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Melika Payvand (Member, IEEE) received the M.S. and Ph.D. degrees in electrical and computer engineering from the University of California Santa Barbara, in 2012 and 2016, respectively. She is currently a Research Scientist with the Institute of Neuroinformatics, University of Zurich and ETH Zurich. Her research activities and interest are in exploiting the physics of the computational substrate for online learning and sensory processing. She is a part of the Scientific Committee of the Capocaccia Workshop for Neuromorphic Intelligence. She is serving as a Technical Member of the Neural Systems, Applications and Technologies in Circuits and System Society and as a Technical Program Committee for International Symposium on Circuits and Systems (ISCAS). She is a Guest Editor of the Frontiers in Neuroscience and is the winner of the Best Neuromorph Award of the 2019 Telluride Neuromorphic Workshop.
Arash Behboodi is a machine learning research scientist (senior staff engineer/manager) at Qualcomm AI Research. He is doing research on machine learning design for wireless communication. His research interests include learning theory, machine learning for inverse problems, compressed sensing, and information theory. He graduated from Ecole Superieure d’electricite (now CentraleSupelec), France in 2012. From 2012 to 2019, he has been senior researcher at TU Berlin and RWTH Aachen.
Naila Murray obtained a BSE in electrical engineering from Princeton University in 2007. In 2012, she received her Ph.D. from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined Xerox Research Centre Europe in 2013 as a research scientist in the computer vision team, working on topics including fine-grained visual categorization, image retrieval, and visual attention. From 2015 to 2019, she led the computer vision team at Xerox Research Centre Europe and continued to serve in this role after its acquisition and transition to becoming NAVER LABS Europe. In 2019, she became the director of science at NAVER LABS Europe. In 2020, she joined Meta AI’s FAIR team, where she served as a senior research manager. She now leads as the director of AI research at Meta. She has served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and CVPR 2022, and program chair for ICLR 2021. Her current research interests include few-shot learning and domain adaptation.
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
"We are at a critical point in the global response to COVID-19 – we need everyone to get involved in this massive effort to keep the world safe." - WHO Director-General Dr. Tedros Adhanom Ghebreyesus Since the beginning of the coronavirus pandemic, we’ve seen an outpouring of interest on the part of data scientists and AI practitioners wanting to make a contribution. At the same time, some of the resulting efforts have been criticized for promoting the spread of misinformation or being disconnected from the applicable domain knowledge. In this discussion, we explore how data scientists and ML/AI practitioners can responsibly contribute to the fight against coronavirus and COVID-19. Four experts: Rex Douglass, Rob Munro, Lea Shanley, and Gigi Yuen-Reed shared a ton of valuable insight on the best ways to get involved. In case you missed it, check out the replay above! We've also gathered all the resources that our panelists discussed during the conversation, you can find those below. Keep an eye out for the next panel, which we’ve tentatively scheduled for Wednesday, May 13th. To stay up to date you can subscribe to our Youtube page or follow us on Twitter for updates. Big shout out to IBM for their support in helping to make this panel possible! IBM continues to support major initiatives -- applying data, knowledge, computing power and insights, to solve the challenging problems presented by the coronavirus. Some of these initiatives include their work with the High-Performance Computing Consortium, providing detailed virus tracking information on the Weather Channel, and offering free access to Watson Assistant for COVID-19 related applications. Click here to find out more about IBM’s response.
To hear about Josh Tobin’s various projects in robot learning, check out the full interview! The discussion on projects outside of the NeurIPS paper picks up at 15:58 in the podcast. Enjoy! Robots have a particularly hard time with “reading a room.” In order to know how to act, they first need to understand their environments. It’s like learning where everything is when you enter a grocery store for the first time. For machines, processing the world in terms of three dimensional spatial awareness–figuring out where objects are, how they are positioned, and even the robot’s own placement in the environment–is incredibly complex. In the real-world, scenarios can be unpredictable and challenging to simulate, so how can we improve perception so machines can successfully understand the world around them? Josh Tobin has dedicated his research to improving robots’ ability to accomplish real-world tasks. He finished his PhD at UC Berkeley under Pieter Abbeel, who we had the opportunity to interview for a Reinforcement Learning Deep Dive. Josh recently took a break from his role at OpenAI and joins us from NeurIPS 2019, where he presented his paper on Geometry Aware Neural Rendering, where they successfully improve 3D modeling to more complex, higher dimensional scenes. Neural Rendering and Generative Query Networks Neural rendering is the practice of observing a scene from multiple viewpoints and having a neural network model render an image of that scene from a different, arbitrary viewpoint. For example, if you have three cameras taking in different perspectives of an environment, the model would use that information to reconstruct a fourth viewpoint. The intuition behind this process is that the model has demonstrated a good understanding of the space if it can predict a strong representation of that view. Tobin’s work is an extension of the ideas presented in a paper from DeepMind on Neural Scene Representation and Rendering. The paper introduces Generative Query Networks (GQN), which the DeepMind paper refers to as “a framework within which machines learn to represent scenes using only their own sensors.” The significance of GQNs is that they do not rely on a massive amount of human-labeled data to produce scene representations. Instead, the model gleans the “essentials” of a scene from images, “constructs an internal representation, and uses this to predict the appearance of that scene.” In GQN, they take the problem of neural rendering and set up a model structure that works with an encoder-decoder architecture. As Josh describes, “The encoder takes each of the viewpoints and maps them through a convolutional neural network independently, so you get a representation for each of those viewpoints. Those representations are summed.” This creates a representation for the entire scene which is then passed on to the decoder. “The decoder’s job is to…go through this multi-step process of turning [the representation] into what it thinks the image from that viewpoint should look like.” GQNs are a powerful development, but there is still a bottleneck that occurs when the representation produced by the encoder is passed to the decoder. This is where the geometry aware component (the main contribution of Tobin’s paper) comes in. Geometry Awareness: Attention Mechanism and Epipolar Geometry Josh’s primary goal “was to extend GQN to more complex, more realistic scenes” meaning, “higher-dimensional images, higher-dimensional robot morphologies, and more complex objects.” Their approach was to use a scaled dot-product attention mechanism. “The way the attention mechanism works is by taking advantage of this fact of 3D geometry called epipolar geometry,” which refers to viewing something from two points and defining the relationship between them. In this case, epipolar geometry refers to knowing “the geometry of the scene, so where the cameras are relative to one another.” If you’re a machine trying to render an image from a particular viewpoint, you want to “go back and look at all of the images that you’ve been given as context, and search over those images for relevant information. It turns out, if you use the geometry of the scene [epipolar geometry]… then you can constrain that search to a line in each of the contexts viewpoints” and attend to the pixels that are most relevant to the image you’re constructing. “For each pixel, we’re constructing this vector that represents a line. When you aggregate all of those you have two spatial dimensions for the image. So you get this 3D tensor and you’re dot-producting the image that you’re trying to render…and that’s the attention mechanism.” The New Data Sets In order to evaluate the performance of the new model they developed several new data sets: In-Hand OpenAI Block Manipulation. This is a precursor to Open AI’s Rubik’s Cube project. In this data set, “You have a bunch of cameras looking at a robot hand that’s holding a block. The colors of the cube, the background, and the hand are randomized and the hand and the cube can be in any pose.” Disco Humanoid. This is Josh’s term for the data set because it looks like a “humanoid model that’s doing crazy jumping-in-the-air dance moves.” It’s similar to the MuJoCo humanoid model except that the colors, poses of the joints, and the lighting are completely randomized. It’s meant to test “whether you can model robots that have complex internal states rates with this high-dimensional robot that you need to model in any pose.” Rooms-Random-Objects. The most challenging data set they introduced involved simulations of a room with objects taken from ShapeNet, a data set with over 51,000 3D models. “Each of the million scenes that we generated had a different set of objects placed in the scene. It’s really challenging because the model needs to understand how to render essentially any type of object.” Randomization is a key part in each data set. As Josh believes “If you want to train a model and simulation that generalizes the real world, one of the most effective ways of doing that is to massively randomize every aspect of the simulator.” Evaluating Performance and Results To evaluate their results, the team compared their model with GQN using several metrics, including the lower bound on the negative log likelihood (the ELBO), per-pixel mean absolute error (L1 and L2), and by qualitatively reviewing rendered images of actual scenes. “The main result of the paper is we introduced a few new data sets that capture some of those properties, and then we showed that our attention mechanism produces qualitatively and quantitatively much better results on new, more complex datasets.” They have yet to test the system in the real world. Josh confirms “right now, it only captures 3D structure, it doesn’t capture semantics, or dynamics, or anything like that, but I think it’s an early step along that path.” Josh is working on various related projects around data efficiency in reinforcement learning and some fascinating work on sim-to-real applications. To hear about them, check out the full interview! The discussion on projects outside of the NeurIPS paper picks up at 15:58 in the podcast. Enjoy!
Today we're joined by Sergey Levine, an Assistant Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. We last heard from Sergey back in 2017, where we explored Deep Robotic Learning. We caught up with Sergey at NeurIPS 2019, where Sergey and his team presented 12 different papers -- which means a lot of ground to cover! Sergey and his lab's recent efforts have been focused on contributing to a future where machines can be "out there in the real world, learning continuously through their own experience." Sergey shares how many of the papers presented at the most recent NeurIPS conference are working to make that happen. Some of the major developments have been in the research fields of model-free reinforcement learning, causality and imitation learning, and offline reinforcement learning.
How does LinkedIn allow its data scientists to access aggregate user data for exploratory analytics while maintaining its users' privacy? That was the question at the heart of our recent conversation with Ryan Rogers, a senior software engineer in data science at the company. The answer, it turns out, is through differential privacy, a topic we've covered here on the show quite extensively over the years. Differential privacy is a system for publicly sharing information about a dataset by describing patterns of groups within the dataset, the catch is you have to do this without revealing information about individuals in the dataset (privacy). Ryan currently applies differential privacy at LinkedIn, but he has worked in the field, and on the related topic of federated learning, for quite some time. He was introduced to the subject as a PhD student at the University of Pennsylvania, where he worked closely with Aaron Roth, who we had the pleasure of interviewing back in 2018. Ryan later worked at Apple, where he focused on the local model of differential privacy, meaning differential privacy is performed on individual users' local devices before being collected for analysis. (Apple uses this, for example, to better understand our favorite emojis 🤯 👍👏). Not surprisingly, they do things a bit differently at LinkedIn. They utilize a central model, where the user's actual data is stored in a central database, with differential privacy applied before the data is made available for analysis. (Another interesting use case that Ryan mentioned in the interview: the U.S. Census Bureau has announced plans to publish 2020 census data using differential privacy.) Ryan recently put together a research paper with his LinkedIn colleague, David Durfee, that they presented as a spotlight talk at NeurIPS in Vancouver. The title of the paper is a bit daunting, but we break it down in the interview. You can check out the paper here: Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. There are two major components to the paper. First, they wanted to offer practical algorithms that you can layer on top of existing systems to achieve differential privacy for a very common type of query: the "Top-k" query, which means helping answer questions like "what are the top 10 articles that members are engaging with across LinkedIn?" Secondly, because privacy is reduced when users are allowed to make multiple queries of a differentially private system, Ryan's team developed an innovative way to ensure that their systems accurately account for the information the system returns to users over the course of a session. It's called Pay-what-you-get Composition. One of the big innovations of the paper is discovering the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel noise, which is commonly used in machine learning. One of the really nice connections that we made in our paper was that actually the exponential mechanism can be implemented by adding something called Gumbel noise, rather than Laplace noise. Gumbel noise actually pops up in machine learning. It's something that you would do to report the category that has the highest weight, [using what is] called the Gumbel Max Noise Trick. It turned out that we could use that with the exponential mechanism to get a differentially private algorithm. [...] Typically, to solve top-k, you would use the exponential mechanism k different times⁠ —you can now do this in one shot by just adding Gumbel noise to [existing algorithms] and report the k values that are in the the top […]which made it a lot more efficient and practical. When asked what he was most excited about for the future of differential privacy Ryan cited the progress in open source projects. This is the future of private data analytics. It's really important to be transparent with how you're doing things, otherwise if you're just touting that you're private and you're not revealing what it is, then is it really private? He pointed out the open-source collaboration between Microsoft and Harvard's Institute for Quantitative Social Sciences. The project aims to create an open-source platform that allows researchers to share datasets containing personal information while preserving the privacy of individuals. Ryan expects such efforts to bring more people to the field, encouraging applications of differential privacy that work in practice and at scale. Listen to the interview with Ryan to get the full scope! And if you want to go deeper into differential privacy check out our series of interviews on the topic from 2018. Thanks to LinkedIn for sponsoring today's show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn's highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.
On the heels of TWIMLcon, we bring you the final edition of #TWIMLcon Shorts. During and after day 2 of TWIMLcon, we were joined by a few of the awesome sponsors, attendees, and even some former podcast guests, to chat about what they are working on, their favorite TWIML podcast episode, parting thoughts on  #TWIMLcon and more! Manasi Vartak Founder and CEO of Verta.AI, a sponsor of TWIMLcon: AI Platforms Walter Roth Sales advisor at Founding Sponsor Dotscience, and sales surgery expert! Eero Laaksonen who you might remember from Day 1, joins us to chat about Sam's love of linguistics, the podcast music, and his thoughts on the conference. Fredrik Rönnlund Chief Growth Officer at TWIMLcon sponsor Valohai joined us to chat about his experience at the event. Prasad Vellanki CEO of One Convergence, joins us to discuss his experience at the conference, as well as his work at One Convergence. Kaiwen Zhong Product Manager of Data Science at Veeva Systems, and long-time listener of the podcast details her exciment around and biggest takeaways from the conference. Make sure you follow Kaiwen on IG! John Bohannon Last but not least, we're joined by John Bohannon, Director of Science at Primer AI. John, who joined Sam on the podcast on episode 136, gives us his thoughts on what made TWIMLcon special, Sam's foray into journalism, and Primer Science.  
Another #TWIMLcon short with the wonderful Rosie Pongracz and Trisha Mahoney, from a Founding sponsor who you all know, IBM. Rosie is the  World Wide Director of Technical Go-to-Market and Evangelism for Data Science and Trisha is a Senior Tech Evangelist. We chat about the latest IBM research, projects, and products, including AI Fairness 360, which will be the focus of Tricia’s session at TWIMLcon. The IBM booth also promises to bring the heat, with a variety of open source projects and resources for the data science community. See you there! Sam Charrington: [00:00:00] All right everyone. I've got Rosie Pongracz and Trisha Mahoney from IBM on. Rosie is the Worldwide Director of Technical go-to-market and Evangelism for Data Science and AI and Trisha is a Senior Tech Evangelist in Machine Learning & AI and they are both instrumental in IBM's support for the TWIMLcon: AI Platforms conference. Rosie and Trisha, it's so exciting to be able to talk to you. Rosie Pongracz: [00:00:27] We are excited to be here, Sam! So happy to be a supporter of TWIMLcon and all the great work you do. Trisha Mahoney: [00:00:33] Thanks for having us, Sam. Sam Charrington: [00:00:34] Absolutely. Thank you. So, I don't know if it makes sense to say who is IBM? [laughs] You know in this context, I think most people who hear this know what IBM is but, you know, maybe you can talk a little bit about the company's involvement in the AI Platform space and, you know, why you're, you know what really kind of created the interest in supporting this, this conference. Rosie Pongracz: [00:01:00] Absolutely. So, yes, I would imagine most of the listeners already know IBM. We are long-standing, I'd say, evangelist, product producer, supporters of open source, anything for AI. And I'd say most of the current recognition goes back to Watson, of course, and the Jeopardy challenge.   But from that, IBM has evolved...what was that, almost ten years ago, to create some significant products. Not only have we made our way to the cloud should I say, and supports hybrid clouds for our clients and bringing them through the digital transformation, but we also have a good range of, of tools that help people not only do data science and machine learning but also scaled those, operationalized those, and bring them to production. I think if anything, IBM is known for its expertise in enterprise-scale and wide range of industry solutions. And that's really what we're doing. We're involved in open source. So quite a few open-source projects that are AI and data science and ML related, as well as products that can help our clients bring that AI to their business.  Sam Charrington: [00:02:16] Awesome. And I know that I've covered some of those products in our recent e-books in the platform space. Both the Fabric for Deep Learning open source project, which I talked about in our Kubernetes for ML and DL e-book, as well as the Watson Studio products which I believe came up in the ML Platforms e-book. Are there other products that IBM is kind of focused on in this space? Rosie Pongracz: [00:02:43] I think you captured the main ones. Especially the ones my team has been involved in. There's Watson Studio, Watson Machine Learning, Watson Open Scale. And if you look at Studio, it's more or less it's an IDE of sorts for data scientists, built on Jupiter Notebooks. ML, uh Watson ML is for running those machine learning algorithms. And then Watson Open Scale is for at scale.  And actually one of the big pieces of that pipeline, if you look at all those pieces along the pipeline or the platform, if you will is one of the areas that Trisha's going to be talking about which is the AI fairness and bias, which is a really important piece of the pipeline that we're proud to be incorporating.  I think you caught all the products. There's a significant amount of open-source that we're also involved in and, like I said, bringing those into our products and also supporting those communities like the Jupiter community, like the Linux Foundation, AI. Those are also very important projects and places where IBM has been involved as well.  Sam Charrington: [00:03:53] That's right. We recently did a podcast with Luciano Resende, who is at IBM and works on the Jupiter Enterprise Hub project, I believe is the name of it? Rosie Pongracz: [00:04:03] Yup. Jupiter Enterprise Gateway is correct. Yes. Sam Charrington: [00:04:05] Got it. Jupiter Enterprise Gateway. Rosie Pongracz: [00:04:07] Yeah. Sam Charrington: [00:04:08] So in addition to all of the products and open-source that you're working on in this space, you're also out there evangelizing the whole idea of ML Ops. You ran a workshop on this topic at the OzCon Conference recently. Maybe talk a little bit about your perspective on ML Ops and why that's so interesting to you. Rosie Pongracz: [00:04:29] Yeah. I think it goes back to where, where IBM can really make a difference is that we have, we'll have literally hundreds of years, decades, of experience in helping our enterprise clients do things at scale. And that is across industry. So if you look at all of the products that we have and you also look at something like cloud pak for data, which is bringing those containerized applications to any cloud, really, it is about giving our clients flexibility, helping them modernize. It's helping do things at scale.   Now a lot of our clients also have businesses that they're trying to transform so when you talk about ML Ops, certainly, you look at data science, I kind of look at that akin to a desktop where a developer works on. It's great to be able to develop those algorithms on your desktop and test that out on data sets, but when you really want to implement it, you're talking there's a whole kind of dev-ops cycle, if you will, applying that to AI and then machine learning.   And IBM has been there with its clients in the early days of Java. It's been there in the early days of cloud. And we're also taking that now into kind of the next realm if you will, the next era of bringing AI to businesses at scale. So how do you take your current applications and embed AI in those? Or how are you creating new ways to use your data and to modernize your business? And IBM you know, it's just near and dear to our client's heart. It's near and dear to who we are as a company in being able to do things at scale. And you have to have a platform. You have to have a way to operationalize that. It's great to run little science experiments to try things out and test things and fail fast, but when you start to operationalize, that's where the ML at scale, ML Ops, is really going to start to be important. Sam Charrington: [00:06:25] Mm-hmm [affirmative]. I was at the last IBM Think Conference, which is its big user conference and had an opportunity to hear Rob Thomas talk about, you know, one of the key things that he sees as being a determinant of enterprises finding success in machine learning and AI is the number of experiments that they're able to run and being able to scale that so that they can run those experiments en masse. Rosie Pongracz: [00:06:51] Yeah absolutely. That's an important piece of what IBM is helping enable our clients to do. And with our products that is definitely what we're striving for. You've got to be able to experiment. And then when you do want to operationalize, you got to be able to do that at scale.   Some of the clients we work with have some of the biggest applications running for their enterprise for their customers. And they depend on IBM to do that. So how do we bring that into, you know, this experimentation mode? Because you're absolutely right. Now it's not, you know, much more in...it's not about, you know, building one app and then releasing that. It's, as you know, the world is very much agile, you've got to fail fast. You've got to experiment. You've got to understand.   And with data science, that is absolutely sort of the MO. That's sort of the way you operate; is how do you, how do you know what works? And then if, when you...you know, you also have to retrain. So there's a lot of differences to building AI and building data science in a [inaudible] scale that is slightly different than just building applications if you will. Sam Charrington: [00:07:55] Mm-hmm [affirmative]. Mm-hmm [affirmative]. So, Trisha, you're going be speaking at the conference. Tell us a little bit about your topic and what attendees can expect when they come to your session. Trisha Mahoney: [00:08:06] Right. So, I'm going to be speaking on AI Fairness 360. And this is a comprehensive toolkit created by IBM researchers. And what we focus on is detecting and understanding and mitigating unwanted machine learning bias. So the toolkit is open source. It's in Python and it contains over 75 fairness metrics, ten bias mitigation algorithms, and fairness metrics with explanations. So one of the key components to this is that it has some of the most cutting edge metrics and algorithms across academia and industry today. So it's not just an IBM thing, it includes the algorithms from researchers from Google, Stanford, Cornell. That's just a few.   But what it really focuses on is teaching people how to learn to measure bias in their data sets and models. And how to apply fairness algorithms throughout the pipeline. So you know the big focus is on data science leaders, practitioners, and also legal and ethic stakeholders who would be a part of this.  So, just a few things that I'll go through in the talk is when you would apply pre-processing algorithms to manipulate your training data, in- processing algorithms, for incorporating fairness into your training algorithms itself, as well as post-processing, de-biasing algorithms. And, you know, one of the key things we wanted to get across is, I'm working on an O'Reilly book on AI fairness and bias with our researchers. So, you know, the key thing is that you know, this is a problem we think may prevent AI from reaching its full potential if we can't remove bias.   So, the thing we want to get across is that this is a long data science initiative. If you want to remove bias throughout your pipeline, so it involves a lot of stakeholders in your company, and that it can be very complex. So the way you define fairness and bias leads down into the types of metrics and algorithms you use. So, you know there are a lot of complexities. And the hope is that data science teams need to work with people throughout their org; they can't really make these decisions on their own, as they may actually break the law in some cases with their algorithms.   So, you know, I'll go into in the short period of time, kind of some of the trade-offs that data science teams have to make between model accuracy and removing bias, and talk about what they do for acceptable thresholds for each.   And the last thing on the ML Ops piece is I'll also do a demo in Watson Open Scale. And this is where you, you know, have models in production and you need to detect and remove bias from models, you know that are, aren't in an experimentation environment, right? So within Watson Open Scale, you can automatically detect fairness, issues at run time. And we essentially just do this by comparing the difference between rates at which different groups receive the same outcomes.  So are different minority groups, or men or women being approved for loans at the same time. So that's just an example. So that's kind of the top things that I'll go through on the toolkit and, I've heard many people say that others do bias talks on the problem that we have. But AI Fairness 360 is one of the few that's bringing a solution to the table on how to fix this within the machine learning pipeline.  Sam Charrington: [00:11:29] Yeah, I think that's one of the most exciting things about the talk from our perspective is that it's not just talking about the challenges that exist, but also how to integrate a concrete toolkit into your pipeline. And whether it's Fairness 360 or something else, but how to, integrate tools into your pipeline so that you can detect and mitigate bias, just very concretely as opposed to talking about it abstractly. Trisha Mahoney: [00:11:58] Correct. And I think the bridge that this creates is, you know, there are a lot of new fairness research techniques out there, but this toolkit sort of gets them into production and accessible in a way that data scientists can use. So, I think this is considered the most comprehensive toolkit to do that on the market today. Sam Charrington: [00:12:18] Mm-hmm [affirmative]. So Rosie in addition to Trisha's session, you'll also be exhibiting at the conference in our community hall. What can attendees expect to see at the IBM booth there? Rosie Pongracz: [00:12:30] Yeah, we're excited to be there too. So you'll see several things. We are going to be talking about the relevant open source projects like AI Fairness 360 that Trisha mentioned and also AI Explainability 360, which is another new toolkit. And we have, actually, a whole host of, projects that I won't go into here, but we can talk through those and see where IBM is contributed and working on open source projects like the Jupiter Enterprise Gateway that you mentioned as well.   They'll also see our, our products, and how those work together in helping operationalize and bring AI platforms to reality. And we'll also be talking about our data science community, which is a place that not only can product users go and share and collaborate, but also we have some great technical solution type content on there, with the goal of that being that IBM has a lot of deep rich solutions that we're building. As I mentioned earlier, industry-specific, or transformation type of projects and those are the types of materials that we're building there.  We've heard many people, both academic and industry, say it's great to talk about all this theoretical AI and what we'd really like to see is how are people putting that to work and solutions. So that's something that we're trying to bring to life on the community with many of [our] IBM experts all across any of our implementation folks, to our research folks. Sam Charrington: [00:14:01] Fantastic. Fantastic. Well, I'm really looking forward to seeing both of you at the event. And I am very gracious for your and IBM's support of the conference. Rosie Pongracz: [00:14:14] We are really excited to support what you're doing, Sam. I know you and I have worked together for many years through some technology transitions, so this is really appropriate and fun and fitting that we get to work together on something as exciting as what you're doing at TWIMLcon. Sam Charrington: [00:14:29] Absolutely. Thank you both. Rosie Pongracz: [00:14:31] Thank you. TWIMLcon: AI Platforms will be held on October 1st and 2nd at the Mission Bay Conference Center in San Francisco. Click here to learn more  
Welcome to #TWIMLcon Shorts - a series where I sit down with some of our awesome Founding Sponsors and talk about their ML/AI journey, current work in the field and what we can expect from them at TWIMLcon: AI Platforms! First up is Luke Marsden, Founder & CEO of Dotscience. Based in Bristol, UK, Luke joins me to share the Dotscience story and why he is most excited for #TWIMLcon next month! From a stellar breakout session featuring the Dotscience manifesto to live demos at their booth, we can’t wait! Sam Charrington: [00:00:00] All right everyone, I am on the line with Luke Marsden. Luke is the founder and CEO of Dotscience a founding sponsor for TWIMLcon: AI Platforms. So Luke, we go back a little bit from your involvement in the docker space. I remember introducing you at a session at Dockercon quite a few years back, but for those who aren't familiar with your background, who are you? Luke Marsden: [00:00:51] So hey Sam, and thanks for having me on. My name is Luke Marsden, I'm the founder and CEO of Dotscience and I come from a devops background. My last startup was called Cluster HQ, and we were solving the problem of running stateful containers in docker. And so I'm a sort of serial entrepreneur based out of the UK. I live in the beautiful city of Bristol in the southwest and very excited to be involved with TWIML. Sam Charrington: [00:01:28] Awesome. So tell us a little bit about Dotscience and what the company is up to in the AI platform space.  Luke Marsden: [00:01:36] Yeah, sure. So we started Dotscience a couple of years ago. Initially, we were targeting the area of data versioning and devops but we quickly realized that the tool that we built which is an open source project called dotmesh was actually much more relevant and important to the world of AI and machine learning which has a big data versioning and reproducibility problems. So we pivoted to that about a year in, and we've been building an AI platform around that core concept of data versioning.  Sam Charrington: [00:02:13] So tell me a little bit more about that. How are you taking on data versioning? And why is that an important element of the puzzle for folks that are doing AI? Luke Marsden: [00:02:25] Absolutely. So there's really sort of four main pieces of the puzzle that I believe need to be solved to achieve devops for AI, devops for machine learning, and number one is reproducibility - and that's where the data versioning piece comes in. So what we've seen is that there's a lot of chaos and pain that happens when AI or ML teams start trying to operationalize the models that they're developing. And one of the big pain points is if you can't actually get back to the exact version of the data that you use to train your model, then you can't go back and solve problems with it. You can't fix bugs in the model or or really reliably understand sort of exactly where that model came from. So that's kind of that fundamental problem of like which version of the data that I trained is this model on and that's what we solve with with Dotscience. Every time you train a model in Dotscience, you are automatically versioning all of the dependent data sets that that model training happens on. And by using copy-on-write technology, which is a file system technology and in dotmesh, which is part of the Dotscience platform, it does that very efficiently using no more disk space than is required to achieve reproducibility. Sam Charrington: [00:03:52] Awesome. So tell me why are you excited about TWIMLcon: AI Platforms? Luke Marsden: [00:03:59] TWIMLcon looks to be an awesome event. We were actually planning on hosting our own event around the same time in San Francisco to promote Dotscience, but TWIML was such a good fit for what we're trying to do, and the themes and the topics that are being discussed in the space, that we decided to join forces with you guys and become a Founding sponsor rather than running our own things. So yeah, really, really excited and looking forward to it.  Sam Charrington: [00:04:34] That's fantastic and we are super appreciative to have you on board as a Founding sponsor, it is great to have your support in that way. When folks come to your breakout session at TWIMLcon, tell us a little bit about what you'll be covering there, who will be presenting, what can attendees expect to learn from the breakout session. Luke Marsden: [00:04:57] Yes, so the session will be run by my colleague Nick who's our principal data scientist, and the basic premise of the talk really touches on some of the things I mentioned earlier. There's a lot of chaos and pain trying to operationalize AI and that we have this manifesto of things that we believe are needed to go from, sort of the "no-process" process that is the default. So when you start an AI or machine learning project and you have maybe a small number of data scientists or machine learning engineers doing that work, they'll invent a process, right? Any technical group that's doing technical work will make up a process as they go based on the tools that they're familiar with and they'll do their best. But the point of the talk is that the "no-process process," it gets your first model into production when your team is small, but that's really where the problems begin and (Laughter) you end up with this sort of this kind of mess of models and data sets and deployments and hyperparameters and metrics and all these different things flying around, because machine learning is fundamentally more complicated than software development software engineering. And so, by just sort of doing things in an ad-hoc way, you get yourself into this sort of mess quite quickly, and this is something we've seen across hundreds of companies that we've spoken to in the industry. And so basically what we're proposing is a Manifesto, that you should make your machine learning process, the whole process of building, training, deploying, monitoring machine learning models that you should make that that whole process reproducible, accountable, collaborative, and continuous.  And so what I mean by reproducible is that somebody else should be able to come and reproduce the model that I trained now, like 9 or 12 months later without me still needing to be there, without me needing to have kept meticulous manual documentation. Somebody else should be able to go and rerun that model training against the same version of the data with the same version of Tensorflow with the same code, with the same hyperparameters, and get the same accuracy score to within a few percent. If your development environment isn't reproducible, then you won't be able to do that, but we believe that that is key to achieving devops for ML.  So anyway, that's kind of a snapshot of some of the things we'll be talking about in the session. So yeah, please please come along.   Sam Charrington: [00:08:00] Awesome. You'll also be present in TWIMLcon's Community Hall, what can attendees expect to see at the company's booth? Will they be able to get hands on?  Luke Marsden: [00:08:15] Absolutely, so we'll have live demos at the booth. You can see the full end-to-end platform and our Engineers as I speak in the early part of September today, are busily working on the latest features that we're going to have ready in time for the conference in true startup conference driven development mode. (Laughter) So, we will have the deploy to production and statistical monitoring pieces ready in time for the conference. So, it's probably going to be the first time that you can come and see those pieces of the product and and get hands-on with the product will be at TWIML, so please come and check it out. Sam Charrington: [00:09:00] Fantastic.  Luke, thanks so much for chatting with me about what you're up to and what you'll be showing at the event, we are super excited to have you on board with us for TWIMLcon: AI Platforms.   Luke Marsden: [00:09:10] Awesome. Thank you Sam. TWIMLcon: AI Platforms will be held on October 1st and 2nd at the Mission Bay Conference Center in San Francisco. Click here to learn more
Bits & Bytes Google announces TensorFlow 2.0 Alpha, TensorFlow Federated, TensorFlow Privacy. At the 3rd annual TensorFlow Developer Summit, Google announced the first alpha release of TensorFlow 2.0 and several other new releases such as: TensorFlow Federated – a new open-source framework that allows developers to use all the ML-training features from TF while keeping the data local; TensorFlow Privacy – which uses differential privacy to process data in a private manner; extensions to TensorFlow Extended (TFX), a platform for end-to-end machine learning; and Activation Atlases – which attempts to visualize and explain how neural networks process images. Google open sources GPipe, a library for parallel training of large-scale neural networks. GPipe, which is based on the Lingvo (a TensorFlow framework for sequence modeling), is applicable to any network consisting of multiple sequential layers and allows researchers to “easily” scale performance. [Paper] Facebook AI researchers create a text-based adventure to study how AI speak and act. Researchers from Facebook and University College London specifically investigated the impact of grounding dialogue – a collection of mutual knowledge, beliefs, and assumptions essential for communication between two people–on AI agents. Google announces Coral platform for building IoT hardware with on-device AI. Coral targets developers creating IoT hardware from prototyping to production. It is powered by a TPU that is specifically designed to run at the edge and is available in beta. Google and DeepMind are using AI to predict the energy output of wind farms. Google announced that it has made energy produced by wind farms more viable using DeepMind’s ML algorithms to better predict the wind output. Ben-Gurion U. develops new AI platform for ALS care. Researchers at Ben-Gurion University have used ML models to develop a new method of monitoring and predicting the progression of neurodegenerative and help identify markers for personalized patient care and improve drug development. Google rolls out AI grammar checker for G Suite users. Google applies ML techniques to understand complex grammar rules and identify “tricky” grammatical errors by G Suite users. Dollars & Sense PolyAI, a London, UK-based platform for conversational AI, raised $12M in Series A funding Wade & Wendy, a NYC-based AI recruitment platform, closed a $7.6M Series A funding Brodmann17, a Tel Aviv, based provider of vision-first technology for automated driving, raised $11M in Series A funding Paradox.ai, a Scottsdale-based assistive intelligence platform raised $13.34M series A funding Apple acquires patents from AI security camera maker Lighthouse Horizon Robotics, China-based AI chip maker raises $600M ELSA, US-based AI language learning app, raised $7M Modulate, a Cambridge-based ML startup raised $2M in seed funding Zone7, which uses AI to predict injuries in sports, has secured $2.5M DataRobot acquires a data collaboration platform company, Cursor Splice Machine announced that it has raised $16M for unified ML platform Senseon has raised $6.4M to tackle cybersecurity threats with an AI ‘triangulation’ approach Ctrl-labs, a New York startup announced that it has raised $28M in a funding round led by GV, Google’s venture capital arm Armorblox, a Sunnyvale, CA-based provider of a natural language understanding platform for cybersecurity, raised $16.5M Series A funding ViSenze, Singapore-based AI startup, has raised $20M in series C funding BlackBerry announces the acquisition of Cylance, a cybersecurity and AI firm To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.
Bits & Bytes IBM takes Watson AI to AWS, Google, Azure. In an effort to broaden the distribution of its AI technology, IBM announced that it is making IBM Watson available across competing public cloud services such as AWS, Google, and Azure. FAIR open-sources new ELF OpenGo data set, research, and insights. Facebook AI Research (FAIR’s) new features and research results include a completely retrained model of ELF OpenGo using reinforcement learning. The open-sourcing of these models provide an important benchmark for future research. Google collaborates with DeepMind to announce PlaNet for reinforcement learning. The Deep Planning Network (PlaNet) agent learns a world model from image inputs and leverages it for planning. It’s able to solve a variety of image-based tasks with up to 5,000 percent the data efficiency. Cogitai launches Continua, a cloud-based, self-learning AI platform for businesses. The SAAS platform for self-learning AI, Continua platform aims to turn any process, system, software bot, or real robot into a self-learning autonomous service. IBM, SUNY Poly creating AI “hardware lab” in Albany. IBM has agreed to extend its presence at SUNY Poly’s Center for Semiconductor Research through 2023, with an option for another five-year agreement after that. IBM’s total spending under the deal will be $2 billion. Google announces Live Transcribe for real-time continuous transcription. Live Transcribe captions conversations in real-time, with support for over 70 languages. Unity announces forthcoming Obstacle Tower Challenge. The new AI challenge aims to test the vision, control, planning, and generalization abilities of intelligent agents in an end-to-end manner. It’s based on Obstacle Tower, a new research environment combining platform-style gameplay with puzzles and planning problems, spanning an endless number of floors that become progressively more difficult as an agent advances. Dollars & Sense Peltarion, a Swedish startup announced that it has raised a $20M Series A funding Polly.ai, a Seattle, based company has raised a $7M Series A funding round TuSimple, a self-driving truck startup has raised $95M in a Series D funding round Apple has acquired AI startup called PullString that specializes in helping companies build conversational voice apps Sensory Inc. announced acquisition of Vocalize.ai, which provides benchmarking, accuracy assessments and bias evaluations for speech technologies and natural language interface Databricks, announced it has raised $250M in a Series E funding round led by Andreessen Horowitz. Coatue Management, Microsoft Aire, a London based credit scoring system company, raised $11M in Series B funding KenSci has raised $22M to fuel the growth of its ML and AI-powered technology New Relic, Inc., provider of real-time insights for software-driven businesses, announced that it has acquired SignifAI Avalara, Inc. announced it has acquired AI technology and expertise from Indix Facebook has acquired visual shopping and AI startup GrokStyle To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.
Bits & Bytes Facebook and Google researchers build RL framework to study language evolution. Their research paper proposes a computational framework allowing agents to interact in a series of games and uses it to demonstrate that symmetric communication protocols (i.e. languages) emerge and evolve without any innate, explicit mechanisms built in the agent. Google releases synthetic speech dataset to help researchers combat “deep fakes.” Google has published a dataset of synthetic speech containing thousands of phrases spoken by deep learning based text-to-speech models. By training models on both real and computer-generated speech, researchers can develop systems that learn to distinguish between the two. Carbon Relay to optimize energy efficiency in data centers with AI. Carbon Relay launched a new data center energy management product using deep reinforcement learning and augmented intelligence to offer customers energy efficiency improvements. Google announced its success with a similar internal project last year. AWS open sources Neo-AI project to accelerate ML on edge devices. Recall that the project extends TensorFlow, MXNet, PyTorch, ONNX, and XGBoost models to perform at up to twice the speed of the original model with no loss in accuracy on multiple hardware platforms. It’s based on the TVM and Treelite compilers developed at the University of Washington. Microsoft and MIT work to detect ‘blind spots’ in self-driving cars. A model developed by MIT and Microsoft researchers identifies instances where autonomous cars have learned actions from training examples that could cause real-world errors on the road. Amazon facial-identification software used by police falls short on tests for accuracy and bias. AWS may be digging itself a deeper and deeper hole as it attempts to refute claims of bias for its facial-recognition software, Rekognition, marketed to local and federal law enforcement as a crime-fighting tool, struggles to pass basic tests of accuracy. Spell expands cloud AI platform. The Spell platform uses Kubernetes to automatically scale models as necessary and provides metrics, monitoring, and logs for everything running in real time. Its latest edition adds team and collaboration features. The company also announced new funding; see below. Dollars & Sense Israel-based start-up, Augury which provides AI solution to predict industrial equipment failure, raised $25M in a Series C funding Aureus Analytics, a predictive analytics platform for the insurance industry, has secured $3.1M in funding Mimiro, a London, UK-based ML platform for analyzing the risk of financial crime, raised $30M in Series B funding Carbon Relay, a Boston and Washington, D.C.based company raised $5M to tackle data center cooling with AI Verbit Software Ltd, a provider of automated video and speech transcription services powered by AI has raised $23M round of funding led by Viola Ventures Cinnamon AI, a provider of AI solutions, has secured $15M in Series B funding Sonasoft Corp. announces that it has signed a letter of intent to acquire Hotify Rover180 has acquired an AI automation and ML Indiana-based company, Vemity Sherpa.ai, Palo Alto, California-based AI-powered digital predictive assistant provider, raised $8.5M in Series A funding AInnovation, a Chinese AI solutions provider raised approximately $60M in Series A and A+ financing round Zeta Global has acquired Silicon Valley-based AI company, Temnos Adjust announced that it has entered into a definitive agreement to acquire cybersecurity and AI start-up, Unbotify Spell closed $15M in new funding from Eclipse Ventures and Two Sigma Ventures. To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.
Bits & Bytes Google introduces Feast: an open source feature store for ML. GO-JEK and Google announced the release of Feast that allows teams to manage, store, and discover features to use for ML projects. Amazon CEO, Jeff Bezos, is launching a new conference dedicated to AI. The new AI specific conference, re:MARS, will be held in Las Vegas between June 4th and 7th this year. Should be an interesting event. Mayo Clinic research uses AI for early detection of silent heart disease. Mayo Clinic study finds that applying AI to electrocardiogram (EKG) test results offers a simple, affordable early indicator of asymptomatic left ventricular dysfunction, a precursor to heart failure. Microsoft announces ML.NET 0.9. Microsoft’s open-source and cross-platform ML framework, ML.NET, was updated to version 0.9. New and updated features focus on expanded model interpretability capabilities, GPU support for ONNX models, new Visual Studio project templates in preview, and more. Intel and Alibaba team up on new AI-powered 3D athlete tracking technology. At CES 2019, Intel and Alibaba announced the new collaboration to develop AI-powered 3D athlete tracking technology to be deployed at the 2020 Olympic Games. Baidu unveils open source edge computing platform and AI boards. OpenEdge, an open source computing platform enables developers to build edge applications with more flexibility. The company also announced new AI hardware development platforms BIE-AI-Box with Intel for in-car video analysis, and BIE-AI-Board, co-developed with NXP, for object classification. Qualcomm shows off an AI-equipped car cockpit at CES 2019. At CES, Qualcomm introduced the third generation of its Snapdragon Automotive Cockpit Platforms. The upgraded version covers various aspects of the in-car experience from voice-activated interfaces to traditional navigation systems. Their keynote featured a nice demo of “pedestrian intent prediction” based on various computer vision techniques including object detection and pose estimation. Dollars & Sense Fractal Analytics, an AI firm based in India which, among other things, owns Qure.ai (see my interview with CEO Prashant Warier), raised $200M from private equity investor Apax Standard has acquired Explorer.ai, a mapping and computer vision start-up Israeli AI-based object recognition company, AnyVision, has raised $15M from Lightspeed Venture Partners Spell, an NYC-based AI and ML platform startup raised $15M HyperScience, an edge ML company has raised $30M WeRide.ai, a Chinese autonomous driving technology specialist raised series A funding from SenseTime Technology and ABC International in series A funding UK-based Exscientia, AI-driven drug discovery company, has raised $26 million CrowdAnalytix raises $40 million in strategic investment for crowdsourced AI algorithms To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.
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.