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Technical Program Manager with over 20 years experience in the field as developer, development lead and program manager. I have experience presenting to large audiences, I enjoy technically challenging management roles, and I thrive in a fast paced culture focused on building high quality products. I started my career in US in 1998 working on Windows 2000 as a developer. I have shipped Windows 2000, Windows XP, Windows 2003 and Windows 7, working on various technologies from Print Spooler Service, Active Directory, 64bit Interop and RPC, TCP/IP and USB device connectivity, XPS document printing, Winb32 and .Net System APIs. I progressed from developer to development lead and I lead a team of 6 developers for 3 years. From 2009 to 2014 I have worked on Windows Phone as Senior Program Manager, driving multiple key projects from defining the process for Application certification of apps in the Windows Phone Store to Fast Application Switching and Fast Application Resume, app pre-compilation, deployment of phone apps in the Windows Store, multitasking for Location apps, VOIP, Audio, etc. As a Principal Program Manager Lead, I've lead a team of 5 Program Managers, driving the feature definition for the Windows Phone Execution Model and App Lifecycle, Resource Management, Multitasking, App-to-App and Page Navigation Model. From 2014 to 2018 I have worked on Speech Recognition for Cortana on PC, Windows Virtual Reality and Hololens experiences. I have worked on improving speech recognition accuracy via personalization of user's language models and acoustic models. I have been on point for enabling 3rd party skills for Cortana via voice commands and for enabling voice input for chat bots built with Microsoft Bot Framework. Since January 2019 I am working on Artificial Intelligence for Cognitive Services and Computer Vision at Microsoft, driving product requirements and working with Engineering and Research teams through the product lifecycle. I drove the product requirements and the engineering release for Computer Vision for Spatial Analysis. For more on Spatial Analysis and Azure Cognitive Services see https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
Sarah's work focuses on research and emerging technology strategy for AI products in Azure. Sarah works to accelerate the adoption and positive impact of AI by bringing together the latest innovations in research with the best of open source and product expertise to create new tools and technologies. Sarah is currently leading Responsible AI for the Azure Cognitive Services. Prior to joining the Cognitive Services, Sarah lead the development of responsible AI tools in Azure Machine Learning. She is an active member of the Microsoft AETHER committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Sarah was one of the founding researchers in the Microsoft FATE research group and prior to joining Microsoft worked on AI fairness in Facebook. Sarah is active contributor to the open source ecosystem, she co-founded ONNX, Fairlearn, and OpenDP's SmartNoise was a leader in the Pytorch 1.0 and InterpretML projects. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She co-founded the MLSys research conference and the Learning Systems workshops. She has a Ph.D. in computer science from UC Berkeley advised by Dave Patterson, Krste Asanovic, and Burton Smith.
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
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!
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!)
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.
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.
This video is a recap of our October 2018 Americas TWIML Online Meetup. In this month's community segment, we briefly discuss Entity Embeddings, the topic of the next month’s meetup, along with the recent announcement that MIT made a $1 billion commitment to address the global opportunities and challenges presented by the prevalence of computing and the rise of artificial intelligence (AI). In our presentation segment, Mark Meketon joins us to cover TWIML Talk veteran Ksenia Konyushkova's paper “Learning Active Learning from Data." For links to the papers, podcasts, and more mentioned above or during this meetup, for more information on previous meetups, or to get registered for upcoming meetups, visit twimlai.com/meetup! https://youtu.be/IiRBY7gP0II Paper: Learning Active Learning with Data
This video is a recap of our October 2018 EMEA TWIML Online Meetup. In this month's community segment we discuss the upcoming topics for the EMEA meetup group, along with our ongoing and upcoming Fast.AI study groups. We take a quick look at the newly released fastai v1 library, Jeremy Howard’s upcoming book, and the podcast we recorded with him earlier this week. Finally, we discuss a few dataset search engines including the recently released Google Dataset Search Engine Beta. In our presentation segment, Arvind Kumar leads us in a breakdown of the paper “Including multi-feature interactions and redundancy for feature ranking in mixed datasets,” which he co-authored along with other researchers from Bosch and the Hasso Plattner Institute in Germany. 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/W5-fYQ4bvVc Paper - Including multi-feature interactions and redundancy for feature ranking in mixed datasets - Slides Mentioned in the Community Segment - Jeremy Howard’s upcoming book, The Mechanics of Machine Learning - The Fastai v1 Deep Learning Framework with Jeremy Howard - Jeremy Howard's panel discussion at Pytorch Conference(Starts at 1:18:47) - Google Dataset Search Engine - CV Data Search Engine - Google Earth Engine
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 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 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 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.
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 Apple hires Google’s AI head Google forms A.I. business unit. The latest in the AI talent wars, John Giannanderea, previously Google's chief of search and AI, was hired to run Apple’s “machine learning and A.I. strategy.” It’s an important victory for Apple who has lagged behind in AI. Google took the change as an opportunity to put AI into its own business unit under recent TWIML guest Jeff Dean. As the AI “arms race” intensifies, larger players are putting ever more resources into solidifying their positions. Last week we shared a similar story from Microsoft on its own reorg to better focus on AI. Researchers at MIT-IBM Watson AI Lab train models to recognize dynamic events. It’s easy for humans to recognize dynamic events, for example, opening a door, a book, or a bottle. MIT-IBM researchers hope to train models to recognize these types of dynamic events. They've released a Moments in Time dataset and are hosting a Moments in Time competition at CVPR. Note: I recently discussed similar work from the Univerisity of Montreal and startup Twenty Billion Neurons with its chief scientist Roland Memisevic. GridGain's newest release includes continuous learning framework. The company's in-memory computing framework based on Apache Ignite now includes machine learning and a multilayer perceptron (MLP) neural network that enables companies to run ML and deep learning algorithms against petabyte-scale operational datasets in real-time. Amazon SageMaker update. They’ve added support for more instance sizes and open sourced their MXNet and Tensorflow containers. The updated containers can be downloaded to support local development. Data scientist uses cloud ML to classify bowls of ramen. Nevermind hot dog/not hot dog... Data scientist Kenji Doi used Google Cloud AutoML Vision to successfully identify the exact shop each bowl is made at. A very impressive feat when you consider how similar the bowls of ramen actually look. Dollars and Sense Insider, an AI-enabled growth marketing platform, raises $11 million Comet.ml, a platform for managing AI projects, raises $2.3 million Audioburst, an AI-enabled audio search platform, raises $4.6 million from Samsung Conga to acquire, the contract discovery and analytics company Counselytics, to bolster AI strategy and document automation capabilities Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Last week on the podcast I interviewed Clare Gollnick, CTO of Terbium Labs, on the reproducibility crisis in science and its implications for data scientists. We also got into an interesting conversation about the philosophy of data, a topic I hadn’t previously thought much about. The interview seemed to really resonate with listeners, judging by the number of comments we’ve received via the show notes page and Twitter. I think there are several reasons for this. I’d recommend listening to the interview if you haven't already. It’s incredibly informative and Clare does an excellent job explaining some of the main points of the reproducibility crisis. The short of it though is that many researchers in the natural and social sciences report not being able to reproduce each other’s findings. A 2016 “Nature” survey demonstrated that more than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments. This concerning finding has far-reaching implications for the way scientific studies are performed. Gollnick suggests that one contributing factor is the idea of “p-hacking”–that is, examining one’s experimental data until patterns are found that meet the criteria for statistical significance, before determining a specific hypothesis about the underlying causal relationship. P-hacking is also known as “data fishing” for a reason: You’re working backward from your data to a pattern, which breaks the assumptions upon which statistical significance is determined in the first place. Clare points out that data fishing is exactly what machine learning algorithms do though–they work backward from data to patterns or relationships. Data scientists can thus fall victim to the same errors made by natural scientists. P-hacking in the sciences, in particular, is similar to developing overfitted machine learning models. Fortunately for data scientists, it is well understood that cross-validation, by which a hypothesis is generated on a training dataset and then tested on a validation dataset, is a necessary practice. As Gollnick points out, testing on the validation set is a lot like making a very specific prediction that’s unlikely to occur unless your hypothesis is true, which is essentially the scientific method at its purest. Beyond the sciences, there’s growing concern about a reproducibility crisis in machine learning as well. A recent blog post by Pete Warden speaks to some of the core reproducibility challenges faced by data scientists and other practitioners. Warden refers to the iterative nature of current approaches to machine and deep learning and the fact that data scientists are not easily able to record their steps through each iteration. Furthermore, the data science stack for deep learning has a lot of moving parts, and changes in any of these layers–the deep learning framework, GPU drivers, or training or validation datasets–can all impact results. Finally, with opaque models like deep neural networks, it’s difficult to understand the root cause of differences between expected and observed results. These problems are further compounded by the fact that many published papers fail to explicitly mention many of their simplifying assumptions or implementation details, making it harder for others to reproduce their work. Efforts to reproduce deep learning results are further confounded by the fact that we really don’t know why, when or to what extent deep learning works. During an award acceptance speech at the 2017 NIPS conference, Google’s Ali Rahimi likened modern machine learning to alchemy for this reason. He explained that while alchemy gave us metallurgy, modern glass making, and medications, alchemists also believed they could cure illnesses with leeches and transmute base metals into gold. Similarly, while deep learning has given us incredible new ways to process data, Rahimi called for the systems responsible for critical decisions in healthcare and public policy to be “built on top of verifiable, rigorous, thorough knowledge.” Gollnick and Rahimi are united in advocating for a deeper understanding of how and why the models we use work. Doing so might mean a trip back to basics–as far back as the foundations of the scientific method. Gollnick mentioned in our conversation that she’s been fascinated recently with the “philosophy of data,” that is, the philosophical exploration of scientific knowledge, what it means to certain of something, and how data can support these. It stands to reason that any thought exercise that forces us to face tough questions about issues like explainability, causation, and certainty, could be of great value as we broaden our application of modern machine learning methods. Guided by the work of science philosophers like Karl Popper, Thomas Kuhn, and as far back as David Hume, this type of deep introspection into our methods could prove useful for the field of AI as a whole. What do you think? Does AI have a reproducibility crisis? Should we bother philosophizing about the new tools we’ve made, or just get to building with them? 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.
Bits and Bytes Amazon text-to-speech service, Polly releases new Breath feature. The new feature more closely mirrors human speech patterns by adding in pauses and breaths. The breaths can be added both manually and via an automated algorithm. IBM sets 46x faster benchmark record with POWER9 and NVIDIA GPUs. The test was done using IBM’s SnapML AI software. They were able to train a logistic regression classifier model in just 1.53 seconds with the fastest previous time for the model and dataset being 70 minutes on Google Cloud with Tensorflow. Apple partners with IBM to add Watson Visual Recognition to CoreML. Apple, which has been weak in its cloud services offerings, is likely trying to keep up with the new AI demands of mobile developers that may be swayed by Google’s robust suite of AI tools for the Android platform. (I'm quoted to this effect in the linked article.) Paperspace launches Gradient, an AI PaaS. The platform offers fully configured ML environments compatible with a large suite of leading AI development tools. IBM Launches Watson Data Kits to help accelerate enterprise AI adoption. The kits will provide companies with pre-enriched industry-specific data that can be used to scale AI across their business. The service will initially only be available for travel and transportation and food industries. Dollars & Sense Mythic, an Austin-based AI hardware startup, raised $40 million. Skyline AI, a real-estate investment service powered by AI, raised $3 million. Beijing Infervision, a company that develops AI medical imaging tech, raised $47 million. Sift Science, a cybersecurity company, has raised $53 million. AllyO, a provider of AI recruiting technology, raised $14m. Vision Critical, a customer relationship intelligence software company, has bought assets of AI-startup Aida Software. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes Google open sources exoplanet discovery AI. The project came out of a collaboration between Google Brain software engineer Chris Shallue and astrophysicist Andrew Vanderburg. The team was able to discover several new exoplanets and have now open-sourced their project to the public. I got a chance to talk with Chris Shallue about his work not too long ago, check out the show to learn more. Microsoft matches human performance translating news from Chinese to English. The research incorporated novel methods of training translation models including dual learning, deliberation, joint training and agreement regularization. Google's NSynth Super is an AI synth made of Raspberry Pis. The tool comes out of Magenta, Google’s creative AI applications project. The synthesizer uses open source AI software to generate new sounds. I talked with Doug Eck, the Magenta project lead, about his work on generative AI for music a little while back; give it a listen. Gluon models now deployable to AWS DeepLens. Gluon is an open source deep learning interface developed by AWS and Microsoft. It’s now deployable to AWS DeepLens instances for computer vision applications. Google open-sources the AI-powered tool for portrait mode on their Pixel devices. The tool uses semantic image segmentation to identify optimal focal areas or areas that need higher or lower exposure. Dollars & Sense SambaNova Systems, a start-up building computer processors and software for AI raises $56 million in funding led by Alphabet. Voci Technologies Incorporated, a provider of enterprise speech-to-text transcription and analytics, raises $8m in Series B funding. Percipient.ai, a provider of analytics for national security and now corporate security missions, raises $14.7M in Series A funding. Airspace Systems, Inc., a manufacturer of comprehensive drone defense systems, raised $20m in Series A funding. TaoData, a Chinese fintech startup, has raised $15.8 million in a series B round. Fractal Analytics, an AI solutions and analytics company, announced the acquisition of behavioral AI company Final Mile. L’Oréal announces the acquisition of ModiFace, an augmented reality and AI-powered beauty company. Avaya Holdings Corp. announced its acquisition of Spoken Communications, a Contact Center as a Service solutions application built on conversational artificial intelligence. Sign up for our Newsletter to receive the Bits & Bytes 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 Philips and Microsoft launch AI products for healthcare. The HIMSS Conference is going on this week, so healthcare AI is in the news. Philips announced a suite of AI-powered tools for diagnostic imaging, patient monitoring, oncology, genomics and other applications. Meanwhile, Microsoft launched a number of new healthcare tools and products powered by AI and Azure. Microsoft also updated several of its hosted artificial intelligence algorithms. Their Custom Vision service (see my article on Google’s AutoML) is now in public preview, and they’ve updated their Face API. Amazon Alexa’s head AI researcher, Ashwin Ram has left for Google. Ram will be joining Google as the technical director of AI for Google Cloud. Amazon has a reputation for being fairly litigious when it comes to departing talent, so I wonder if there’s a lawsuit in the works here. Google and Microsoft announce new AI learning portals. Google’s offering includes lessons on core ML concepts as well as in-depth Tensorflow tutorials. Microsoft’s is similarly structured, with boot camp style content as well as more detailed product guides. AI2 launches Project Alexandria to develop “common sense” AI. Paul Allen, Microsoft’s co-founder, announced a $125m fund for the Allen Institute for Artificial Intelligence (AI2) to kickstart new research into common sense AI. The first challenge is to develop a set of standard measurements for the common sense abilities of an AI system, which sounds like a great place to start! Dollars & Sense Spring Discovery, an AI-based drug discovery platform, lands $4.25 million seed. Spruce Up, a company using machine learning for interior design raises $1.5M. CounterFlow AI, a cybersecurity startup closes $2.7M in seed funding. Wecash, the Chinese big data startup, raises $160m led by ORIX Asia, Sea Group. Tellius raises $7.5M to transform business analytics with AI. Genesys, a customer support and contact center solution company acquires Altocloud to bolster analytics offerings. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
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.
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.
In this recap of the TWIML Online Meetup, held on Dec 13, 2017, we focus on the paper "Understanding Deep Learning Requires Rethinking Generalization" by Google Brain researchers Chiyuan Zhang, Samy Bengio and others. We also recap some of the major ML and AI advancements of the year, and take a look ahead to some of the key trends we’ll be following in 2018, such as deep reinforcement learning, capsule and schema networks and more. Thanks again to our presenter Bruno Gonçalves! Make sure you Like this video, and Subscribe to our channel above! https://youtu.be/mEYerIMYb5Q Full paper: Understanding Deep Learning Requires Rethinking Generalization To register for the next meetup, visit twimlai.com/meetup
A potentially interesting survey crossed the wires this week, and I while I’m bringing it up here, I do so with caveats, because the numbers seem a bit wonky. The survey, titled “Outlook on Artificial Intelligence in the Enterprise 2016” was published by Narrative Science, a “data storytelling” company that uses natural language generation to turn data into narratives. Narrative Science had help from the National Business Research Institute, a survey company that did the data collection for them. The headline of the survey announcement seems to be that 38% of those surveyed are already using AI technologies, while 56% of those that aren’t expect to do so by 2018. But, if that’s the case, then my math says that 73% of respondents’ organizations expect to have AI deployed by 2018, but the official report cites this number as 62%. Also, an infographic published by the same group says that only 24% of organizations surveyed are currently using AI, instead of the 38% quoted in their news release. This discrepancy could be due to the fact that a large percentage of organizations represented by the survey had more than one respondent, but it’s very confusing and I’d certainly expect more from a “data storytelling” company. Unless of course their press release and infographic where totally created by a generative AI, in which case I’m very impressed but also a bit horrified. Of course, the articles reporting on the survey don’t do anything to clear this up, with one of them reporting that 74% of organizations have already adopted AI. In any case, I feel we do need more data about enterprise adoption of AI, so some credible numbers here would be great but for now this ends up being just a cautionary tale about questioning your data. I have tweeted out to the company for clarification, and I’ll share whatever I find out. Subscribe: iTunes / Youtube / Spotify / RSS