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Peter Skomoroch is an entrepreneur, investor, and the former Head of Data Products at Workday and LinkedIn. He was Co-Founder and CEO of SkipFlag, a venture backed deep learning startup acquired by Workday in 2018. Peter is a senior executive with extensive experience building and running teams that develop products powered by data and machine learning. He was an early member of the data team at LinkedIn, the world's largest professional network with over 500 million members worldwide. As a Principal Data Scientist at LinkedIn, he led data science teams focused on reputation, search, inferred identity, and building data products. He was also the creator of LinkedIn Skills and Endorsements, one of the fastest growing new product features in LinkedIn's history. Before joining LinkedIn, Peter was Director of Analytics at Juice Analytics and a Senior Research Engineer at AOL Search. In a previous life, he developed price optimization models for Fortune 500 retailers, studied machine learning at MIT, and worked on Biodefense projects for DARPA and The Department of Defense. Peter has a B.S. in Mathematics and Physics from Brandeis University and research experience in Machine Learning and Neuroscience.
There are few things I love more than cuddling up with an exciting new book. There are always more things I want to learn than time I have in the day, and I think books are such a fun, long-form way of engaging (one where I won’t be tempted to check Twitter partway through). This book roundup is a selection from the last few years of TWIML guests, counting only the ones related to ML/AI published in the past 10 years. We hope that some of their insights are useful to you! If you liked their book or want to hear more about them before taking the leap into longform writing, check out the accompanying podcast episode (linked on the guest’s name). (Note: These links are affiliate links, which means that ordering through them helps support our show!) Adversarial ML Generative Adversarial Learning: Architectures and Applications (2022), Jürgen Schmidhuber AI Ethics Sex, Race, and Robots: How to Be Human in the Age of AI (2019), Ayanna Howard Ethics and Data Science (2018), Hilary Mason AI Sci-Fi AI 2041: Ten Visions for Our Future (2021), Kai-Fu Lee AI Analysis AI Superpowers: China, Silicon Valley, And The New World Order (2018), Kai-Fu Lee Rebooting AI: Building Artificial Intelligence We Can Trust (2019), Gary Marcus Artificial Unintelligence: How Computers Misunderstand the World (The MIT Press) (2019), Meredith Broussard Complexity: A Guided Tour (2011), Melanie Mitchell Artificial Intelligence: A Guide for Thinking Humans (2019), Melanie Mitchell Career Insights My Journey into AI (2018), Kai-Fu Lee Build a Career in Data Science (2020), Jacqueline Nolis Computational Neuroscience The Computational Brain (2016), Terrence Sejnowski Computer Vision Large-Scale Visual Geo-Localization (Advances in Computer Vision and Pattern Recognition) (2016), Amir Zamir Image Understanding using Sparse Representations (2014), Pavan Turaga Visual Attributes (Advances in Computer Vision and Pattern Recognition) (2017), Devi Parikh Crowdsourcing in Computer Vision (Foundations and Trends(r) in Computer Graphics and Vision) (2016), Adriana Kovashka Riemannian Computing in Computer Vision (2015), Pavan Turaga Databases Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases (2021), Xin Luna Dong Big Data Integration (Synthesis Lectures on Data Management) (2015), Xin Luna Dong Deep Learning The Deep Learning Revolution (2016), Terrence Sejnowski Dive into Deep Learning (2021), Zachary Lipton Introduction to Machine Learning A Course in Machine Learning (2020), Hal Daume III Approaching (Almost) Any Machine Learning Problem (2020), Abhishek Thakur Building Machine Learning Powered Applications: Going from Idea to Product (2020), Emmanuel Ameisen ML Organization Data Driven (2015), Hilary Mason The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI (2019), David Carmona MLOps Effective Data Science Infrastructure: How to make data scientists productive (2022), Ville Tuulos Model Specifics An Introduction to Variational Autoencoders (Foundations and Trends(r) in Machine Learning) (2019), Max Welling NLP Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics (2013), Emily M. Bender Robotics What to Expect When You’re Expecting Robots (2021), Julie Shah The New Breed: What Our History with Animals Reveals about Our Future with Robots (2021), Kate Darling Software How To Kernel-based Approximation Methods Using Matlab (2015), Michael McCourt
This study group will follow the text Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition, 2018 MIT Press. The group meets every Sunday at 10 am PT starting on April 11, 2021. The study group slack channel is #rl_2021 (you can join our slack community by clicking on "join us" at twimlai.com/community). Also, the study group leaders are looking for volunteers from the community to help lead the sessions so don’t hesitate to post a note in the channel if you’d like to volunteer.
Getting to know Andrea Banino Andrea’s background as a neuroscientist informed his work in deep learning. At DeepMind, Andrea’s research falls in the realm of Artificial General Intelligence, specifically memory, along with investigating ways to shape deep learning systems so they better mimic the human brain. “I think for us, we have a different sort of memory. We have very long-term memory, we have short-term memory. I argue that agents should be equipped with these sort of different timescale memory.” Introduction to Human and Machine Memory Human memory can be broadly categorized into two kinds: short-term, sometimes called “working” memory, and long-term memory. Working memory deals with immediate phenomena and manipulates it for other cognitive functions. Tasks like counting, drawing a still life, or putting together a puzzle, where you use recently encountered information to accomplish a goal involve working memory. Recurrent neural networks and LSTMs are working memory equivalent models which hold information “online” to solve a problem, and then usually let it go afterwards. Long-term memory can be further subdivided into episodic and semantic memory. Episodic memory, also called autobiographical memory, catalogues personal experiences and stores them in memories. This differs from semantic memory, which generally stores knowledge and concepts. For example, knowing what a bike looks like and what it does is semantic memory, while remembering a specific bike ride with a friend is stored in autobiographical memory. Andrea’s research background is in long-term episodic memory. There isn’t a really good long-term memory equivalent in ML models yet, but Andrea and his team have experimented with a few different arrangements. Long-Term Memory Models One interesting model Andrea explored is a memory-augmented neural network. This is a neural network connected to an external memory source, which allows it to write previous computations and reuse previous computation procedures when it encounters similar problems. Retrieval augmented models are another long-term memory equivalent that have the ability to look things up in their memory. However, unlike human minds, they don’t update or reconsolidate their memory based on new information; it’s just a constant cycle of check and replicate. Transformer models also seem promising as a substrate for long-term memory. However, Andrea notes that they have only been used to model language so far, so still limited data. One downside is that transformers are computation-heavy and difficult to scale, so it’s definitely an open area of research. Overfitting, in models and humans A common critique of deep learning models is that they have a tendency to overfit to their data set, and have difficulty generalizing as a result. While this is certainly an issue, Andrea brought up another really interesting point. Humans also memorialize, and there’s always the potential for overfitting as a person. One way evolution has helped prevent against that is by increasing the data set over time, as the set of human experiences our brains pull from increases as we age. Andrea mentioned that even humans are limited in our generalizability — limited by the data we take in. The link between memory and learning is that consistent experience enables generalization, so people take memories and use them to predict the future. In some ways, our brains aim to minimize uncertainty, and incorporating previously-known information about the environment helps us predict what’s going to happen in the future. Neural Network Navigation Task In 2018, Andrea and his colleagues published a paper that explored agent navigation via representation. The model they built was programmed to mimic the human hippocampus. To understand what this model looked like, Andrea explained the three types of cells in the hippocampus that work together for spatial analysis. Head direction cells fire when a person is facing a specific direction relative to their environment. Place cells on the other hand fire in a specific place, such as the town square or even one’s own bedroom. Grid cells fire in a hexagonal lattice format and are theorized to be the cells that allow us to calculate shortcuts. Andrea et al. trained a neural network with models that mimicked each of these three traits. Via experimentation, using methods like dropout and introducing noise, Andrea and his team were able to determine that all three artificial cell types were necessary for successful shortcut navigation. “We managed to make the representation emerge in our neural network, trained it to do path integration, a navigation task. And we proved that that was the only agent able to take a shortcut. So, it was an empirical paper to prove what the grid cells are for.” Ponder Net: an algorithm that prioritizes complexity Andrea’s most recent development is an algorithm called Ponder Net. As a general rule, the amount of computational power required for a neural network to make an inference increases as the size of a model’s input (like its feature dimensionality) increases, while the required computational power has no necessary relation to the complexity of a particular problem or prediction. By contrast, the amount of time it takes a human to solve a problem is directly related to the problem’s complexity. Ponder Net attempts to create neural networks that budget computational resources based on problem complexity. It does so with the introduction of a halting algorithm which helps to conserve inference time, so if the computer is confident about the solution, it can stop calculating early. How does it work? Pondering steps & the halting algorithm Ponder Net is based on previous work called adaptive computation time. Adaptive computation time (ACT) minimizes the number of pondering steps with a halting algorithm. In ACT, the algorithm finds a weighted average of the prediction, instead of a specific prediction. With Ponder Net, the probability of halting is found for each time step in the sequence. Andrea explained that the probability of halting is a Bernoulli random variable (think coin flip) which tells you the probability of halting at the current step, given that you have not halted at the previous step. From there, Ponder Net calculates a probability distribution by multiplying the probability at each time step in order to form a proper geometric distribution. Once we have that, the algorithm can then calculate the loss for each prediction in the sequence that we made. The loss can then be weighted by the probability where we altered that particular step. Andrea sees Ponder Net as a technique that can be applied in many different architectures, and he tested it on a number of different tasks. The team reported above state-of-the art performance, and that Ponder Net was able to succeed at extrapolation tests where traditional neural networks fail. Transformers & Reinforcement Learning Another project Andrea mentioned was a BERT-inspired combined transformer and LSTM algorithm he published in a recent paper. While LSTMs work great for reinforcement learning tasks, they do suffer from a recency bias which makes them less suited to long-term memory problems. Transformers perform better over a long string of information, however their reward system is more complicated and they have noisier gradients. Andrea’s algorithm applied a BERT masking training to features from a CNN which were then reconstructed. Figure 1 from CoBERL paper Combining the LSTM with a transformer reduced the size and increased the speed of the algorithm. Something clever Andrea did was letting the agent choose whether to use the LSTM alone or to combine with the transformer “I think there’s lots of stuff we can do to improve transformers and memory in general, in reinforcement learning, especially in relation to the length of the context that we can process.” Check out the podcast episode to learn more about Ponder Net, and reinforcement learning!
Running out of gift ideas and need a little inspiration? The TWIML team has you covered! We put together a Holiday Gift Guide featuring some of our favorite AI-enabled products. It’s probably no surprise if you listen to the podcast, but AI has found its way into a bunch of different areas. This is just a small sampling of some of the nifty gadgets and services that caught our attention this holiday season. Surprise the AI enthusiast (or non-enthusiast) in your life with: The Drone: ActiveTrack + Advanced Pilot Asset System am felt this list wouldn’t be complete without at least one drone, and this is the one on his wish list. The DJI Mavic Air 2 has all the usual drone things: good sensors, a nice camera, etc., but what makes the drone unique is the stellar AI software. ActiveTrack 3.0 and Advanced Pilot Assist System 3.0 features allow you to focus on a subject and have it tracked and filmed while the drone is in flight. There’s a pretty good review of the drone here. Personal Trainer for Running For those of us missing the gym, running is often the only refuge. We like the Vi app because it uses AI to personalize workouts, offer to coach, and give daily challenges– all helpful qualities when you’re trying to build healthy habits! Coding Robots for Kids & Adults This one is for the kiddos! (And anyone learning to code). We’re fans of this little smart Root Coding Robot that complements any level of coding experience. It’s super interactive, with 3 learning levels full of lessons, projects, and activities. If you’re curious about how iRobot is using AI, check out this TWIML interview on Re-Architecting Data Science at iRobot with Angela Bassa, the company’s Global Global Head of Analytics & Data Science. For older kids or young-at-heart adults, DJI’s Robomaster S-1 is a neat choice too and allows users to program with some simple AI building blocks like person detection. Data-based Skincare In an effort to create a skincare program based on data science, Proven established The Skin Genome ProjectTM, which became the most comprehensive skincare database you can find and winner of MIT’s 2018 Artificial Intelligence Award. With this database that accounts for over 20,000 skincare ingredients, 100,000 products, 8 million testimonials, and even the climate you live in – they’re able to curate skin care formulas based on your skin. We hope you enjoy our top picks!
The issue of bias in AI was the subject of much discussion in the AI community last week. The publication of PULSE, a machine learning model by Duke University researchers, sparked a great deal of it. PULSE proposes a new approach to the image super-resolution problem, i.e. generating a faithful higher-resolution version of a low-resolution image. In short, PULSE works by using a novel technique to efficiently search space of high-resolution artificial images generated using a GAN and identify ones that are downscale to the low-resolution image. This is in contrast to previous approaches to solving this problem, which work by incrementally upscaling the low-resolution images and which are typically trained in a supervised manner with low- and high-resolution image pairs. The images identified by PULSE are higher resolution and more realistic than those produced by previous approaches, and without the latter’s characteristic blurring of detailed areas. However, what the community quickly identified was that the PULSE method didn’t work so well on non-white input images. An example using a low res image of President Obama was one of the first to make the rounds, and Robert Ness used a photo of me to create this example: I’m going to skip a recounting of the unfortunate Twitter firestorm that ensued following the model’s release. For that background, Khari Johnson provides a thoughtful recap over at VentureBeat, as does Andrey Kurenkov over at The Gradient. Rather, I’m going to riff a bit on the idea of where bias comes from in AI systems. Specifically, in today’s episode of the podcast featuring my discussion with AI Ethics researcher Deb Raji I note, “I don’t fully get why it’s so important to some people to distinguish between algorithms being biased and data sets being biased.” Bias in AI systems is a complex topic, and the idea that more diverse data sets are the only answer is an oversimplification. Even in the case of image super-resolution, one can imagine an approach based on the same underlying dataset that exhibits behavior that is less biased, such as by adding additional constraints to a loss or search function or otherwise weighing the types of errors we see here more heavily. See AI artist Mario Klingemann’s Twitter thread for his experiments in this direction. Not electing to consider robustness to dataset biases is a decision that the algorithm designer makes. All too often, the “decision” to trade accuracy with regards to a minority subgroup for better overall accuracy is an implicit one, made without sufficient consideration. But what if, as a community, our assessment of an AI system’s performance was expanded to consider notions of bias as a matter of course? Some in the research community choose to abdicate this responsibility, by taking the position that there is no inherent bias in AI algorithms and that it is the responsibility of the engineers who use these algorithms to collect better data. However, as a community, each of us, and especially those with influence, has a responsibility to ensure that technology is created mindfully, with an awareness of its impact. On this note, it’s important to ask the more fundamental question of whether a less biased version of a system like PULSE should even exist, and who might be harmed by its existence. See Meredith Whittaker’s tweet and my conversation with Abeba Birhane on Algorithmic Injustice and Relational Ethics for more on this. A full exploration of the many issues raised by the PULSE model is far beyond the scope of this article, but there are many great resources out there that might be helpful in better understanding these issues and confronting them in our work. First off there are the videos from the tutorial on Fairness Accountability Transparency and Ethics in Computer Vision presented by Timnit Gebru and Emily Denton. CVPR organizers regard this tutorial as “required viewing for us all.” Next, Rachel Thomas has composed a great list of AI ethics resources on the fast.ai blog. Check out her list and let us know what you find most helpful. Finally, there is our very own Ethics, Bias, and AI playlist of TWIML AI Podcast episodes. We’ll be adding my conversation with Deb to it, and it will continue to evolve as we explore these issues via the podcast. I'd love to hear your thoughts on this. (Thanks to Deb Raji for providing feedback and additional resources for this article!)
Stefan Lee is involved in a number of projects around emergent communication. To hear about more of them, in addition to the ViLBERT model, check out the full interview! TWiML Talk #358 with Stefan Lee. One of the major barriers keeping robots from full immersion in our daily lives, is the reality that meaningful human interaction is dependent on the interpretation of visual and linguistic communication. Humans (and most living creatures) rely on these signals to contextualize, operate and organize our actions in relation to the world around us. Robots are extremely limited in their capacity to translate visual and linguistic inputs, which is why it remains challenging to hold smooth conversation with a robot. Ensuring that robots and humans can understand each other sufficiently is at the center of Stefan Lee’s research. Stefan is an assistant professor at Oregon State University for the Electrical Engineering and Computer Science Department. Stefan, along with his research team, held a number of talks and presentations at NeurIPS 2019. One highlight of their recent work is the development of a model called ViLBERT (Vision and Language BERT), published in their paper, ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. BERT Models for Natural Language Processing BERT (Bidirectional Encoder Representations from Transformers) is Google’s popularized model that has revolutionized natural language processing (NLP). BERT is what’s called a language model, meaning it is trained to predict the future words in a sentence based on past words. Another example is GPT-2, which caused a controversy when released by OpenAI last year. Part of what makes them so special is that they are bidirectional, meaning they can contextualize language by reading data from both the left and right. This builds relationships between words and helps the model make more informed predictions about related words. They also pre-train models on large sets of unlabeled data using a transformer architecture, so data sequences don’t need to be processed in order, enabling parallel computations. Models like BERT work by taking “a large language corpus and they learn certain little things to build supervision from unlabeled data. They’ll mask out a few words and have them re-predict it based on the other linguistic context, or they’ll ask if a sentence follows another sentence in text.” Extending BERT to the Visual Domain ViLBERT is an extension of the BERT technique. To apply the BERT model to vision and language, the team worked with a data set called Conceptual Captions, composed of around 3 million images paired with alt-text. Their method is to mask out random parts of the image, and then ask the model to reconstruct the rest of the image given the associated alt-text. “Likewise, we’re asking, does this sentence match with this image or not? or masking out parts of the language and having it reconstruct from the image and the text. We’re designing this self-supervised multi-model task with this large weekly supervised data source.” Visual Grounding Stefan describes that “Most of natural language processing is just learning based on its association with other words. Likewise on the visual side, you’re learning to represent some sparse set of classes. Those classes often relate to specific nouns, but they don’t have a sense of closeness, so there’s no idea that the feature for a cat should be close to the feature for a tiger…The point of ViLBERT is to try to learn these associations between vision and language directly. This is something we usually call visual grounding of a word.” Humans do this naturally because we often have the inherent context to imagine a visual representation of something. For example, the words “wolf head” might bring up certain imagery of wolves, but machines lack the same visual associations. What Stephan is working towards with ViLBERT is to present the agent with something like, “red ball” and have it interpret that to reconstruct an image of a red ball. How Vilbert Works: Bounding Boxes and Co-Attentional Transformer In object detection, bounding boxes are used to describe the target area of the object being observed. For the purpose of ViLBERT, an image is dissected as a set of bounding boxes that are independent of each other. Each box is given a positional encoding that shows where the box was pulled from, but ultimately the order of the sequence is not important. BERT works in the same way where you have a sequence of words (a sentence) that are treated as independent inputs and given a positional embedding. “It’s just a set. It’s an unordered set. In fact, the actual input API for BERT looks the same in our model for the visual side and the linguistic side.” The bounding boxes are output by an R-CNN model trained on the Visual Genome. The R-CNN model “can produce quite a lot of bounding boxes and you can sample from it if you’d like to increase the randomness.” Something to note is that many of the bounding boxes are not well aligned and the data could come back as fairly noisy. “Sometimes you’ll have an object like a road…it doesn’t do a great job of honing in on specific objects.” While the model is not trained on the visuals from scratch, it still has to learn the association even when it might not be obvious. To train the model, certain parts of the image (bounding boxes) are removed, and the model is asked about alignment between the alt-text and the image. The distinction between BERT is that “In BERT, it’s a sentence and the next sentence, and you’re predicting whether one comes after the other, but in our case, it’s an image and a sentence, and we’re asking does this align or not? Is this actually a pair from conceptual captions?” At the end of the process, “what we get is a model that has built some representations that bridge between vision and language” which can then be fine-tuned to fit a variety of other tasks. Applications and Limitations Since the ViLBERT paper, the model has been applied to around a dozen different vision and language tasks. “You can pre-train this and then use it as a base to perform fairly well, fairly quickly, on a wide range of visual and language reasoning tasks.” In addition to fine-tuning for specific task types, you can adjust for specific types of data or data language relationships. One useful adaptation is for Visual Question and Answering (VQA) to help those who are visually impaired ask questions about the world around them, and receive descriptive answers in return. To modify the model for VQA, you could feed the questions as the text inputs and “train an output that predicts my subset of answers.” ViLBERT is pre-trained on a dataset of images and captions as the text input. For VQA, you would use the questions as the text input and have the model reconstruct answers as the output. While ViLBERT is a solid starting point for the field, Stefan notes that the grounding component of the research is still underdeveloped. For example, if the model is trained for VQA on a limited dataset like COCO images, there may be objects that are not accounted for because the machine never learned they existed. “One example that I like to show from a recent paper is that [COCO images] don’t have any guns. If we’ve fed this caption trained on COCO with an image of a man in a red hat with a red shirt holding a shotgun, and the caption is a man in a red hat and a red shirt holding a baseball bat, because he’s wearing what looks like a baseball uniform and he’s got something in his hands. It might as well be a baseball bat. If we talk back to these potential applications of helping people with visual impairment, that kind of mistake doesn’t seem justifiable.” Future Directions For Visual Grounding One related area of research that Stefan has started to branch into is the interpretation of motion. The problem with images is that it can often be difficult to distinguish between active behaviors and stationary behaviors. “For a long time in the community, grounding has been on static images, but there’s actually a lot of concepts that rely on motion, that rely on interaction to ground. I could give you a photo and you could tell me it looks like people are talking, but they could just be sitting there quietly as well.” There is less emphasis on the interaction, which is a key element not only to understanding communication, but for accuracy in reading a social situation. Machines are not yet able to catch on to these distinctions and it’s a growing area of interest for Stefan. For more on what Stefan is up to, be sure to check out the full interview with Stefan Lee for a deeper understanding of ViLBERT and the numerous projects the team is working on.
How does LinkedIn allow its data scientists to access aggregate user data for exploratory analytics while maintaining its users' privacy? That was the question at the heart of our recent conversation with Ryan Rogers, a senior software engineer in data science at the company. The answer, it turns out, is through differential privacy, a topic we've covered here on the show quite extensively over the years. Differential privacy is a system for publicly sharing information about a dataset by describing patterns of groups within the dataset, the catch is you have to do this without revealing information about individuals in the dataset (privacy). Ryan currently applies differential privacy at LinkedIn, but he has worked in the field, and on the related topic of federated learning, for quite some time. He was introduced to the subject as a PhD student at the University of Pennsylvania, where he worked closely with Aaron Roth, who we had the pleasure of interviewing back in 2018. Ryan later worked at Apple, where he focused on the local model of differential privacy, meaning differential privacy is performed on individual users' local devices before being collected for analysis. (Apple uses this, for example, to better understand our favorite emojis 🤯 👍👏). Not surprisingly, they do things a bit differently at LinkedIn. They utilize a central model, where the user's actual data is stored in a central database, with differential privacy applied before the data is made available for analysis. (Another interesting use case that Ryan mentioned in the interview: the U.S. Census Bureau has announced plans to publish 2020 census data using differential privacy.) Ryan recently put together a research paper with his LinkedIn colleague, David Durfee, that they presented as a spotlight talk at NeurIPS in Vancouver. The title of the paper is a bit daunting, but we break it down in the interview. You can check out the paper here: Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. There are two major components to the paper. First, they wanted to offer practical algorithms that you can layer on top of existing systems to achieve differential privacy for a very common type of query: the "Top-k" query, which means helping answer questions like "what are the top 10 articles that members are engaging with across LinkedIn?" Secondly, because privacy is reduced when users are allowed to make multiple queries of a differentially private system, Ryan's team developed an innovative way to ensure that their systems accurately account for the information the system returns to users over the course of a session. It's called Pay-what-you-get Composition. One of the big innovations of the paper is discovering the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel noise, which is commonly used in machine learning. One of the really nice connections that we made in our paper was that actually the exponential mechanism can be implemented by adding something called Gumbel noise, rather than Laplace noise. Gumbel noise actually pops up in machine learning. It's something that you would do to report the category that has the highest weight, [using what is] called the Gumbel Max Noise Trick. It turned out that we could use that with the exponential mechanism to get a differentially private algorithm. [...] Typically, to solve top-k, you would use the exponential mechanism k different times⁠ —you can now do this in one shot by just adding Gumbel noise to [existing algorithms] and report the k values that are in the the top […]which made it a lot more efficient and practical. When asked what he was most excited about for the future of differential privacy Ryan cited the progress in open source projects. This is the future of private data analytics. It's really important to be transparent with how you're doing things, otherwise if you're just touting that you're private and you're not revealing what it is, then is it really private? He pointed out the open-source collaboration between Microsoft and Harvard's Institute for Quantitative Social Sciences. The project aims to create an open-source platform that allows researchers to share datasets containing personal information while preserving the privacy of individuals. Ryan expects such efforts to bring more people to the field, encouraging applications of differential privacy that work in practice and at scale. Listen to the interview with Ryan to get the full scope! And if you want to go deeper into differential privacy check out our series of interviews on the topic from 2018. Thanks to LinkedIn for sponsoring today's show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn's highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.
Sam Charrington: Hey, what's up everyone? This is Sam. A quick reminder that we've got a bunch of newly formed or forming study groups, including groups focused on Kaggle competitions and the fast.ai NLP and Deep Learning for Coders part one courses. It's not too late to join us, which you can do by visiting twimlai.com/community. Also, this week I'm at re:Invent and next week I'll be at NeurIPS. If you're at either event, please reach out. I'd love to connect. All right. This week on the podcast, I'm excited to share a series of shows recorded in Orlando during the Microsoft Ignite conference. Before we jump in, I'd like to thank Microsoft for their support of the show and their sponsorship of this series. Thanks to decades of breakthrough research and technology, Microsoft is making AI real for businesses with Azure AI, a set of services that span vision, speech, language processing, custom machine learning, and more. Millions of developers and data scientists around the world are using Azure AI to build innovative applications and machine learning models for their organizations, including 85% of the Fortune 100. Microsoft customers like Spotify, Lexmark, and Airbus, choose Azure AI because of its proven enterprise grade capabilities and innovations, wide range of developer tools and services and trusted approach. Stay tuned to learn how Microsoft is enabling developers, data scientists and MLOps and DevOps professionals across all skill levels to increase productivity, operationalize models at scale and innovate faster and more responsibly with Azure machine learning. Learn more at aka.ms/azureml. All right, onto the show! Erez Barak: [00:02:06] Thank you. Great to be here with you, Sam. Sam Charrington: [00:02:08] I'm super excited about this conversation. We will be diving into a topic that is generating a lot of excitement in the industry and that is Auto ML and the automation of the data science process. But before we dig into that, I'd love to hear how you got started working in ML and AI. Erez Barak: [00:02:30] It's a great question because I've been working with data for quite a while. And I think roughly about five to 10 years ago, it became apparent that the next chapter for anyone working with data has to weave itself through the AI world. The world of opportunity with AI is really only limited by the amount of data you have, the uniqueness of the data you have and the access you have to data. And once you're able to connect those two worlds, a lot of things like predictions, new insights, new directions, sort of come out of the woodwork. So seeing that opportunity, imagining that potential, has naturally led me to work with AI. I was lucky enough to join the Azure AI group, and there's really three focal areas within that group. One of them is machine learning. How do we enable data scientists of all skills to operate through the machine learning lifecycle, starting from the data to the training, to registering the models to putting them in productions and managing them, a process we call ML Ops. So just looking at that end to end and understanding how we enable others to really go through that process in a responsible trusted and known way has been a super exciting journey so far. Sam Charrington: [00:03:56] And so do you come at this primarily from a data science perspective, a research perspective, an engineering perspective? Or none of the above? Or all of the above? Erez Barak: [00:04:07] I'm actually going to go with all of the above. I think it'd be remiss to think that if you're  a data science perspective, and you're trying to build a product and really looking to build the right set of products for people to use as they go through their AI journey, you'd probably miss out on an aspect of it. If you just think about the engineering perspective, you'll probably end up with great info that doesn't align with any of the data science. So you really have to think between the two worlds and how one empowers the other. You really have to figure out where most data scientists of all skills need the help, want the help, are looking for tools and products and services on Azure to help them out, and I think that's the part I find most compelling. Sort of figuring that out and then really going deep where you landed, right? 'Cause if we end up building a new SDK, we're going to spend a whole lot of time with our data science customers, our data science internal teams and figure out, "Well, how should our SDK look like?" But if you're building something like Auto ML that's targeted not only at the deeper data scientist, but also the deeper rooted data professionals, you're going to spend some time with them and understand not only what they need, but also how that applies to the world of data science. Sam Charrington: [00:05:27] And what were you working on before Azure AI? Erez Barak: [00:05:31] So before Azure AI, in Microsoft, I worked for a team called Share Data, which really created a set of data platforms for our internal teams. And prior to joining Microsoft, I worked in the marketing automation space, at a company called Optify. and again the unique assets we were able to bring to the table as part of Optify in the world of marketing automations were always data based. We were always sort of looking at the data assets the marketers had and said, "what else can we get out of it?" Machine learning wasn't as prevalent at the time, but you could track back to a lot of what we did at that time and how machine learning would've helped if it was used on such a general basis. Sam Charrington: [00:06:12] Yeah, one of the first machine learning use cases that I worked with were with folks that were doing trying to do lead scoring and likelihood to buy, propensity to buy types of use cases. I mean that's been going on for a really long time. Erez Barak: [00:06:30] So we're on a podcast so you can't see me smiling, but we did a lot of work around building lead scoring...and heuristics and manual heuristics, and general heuristics, and heuristics that the customer could customize. And today, you've seen that to really evolve to a place where there's a lot of machine learning behind it. I mean, it's perfect for machine learning, right? You've got all this data. It's fresh. It's coming  in new. There's insights that are really hard to find out. Once you've start slicing and dicing it by regions or by size of customers, it gets even more interesting so all the makings for having machine learning really make it shine. Sam Charrington: [00:07:07] Yeah you are getting pretty excited I think. Erez Barak: [00:07:08] Oh, no, no, no. It's a sweet spot there. Yes. Sam Charrington: [00:07:12] Nice. You want to dive into talking about Auto ML? For the level of excitement and demand for Auto ML and enthusiasm that folks have for the topic, not to mention the amount of confusion that there is for the topic, I've probably not covered it nearly enough on the podcast. Certainly when I think of Auto ML, there's a long academic history behind the technical approaches that drive it. But it was really popularized for many with Google's Cloud Auto ML in 2018, and before that they had this New York Times PR win that was a New York Times article talking about how AI was going to create itself, and I think that contributed a lot to, 'for lack of a better term in this space', but then we see it all over the place. There are other approaches more focused on citizen data science. I'd love to just start with how you define Auto ML and what's your take on it as a space and its role and importance, that kind of thing. Erez Barak: [00:08:42] Yeah, I really relate to many of the things you touched on. So maybe I'll start - and this is true for many things we do in Azure AI but definitely for Auto ML - on your point around academic roots. Microsoft has this division called MSR, Microsoft Research, and it's really a set of researchers who look into bleeding edge topics and drive the world of research in different areas. And that is when we first got, in our team, introduced to Auto ML. So a subset of that team has been doing research around the Auto ML area for quite a few years. They've been looking at it, they've been thinking. It yes, I've heard the sentence, "AI making AI." That's definitely there. But when you start reading into it like what does it mean and to be honest, it means a lot of things to many people. It's quite overused. I'll be quite frank. There's no one industry standard definition that says, "Hmm, here's what Auto ML is." I can tell you what it is for us. I can tell you what it is for our customers. I can tell you where we're seeing it make a ton of impact. And it comes to using machine learning capabilities in order to help you, being the data scientist, create machine capabilities in a more efficient, in a more accurate, in a more structured fashion. Sam Charrington: [00:10:14] My reaction to that is that it's super high level. And it leaves the door open for all of this broad spectrum of definitions that you just talked about. For example, not to over index on what Google's been doing, but Cloud Auto ML Vision when it first came out was a way for folks to do vision cognitive services, but use some of their own data to tune it. Right? Which is a lot different. In fact, they caught a lot of flack from the academic Auto ML community because they totally redefined what that community had been working for for many years and started creating the confusion. Maybe a first question is, do you see it as being a broad spectrum of things or is it how do we even get to a definition that separates the personalized cognitive services trained with my own data versus this other set of things? Erez Barak: [00:11:30] I think you see it as more of that general sense, so I would say probably not. I see it as a much more concrete set of capabilities that adhere to a well known process. That actually is agreed upon across the industry. When you build a model, what do you do? You get data, you featurize that data. Once the features are in place, you choose a learner, you choose an algorithm. You train that algorithm with the data, creating a model. At that point, you want to evaluate the model, make sure it's accurate. You want to get some understanding of what are the underlining features that have most affected the model. And you want to make sure, in addition, that you can explain that model is not biased, you can explain that model is really fair towards all aspects of what it's looking at. That's a well-known process. I think there's no argument around that in the sort of the machine learning field that's sort of the end to end. Auto ML allows automating that process. So at its purest, you feed Auto ML the data and you get the rest for free if you may. Okay? that would be sort of where we're heading, where we want to be. And I think that's at the heart of Auto ML. So, where does the confusion start? I could claim that what we or others do for custom vision follows that path, and it does. I can also claim that some of what we do for custom vision is automated. And then there's  the short hop to say, "Well, therefore it is Auto ML." But I think that misses the general point of what we're trying to do with Auto ML. Custom vision is a great example where Auto ML can be leveraged. But Auto ML can be leveraged wherever that end to end process happens in machinery. Sam Charrington: [00:13:27] Nice. I like it. So maybe we can walk through that end to end process and talk about some of the key areas where automation is applied to contribute to Auto ML. Erez Barak: [00:13:44] So I'd like to start with featurization. And at the end of the day, we want an accurate model. A lot of that accuracy, a lot of the insights we can get, the predictions we can get, and the output we can get from any model is really hinged on how effective your featurization is. So many times you hear that, "Well, 80% of the time data scientists spend on data." Can I put a pin on, do you know where that number comes from? Oh of course. Everyone says that's the number, everyone repeats it. It's a self-fulfilling prophecy. I'm going to say 79% of it just to be sure. But I think it's more of an urban legend at that point. I am seeing customers who do spend that kind of percentages  I am seeing experiments rerun that take that amount of time. Generalizing that number is just too far now to do. Sam Charrington: [00:14:42] I was thinking about this recently, and wondering if there's some institute for data science that's been tracking this number over time. It would be interesting to see how it changes over time I think is the broader curiosity. Erez Barak: [00:14:55] It would. I should go figure that out. [laughs] So anyone who builds a model can quickly see the effect of featurization on the output. Now, a lot of what's done, when building features, can be automated. I would even venture to say that a part of it can be easily automated. Sam Charrington: [00:15:24] What are some examples? Erez Barak: [00:15:25] Some examples are like, "I want to take two columns and bring them together into one." "I want to change a date format to better align with the rest of my columns." And even a easy one, "I'd like to enhance my data with some public holiday data when I do my sales forecasting because that's really going to make it more accurate." So it's more data enhancement, but you definitely want to build features into your data to do that. So getting that right is key. Now start thinking of data sets that have many rows, but more importantly have many columns. Okay? And then the problem gets harder and harder. You want to try a lot more options. There's a lot more ways of featurizing the data. Some are more effective than others. Like we recently in Auto ML, have incorporated the BERT model into our auto featurization capability. Now that allows us to take text data we use for classification and quickly featurize it. It helps us featurize it in a way that requires less input data to come in for the model to be accurate. I think that's a great example of how deep and how far that can go. Sam Charrington: [00:16:40] You mentioned that getting that featurization right is key. To what extent is it an algorithmic methodological challenge versus computational challenge? If you can even separate these two. Meaning, there's this trade off between... Like we've got this catalog of recipes like combining columns and bending things and whatever that we can just throw at a data set that looks like it might fit. Versus more intelligent or selective application of techniques based on nuances whether pre-defined or learned about the data. Erez Barak: [00:17:28] So it extends on a few dimensions. I would say there are techniques. Some require more compute than others. Some are easier to get done. Some require a deeper integration with existing models like I mentioned BERT before, to be effective. But that's only one dimension. The other dimension is the fit of the data into a specific learner. So we don't call it experiments in machine learning for nothing. We experiment, we try. Okay? Nobody really knows exactly which features would affect the model in a proper way, would drive accuracy. So there's a lot of iteration and experimentation being done. Now think of this place where you have a lot of data, creating a lot of features and you want to try multiple learners, multiple algorithms if you may. And that becomes quickly quite a mundane process that automating can really, really help with. And then add on top of that, we're seeing more and more models created with just more and more features. The more features you have, the more nuanced you can get about describing your data. The more nuanced the model can get about predicting what's going to happen next, or we're now seeing models with millions and billions of features coming out. Now, Auto ML is not yet prepared to deal with the billion feature model, but we see that dimension extend. So extend compute, one, extend the number of iterations you would have, extend to the number of features you have. Now you got a problem that's quickly going to be referred to as mundane. Hard to do. Repetitive. Doesn't really require a lot of imagination. Automation just sounds perfect for that. So that's why one of the things we went after in the past, I'd say six to twelve months is how we get featurization to a place where you do a lot of auto featurization. Sam Charrington: [00:19:22] I'm trying to parse the extent to which, or whether, you agree with this dichotomy that I presented. You've got this mundane problem that if a human data scientist was doing would be just extremely iterative, and certainly one way of automating is to just do that iteration a lot quicker because the machine can do that. Another way of automating is... let's call it more intelligent approaches to navigating that feature space or that iteration space, and identifying through algorithmic techniques what are likely to be the right combinations of features as opposed to just throwing the kitchen sink at it and putting that in a bunch of loops. And certainly that's not a dichotomy, right? You do a bit of both. Can you elaborate on that trade off or the relationship between those two approaches? Is that even the right way to think about it or is that the wrong way to think about it? Erez Barak: [00:20:33] I think it's a definitely a way to think about it. I'm just thinking through that lens for a second. So I think you describe the brute force approach to it. On one side. The other side is how nuanced can you get about it? So what we know is you can get quite nuanced. There's things that are known to work, things that are not known to work. Things that work with a certain type of data set that don't work with another. Things that work with a certain type of data set combined with the learner that don't work with others. So as we build Auto ML, I talked about machine learning used to help with machine learning. We train a model to say, "Okay, in this kind of event, you might want to try this kind of combination first." Because if you're... I talked about the number of features, brute force is not an option. So we have have toto get a lot more nuanced about it, so what Auto ML does is given those conditions if you may, or those features for that model, it helps shape the right set of experiments before others. That's allowing you to get to a more accurate model faster. So I think that's one aspect of it. I think another aspect, which you may have touched on, and I think is really important throughout Auto ML, but definitely in featurization, is why people are excited about that. The next thing you are going to hear is that I want to see what you did. And you have to show what kind of features you used. And quickly follows is, "I want to change feature 950 out of the thousand features you gave me. And I want to add two more features at the end because I think they're important." That's where my innovation as a data scientist comes into play. So you've got to, and Auto ML allows you to do that, be able to open up that aspect and say, "Here's what I've come up with. Would you like to customize? Would you like to add? Would you like to remove?" Because that's where you as a data scientist shine and are able to innovate. Sam Charrington: [00:22:39] So we started with featurization. Next step is learner/model selection? Erez Barak: [00:22:45] I think it's probably the best next step to talk about. Yes. I think there's a lot of configuration that goes into this like how many iterations do I want to do?For instance. How accurate do I want to get? What defines accuracy? But those are more manual parameters we ask the user to add to it. But then automation again comes into play as learner selection. So putting Auto ML aside, what's going to happen? Build a set of features, choose a learner, one that I happen to know is really good for this kind of problem and try it out. See how accurate I get. If it doesn't work, but even if it works, you are going to try another. Try another few. Try a few options. Auto ML at the heart of it is what it does. Now, going to what we talked about in featurization, we don't take a brute force approach. We have a model that's been trained over millions of experiments, sort of knows what would be a good first choice given the data, given the type of features, given the type of outcome you want. What do we try first? Because people can't just run an endless number of iterations. It takes time, takes cost, and sort of takes the frankly it takes a lot of the ROI out of something you expect from Auto ML. So you want to get there as fast as possible based on learnings from the past. So what we've automated is that selection. Put in the data, set a number of iterations or not set them. We have a default number that goes in. And then start using the learners based on the environment we're seeing out there and choosing them out from that other model we've trained over time. By the way, that's a place where we really leaned on the outputs we got from MSR. That's a place where they, as they were defining Auto ML, as they were researching it, really went deep into, and really sort of created assets we were then able to leverage. A product sort of evolves over time and the technology evolves over time, but if I have to pick the most, or the deepest rooted area, we've looked at from MSR, it's definitely the ability to choose the right learner for the right job with a minimal amount of compute associated with it if you may. Sam Charrington: [00:24:59] And what are some of the core contributions of that research if you go to the layer deeper than that? Erez Barak: [00:25:10] Are you asking in context of choosing a model or in general? Sam Charrington: [00:25:13] Yeah, in the context of choosing a model. For example, as you described, what is essentially a learner, learning which model to use, that created a bunch of questions for me around like, "Okay how do you  represent this whole, what are the features of that model? And what is the structure of that model?" And I'm curious if that's something that came out of MSR or that was more from the productization and if there are specific things that came out of that MSR research that come to mind as being pivotal to the way you think about that process. Erez Barak: [00:25:57] So I recall the first version coming out of MSR wasn't really of the end to end product, but at the heart of it was this model that helps you pick learners as it relates to the type size of data you have and the type of target you have. This is where a lot of the research went into. This is where we publish papers around, "Well, which features matter when you choose that?" This is where MSR went and collected a lot of historical data around people running experiments and trained that model. So the basis at the heart of our earliest versions, we really leaned on MSR to get that model in place. We then added the workflow to it, the auto featurization I talked about, some other aspects we'll talk about in a minute, but at the heart of it, they did all that research to understand... Well, first train that model. Just grabbing the data. Sam Charrington: [00:26:54] And what does that model look like? Is it a single model? Is it relatively simple? Is it fairly complex? Is it some ensemble? Erez Barak: [00:27:06] I'll oversimplify a little bit, but it profiles your data. So it takes a profile of your data, it profiles your features, it takes a profile of your features. It looks at the kind of outcome you want to achieve. Am I doing time series forecasting here? I'm doing classification. I'm doing regression that really matters. And based on those features picks the first learner to go after. Then what it does is uses the result of that first iteration, which included all the features I'm talking about, but also now includes, "Hey, I also tried learner X and I got this result." And that helps it choose the next one. So what happens is you look at the base data you have, but you constantly have additional features that show you, "Well, what have I tried and what were the results?" And then the next learner gets picked based on that. And that gets you in a place where the more you iterate, the closer you get to that learner that gives you more accurate result. Sam Charrington: [00:28:14] So I'm hearing elements of both supervised learning. You have a bunch of experiments and the models that were chosen ultimately, but also elements of something more like simple reinforcement learning, contextual bandits, explore, exploit kind of things as well. Erez Barak: [00:28:37] It definitely does both. If I could just touch on one point, reinforcement learning, as it's defined, I wouldn't say we're doing reinforcement learning there. Saying that, we're definitely... every time we have an iteration going or every X times we have that, we do fine tune the training of the model to learn as it runs more and more. So I think reinforcement learning is a lot more reactive. But taking that as an analogy, we do sort of continuously collect more training data and then retrain the model that helps us choose better and better over time. Sam Charrington: [00:29:15] Interesting. So we've talked about a couple of these aspects of the process. Feature engineering, model selection, next is once you've identified the model, tuning hyper-parameters and optimization. Do you consider that its own step or is that a thing that you're doing all along? Or both? Erez Barak: [00:29:38] I consider it part of that uber process I talked about earlier. We're just delving into starting to use deep learning learner within Auto ML. So that's where we're also going to automate the parameter selection, hyper-parameter selection. A lot of the learners we have today are classic machine learning if you may, so that's where hyper-parameter tuning is not as applicable. But saying that, every time we see an opportunity like that, I think I mentioned earlier in our forecasting capabilities, we're now adding deep learning models. In order to make the forecasting more accurate, that's where that tuning will also be automated. Sam Charrington: [00:30:20] Okay, actually elaborate. I think we chatted about that pre-interview, but you mentioned that you're doing some stuff with TCN and Arema around times series forecasting. Can you elaborate on that? Erez Barak: [00:30:36] Yeah, so I talked about this process of choosing a learner. Now you also have to consider what is your possible set of learners you can choose from. And what we've added recently are sort of deep learning models or networks that actually are used within that process. So TCN and Arema are quite useful when doing times series forecasting. Really drive the accuracy based on the data you have. So we've now embedded those capabilities within our forecasting capability. Sam Charrington: [00:31:12] So when you say within forecasting, meaning a forecasting service that you're offering as opposed to within... Erez Barak: [00:31:21] No, let me clarify. There's three core use cases we support as part of Auto ML. One for classification, the other for regression, and the third for times series forecasting. So when I refer to that, I was referring more to that use case within Auto ML. Sam Charrington: [00:31:42] Got it. So in other words in the context of that forecasting use case, as opposed to building a system that is general and applying it to time series and using more generalized models, you're using now TCN and Arema as core to that, which are long-proven models for times series forecasting. Erez Barak: [00:32:07] Yeah, I would argue they're also a bit generalized, but in the context of forecasting. But let me tell you how we're thinking about it. There's generally applicable models. Now, we're seeing different use cases like in forecasting there are generally applicable models for that area, that are really useful in that area. That's sort of the current state we're in right now. And we want to add a lot more known generally applicable models to each area. In addition to that, sort of where we're heading and as I see this moving forward, more and more customers will want to add their own custom model. We've done forecasting for our manufacturing. We've tuned it to a place where it's just amazing for what we do because we know a lot more about our business than anyone else. We'd like to put that in the mix every time your Auto ML considers the best option. I think we're going to see- I'm already seeing a lot of that, sort of the 'bring your own model'. It makes sense. Sam Charrington: [00:33:06] That's an interesting extension to bring your own data, which was one of the first frontiers here. Erez Barak: [00:33:11] I mean you're coming in to a world now, it's not "Hey, there's no data science here. There's a lot of data science going on so I'm the data scientist. I've worked on this model for the past, you name it, weeks? Months? Years? And now this Auto ML is really going to help me be better? I don't think that's a claim we even want to make. I don't think that's a claim that's fair to make. The whole idea is find the user where they are. You have a custom model? Sure, let's plug that in. It's going to be considered with the rest in a fair and visible way, maybe with the auto featurization it even goes and becomes more accurate. Maybe you'll find out something else, you want to tune your model. Maybe you have five of those models, and you're not sure which one is best so you plug in all five. I think that's very much sort of where we're heading, plugging into an existing process that's already deep and rich wherever it lands. Sam Charrington: [00:34:07] The three areas that we've talked about, again featurization, model selection, and parameter tune or optimization are I think, what we tend to think of as the core of Auto ML. Do you also see it playing in the tail end of that process like the deployment after the model's deployed? There's certainly opportunities to automate there. A lot of that is very much related to dev ops and that kind of thing, but are there elements of that that are more like what we're talking about here? Erez Barak: [00:34:48] I think there's two steps, if you don't mind I'll talk about two steps before that. I think there's the evaluation of the model. Well, how accurate is it, right? But again you get into this world of iterations, right? So that's where automation is really helpful. That's one. The other is sort of the interpretation of the model. That's where automation really helps as well. So now, especially when I did a bunch of automation, I now want to make sure, "Well, which features really did affect this thing? Explain them to me. And work that into your automated processes. Did your process provide a fair set of data for my model to learn from? Does it represent all all genders properly? Does it represent all races properly? Does it represent all aspects of my problem, uses them in a fair way? Where do you see imbalance?" So I think automating those pieces are right before we jump into deployment, I think it's really mandatory when you do Auto ML to give that full picture. Otherwise, you're sort of creating the right set of tools, but I feel without doing that, you're sort of falling a bit short of providing everyone the right checks and balances to look at the work they're doing. So when I generalize the Auto ML process, I definitely include that. Back to your question on do I see deployment  playing there? To be honest, I'm not sure. I think definitely the way we evaluate success is we look at the models deployed with Auto ML or via Auto ML or that were created via Auto ML and are now deployed. We looked at their inferences. We look at their scoring, and we provide that view to the customer to assess the real value of their model. Automation there I think if I have to guess, yes. Automation will stretch there. Do I see it today? Can I call it that today? Not just yet. Sam Charrington: [00:36:54] Well, a lot of conversation  around this idea of deploying a model out into production, and thankfully I think we've convinced people that you can, it's not just deploy once and you're not thinking about it anymore. You have to monitor the performance of that model and there's a limited lifespan for most of the models that we're putting into production and then the next thing that folks get excited about is, "Well I can just see when my model falls out of tolerance and then auto-retrain..." It's one of these everyone's talking about it, few are actually doing it. it sounds like you're in agreement with that like we're not there yet at scale or no? Erez Barak: [00:37:42] So I think we often refer to that world as the world of ML ops. Machine learning operations in a more snappy way. I think there's a lot of automation there. If you look at automation, you do it dev ops for just code. I mean, forget machine learning code, but code, let alone models, is very much automation we need. I do think there're two separate loops that have clear interface points. Like deployed models, like maybe data about data drift. But they sort of move in different cycles at different speeds. So we're learning more about this but I suspect that iteration of training, improving accuracy, getting to a model where the data science team says, "Oh, this one's great. Let's use that." I suspect that's one cycle and frankly that's where we've been hyper-focused on automating with Auto ML. There's naturally another cycle of that, operations that we're sort of looking at automation opportunities with ML ops. Do they combine into one automation cycle? Hmm, I'm not sure. Sam Charrington: [00:38:58] But it does strike me that when for example, the decision "Do I retrain from scratch? Do I incrementally retrain? Do I start all the way over?" Maybe that decision could be driven by some patterns or characteristics in the nature of the drift in the performance shift that a model could be applied to. And then,  there're aspects of what we're thinking about and talking about as Auto ML that are applied to that dev ops-y part. Who knows? Erez Barak: [00:39:37] No, I'd say who knows. Then listening to you I'm thinking oh, to myself that while we sort of have a bit of a fixed mindset on the definition we'd definitely need to break through some of that and open up and see, "Well, what is it that we're hearing from the real world that should shape what we automate, how we automate and under which umbrella we put it?" I think, and you will notice, it's moving so fast, evolving so fast. I think we're just at the first step of it. Sam Charrington: [00:40:10] Yeah. A couple quick points that I wanted to ask about. Another couple areas that are generating excitement under this umbrella are neural architecture surge and neural evolution and techniques  like that. Are you doing anything in those domains? Erez Barak: [00:40:30] Again, we're incorporating some of those neural architectures into Auto ML today. I talked about our deeper roots with MSR and how they got us that first model. Our MSR team is very much looking deeper into those areas. They're not things that formulated just yet but the feeling is that the same concepts we put into Auto ML, or automated machine learning can be used there, can be automated there. I'm being a little vague because it is a little vague for us, but the feeling is that there is something there, and we're lucky enough to have the MSR arm that, when there's a feeling there's something there, some research starts to pan out, and they're thinking of different ideas there but to be frank, I don't have much to share at that point in terms of more specifics yet. Sam Charrington: [00:41:24] And my guess is we've been focused on this Auto ML as a set of platform capabilities that helps data scientists be more productive. There's a whole other aspect of Microsoft delivering cognitive services for vision, and other things where they're using Auto ML internally and where it's primarily deep learning based, and I can only imagine that they're throwing things like architecture surge and things like that at the problem. Erez Barak: [00:41:58] Yeah. So they do happen in many cases I think custom vision is a good example. We don't see the general patterns just yet and for the ones we do see, the means of automation haven't put out yet. So if I look at where we were with the Auto ML thinking probably a few years back is where that is right now. Meaning, "Oh, it's interesting. We know there's something there." The question is how we further evolve into something more specific. Sam Charrington: [00:42:30] Well, Erez, thanks so much for taking the time to chat with us about what you're up to. Great conversation and learned a ton. Thank you. Erez Barak: [00:42:38] Same here. Thanks for your time and the questions were great. Had a great time.
Bits & Bytes Microsoft leads the AI patent race. As per EconSight research findings, Microsoft leads the AI patent race going into 2019 with 697 patents that the firm classifies as having a significant competitive impact as of November 2018. Out of the top 30 companies and research institutions as defined by EconSight in their recent analysis, Microsoft has created 20% of all patents in the global group of patent-producing companies and institutions. AI hides data from its creators to cheat at its appointed task. Research from Stanford and Google found that the ML agent intended to transform aerial images into street maps and back was found to be hiding information it would need later. Tech Mahindra launches GAiA for enterprises. GAiA is the first commercial version of the open source Acumos platform, explored in detail in my conversation with project sponsor Mazin Gilbert about a year ago. Taiwan AI Labs and Microsoft launch AI platform to facilitate genetic analysis. The new AI platform “TaiGenomics” utilizes AI techniques to process, analyze, and draw inferences from vast amounts of medical and genetic data provided by patients and hospitals. Google to open AI lab in Princeton. The AI lab will comprise a mix of faculty members and students. Elad Hazan and Yoram Singer, who both work at Google and Princeton and are co-developers of the AdaGrad algorithm, will lead the lab. The focus of the group is developing efficient methods for faster training. IBM designs AI-enabled fingernail sensor to track diseases. This tiny, wearable fingernail sensor can track disease progression and share details on medication effectiveness for Parkinson’s disease and cardiovascular health. ZestFinance and Microsoft collaborate on AI solution for credit underwriting. Financial institutions will be able to use the Zest Automated Machine Learning (ZAML) tools to build, deploy, and monitor credit models using the Microsoft Azure cloud and ML Server. Dollars & Sense Swiss startup  Sophia Genetics raises $77M to expand its AI diagnostic platform Baraja, LiDAR start-up, has raised $32M in a series A round of funding Semiconductor firm QuickLogic announced that it has acquired SensiML, a specialist in ML for IoT applications Donnelley Financial Solutions announced the acquisition of eBrevia, a provider of AI-based data extraction and contract analytics software solutions Graphcore, a UK-based AI chipmaker, has secured $200M in funding, investors include BMW Ventures and Microsoft Dataiku Inc, offering an enterprise data science and ML platform, has raised $101M in Series C funding Ada, a Toronto-based co focused on automating customer service, has raised $19M in funding To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.
Happy New Year! I've spent the week at CES in Las Vegas this week, checking out a bunch of exciting new technology. (And a bunch of not-so-exciting technology as well.) I'll be writing a bit more about my experiences at CES on the TWIML blog, but for now I'll simply state the obvious: AI was front and center at this year's show, with many interesting applications, spanning smart home and city to autonomous vehicles (using the term vehicle very broadly) to health tech and fitness tech. I focused on making videos this time around, and we'll be adding a bunch from the show to our CES 2019 playlist over on Youtube, so be sure to check that out and subscribe to our channel while you're there. In other news, we just wrapped up our AI Rewind 2018 series in which I discussed key trends from 2018 and predictions for 2019 with some of your favorite TWIML guests. This series was a bit of an experiment for us and we're excited to have received a lot of great feedback on it. If you've had a chance to check it out I'd love to hear your thoughts. Cheers, Sam P.S. We're always looking for sponsors to support our work with the podcast. If you think your company might benefit from TWIML sponsorship, I'd love your help getting connected to the right people. P.P.S. I'm planning a visit to the Bay Area for the week of January 21st. I've got a few open slots for briefings and meeting up with listeners. If you're interested in connecting give me a holler.   To receive this to your inbox, subscribe to our Newsletter.
In the final episode of our AI Rewind series, we're excited to have Siddha Ganju back on the show. Siddha, who is now an autonomous vehicles solutions architect at Nvidia shares her thoughts on trends in Computer Vision in 2018 and beyond. We cover her favorite CV papers of the year in areas such as neural architecture search, learning from simulation, application of CV to augmented reality, and more, as well as a bevy of tools and open source projects.
In this episode of our AI Rewind series, we're bringing back one of your favorite guests of the year, Jeremy Howard, founder and researcher at Fast.ai. Jeremy joins us to discuss trends in Deep Learning in 2018 and beyond. We cover many of the papers, tools and techniques that have contributed to making deep learning more accessible than ever to so many developers and data scientists.
Bits & Bytes IBM, Nvidia pair up on AI-optimized converged storage system.  IBM SpectrumAI with Nvidia DGX, is a converged system that combines a software-defined file system, all-flash storage, and Nvidia's DGX-1 GPU system. The storage system supports AI workloads and data tools such as TensorFlow, PyTorch, and Spark. Google announces Cloud TPU Pods, availability in alpha.  Google Cloud TPU Pods alpha are tightly-coupled supercomputers built with hundreds of Google’s custom Tensor Processing Unit (TPU) chips and dozens of host machines. Price/performance benchmarking shows a 27x speedup for nearly 40% lower cost in training a ResNet-50 network. MediaTek announces the Helio P90.  The Helio P90 system-on-chip (SoC) uses the company's APU 2.0 AI architecture. APU 2.0 is a leading fusion AI architecture designed by MediaTek can deliver a new level of AI experiences that are 4X more powerful than the Helio P70 and Helio P60 chipsets. Facebook open sources PyText for faster NLP development. Facebook has open sourced the PyText modeling framework for NLP experimentation and deployment. The library is built on PyTorch and supports use cases such as document classification, sequence tagging, semantic parsing, and multitask modeling. On scaling AI training. This interesting article from OpenAI proposes that the gradient noise scale metric can be used to predict parallelizability of training for a wide variety of tasks, and explores the relationship between gradient noise scale, batch size, and training speed. Dollars & Sense TechSee, Tel Aviv-based provider of AI-powered visual customer engagement solutions, has secured $16M in Series B funding Zesty.ai, an Oakland, CA-based AI startup, closed US$13M Series A financing Walmart Labs India, the product development division of the US retail giant, announced that it has acqui-hired AI and data analytics startup Int.ai Avnet, Inc announced that it will acquire Softweb Solutions, Inc., a software and AI company that provides software solutions for IoT Sign up for our Newsletter to receive this weekly to your inbox.
A couple of weeks ago I spent the week in Las Vegas at the Amazon Web Services (AWS) re:Invent conference, and we shared a few of my interviews from the event in our AWS re:Invent Series. I’ll share a bit of the news coming out of the event in this post, but if you’re interested in machine learning and artificial intelligence, and especially in the intersection of ML/AI and cloud computing, I really recommend that you tune into our 2nd Annual re:Invent Roundup Roundtable. The Roundtable is a fun TWIML “tradition” in which myself and a couple of panelists get together at re:Invent to recap the week’s announcements. (Note to self: I really like this format and need to do it more often.) This year, the Roundtable included veteran participant Dave McCrory (VP of Engineering at Wise.io at GE Digital) and Val Bercovici (CEO of startup Pencil Data). Dave, Val, and I cover all of the interesting AI news and highlights from this year’s conference. Here are a bunch of the main machine learning and artificial intelligence announcements made by AWS around re:Invent: New Features for Amazon SageMaker: Workflows, Algorithms, and Accreditation. In the run-up to re:Invent (aka “pre:Invent”), Amazon announced new automation, orchestration, and collaboration features to make it easier to build, manage, and share machine learning workflows. SageMaker RL aims to bring reinforcement learning to the masses. Newreinforcement learning extensions to SageMaker support 2D & 3D simulation and physics environments as well as OpenAI’s OpenGym. Robotics tools Sumerian, RoboMaker, and ROS are also supported. AWS also announced AWS DeepRacer, a new 1/18th scale autonomous model race car for developers, driven by reinforcement learning. SageMaker Ground Truth simplifies data labeling. The service aims to allow developers to build highly accurate training datasets using machine learning while reducing data labeling costs by up to 70% using active learning. SageMaker Neo to optimize AI performance at the edge. Neo is an open source compiler and runtime targeting edge devices. It provides automatic optimization and will compile deep learning models to run on any edge device at up to 2x the performance & 1/10th the size. Amazon Textract allows developers to extract text from virtually any document. The new service automatically extracts text and tabular data from scanned documents. Amazon Transcribe now supports real-time transcriptions. With the new feature called Streaming Transcription, Amazon Transcribe (speech-to-text service) will be able to create text transcripts from audio in real time. Amazon Rekognition announces updates to its face detection, analysis, and recognition capabilities. The new updates will enhance the ability to detect more faces from images, perform higher accuracy face matches, and obtain improved age, gender, and emotion attributes for faces in images. Amazon launches ML-based platform for healthcare. Amazon Comprehend Medical platform allows developers to process unstructured medical text and identify information such as patient diagnosis, treatments, dosages, symptoms and signs, and more. Amazon announces new ML chip. AWS’ new ML chip, Inferentia, is a high-throughput, low-latency, and a cost-effective processor. It supports multiple ML frameworks, including TensorFlow, Caffe2, and ONNX. NVIDIA AI Software is now available on AWS Marketplace. This will simplify access to NVIDIA software on Amazon ECS and Amazon EKS. Six NVIDIA containers will be available on AWS marketplace; including CUDA, MXNet, PyTorch, TensorFlow, TensorRT, and TensorRT Inference Server. Amazon QuickSight announces ML Insights in preview. Amazon announced that it is adding three new features to Amazon QuickSight to provide customers with ML-powered insights beyond visualizations. New features will provide hidden insights, forecasting, and narrative description of the dashboard Amazon announced new personalization and forecasting services. AWS announced a Amazon Personalize and Amazon Forecast. These fully-managed services incorporate Auto-ML features to put ML-powered personalization and forecasting capabilities into the hands of developers with little ML experience. Amazon SageMaker now comes with a search feature. The SageMaker Search capability lets you find and evaluate the most relevant model training. The new feature will accelerate the model development and will reduce overall time to market ML-based solutions. AWS introduces dynamic training for DL with Amazon EC2. Dynamic Training (DT) for DL models allows DL practitioners to reduce model training cost and time by leveraging the cloud’s elasticity and economies of scale. Amazon’s ‘Machine Learning University’ is now available to all developers. AWS announced that its ML courses to train engineers at Amazon are now available to all developers. There are more than 30 self-service, self-paced digital courses with more than 45 hours of courses, videos, and labs for four key groups: developers, data scientists, data platform engineers, and business professionals. As you can see, AWS announced a ton of new ML and AI capabilities this year, making it fun, and challenging, to try to keep up with it all. In addition to the Roundup, you should also check out my conversation with Jinho Choi of Emory University, discussing key challenges faced with the cloud-based NLP platform and his vision for his group’s ELIT platform, and my conversation with Thorsten Joachims of Cornell University, discussing the inherent and introduced biases in recommender systems, and how inference techniques can be used to make learning algorithms more robust to them.
Bits & Bytes ONNX Runtime for ML inference now in preview. Microsoft released a preview of the ONNX Runtime, a high-performance inference engine for Open Neural Network Exchange (ONNX) models. It is compatible with ONNX version 1.2 and comes in Python packages that support both CPU and GPU. Uber describes new platform for rapid Python ML development. Uber shared Michelangelo PyML, an extension to its Michelangelo platform providing for faster development and experimentation based on Docker containers. NYU and Facebook release cross-language NLU data set. As researchers look to increase the number of languages NLU systems can understand, gathering and annotating data in every language is a bottleneck. One alternative is to train a model on data in one language and then test that model in other languages. The Cross-Lingual Natural Language Inference (XNLI) data set advances this approach by providing that test data in languages. Malong researchers develop a technique to train deep neural networks. In this new paper, Malong introduces CurriculumNet, a training strategy leveraging curriculum learning to increase performance while decreasing noise when working on large sets of data. The code is now available on GitHub as well. Facebook launches Horizon reinforcement learning platform. Facebook has open-sourced Horizon, an end-to-end applied reinforcement learning platform. Unlike other open-source RL platforms focused on gameplay, Horizon targets real-world applications and is used at Facebook to optimize notifications, video streams, and chatbot suggestions. Google launches AdaNet for combining algorithms with AutoML. Google launched AdaNet, an open-source tool for automatically creating high-quality models based on neural architecture search and ensemble learning. Users can add their own model definitions to AdaNet using high-level TensorFlow APIs. Dollars & Sense People.ai announced that it has raised $30M in Series B funding led by Andreessen Horowitz DataRobot, a Boston-based automated ML company, raised $100M in Series D funding Syntiant Corp, an Irvine-based AI semiconductor company, raised $25M in Series B funding led by M12, Microsoft’s VC arm Oracle announced that it has acquired data management and AI solutions provider DataFox eSentire has acquired Seattle-based cybersecurity AI company Versive (formerly Context Relevant) AppZen, an AI auditing solutions provider, announced $35 million funding led by Lightspeed Venture Partners Validere, which provides an AI and IoT platform for oil and gas, raised $7m in seed funding Esperanto Technologies a hardware company focused on energy efficient systems for AI, ML, and DL, closed $58m Series B funding Conversica, offering conversational AI products for sales and marketing, announced it has secured a $31 million Series C funding Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Researchers develop AI to detect musical mood. Deezer researchers have developed a deep learning system which can identify the mood and intensity of songs based on audio and lyrics. Microsoft announces automated machine learning service. The new service aims to identify the best machine learning pipeline for the user’s labeled data. Automated ML is integrated with Azure Machine Learning and includes an SDK for integration with Python development environments including Visual Studio Code, PyCharm, Azure Databricks notebooks and Jupyter notebooks. Microsoft contributes $40 million for humanitarian AI. Microsoft launched a new $40-million program aimed at harnessing the power of AI for humanitarian action. The program targets causes such as disaster recovery, helping children, and protecting refugees. Microsoft features Shell industrial AI use cases. Early in his Ignite keynote, Microsoft CEO Satya Nadella highlighted work by Microsoft and Bonsai, which it acquired earlier this year, performed at Shell. The join work aims to enhance safety by applying AI and IoT in a number of areas including at retail gas stations. DeepMind outlines Technical AI Safety program. Interesting post by Google DeepMind researchers outlining the key tenets—specification, robustness, and assurance—of their AI safety research program. AI’s new muse: our sense of smell. Artificial neural networks have been loosely inspired by the brain, specifically the visual cortex. This article describes recent work being done to draw inspiration from our olfactory circuits as well. Dollars & Sense Netradyne, which applies AI to driver and fleet safety, has raised Series B funding of $21 million Olis Robotics, announced the acquisition of White Marsh Forests, a machine learning startup based in Seattle Slack, announced that it has acquired Astro, a messaging startup that applied AI to email Marketing agency Impact Group acquired ad platform startup Cluepto help connect retail brands to consumers Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes IBM launches tool aimed at detecting AI bias. IBM Research has launched the AI Fairness 360 Kit to scan for signs of AI bias and make recommendation adjustments. The open source Python package contains nine different algorithms, developed by the broader algorithmic fairness research community, to mitigate unwanted bias. Microsoft adds Tensorflow scoring to ML.Net. The company has added TensorFlow model scoring to version 0.5 of its ML.Net open source machine learning framework, enabling the use of existing TensorFlow models in ML.Net experiments. Tencent announces new AI services. The new Tencent Open AI Platform, called "AI.QQ.COM," aims to build a services ecosystem leveraging Tencent’s various AI capabilities. The platform makes more than 100 AI APIs available to industry. Accenture Introduces new healthcare bots. The virtual-assistant bots “Ella” and “Ethan” join the Accenture Intelligent Patient Platform to make intelligent recommendations for interactions between life sciences companies, patients, health care providers, and caregivers. Miso Robotics enhances AI platform to include frying skills. The cloud-connected Miso AI platform now enables Flippy, the company's autonomous robotic kitchen assistant, to perform frying tasks in addition to grilling. Dollars & Sense Ontario-based DarwinAI, which develops tools for optimizing deep neural nets, raised $3 million, led by Obvious Ventures and iNovia Capital Leena AI, an HR bot startup has raised $2 million in a seed funding round from a group of Silicon Valley investors Oxbotica, an Oxford, UK-based autonomous vehicle software company, completed a £14m funding round Orbital Insight announced its acquisition of a Boston-based “FeatureX,” which specializes in computer vision for satellite imagery Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Executive change at Google Cloud AI. Google Cloud AI head Dr. Fei-Fei Li has left the organization and will be returning to her professorship at Stanford. Dr. Andrew Moore, Dean of the School of Computer Science at CMU, will replace her by year end. Cisco unveils server for Artificial Intelligence and Machine Learning. Cisco has launched new servers aimed at speeding up deep learning workloads. Facebook's 'Rosetta' can extract text from a billion images daily. Facebook has developed a machine learning system called Rosetta for contextual extraction of text in images. The system supports image search and will also help Facebook identify inappropriate or harmful content. NVIDIA launches new data center inference. NVIDIA launched “TensorRT Hyperscale” offering inference acceleration for voice, video, image and recommendation services. The new platform features NVIDIA Tesla T4 GPUs based on the Turing architecture. Facebook developed an AI-based debugging tool. The tool, called “SapFix,” aims to help programmers by finding and fixing software bugs automatically. Google open sources ‘What-If Tool’ for code-free ML Experiments. Google’s AI research team has developed What-If Tool, a new TensorBoard feature allowing users to analyze an ML model without writing code. The tool offers a visual interface for exploring different model results. Dollars & Sense Syllable.ai, which offers a healthcare chat platform, raises $13.7M Integrate.ai, a Toronto-based AI software company, secures $30 million in Series A funding Microsoft announced the acquisition of San Francisco based Lobe, whose slick demo of code-free deep learning swept the Twitters a few months ago Deloitte announced its acquisition of Magnetic Media Online's artificial intelligence platform business. Magnetic is a marketing technology company headquartered in New York City Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
This video is a recap of our September 2018 EMEA TWIML Online Meetup. In this month's community segment we briefly look at Remote Sensing and Auto Encoders for Weather Tracking. We also discuss the status of upcoming Fast.AI Study Groups, including groups looking to reengage in part 1 again, Starting Part 2, and the release up the updated Version 2 (Lesson 1). In our presentation segment, Kai Lichtenberg leads us in a breakdown of Geoffrey Hinton’s CapsNets paper. Topics covered include: - Whats wrong with CNNs - Why our brain is probably doing “Inverse Graphics.” - What is capsule? - CapsNet and Dynamic Routing. https://youtu.be/G7lvnt1eRjw For links to the papers mentioned above and more information on this and previous meetups, or to get registered for upcoming meetups, visit twimlai.com/meetup! Paper: Dynamic Routing Between Capsules Paper: Matrix Capsules with EM Routing
Bits & Bytes Diffbot launches knowledge graph as-a-service. The startup, whose roots are in web scraping, applied machine learning, computer vision, and natural language processing to create a database of ‘all the knowledge of the Web,’ spanning over 10 billion entities and 1 trillion facts. Automatic transliteration helps Alexa find data across language barriers. Amazon researchers have developed a multilingual “named-entity transliteration system” to help Alexa overcome language barriers in multilingual environment. Oracle open sources GraphPipe for model deployment. Though Oracle has a strained relationship with open source, they recently released a new open source tool called GraphPipe, designed to simplify and standardize the deployment of machine learning models. Google turns datacenter cooling controls over to AI. Google was already using AI to optimize data center energy efficiency. Now they’ve handed over complete control of data center cooling to AI. Instead of humans implementing AI-generated recommendations, the system is now directly controlling data center cooling. IBM researchers propose ‘factsheets’ for AI transparency. Expanding on ideas like the Datasheets for Datasets paper I discussed previously, an IBM Research team has suggested a factsheet based approach for AI developers to ensure transparency. Facebook and NYU researchers speed up MRI scans with AI. Facebook announced the fastMRI project, in collaboration with NYU, which aims to apply AI to accelerate MRI scans by up to 10 times. Google releases Dopamine reinforcement learning research framework. Google announced the new TensorFlow-based framework, which aims to provide flexibility, stability, and reproducibility for new and experienced RL researchers. Baidu launches EZDL, a coding-free deep learning platform. Chinese firm Baidu EZDL, an online tool enabling anyone to build, design, and deploy models without writing code. Dollars & Sense Canvass Analytics, a Toronto-based provider of AI-enabled predictive analytics for IIOT, raised $5M in funding. Cloudalize, a cloud platform for running GPU-accelerated applications, has secured a €5 million funding round. Intel announced that it is buying Vertex.ai, a startup developing a platform-agnostic model suite, for an undisclosed amount. Zscaler, announced that it has acquired AI and ML technology and the development team of stealth security startup TrustPath. New Knowledge, an Austin-based cybersecurity company that protects corporations from covert, coordinated disinformation campaigns, raised $11M in Series A funding. Phrasee, a London based marketing technology company that uses AI to generate optimized marketing copy, closed a $4m Series A funding round. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes IBM Research presents 'DeepLocker,' AI-powered malware. IBM researchers have developed DeepLocker, a new breed of highly targeted and evasive attack tools powered by AI. The malware remains dormant until identifying its target through indicators like facial recognition, geolocation and voice recognition. The project is meant to raise awareness of the possibilities when AI is combined with current malware techniques. Microsoft Updates ML.NET Machine Learning Framework Microsoft has updated ML.NET, its cross-platform, open-source machine learning framework for .NET developers. This initial preview release of ML.NET enables simple tasks like classification and regression and brings the first draft of .NET APIs for training and inference. The framework can be extended to add support for popular ML libraries like TensorFlow, Accord.NET, and CNTK. Tesla dumps Nvidia, goes it alone on AI hardware. Tesla confirmed that it has developed its own processor for AutoPilot and other on-vehicle AI workloads. The company mentioned that the new processor is 10 times faster than the one it was using from Nvidia, and the linked article’s author estimates the company could save billions by developing its own. Doc.ai and Anthem to introduce health data trial powered by AI and blockchain. The companies have partnered to launch an AI data trial on the blockchain. doc.ai will attempt to identify models for predicting allergies based upon collected phenome (e.g., age, height, weight), exposome (e.g., exposure to weather/pollution based on location) and physiome (e.g., physical activity, daily steps) data. NetApp and NVIDIA launch new deep learning architecture. NetApp introduced the NetApp® ONTAP® AI architecture, powered by NVIDIA DGX™ supercomputers and NetApp AFF A800 cloud-connected all-flash storage to simplify, speed, and scale the deep learning data pipeline. Getty Images launches AI tool for media publishers. Getty Images launched Panels by Getty Images, a new artificial intelligence tool for media publishers that recommends the best visual content to accompany a news article. Dollars & Sense Malong Technologies, a Shenzhen, China-based computer vision startup, received a minority investment from Accenture Test.ai, which AI to the challenge of testing apps, has raised an $11 million Series A round led by Gradient Ventures Skyline AI, a New York based real estate investech firm using AI and data science, raised $18M in Series A funding DefinedCrowd, which provides crowd-sourced data for training AI, has raised a $11.8 million funding round led by Evolution Equity Partners Dbrain, whose platform links crowdworkers and data scientists via the blockchain, has raised a $3 million funding round Racetrack.ai, which applies AI to accelerating business sales, has raised $5 million in a pre-Series A round Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Elon Musk, DeepMind co-founders promise never to make killer robots. The founders have signed on to the Future of Live Institute’s pledge to develop, manufacture or use killer robots, which was published at the annual International Joint Conference on Artificial Intelligence in Stockholm, Sweden Huawei plans AI chips to rival Nvidia, Intel. The company is reportedly developing AI chips for both networking equipment and the datacenter in an effort to strengthen its position in growing AI market and to compete with the likes of Nvidia and Intel. Let the sniping continue. Facebook has hired Shahriar Rabii to lead its chip initiative. Rabii previously worked at Google, where he helped lead the team in charge of building the Visual Core chip for the company’s Pixel devices. Apple has appointed former Google AI exec John Giannandrea to lead a new artificial intelligence and machine learning team, to include the Siri unit. Interesting projects. Researchers at Nvidia, MIT, and Aalto University presented an approach to automatically removing noise, grain, and even watermarks from photos at ICML. A Google researcher along with collaborators from academia have developed a deep learning-based system for identifying protein crystallization, achieving a 94% accuracy rate and potentially improving the drug discovery process by making it easier to map the structures of proteins. Google revealed “Move Mirror,” an ML experiment that matches user’s poses with images of other people in the same pose. Dollars & Sense R4 Technologies, a Ridgefield, Connecticut-based AI startup created by Priceline.com founders and executives, secured $20m in Series B funding Cambridge-based SWIM.AI, which provides edge intelligence software for IoT applications, announced $10 million in Series B funding Viz.ai, a company applying AI in healthcare secured $21 million in Series A funding Computer vision technology provider AnyVision announced at it has secured $28 million in Series A financing Salesforce has signed a definitive agreement to acquire Datorama, an AI-powered marketing intelligence platform Workday announced that it has acquired Stories.bi, which uses AI to automate analytics and generate natural language business stories Robotic retail inventory specialist Bossa Nova announced the acquisition of AI video surveillance company, HawXeye Self-driving car company Pony.ai raised $102 million, putting it close to a billion dollar valuation Box announced that it has acquired Butter.ai, a startup focused on cross-silo enterprise search DataRobot announced that it has acquired Nexosis, an ML platform company whose founders we interviewed in TWIML Talk #69 Accenture has acquired Kogentix to strengthen Accenture Applied Intelligence’s growing data engineering business Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
This video is a recap of our July 2018 TWIML Online Meetup. In this month's community segment we look at the ongoing Fast.ai Study Group, the upcoming meetup presenter schedule, the recent Glow paper from the folks at OpenAI, and entity embeddings. In our presentation segment, Nicholas Teauge leads us in a discussion on the paper Quantum Machine Learning by Jacob Biamonte et al, which explores how to devise and implement concrete quantum software that outperforms classical computers on machine learning tasks. For links to the papers mentioned above and more information on this and previous meetups, or to get registered for upcoming meetups, visit twimlai.com/meetup! https://youtu.be/-ftniM7248I Paper: Quantum Machine Learning OpenAI Glow
In this episode I'm joined by Amir Zamir, Postdoctoral researcher at both Stanford & UC Berkeley. Amir joins us fresh off of winning the 2018 CVPR Best Paper Award for co-authoring "Taskonomy: Disentangling Task Transfer Learning." In this work, Amir and his coauthors explore the relationships between different types of visual tasks and use this structure to better understand the types of transfer learning that will be most effective for each, resulting in what they call a "computational taxonomic map for task transfer learning." In our conversation, we discuss the nature and consequences of the relationships that Amir and his team discovered, and how they can be used to build more effective visual systems with machine learning. Along the way Amir provides a ton of great examples and explains the various tools his team has created to illustrate these concepts.
Bits & Bytes AI around the world. This interesting post summarizes the national AI strategies of the 15 nations that have formally published them. Baidu unveils AI chipset. Baidu launches Kunlun, China's first “cloud-to-edge” AI chips. The chips were built to accommodate a variety of AI scenarios–such as voice recognition, search ranking, natural language processing, autonomous driving and large-scale recommendations–and can be applied to both cloud and edge scenarios, including data centers, public clouds, autonomous vehicles, and other devices. Kunlun includes distinct chips for both training and inference. New research explores identification of Photoshopped images. At the recent CVPR conference, University of Maryland researchers presented a method for identifying edited pictures using deep learning. Stanford AI recreates periodic table. Applying techniques borrowed from NLP, Stanford researchers created “Atom2Vec.” The tool analyzed a list of chemical compound names from an online database and proceeded to re-create the periodic table of elements in a few hours. ‘AI eating software’ roundup. Silicon design tools vendor NetSpeed incorporates AI features into new SoCBuilder design and integration platform. AMFG launches new AI software platform for industrial 3D printing. Energy industry ERP provider Quorum Software adds a new cognitive services layer providing intelligent ingest, compliance and reporting capabilities. Dollars & Sense Facebook has acquired the team behind Bloomsbury AI, a London firm specializing in using ML to understand natural language documents JDA Software to acquire Blue Yonder, a provider of AI solutions for retail D.C. startup QxBranch closed $4.1 million in Series A funding to develop analytics software for quantum computing JASK, an Autonomous Security Operations Center (ASOC) platform provider announced that it has raised $25M in Series B funding Suzhou city-based AISpeech announced $76 million in Series D funding, bringing its total funding to over $121 million Tact.ai, a conversational AI sales platform, announced its $27M Series C raise, bringing total funding to more than $57M Cybersecurity firm Balbix raises $20 million in Series B round led by Singtel Innov8 Ping Identity acquires Elastic Beam, a cybersecurity startup using artificial intelligence to monitor and protect APIs Precision Therapeutics, a company focused on AI-based personalized medicine and drug discovery, announced that its merger with Helomics Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes IBM hosts first AI-Human debate. In a publicity stunt in the spirit of Deep Blue’s match with Garry Kasparov, or IBM Watson’s appearance on Jeopardy, IBM hosted the first ever live public debate between its Project Debater AI and a human in San Francisco last week. The company has published several datasets and technical papersoutlining various components of the system. Nvidia publishes “Super SloMo” for transforming standard video into slow motion At last week’s CVPR, NVIDIA researchers presented research into a deep learning model for interpolating between video frames to produce slow-motion video from a standard 30-frame-per-second video. Amazon SageMaker now supports PyTorch and TensorFlow 1.8: In a recent update, Amazon SageMaker has added support for PyTorch deep learning models. I’m now wondering if the fast.ai course and library can be completed on SageMaker. AWS is also now supporting the latest stable TensorFlow versions. Microsoft to acquire Bonsai, one of my favorite AI companies. Berkeley-based Bonsai, a client of mine and sponsor of last year’s Industrial AI series, offers a deep reinforcement learning platform for enterprise AI. I’m super excited for them and looking forward to seeing how things evolve now that they’ll be part of Microsoft. Tracking the state-of-the-art (SOTA) in NLP. Researcher Sebastian Ruder has put together an interesting project to track the SOTA of a variety of problems in natural language processing. Dollars & Sense AI-powered fitness startup Vi raises $20 million Falkonry, a provider of machine learning software for manufacturing, raised $4.6 million in Series A funding Prifender, whose software uses AI to map PII in enterprise data systems, raised a $5M seed round AI-as-a-service startup Noodle.ai announced a $35 million round led by Dell Technologies WalkMe announced that it has acquired DeepUI, whose ML models seek to understand any software at the GUI level, without the need for an API Twitter has agreed to buy San Francisco-based Smyte, which offers tools to stop online abuse, harassment, and spam, and protect user accounts PayPal announced today that it has agreed to acquire Simility, a leading fraud prevention and risk management platform provider for $120 million Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes CMA CGM to use artificial intelligence on ships. Leading global shipper CMA CGM Group announced a development partnership with Shone, an early-stage startup building autonomous technologies for cargo ships. The agreement gives Shone access to CMA CGM’s vessels and data. Once completed, the Shone product will facilitate the work of crews in decision support, maritime safety, and piloting. Pega paves path to one-to-one marketing with self-optimizing campaigns. New AI-powered marketing capability will reduce marketers’ dependence on traditional segment-based campaigns and transition them towards real-time, one-to-one engagement. The new offering will face stiff competition from SAP and Adobe, which already have offerings in this space, when it launches as part of PegaInfinity in Q3. IBM and H2O.ai collaborate on POWER9 support. IBM Power Systems and H2O have announced a collaboration to accelerate the latter’s Driverless AI software on IBM POWER9 systems. Google publishes Responsible AI Practices. Following missteps around the launch of Google Duplex and its DOD deal, Google has released a set of principles which will direct its future use of AI technology. The principles aim to ensure that the company’s use of AI is socially beneficial, accountable, incorporates privacy-by-design, and adheres to ethical standards. Databricks introduces MLflow, an open source ML platform. Databricks has released MLflow, a new open source project for managing the lifecycle of machine learning models. Apple has released Core ML 2. Apple released Core ML 2, a new version of its machine learning SDK for iOS devices, at its annual developer conference. Core ML 2 includes the new Create ML tool which will allow developers to create and train custom machine learning models locally on their Mac and enables Keras and sci-kit learn users to import models directly into Core ML. Dollars & Sense Zebra Medical Vision, an Israeli medical imaging startup that uses machine and deep learning to build tools for radiologists, has raised a $30 million Series C CloudNC, the U.K. startup that is developing AI software to automate part of the manufacturing process, has raised £9 million in Series A funding Black Knight has acquired HeavyWater, a provider of artificial intelligence and machine learning (AI/ML) tools to the financial services industry Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes Google won’t renew its military AI contract. According to company sources, Google is planning to close the military AI project after the current contract expires in March 2019. Google staff had expressed their unhappiness over project Maven earlier this year. Nvidia Introduces HGX-2 for HPC and AI. Nvidia has introduced a unified computing platform HGX-2 for both artificial intelligence and high-performance The new cloud server platform allows high-precision calculations for scientific computing and simulations and will also enable AI training and inference. Nvidia Jetson platform goes GA. Nvidia announced the general availability of Nvidia Isaac, which brings AI capabilities to robots for manufacturing, logistics, agriculture, construction and many other industries. The Isaac platform includes new hardware, software, and a realistic robot simulator. Qualcomm reveals new XR platform. Qualcomm introduced its new dedicated Extended Reality (XR) platform Qualcomm® Snapdragon™ XR1. The XR1 platform includes an on-device AI engine so that Augmented Reality (AR) developers can take advantage of AI features like better pose prediction and object classification. Microsoft’s AI bot also calls humans, but only in Chinese. After Google’s Duplex demo, which showed an AI calling to make a reservation and conversing with an employee, Microsoft CEO Satya Nadella demoed a similar service at an event in London. Unlike Duplex, which is just a demo at this point, Microsoft says that thousands of users in China have conversed with its AI, called Xiaoice and it can also call for the conversation. Intel AI Lab open-sources deep NLP library. Intel AI Lab has open-sourced a library for deep-learning-based natural language processing to help researchers and developers create conversational agents like chatbots and virtual assistants. Facebook researchers demonstrate musical style transfer. Researchers from Facebook Artificial Intelligence Research (FAIR) have developed an AI system that can translate music between different styles. Particularly impressive was the ability to translate a whistled version of the Raiders of the Lost Ark theme song to a variety of instruments and classical styles. The accompanying paper, A Universal Music Translation Network, is available on arXiv. Dollars & Sense Flock, a London-based startup focused on the use of real-time data in insurance, has raised a £2.25M Seed funding ForwardX, maker of Ovis, the self-driving suitcase you never knew you needed, raised $10M Series A funding Chinese facial recognition technology developer SenseTime Group Ltd said it has raised $620 million in a second round of funding Kneron, provider of edge artificial intelligence (AI) solutions, announced completion of their series A1 financing of US$18 millionled by Horizons Ventures Weights & Biases, a San Francisco, CA-based enterprise AI platform, received $5m in Series A funding Kontrol Energy Corp, a leader in the energy efficiency market announced acquisition of iDimax will be adding Artificial Intelligence (AI) across its energy software platform PayPal announced that it has acquired Jetlore, an artificial intelligence-powered prediction platform SafeToNet, the UK-based cyber safety company, has acquired the Toronto-based artificial intelligence (AI) and natural language processing startup, VISR Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
We recently ran a series of shows on differential privacy on the podcast. It’s an especially salient topic given the rollout of the EU’s General Data Protection Regulation (GDPR), which becomes effective this month, not to mention scandals like the Facebook/Cambridge Analytica breach and other attacks on private data. If you hadn’t previously (or haven’t yet) heard the term differential privacy, you’re not alone. The field is relatively new–only about ten years old. Differential privacy attempts to allow data holders to make confidential data available for analysis or use via a data product while simultaneously preserving–actually, guaranteeing–the privacy of the individuals whose data is included in the database or data product. Differential privacy is often introduced in contrast to data anonymization. While anonymization might seem to be a reasonable way to protect the privacy of those data subjects whose information is included in a data product, that information is vulnerable to numerous types of attack. Consider, for example, the Netflix Prize, a frequently cited example. In support of a competition to see if someone could build a better recommendation engine, Netflix made an anonymized movie rating dataset available to the public. A group of researchers, however, discovered a linkage attack that allowed large portions of the data to be de-anonymized by cross-referencing it with publicly available IMDB user data. But what if we don’t want to publish data, but rather use it to create machine learning models that we allow others to query or incorporate into products? It turns out that machine learning models are vulnerable to privacy leakage as well. For example, consider a membership inference attack against a machine learning model. In this kind of attack, patterns in the model’s output are used to extract the data the model was trained on. These types of attacks, powered by ‘shadow’ machine learning models, have been shown to be effective against black-box models trained in the cloud with Google Prediction API and Amazon ML. In another example, an attack called model inversion [pdf] was used to extract recognizable training images (i.e. faces) from cloud-based image recognition APIs. Because these APIs return a confidence score alongside the label of a face submitted for recognition, an adversary could systematically construct an input face that maximizes the APIs confidence in a given label.   Differential privacy is an approach that provides mathematically guaranteed privacy bounds–it’s not a specific algorithm. For any given problem, there can be many algorithms that provide differential privacy. Aaron Roth provided a great example of a simple differential privacy algorithm in our interview. In his example, a polling company wants to collect data about who will vote for Trump in the upcoming election, but are concerned about the privacy of the people they poll. Roth explains that they could use a simple yet differentially private method of collecting the data. Instead of simply asking for the pollees voting data, the company could instruct the individuals to first flip a coin and if the coin is heads, answer the question honestly, but if the coin is tails, give a random answer decided by another coin flip. Because the statistical characteristics of the coin flip are known, you can still make inferences about the wider population even though your data collection has been partially corrupted. At the same time, this method ensures that the individuals in your study are protected by plausible deniability. That is to say, if the data were to be exposed there’s no way of knowing if a given answer was honest or part of the injected noise. Some tech companies are already starting to reap the benefits of differential privacy. For example: Apple. Apple uses differentially private methods of capturing user data to gain insights about user behavior on a large scale. They’re currently using this method for applications as diverse as QuickType and Emoji suggestions, Lookup Hints in Notes, crashing and energy draining domains in Safari, autoplay intent in Safari, and more. Google. In addition to using differential privacy to help understand the effectiveness of search query suggestions in its Gboard keyboard, Google, along with other cloud providers, has a huge incentive to explore these methods due to the public nature of many of the machine learning models they offer. Google has published several papers on the topic so far, including RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response and Deep Learning with Differential Privacy. Bluecore. Bluecore offers software and services to help marketers find and retain their best customers through targeted email marketing. The company uses differential privacy techniques to pool data across companies to improve customer outcomes while preventing any individual customer from being able to gain any insights into competitors’ data. Be sure to check out my interview with Bluecore director of data science Zahi Karam. Uber. Uber uses differential privacy to protect sensitive data against internal and external privacy risks. When company data analysts explore average trip distances in a city, for example, their queries go through an internal differential privacy system called Chorus, which rewrites their queries to ensure differential privacy. Open-source tools for differential privacy are beginning to emerge from both academic and commercial sources. A few examples include: Epsilon is a new differential privacy software system offered by Georgian Partners. Epsilon currently works for logistic regression and SVM models. At this time it’s only offered to the firm’s partners and portfolio companies, but the team behind that project plans to continue expanding the tool’s capabilities and availability. For more check out my interview with Georgian’s Chang Liu. SQL Elastic Privacy is an open source tool from Uber that can be used in an analytics pipeline to determine the level of privacy required by a given SQL query. This becomes a parameter that allows them to fine-tune their differential privacy algorithm. Diffpriv is an R package that aims to make differential privacy easy for data scientists. Diffpriv replaces theoretical sensitivity analysis with sensitivity sampling, helping to automate the creation of privacy assured statistics, models, and other structures. ARX is a more comprehensive open-source offering comprising a GUI-based tool and a Java library implementing a variety of approaches privacy-preserving data analysis, including differential privacy. As you might imagine, differential privacy continues to be an active research topic. According to Roth, hot research areas include the use of differential privacy to create and publish synthetic datasets, especially for medical use cases, as well as better understanding the ‘local method’ of differentially private data collection, in which the noise is injected at the time of collection as opposed to after collection. Differential privacy isn’t a silver bullet capable of fixing all of our privacy concerns, but it’s an important emerging tool for helping organizations work with and publish sensitive data and data products in a privacy-preserving manner. I really enjoyed producing this series and learned a ton. I’m eager to hear about what readers and listeners think about it, so please email or tweet over any comments or comment below. Sign up for our Newsletter to receive this weekly to your inbox.
Bits and Bytes This week news from Google I/O and Microsoft Build have dominated the news. Here are the highlights: Google introduces ML developer kit. The SDK supports text recognition, face detection, barcode scanning, image labeling, and landmark recognition for developers integrating AI tech into iOS and Android. It also boasts the ability to run its models offline. Google demos lifelike voice bot with Google Duplex. The technology is capable of conducting natural conversations to carry out real-world tasks–such as booking an appointment–over the phone. It works with Google Assistant, which also got 6 new voices and the ability to perform complex tasks via voice command. Microsoft and Qualcomm partner for vision at the edge. The partnership allows for Qualcomm’s latest AI hardware accelerators to deliver real-time AI at the edge. The developer kit, which uses Microsoft’s Azure IoT Edge and Azure Machine Learning, can be used to create camera-based IoT solutions. This is part of Microsoft's larger expansion of Azure IoT Edge which received updates at the recent Microsoft Build conference. Microsoft announces Cognitive Services updates. Microsoft's suite of AI services received updates including a unified Speech service and Bing Visual Search, custom object detection, added features for Bing Custom Search, new speech features with custom translator, custom voice, custom speech, and more. Microsoft announces new ML.NET platform. ML.NET is a cross-platform, open source machine learning framework. It will allow .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing ML models. Dollars & Sense Notable, a voice-powered healthcare startup, raises $3M Learning Machine, a software startup making blockchain-based credentials at scale, raises $3M Xnor.ai, a startup focusing on AI edge computations, raises $12M conDati, a digital marketing analytics company, raises $4.75M Gamalon, a startup working on AI capable of creating summaries or explanations of speech or text, raises $20M Innovaccer, a healthcare data platform company, raises $25M Unisound, a Chinese AI solutions company, raises $100M Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes Forgive the Facebook news bias here. There were a few interesting announcements from their F8 developer conference last week. Google’s Kubeflow brings machine learning support to Kubernetes. The open-source Kubeflow project for Kubernetes', Google's open-source container-orchestration system, has actually been around for a few months now and has seen strong interest in the open-source community. With the release of Kubeflow 0.1 it’s now a more robust option for building ML stacks on Kubernetes. Intel rolls out AI Builders Program for Enterprise AI partners. The program provides its members with resources to help bring their solutions to market on Intel AI technologies. These resources include technical enablement, marketing assistance, and in some cases investment. Facebook adds AI labs in Seattle and Pittsburgh. They’ve hired researchers from the University of Washington and Carnegie Mellon, causing some to worry that the AI talent shortage will ultimately be worsened by large companies poaching AI faculty. Facebook forms a special ethics team to prevent bias. The team spearheads a software system called Fairness Flow that is to help monitor implicit bias that might be unintentionally baked into production AI systems. Facebook announces PyTorch 1.0, a more unified AI framework. PyTorch 1.0 combines, Caffe2’s production-oriented capabilities and PyTorch's flexible research-focused design to provide an easier process for deploying of AI systems. Other companies have been quick to announce their compatibilities with the newly released software, like Microsoft and Google. Facebook Open Sources ELF OpenGo. Their AI bot, which is based on PyTorch and their ELF reinforcement learning platform, has successfully defeated 14 professional Go players as well as the strongest publicly available Go bot, LeelaZero. Facebook will be making both the trained model and the code used to create it open to the public. Dollars & Sense Suki, a startup creating a voice assistant for doctors, raises $20M Algolux Inc., a provider of machine-learning stacks for autonomous vision and imaging, raises $10M Passage AI, a provider of AI-powered conversational interfaces, raises$7.3 million MindBridge Analytics, a startup offering a FinTech autonomous auditing system, raises $8.4 Million BenchSci, a search engine to help researchers find antibody usage data, raises $10 million Synyi, a Chinese AI Medical Data startup, raises $15.7 million SoundHound, a competitor to Alexa and Google Assistant, raises$100M Humu, a behavioral-change software company, raises $30m Cisco to acquire Accompany, a company providing an AI-driven relationship intelligence platform for sales ServiceNow acquires Parlo, an artificial intelligence (AI) and natural language understanding (NLU) workforce solution Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Gartner pins “global AI business value” at $1.2 billion in 2018. Not sure what we’re supposed to do with that broadly-defined number beside putting it in our pitch decks, but I get it–AI just might be important ????. The firm describes three sources of AI value: customer experience enhancements, new revenues, cost reductions. Scientists in Europe call for new AI hub. Scientists in Europe pen an open letter outlining the motivations behind ELLIS–a new European AI hub. Nice analysis at The Guardian on this story and an EU commission call for €20 billion investment in the space to compete with the US and China. DeepCode launches to provide AI code copilot. Analogous to a grammar checker for code, the software connects to your GitHub repos and tells you how to fix problems in your Javascript, Java or Python code. AI helping discover new materials. Interesting article on recent Northwestern University research that applies AI to the task of creating new metal-glass hybrids. I did an interview on this topic recently so stay tuned for more. Google gives cloud credits to researchers. Awards are available to academic researchers in qualified countries. Awards are typically for $5k, but more can be requested via the application. Dollars & Sense Baidu has raised over $1.9 billion for a newly spun-off AI-powered financial services company Allegro.ai grabs $11 million to advance DL-as-a-Service platform Marble, a delivery robotics company raises $10 million iGenius, a Milan, Italy based startup, raises $7 million to make enterprise data easily queryable via natural language SalesHero raises $4.5 million seed to help reps keep Salesforce.com up to date Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Intel open sources nGraph neural network compiler. The newly open-sourced compiler, originally announced last summer and discussed on TWIML Talk #31, provides support for multiple deep learning frameworks while optimizing models for different hardware solutions. It supports six deep learning frameworks: TensorFlow, MXNet, neon, PyTorch, CNTK, and Caffe2. Google unveils augmented reality microscope. The prototype, which can detect cancer in real-time, was unveiled at an event organized by the American Association for Cancer Research. The new tool relays its predictions directly into the field of view of the user and has the ability to be retrofitted into existing microscopes. Google extends semantic language capabilities. Building on the hierarchical vector models at the heart of Gmails's Smart Reply feature, the new work extends these ideas by creating vectors for larger chunks of language such as full sentences and small paragraphs. The company published a paper on its Universal Sentence Encoder and launched the Semantic Experiences demonstration site. A pre-trained TensorFlow model was also released. IBM releases Adversarial Robustness Toolbox. The open-source software library aims to support researchers and developers in defending deep neural nets against adversarial attacks. The software, which currently works with TensorFlow and Keras, can assess a DNNs robustness, increase robustness as needed, and offer runtime detection of potential threats. MATLAB 2018a adds deep learning features. Many self-taught data scientists were initially exposed to MATLAB via Octave, the open source clone Andrew Ng used in his original Stanford machine learning online course. Well, the commercial software continues to evolve, with its latest version adding a host of new deep-learning related features including support for regression and bidirectional LSTMs, automatic validation of custom layers, and improved hardware support. Dollars & Sense Sword Health, a Portuguese medtech company, raises $4.6 million LawGeex, a contract review automation business, raises $12 million XpertSea, applying computer vision to aquaculture, raises $10 million Konux, a sensor and AI analytics startup, raises $20 million Citrine, materials data and AI platform, raises $8 million Eightfold.ai launches talent intelligence platform, closes $18 million round Voicera, the AI-powered productivity service, announces acquisition of Wrappup Adobe announces acquisition of voice technology business, Sayspring Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
In this month's community segment we chatted about explainability, Carlos Guestrin’s LIME paper, Europe’s attempt to ban “untrustworthy” AI systems and finally, Community member Nicolas Teague shares a blog post he wrote entitled "A Sight for Obscured Eye, Adversary, Optics, and Illusions,” which explores the parallels between computer vision adversarial examples & human vision optical illusions. In our presentation segment, Philosopie Group Inc. Director of AI, Chris Butler, joins us to discuss Trust in AI. Chris gives us an overview of a number of papers on the topic, including: Humans and Automation: Use, Misuse, Disuse, Abuse Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust Some Observations on Mental Models Overtrust of Robots in Emergency Evacuation Scenarios For links to the papers mentioned above and more information on this and previous meetups, or to get registered for upcoming meetups, visit [twimlai.com/meetup]!(/meetup) SUBSCRIBE AND TURN ON NOTIFICATIONS
My travel comes in waves centered around the spring and fall conference seasons. A couple of weeks ago, in spite of there being no signs of a true springtime here in St. Louis, things shifted into high gear with me attending the Scaled ML conference at Stanford and Nvidia GTC over the course of a few days. Following me on Twitter is the best way to stay on top of the action as it happens, but for those who missed my live-tweeting, I thought I’d reflect a bit on Nvidia and GTC. (You’ll need to check out my #scaledmlconf tweets for my fleeting thoughts on that one.) In many ways, Nvidia is the beneficiary of having been in the right place at the right time with regards to AI. It just so happened that (a) a confluence of advances in computing, data, and algorithms led to explosive progress and interest in deep neural networks, and (b) that our current approach to training these depends pretty heavily on mathematical operations that Nvidia’s graphics cards happened to be really efficient at. That’s not to say that Nvidia hasn’t executed extremely well once the opportunity presented itself. To their credit, they recognized the trend early and invested heavily in it, before it really made sense for them to do so, besting the “innovator’s dilemma” that’s caused many a great (or formerly great) company to miss out. Nvidia has really excelled in developing software and ecosystems that take advantage of their hardware and are deeply tailored to the different domains in which it's being used. This was evidenced in full at GTC 2018, with the company rolling out a number of interesting new hardware, software, application, and ecosystem announcements for its deep learning customers.   A few of the announcements I found most interesting were: New DGX-2 deep learning supercomputer After announcing the doubling of the V100 GPU memory to 32GB, Nvidia unveiled the DGX-2, a deep-learning optimized server containing 16 V100s and a new high-performance interconnect called NVSwitch. The DGX-2delivers 2 petaFLOPS of compute power and offers significant cost and energy savings relative to traditional server architectures. For a challenging representative task like training a FAIRSeq neural machine translation (NMT) model, the DGX-2 completed the task in a day and a half, versus the previous generation DGX-1’s 15 days. Deep learning inference and TensorRT 4 Inference (using DL models, versus training them) was a big focus area for Nvidia CEO Jensen Huang. During his keynote, Jensen spoke to the rapid increase in complexity of AI models and offered a mnemonic for thinking about the needs of inference systems both in the datacenter and at the edge–PLASTER, for Programmability, Latency, Accuracy, Size, Throughput, Energy Efficiency, and Rate of Learning. To meet these needs, he announced the release of TensorRT 4, the latest version of its software for optimizing inference performance on Nvidia GPUs. The new version of TensorRT has been integrated with TensorFlow and also includes support for the ONNX deep learning interoperability framework, allowing it to be used with models developed with the PyTorch, Caffe2, MxNet, CNTK, and Chainer frameworks. The new version's performance was highlighted, including an 8x increase in TensorFlow performance when used with TensorRT 4 vs TensorFlow alone and 45x higher throughput vs. CPUs for certain network architectures. New Kubernetes support Kubernetes (K8s) is an open source platform for orchestrating workloads on public and private clouds. It came out of Google and is growing very rapidly. While the majority of Kubernetes deployments are focused on web application workloads, the software has been gaining popularity among deep learning users. (Check out my interviews with Matroid’s Reza Zadehand OpenAI’s Jonas Schneider for more.) To date, working with GPUs in Kubernetes has been pretty frustrating. According to the official K8s docs, “support for NVIDIA GPUs was added in v1.6 and has gone through multiple backwards incompatible iterations.” Yikes! Nvidia hopes its new GPU Device Plugin (confusingly referred to as “Kubernetes on GPUs” in Jensen’s keynote) will allow workloads to more easily target GPUs in a Kubernetes cluster. New applications: Project Clara and DRIVE Sim Combining its strengths in both graphics and deep learning, Nvidia shared a couple of interesting new applications it has developed. Project Clara is able to create rich cinematic renderings of medical imagery, allowing doctors to more easily diagnose medical conditions. Amazingly, it does this in the cloud using deep neural networks to enhance traditional images, without requiring updates to the three million imaging instruments currently installed at medical facilities. DRIVE Sim is a simulation platform for self-driving cars. There have been many efforts to train deep learning models for self-driving cars using simulation, including using commercial games like Grand Theft Auto. (In fact, the GTA publisher has shut several of these efforts down for copyright reasons). Training a learning algorithm on synthetic roads and cityscapes hasn’t been the big problem though. Rather, the challenge has been that models trained on synthetic roads haven’t generalized well to the real world. I spoke to Nvidia chief scientist Bill Dally about this and he says they’ve seen good generalization by incorporating a couple of techniques proven out in their research, namely by combining real and simulated data in the training set and by using domain adaptation techniques, including this one from NIPS 2017 based on coupled GANS. (See also the discussion around a related Apple paper presented at the very first TWIML Online meetup.) Impressively, for as much as Nvidia announced for the deep learning user, the conference and keynote also had a ton to offer their graphics, robotics and self-driving car users, as well as users from industries like healthcare, financial services, oil and gas, and others. Nvidia is not without challengers in the deep learning hardware space, as I’ve previously written, but the company seems to be doing all the right things. I’m already looking forward to next year’s GTC and seeing what the company is able to pull off in the next twelve months. Sign up for our Newsletter to receive this weekly to your inbox.
Bits and Bytes Last week I attended the GTC - GPU Technology Conference in San Jose. NVIDIA made quite a few announcements so you’ll see quite a few of those in this week’s news run down. Microsoft speeds neural net inference with Project Brainwave. Microsoft Research’s Project Brainwave uses Intel FPGAs to accelerate deep learning inference. The company reports that the system has been deployed for Bing, the search engine, resulting in 10x reductions in inference latency while accommodating a 10x increase in model size. Google launches text-to-speech service for developers. Cloud Text-to-Speech offers 32 different voices from 12 languages and variants. It includes a selection of voices built using WaveNet, a generative model for raw audio created by DeepMind. Microsoft reshuffles to bring more AI into products. With AI competition heating up industry-wide, Microsoft is looking to position itself as a leader in the AI solutions and developer markets. The company will now be split into “Experiences & Devices,” “Cloud + AI Platform,” and the existing branch of Microsoft Research. NVIDIA and Arm partner bring deep learning to IoT devices. NVIDIA and Arm will integrate the former’s open-source Deep Learning Accelerator architecture into the latter’s Project Trillium processors for machine learning inference. TensorFlow bumped to version 1.7 TensorFlow.js released.Version 1.7 of the framework moves Eager Mode, TF’s answer to PyTorch, into core. A GUI debugger is now offered in alpha as well. Support for NVIDIA’s TensorRT library for accelerated inference is included as well, among a bunch of other updates. Separately, the deeplearning.js project joins the TensorFlow family as TensorFlow.js. NVIDIA boosts deep learning platform. The NVIDIA Tesla V100 received at 2x memory boost to 32 GB plus the addition of GPU interconnect that enables up to 16 of the GPUs to communicate simultaneously. They also launched the DGX-2, an impressive machine targeting deep learning, capable of delivering two petaflops of computing power. YOLO v3 increases accuracy, humor. YOLO, short for You Only Look Once, is a popular image object detection system. The new version 3 offers minor improvements in accuracy explained in a very readable and quite funny research paper (PDF). Dollars and Sense Valohai, machine learning platform-as-a-service startup, raises $1.8 million Arraiy, a computer vision for the movie and TV industry, raises over $10M Scotty Labs, a startup working on remote-controlled driverless cars, raises $6 million with backing from Alphabet, Inc. Verbit, an AI transcription software startup, raises $11 million Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Healthcare applications of machine learning and AI have been in the news a bit more than usual recently, concurrent with the recent HiMSS conference in Las Vegas. HiMSS is a 45,000+ attendee conference dedicated to healthcare IT. Surprising no one, AI was a major factor at this year’s event. There was a whole subconference focused on ML & AI, plus a ton of AI-focused sessions in the regular conference and a good number of announcements by industry leaders and startups alike. I’ve only done a couple of healthcare-focused shows on the podcast so far, but I’m planning to dive into this area more deeply this year. Healthcare is arguably one the most promising–not to mention important–areas of AI application. Progress is being made across a good many areas, including: Radiology. Image-based diagnostics like radiology lend themselves to the application of deep learning. There are large amounts of labeled image data to work from and a degree of uniformity that's unmatched in many other vision applications. There’s been a raft of research papers on the application of deep learning to radiology and a lot of speculation about AI eventually replacing radiologists, but also strong arguments against this ever happening. Diagnostics. Radiology aside, machine learning and AI has the potential to help doctors make better diagnostic calls. One company that’s been active in this space is the startup Enlitic. The company–which at one time was lead by Fast.ai founder and former Kaggle president Jeremy Howard–wants to use deep learning to help make diagnostic calls, manage patient triage and screening programs, and identify high-level population health trends. Google Brain, Google DeepMind, and IBM Watson are all very active in this area as well, among others; the first of these recently published interesting research into the use of deep learning to predict cardiovascular diseases using retinal images, as opposed to more invasive blood tests. Health Monitoring. Machine learning is also driving health diagnostics and monitoring into the hands of consumers. Last year Apple unveiled a research study app that uses Apple Watch’s built-in heart rate monitor to collect data on irregular heartbeats and alert patients who may be experiencing atrial fibrillation. FirstBeat, a supplier to other fitness watch makers, uses machine learning to predict wearer’s stress levels and recovery times. I spoke with Ilkka Korhonen, the company’s vice president of technology about physiology-based models for fitness and training earlier this year. Personalized medicine. Personalized, or precision, medicine seeks to tailor medical interventions to the predicted response of the patient based on their genetics or other factors. Applications include selecting the best medicines for each patient and developing custom medications that target pathways based on an individual patient’s genetics. My interview with Brendan Frey of Toronto-based Deep Genomics explored a few of the opportunities in this space. Deep Genomics is working on “biologically accurate” artificial intelligence for developing new therapies. Electronic Health Records. The major EHR vendors–including Allscripts, Athenahealth, Cerner, eClinicalWorks, and Epic–all made announcements at HiMSS about ways that they would be incorporating AI into their products. Allscripts announced a partnership with Microsoft to develop an Azure-powered EHR, while Epic unveiled a partnership with Nuance to integrate their AI-powered virtual assistant into the Epic EHR workflow. Trump Administration advisor Jared Kushner even made an appearance advocating for greater EHR interoperability as a step towards applying AI, machine learning, and big data. Surgery. Researchers are beginning to incorporate AI into the planning and execution of a variety of surgical procedures. A variety of surgical scenarios have been explored, including burn care, limb transplants, craniofacial surgery, cancer treatment, and aesthetic (plastic) surgery. Of course, significant obstacles remain before we see AI fully integrated into healthcare delivery. Naturally, the barrier to releasing new products in healthcare is much higher than other industries since even small mistakes can have life-threatening consequences for patients. The techniques being applied now in research must be made more robust, a clear chain of accountability must be present, and justification for how why and how care decisions are made must be made clear. Improving robustness and performance will require time, a lot of data, and many rounds of testing. Increasing trust will further require new tools and techniques for explaining opaque algorithms like deep learning (the aforementioned Google research using retinal images provides a good example of this). We won’t see the autonomous robo-doctors of science fiction anytime soon, but machine learning and AI will undoubtedly play a significant role in the experience of healthcare consumers and providers in the years to come. Sign up for our Newsletter to receive this weekly to your inbox.
Bits & Bytes Google’s AI is being used on US military drone footage. The project provides the Department of Defense (DoD) with Tensorflow APIs to help flag and classify 1000s of hours of drone footage. The partnership has drawn criticism of both Google and the DoD. Microsoft partners with Esri to launch Geospatial AI on Azure. For geospatial analytics professionals, this product provides AI and predictive analytics capabilities including deep learning and machine learning algorithms. Microsoft announces Windows ML for efficient use of hardware with AI workloads. The software works across multiple hardware types including Intel Vision Processing Units. It allows for seamless integration of on system AI, in the form of personal assistants, enhanced biometric security, smart music, and photo search and recognition. China makes open-source platform to boost AI. This comes as part of a wider initiative to position China as a leader in AI technology by 2030. We’ve covered this in previous newsletters when it was announced China was building an AI-dedicated business park. Cloudera unveils enterprise ML and analytics Platform-as-a-Service. The platform aims to tackle the issue of “big data analytics cloud sprawl” (that’s a mouthful!) in enterprises using machine learning. Google’s Quantum AI Lab announces new ‘Bristlecone’ processor. The quantum processor will be used to research system error rates and scalability of google’s qubit technology as well as applications in quantum simulation, optimization, and machine learning. Dollar & Sense ELSA, an English language-learning tool, raises $3.2M for its A.I.-powered pronunciation assistant Medial Earlysign, an AI-powered health tech company secures $30 million Atomwise, which uses AI to improve drug discovery, raises $45M Series A Voicera, which offers an AI-powered transcribing assistant, raises $14.5 million for AI that draws insights from meeting notes GameSparks recently (leave) picked up by Amazon for $10M to build out its gaming muscle Kngine, an AI search engine startup is acquired by Samsung Kensho, a leader in AI analytics is acquired by S&P for $550 million
Bits & Bytes Google AI to predict heart disease with eye scans. The tech is being developed by Google’s health subsidiary Verily. It works by scanning the back of a patient’s eye, it then uses that image to deduce patient age, blood pressure, smoking status, and their risk of heart attack. It’s still in its early stages, though, and is not ready for clinical use. Google debuts ‘auto ads’ for intelligently ad placement. While Google has long used machine learning to determine the best ads to show on a web page, this new feature reads the target page and selects the best ad placement on the page. Google claims that participating publishers saw ad revenue increases of 10-15%, however, some beta users were not happy about the number of ads being placed on their pages. IBM partners with game dev platform Unity to create IBM Watson Unity SDK. I’ve had my eye on Unity since my interview with Danny Lange, their VP for ML and AI. The new SDK is being launchedon the Unity Asset Store and will allow developers to integrate visual recognition, speech text, and language classification features into their games and AR/VR applications more easily. Qualcomm adds AI engine to Snapdragon mobile platform. The Qualcomm AI Engine consists of software, hardware and APIs meant to support efficient neural network inference on client devicesrunning Snapdragon processors. Accenture launches AI testing service. Accenture’s taking a “Teach and Test” approach to the service, with the former focused on the choice of data, models, and algorithms used to train ML models, and the latter on up-front and ongoing evaluation of model performance, explainability and bias. MindBridge adds NLP to its AI-powered auditing software. The update allows audit professionals to naturally ask query transactional data and gain insight into potential errors and risky transactions. Dollars & Sense Vectra, a cybersecurity startup, raises $36M for global expansion of its AI-Based Security Platform SparkCognition, an AI solutions startup, raises $56.5 million Series B For International Expansion StatusToday, an employee productivity startup, raises $3.91 million to improve employee productivity with AI Prophesee, a machine vision startup, raises $19 million for its machine vision technology Agent IQ, an AI customer service bot startup, raises $6.3M Benevolentai acquires Cambridge research facility to accelerate AI-enabled drug development Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Nest returns to the Google nest in AI push. Google is bringing device maker Nest back under its control as it fights Amazon and Apple for a foothold in the AI-enabled smart home market. Your smart watch could one day detect diabetes. A recent clinical study demonstrated that the Apple Watch, using its heart rate sensor paired with a machine learning algorithm, could detect diabetes with 85 percent accuracy. Photonic AI chip battle brewing. Last week I noted the $10 million fundraising by startup Lightelligence. Not to be outdone, Lightmatter, another startup with MIT roots going after the same light-powered AI chip market, announced its $11 million funding round. Facebook DensePose tools to re-skin videos in real time. The new Facebook AI Research system consists of DensePose-COCO, a human-annotated dataset which maps 2D images to 3D surfaces on the human body, and DensePose-RCNN, a new network architecture for classifying and localizing body surface parts from video frames. Version 0.2 of AWS Model Server for MXNet adds ONNX support. AWS updated its open source Model Server for Apache MXNet (MMS) library, which packages and serves deep learning models. New features include support for Open Neural Network Exchange (ONNX) models and the ability to publish operational metrics directly to Amazon CloudWatch. Dollars & Sense Paige.ai raises $25 million for cancer detection powered by computer vision Loris.ai, a Crisis Text Line spin-out, raises $2 million AdHive closes $5.5M ICO presale in 36 minutes for community-driven video advertising platform Boost AI raises $5M from Alliance Venture for a virtual AI-based custom agents
Bits and Bytes Google used ML to help block 700K bad apps on the Play Store. Thin on details but an interesting use case example, this blog post focuses on Google's use of new machine learning models for identifying violent or hateful language in app store submissions and then flagging them for human review. NVIDIA brings V100 to IBM Cloud. NVIDIA's latest and greatest Tesla V100 GPUs are now available in the IBM Cloud, one of the top three public clouds by revenue. Customers can equip bare-metal cloud servers with up to two of them. Foxconn to invest $340M into AI R&D over five years. According to the company's chairman, their ambition is to become a "global innovative AI platform rather than just a manufacturing company." They plan to start by hiring a bunch of "top AI experts" globally, and tons of AI developers and engineers, so if you're job hunting... Sophos adds deep learning to latest malware detection product. Sophos, a firm known for their network and endpoint security products, is highlighting the addition of deep learning models to their new malware detection product, promising both high accuracy and lower false positives for both existing and zero-day malware. US Army and UT Austin researchers develop new robotic training algorith. New research by the U.S. Army Research Laboratory and University of Texas at Austin focused on a new algorithm called Deep TAMER, which uses imitation learning and human feedback to teach robots how to perform tasks. Dollars and Sense Downstream.ai raises $1.5 million for AI-driven programmatic ad platform Aquabyte, a SF based startup using computer vision techniques to optimize fish farming efficiency, raises $3.5M in Seed Funding Lightelligence, a Boston-based startup, received $10 million in seed funding for new AI acceleration hardware based on photonics. Andrew Ng formally announced his $175 million fund for AI startups Conversica acquires Intelligens.ai to teach its sales chatbots to speak Spanish Mezi, virtual travel assistant, acquired by American Express Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
While at NIPS in December, I attended the Black in AI workshop and dinner, and had a chance to meet a bunch of amazing people. Black in AI brought in participants from all over the world, and featured some great speakers like Charles Onu and Nyalleng Moorosi, both of whom you'll hear from in this series. The workshop included a poster session that showcased the work of students and practitioners alike, and was followed by an incredible dinner (and party of course) where I had the chance to meet and chat with some more awesome people. The event was organized in part by Timnit Gebru, who we had on the show a few months ago, and Moustapha Cisse, who you'll hear from in the first episode of this series, along with a host of other great people. This is a great group and community that I was fortunate enough to spend some time with, as they are doing important work of supporting a diverse pool of AI students and practitioners. This week on the show, we'll be highlighting some of the great work being done by folks in this community.
Bits and Bytes Interesting tidbits from recent news: Microsoft develops AI powered sketch artist. The new bot, based on recent GAN research, is capable of generating “drawings” from caption-like text descriptions. Applications for this technology include the arts, design, and perhaps at some point, police sketches. Overall very cool. IBM and Salesforce announce Watson + Einstein collaboration. The two tech giants are teaming up to integrate their two eponymously named, over-marketed, poorly understood machine learning products. Oh boy! Although it’s not immediately obvious in what ways Watson and Einstein are “combining”, Salesforce and IBM are making it clear that they are prioritizing AI and fleshing out their offerings. #SnarkLevelHigh Baidu grows AI research team. The new hires are Dr. Kenneth Church a pioneer in Natural Language Pioneering, Dr. Jun Huan a big data and data mining expert and Dr. Hui Xiong who specializes in data and knowledge engineering. Dating services firm Lunch Actually to launch ICO for Viola.AI. The dating service aims to not only match couples but also track their relationships, suggest date venues, remind them of new dates and advise them on relationship problems. Potentially a very interesting AI application, but one with tons of potential privacy implications. UC Berkeley & Facebook introduce House3D for reinforcement learning. The two teamed up to enable more robust intelligent agents by publishing a new dataset called “House3D”. House3D contains 45,622 3D scenes of houses, ranging from single-room studios to multi-storeyed houses equipped fully labeled 3D objects. In doing so, the groups aim to push RL research away towards focusing on tasks that more easily applicable to the real world. App claims to predict if an image will “go viral.” ParallelDots released the app with an open API that allows user to upload images then receive a “virality” score. It’s no secret that viral sharing is the dream of many marketers, so it’ll be interesting to see if this type of service could provide beneficial insights when planning ad campaigns. Amazon launched SageMaker BlazingText. BlazingText is an unsupervised learning algorithm for generating word2vec (see TT # 48) embeddings and is the latest addition to Amazon SageMaker’s suite of built-in algorithms. Deal Flow There seemed to be an abundance of deals last week: Smartphone-maker Coolpad has raised $300 million from Chinese property mogul Chen Hua-backed Power Sun Ventures to enhance its artificial intelligence capabilities. Understand.ai, a Karlsruhe, Germany-based machine learning startup for training and validation data in autonomous vehicles, raised $2.8 million in seed funding. C3 IoT, a provider whose software offerings include AI-for-IoT tools, announced a $100 million new round of financing. Data Nerds, a Canada-based developer of data products, raised $3m in Series A funding. Techcyte, Inc. closed a $4.3 million funding round to commercialize its digital pathology platform. Babblabs, a fresh start-up in advanced speech processing, announced today a Series Seed investment of $4 million. Owkin, a NYC-based predictive analytics company that utilizes transfer learning to accelerate drug discovery and development, raised $11m in Series A funding. Pony.ai, a year-old California-based self-driving car startup, announced it recently completed a $112 million Series A funding round. Smartsheet, that builds software for corporate process management, acquires business automation chatbot startup Converse.AI. Workday, the cloud HR and financials SaaS provider, buys SkipFlag to bolster machine learning capabilities. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
This is a recap of the TWIML Online Meetup, held on Jan 16, 2018, we focus on the paper “Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States” by Microsoft Research post doctoral researcher Timnit Gebru. We recap some of the major ML/AI Resolutions for 2018, community predictions for 2018, our favorite TWIML podcast episodes of 2017 and more. Thanks again to our presenter Timnit Gebru! Make sure you Like this video, and Subscribe to our channel below! https://youtu.be/pLdNx3SSxYo Full paper: "Using Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods Across the United States" To register for the next meetup, visit twimlai.com/meetup Using Deep Learning to Estimate Demographic
Bits & Bytes A few interesting ML and AI related tidbits from around the web over the past week or so: China is building a huge AI-focused research and business park. The state-backed $2.1 billion technology park is part of China’s wider initiative to position themselves at the forefront of emerging markets. The 55 hectare park is expected to house 400 businesses and generate nearly $8 billion a year. Richmond-based AI startup, Notch, acquired by Capital One. Fifteen months after Capital One created an in-house “Center for Machine Learning,” the company has reportedly acquired Notch, a data engineering and machine learning consulting firm. LG distributes in house AI development tools throughout company. A few weeks after LG introduced ThinQ, the umbrella brand for the company’s smart home products, the company has announced availability of an in-house deep learning platform, called DeepThinQ, which is meant to facilitate AI technology development across platforms within the company. Google releases preemptible GPUs with a 50% discount incentive. Preemptible GPUs work well with large machine learning tasks and other batch computational jobs, and Google is making them cheaperfor customers. CEVA unveils new family of AI-processors designed for deep learning at the edge. As I mentioned previously, I’ll be keeping an eye on AI acceleration hardware products this year. CEVA's new line is one such offering. A new derivative-free optimization toolbox for deep learning. ZOOpt/ZOOjl is an interesting toolset that allows for optimization across data that is inconsistent or non-continuous, where standard algorithms like gradient descent which require differentiability would fall short. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.