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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
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 open sources Bing vector search. The company published its vector search toolkit, Space Partition Tree and Graph (SPTAG) [Github], which provides tools for building, searching and serving large scale vector indexes. Intel makes progress toward optical neural networks. A new article on the Intel AI blog (which opens with a reference to TWIML Talk #267 guest Max Welling’s 2018 ICML keynote) describes research by Intel and UC Berkeley into new nanophotonic neural network architectures. A fault tolerant architecture is presented, which sacrifices accuracy to achieve greater robustness to manufacturing imprecision. Microsoft research demonstrates realistic speech with little labeled training data. Researchers have crafted an “almost unsupervised” text-to-speech model that can generate realistic speech using just 200 transcribed voice samples (about 20 minutes’ worth), together with additional unpaired speech and text data. Google deep learning model demonstrates promising results in detecting lung cancer. The system demonstrated the ability to detect lung cancer from low-dose chest computed tomography imagery, outperforming a panel of radiologists. Researchers trained the system on more than 42,000 CT scans. The resulting algorithms turned up 11% fewer false positives and 5% fewer false negatives than their human counterparts. Facebook open-sources Pythia for multimodal vision and language research. Pythia [Github] [arXiv] is a deep learning framework for vision and language multimodal research framework that helps researchers build, reproduce, and benchmark models. Pythia is built on PyTorch and designed for Visual Question Answering (VQA) research, and includes support for multitask learning and distributed training. Facebook unveils what secretive robotics division is working on. The company outlined some of the focus areas for its robotics research team, which include teaching robots to learn how to walk on their own, using curiosity to learn more effectively, and learning through tactile sensing. Dollars & Sense Algorithmia raises $25M Series B for its AI platform Icometrix, a provider of brain imaging AI solutions, has raised $18M Quadric, a startup developing a custom-designed chip and software suite for autonomous systems, has raised $15M in a funding Novi Labs, a developer of AI-driven unconventional well planning software, has raised $7M 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.
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.
Bits & Bytes Researchers develop AI to detect musical mood. Deezer researchers have developed a deep learning system which can identify the mood and intensity of songs based on audio and lyrics. Microsoft announces automated machine learning service. The new service aims to identify the best machine learning pipeline for the user’s labeled data. Automated ML is integrated with Azure Machine Learning and includes an SDK for integration with Python development environments including Visual Studio Code, PyCharm, Azure Databricks notebooks and Jupyter notebooks. Microsoft contributes $40 million for humanitarian AI. Microsoft launched a new $40-million program aimed at harnessing the power of AI for humanitarian action. The program targets causes such as disaster recovery, helping children, and protecting refugees. Microsoft features Shell industrial AI use cases. Early in his Ignite keynote, Microsoft CEO Satya Nadella highlighted work by Microsoft and Bonsai, which it acquired earlier this year, performed at Shell. The join work aims to enhance safety by applying AI and IoT in a number of areas including at retail gas stations. DeepMind outlines Technical AI Safety program. Interesting post by Google DeepMind researchers outlining the key tenets—specification, robustness, and assurance—of their AI safety research program. AI’s new muse: our sense of smell. Artificial neural networks have been loosely inspired by the brain, specifically the visual cortex. This article describes recent work being done to draw inspiration from our olfactory circuits as well. Dollars & Sense Netradyne, which applies AI to driver and fleet safety, has raised Series B funding of $21 million Olis Robotics, announced the acquisition of White Marsh Forests, a machine learning startup based in Seattle Slack, announced that it has acquired Astro, a messaging startup that applied AI to email Marketing agency Impact Group acquired ad platform startup Cluepto help connect retail brands to consumers Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Diffbot launches knowledge graph as-a-service. The startup, whose roots are in web scraping, applied machine learning, computer vision, and natural language processing to create a database of ‘all the knowledge of the Web,’ spanning over 10 billion entities and 1 trillion facts. Automatic transliteration helps Alexa find data across language barriers. Amazon researchers have developed a multilingual “named-entity transliteration system” to help Alexa overcome language barriers in multilingual environment. Oracle open sources GraphPipe for model deployment. Though Oracle has a strained relationship with open source, they recently released a new open source tool called GraphPipe, designed to simplify and standardize the deployment of machine learning models. Google turns datacenter cooling controls over to AI. Google was already using AI to optimize data center energy efficiency. Now they’ve handed over complete control of data center cooling to AI. Instead of humans implementing AI-generated recommendations, the system is now directly controlling data center cooling. IBM researchers propose ‘factsheets’ for AI transparency. Expanding on ideas like the Datasheets for Datasets paper I discussed previously, an IBM Research team has suggested a factsheet based approach for AI developers to ensure transparency. Facebook and NYU researchers speed up MRI scans with AI. Facebook announced the fastMRI project, in collaboration with NYU, which aims to apply AI to accelerate MRI scans by up to 10 times. Google releases Dopamine reinforcement learning research framework. Google announced the new TensorFlow-based framework, which aims to provide flexibility, stability, and reproducibility for new and experienced RL researchers. Baidu launches EZDL, a coding-free deep learning platform. Chinese firm Baidu EZDL, an online tool enabling anyone to build, design, and deploy models without writing code. Dollars & Sense Canvass Analytics, a Toronto-based provider of AI-enabled predictive analytics for IIOT, raised $5M in funding. Cloudalize, a cloud platform for running GPU-accelerated applications, has secured a €5 million funding round. Intel announced that it is buying Vertex.ai, a startup developing a platform-agnostic model suite, for an undisclosed amount. Zscaler, announced that it has acquired AI and ML technology and the development team of stealth security startup TrustPath. New Knowledge, an Austin-based cybersecurity company that protects corporations from covert, coordinated disinformation campaigns, raised $11M in Series A funding. Phrasee, a London based marketing technology company that uses AI to generate optimized marketing copy, closed a $4m Series A funding round. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes IBM Research presents 'DeepLocker,' AI-powered malware. IBM researchers have developed DeepLocker, a new breed of highly targeted and evasive attack tools powered by AI. The malware remains dormant until identifying its target through indicators like facial recognition, geolocation and voice recognition. The project is meant to raise awareness of the possibilities when AI is combined with current malware techniques. Microsoft Updates ML.NET Machine Learning Framework Microsoft has updated ML.NET, its cross-platform, open-source machine learning framework for .NET developers. This initial preview release of ML.NET enables simple tasks like classification and regression and brings the first draft of .NET APIs for training and inference. The framework can be extended to add support for popular ML libraries like TensorFlow, Accord.NET, and CNTK. Tesla dumps Nvidia, goes it alone on AI hardware. Tesla confirmed that it has developed its own processor for AutoPilot and other on-vehicle AI workloads. The company mentioned that the new processor is 10 times faster than the one it was using from Nvidia, and the linked article’s author estimates the company could save billions by developing its own. Doc.ai and Anthem to introduce health data trial powered by AI and blockchain. The companies have partnered to launch an AI data trial on the blockchain. doc.ai will attempt to identify models for predicting allergies based upon collected phenome (e.g., age, height, weight), exposome (e.g., exposure to weather/pollution based on location) and physiome (e.g., physical activity, daily steps) data. NetApp and NVIDIA launch new deep learning architecture. NetApp introduced the NetApp® ONTAP® AI architecture, powered by NVIDIA DGX™ supercomputers and NetApp AFF A800 cloud-connected all-flash storage to simplify, speed, and scale the deep learning data pipeline. Getty Images launches AI tool for media publishers. Getty Images launched Panels by Getty Images, a new artificial intelligence tool for media publishers that recommends the best visual content to accompany a news article. Dollars & Sense Malong Technologies, a Shenzhen, China-based computer vision startup, received a minority investment from Accenture Test.ai, which AI to the challenge of testing apps, has raised an $11 million Series A round led by Gradient Ventures Skyline AI, a New York based real estate investech firm using AI and data science, raised $18M in Series A funding DefinedCrowd, which provides crowd-sourced data for training AI, has raised a $11.8 million funding round led by Evolution Equity Partners Dbrain, whose platform links crowdworkers and data scientists via the blockchain, has raised a $3 million funding round Racetrack.ai, which applies AI to accelerating business sales, has raised $5 million in a pre-Series A round Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Google announced a bunch of interesting ML/AI-related news at last week’s Next conference. Here are the highlights, along with a few other tidbits. Google launches new AI-powered contact center solution. The global market for cloud-based contact center solutions is expected to exceed $30B by 2023. It’s no surprise that Google wants a piece of this, and to that end launched the Contact Center AI alpha. The new offering combines Google’s Dialogflow chat platform with other AI technologies—e.g. agent assist and a conversational topic modeler—to help customers reduce wait times, improve customer satisfaction, and gain greater insights. A full host of technology and services partners were announced as well. Furthering its edge initiatives, Google releases new Cloud IoT Edge. Cloud IoT Edge includes Edge IoT Core, which facilitates the connection of edge devices to the Google Cloud and simplifies their management, and Edge ML, which supports running pre-trained TensorFlow Lite models on edge hardware. Cloud IoT Edge is designed to take advantage of the newly announced Edge TPU as well (see below). Google unveils new AI chips for edge machine learning. Google is bringing its TPU accelerator chips from the cloud to the edge with the launch of Edge TPU, currently in early access. Aiming to compete with offerings like the Nvidia Jetson and Intel Movidius product families, Edge TPU brings high-performance ML inference to small, power-constrained devices. Google adds Natural Language and Translation services to the Cloud AutoML family. I covered the launch of Google Cloud AutoML Vision in the newsletter earlier this year. Last week Google pulled back the covers on new AutoML services for natural language classification and translation. Skip the press releases though and check out Rachel Thomas’ great series of posts on these new tools. For more from Google and Next, check out these roundups of all announcements and analytics/ML announcements. Dollars & Sense Snap40, which uses ML/AI for remote patient monitoring, has secured US $8 million in seed financing Zorroa, which provides a platform for managing visual assets, has closed a $7M funding round Shanghai-based Wayz.ai, a smart location and mapping start-up (not to be confused with Waze) announced that it has raised a US$80 million series A Unisound, a Chinese AI solutions provider, specialized in voice recognition and language processing, has received RMB600 million ($89 million) in Series C-plus funding Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Elon Musk, DeepMind co-founders promise never to make killer robots. The founders have signed on to the Future of Live Institute’s pledge to develop, manufacture or use killer robots, which was published at the annual International Joint Conference on Artificial Intelligence in Stockholm, Sweden Huawei plans AI chips to rival Nvidia, Intel. The company is reportedly developing AI chips for both networking equipment and the datacenter in an effort to strengthen its position in growing AI market and to compete with the likes of Nvidia and Intel. Let the sniping continue. Facebook has hired Shahriar Rabii to lead its chip initiative. Rabii previously worked at Google, where he helped lead the team in charge of building the Visual Core chip for the company’s Pixel devices. Apple has appointed former Google AI exec John Giannandrea to lead a new artificial intelligence and machine learning team, to include the Siri unit. Interesting projects. Researchers at Nvidia, MIT, and Aalto University presented an approach to automatically removing noise, grain, and even watermarks from photos at ICML. A Google researcher along with collaborators from academia have developed a deep learning-based system for identifying protein crystallization, achieving a 94% accuracy rate and potentially improving the drug discovery process by making it easier to map the structures of proteins. Google revealed “Move Mirror,” an ML experiment that matches user’s poses with images of other people in the same pose. Dollars & Sense R4 Technologies, a Ridgefield, Connecticut-based AI startup created by Priceline.com founders and executives, secured $20m in Series B funding Cambridge-based SWIM.AI, which provides edge intelligence software for IoT applications, announced $10 million in Series B funding Viz.ai, a company applying AI in healthcare secured $21 million in Series A funding Computer vision technology provider AnyVision announced at it has secured $28 million in Series A financing Salesforce has signed a definitive agreement to acquire Datorama, an AI-powered marketing intelligence platform Workday announced that it has acquired Stories.bi, which uses AI to automate analytics and generate natural language business stories Robotic retail inventory specialist Bossa Nova announced the acquisition of AI video surveillance company, HawXeye Self-driving car company Pony.ai raised $102 million, putting it close to a billion dollar valuation Box announced that it has acquired Butter.ai, a startup focused on cross-silo enterprise search DataRobot announced that it has acquired Nexosis, an ML platform company whose founders we interviewed in TWIML Talk #69 Accenture has acquired Kogentix to strengthen Accenture Applied Intelligence’s growing data engineering business Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes AI around the world. This interesting post summarizes the national AI strategies of the 15 nations that have formally published them. Baidu unveils AI chipset. Baidu launches Kunlun, China's first “cloud-to-edge” AI chips. The chips were built to accommodate a variety of AI scenarios–such as voice recognition, search ranking, natural language processing, autonomous driving and large-scale recommendations–and can be applied to both cloud and edge scenarios, including data centers, public clouds, autonomous vehicles, and other devices. Kunlun includes distinct chips for both training and inference. New research explores identification of Photoshopped images. At the recent CVPR conference, University of Maryland researchers presented a method for identifying edited pictures using deep learning. Stanford AI recreates periodic table. Applying techniques borrowed from NLP, Stanford researchers created “Atom2Vec.” The tool analyzed a list of chemical compound names from an online database and proceeded to re-create the periodic table of elements in a few hours. ‘AI eating software’ roundup. Silicon design tools vendor NetSpeed incorporates AI features into new SoCBuilder design and integration platform. AMFG launches new AI software platform for industrial 3D printing. Energy industry ERP provider Quorum Software adds a new cognitive services layer providing intelligent ingest, compliance and reporting capabilities. Dollars & Sense Facebook has acquired the team behind Bloomsbury AI, a London firm specializing in using ML to understand natural language documents JDA Software to acquire Blue Yonder, a provider of AI solutions for retail D.C. startup QxBranch closed $4.1 million in Series A funding to develop analytics software for quantum computing JASK, an Autonomous Security Operations Center (ASOC) platform provider announced that it has raised $25M in Series B funding Suzhou city-based AISpeech announced $76 million in Series D funding, bringing its total funding to over $121 million Tact.ai, a conversational AI sales platform, announced its $27M Series C raise, bringing total funding to more than $57M Cybersecurity firm Balbix raises $20 million in Series B round led by Singtel Innov8 Ping Identity acquires Elastic Beam, a cybersecurity startup using artificial intelligence to monitor and protect APIs Precision Therapeutics, a company focused on AI-based personalized medicine and drug discovery, announced that its merger with Helomics Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes IBM hosts first AI-Human debate. In a publicity stunt in the spirit of Deep Blue’s match with Garry Kasparov, or IBM Watson’s appearance on Jeopardy, IBM hosted the first ever live public debate between its Project Debater AI and a human in San Francisco last week. The company has published several datasets and technical papersoutlining various components of the system. Nvidia publishes “Super SloMo” for transforming standard video into slow motion At last week’s CVPR, NVIDIA researchers presented research into a deep learning model for interpolating between video frames to produce slow-motion video from a standard 30-frame-per-second video. Amazon SageMaker now supports PyTorch and TensorFlow 1.8: In a recent update, Amazon SageMaker has added support for PyTorch deep learning models. I’m now wondering if the fast.ai course and library can be completed on SageMaker. AWS is also now supporting the latest stable TensorFlow versions. Microsoft to acquire Bonsai, one of my favorite AI companies. Berkeley-based Bonsai, a client of mine and sponsor of last year’s Industrial AI series, offers a deep reinforcement learning platform for enterprise AI. I’m super excited for them and looking forward to seeing how things evolve now that they’ll be part of Microsoft. Tracking the state-of-the-art (SOTA) in NLP. Researcher Sebastian Ruder has put together an interesting project to track the SOTA of a variety of problems in natural language processing. Dollars & Sense AI-powered fitness startup Vi raises $20 million Falkonry, a provider of machine learning software for manufacturing, raised $4.6 million in Series A funding Prifender, whose software uses AI to map PII in enterprise data systems, raised a $5M seed round AI-as-a-service startup Noodle.ai announced a $35 million round led by Dell Technologies WalkMe announced that it has acquired DeepUI, whose ML models seek to understand any software at the GUI level, without the need for an API Twitter has agreed to buy San Francisco-based Smyte, which offers tools to stop online abuse, harassment, and spam, and protect user accounts PayPal announced today that it has agreed to acquire Simility, a leading fraud prevention and risk management platform provider for $120 million Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
This video is a recap of our June 2018 TWIML Online Meetup. In this month's community segment we briefly cover the us use of machine learning in agriculture use cases, a recent Google "AI Principles” blog post, recently released researched coming from our friends at OpenAI: Improving language understanding with unsupervised learning. In our presentation segment, Kelvin Ross, director with IntelliHQ, a healthcare AI firm based in Queensland, Australia, joins us to present the paper Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks describing an algorithm developed by Stanford researchers that has been able to exceed human expert performance in identifying cardiac arrhythmias based on raw ECG readings. We also covered the broader issues associated with data capture and labelling, as well as longer term heart-rate variability, which can be used as a predictor or early warning for sepsis, fatigue, shock, concussion, heart attack, stroke, and more. https://youtu.be/k3cCgh5WZ7I 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! Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks Presentation Slides
Bits and Bytes CMA CGM to use artificial intelligence on ships. Leading global shipper CMA CGM Group announced a development partnership with Shone, an early-stage startup building autonomous technologies for cargo ships. The agreement gives Shone access to CMA CGM’s vessels and data. Once completed, the Shone product will facilitate the work of crews in decision support, maritime safety, and piloting. Pega paves path to one-to-one marketing with self-optimizing campaigns. New AI-powered marketing capability will reduce marketers’ dependence on traditional segment-based campaigns and transition them towards real-time, one-to-one engagement. The new offering will face stiff competition from SAP and Adobe, which already have offerings in this space, when it launches as part of PegaInfinity in Q3. IBM and H2O.ai collaborate on POWER9 support. IBM Power Systems and H2O have announced a collaboration to accelerate the latter’s Driverless AI software on IBM POWER9 systems. Google publishes Responsible AI Practices. Following missteps around the launch of Google Duplex and its DOD deal, Google has released a set of principles which will direct its future use of AI technology. The principles aim to ensure that the company’s use of AI is socially beneficial, accountable, incorporates privacy-by-design, and adheres to ethical standards. Databricks introduces MLflow, an open source ML platform. Databricks has released MLflow, a new open source project for managing the lifecycle of machine learning models. Apple has released Core ML 2. Apple released Core ML 2, a new version of its machine learning SDK for iOS devices, at its annual developer conference. Core ML 2 includes the new Create ML tool which will allow developers to create and train custom machine learning models locally on their Mac and enables Keras and sci-kit learn users to import models directly into Core ML. Dollars & Sense Zebra Medical Vision, an Israeli medical imaging startup that uses machine and deep learning to build tools for radiologists, has raised a $30 million Series C CloudNC, the U.K. startup that is developing AI software to automate part of the manufacturing process, has raised £9 million in Series A funding Black Knight has acquired HeavyWater, a provider of artificial intelligence and machine learning (AI/ML) tools to the financial services industry Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes Google won’t renew its military AI contract. According to company sources, Google is planning to close the military AI project after the current contract expires in March 2019. Google staff had expressed their unhappiness over project Maven earlier this year. Nvidia Introduces HGX-2 for HPC and AI. Nvidia has introduced a unified computing platform HGX-2 for both artificial intelligence and high-performance The new cloud server platform allows high-precision calculations for scientific computing and simulations and will also enable AI training and inference. Nvidia Jetson platform goes GA. Nvidia announced the general availability of Nvidia Isaac, which brings AI capabilities to robots for manufacturing, logistics, agriculture, construction and many other industries. The Isaac platform includes new hardware, software, and a realistic robot simulator. Qualcomm reveals new XR platform. Qualcomm introduced its new dedicated Extended Reality (XR) platform Qualcomm® Snapdragon™ XR1. The XR1 platform includes an on-device AI engine so that Augmented Reality (AR) developers can take advantage of AI features like better pose prediction and object classification. Microsoft’s AI bot also calls humans, but only in Chinese. After Google’s Duplex demo, which showed an AI calling to make a reservation and conversing with an employee, Microsoft CEO Satya Nadella demoed a similar service at an event in London. Unlike Duplex, which is just a demo at this point, Microsoft says that thousands of users in China have conversed with its AI, called Xiaoice and it can also call for the conversation. Intel AI Lab open-sources deep NLP library. Intel AI Lab has open-sourced a library for deep-learning-based natural language processing to help researchers and developers create conversational agents like chatbots and virtual assistants. Facebook researchers demonstrate musical style transfer. Researchers from Facebook Artificial Intelligence Research (FAIR) have developed an AI system that can translate music between different styles. Particularly impressive was the ability to translate a whistled version of the Raiders of the Lost Ark theme song to a variety of instruments and classical styles. The accompanying paper, A Universal Music Translation Network, is available on arXiv. Dollars & Sense Flock, a London-based startup focused on the use of real-time data in insurance, has raised a £2.25M Seed funding ForwardX, maker of Ovis, the self-driving suitcase you never knew you needed, raised $10M Series A funding Chinese facial recognition technology developer SenseTime Group Ltd said it has raised $620 million in a second round of funding Kneron, provider of edge artificial intelligence (AI) solutions, announced completion of their series A1 financing of US$18 millionled by Horizons Ventures Weights & Biases, a San Francisco, CA-based enterprise AI platform, received $5m in Series A funding Kontrol Energy Corp, a leader in the energy efficiency market announced acquisition of iDimax will be adding Artificial Intelligence (AI) across its energy software platform PayPal announced that it has acquired Jetlore, an artificial intelligence-powered prediction platform SafeToNet, the UK-based cyber safety company, has acquired the Toronto-based artificial intelligence (AI) and natural language processing startup, VISR Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
We recently ran a series of shows on differential privacy on the podcast. It’s an especially salient topic given the rollout of the EU’s General Data Protection Regulation (GDPR), which becomes effective this month, not to mention scandals like the Facebook/Cambridge Analytica breach and other attacks on private data. If you hadn’t previously (or haven’t yet) heard the term differential privacy, you’re not alone. The field is relatively new–only about ten years old. Differential privacy attempts to allow data holders to make confidential data available for analysis or use via a data product while simultaneously preserving–actually, guaranteeing–the privacy of the individuals whose data is included in the database or data product. Differential privacy is often introduced in contrast to data anonymization. While anonymization might seem to be a reasonable way to protect the privacy of those data subjects whose information is included in a data product, that information is vulnerable to numerous types of attack. Consider, for example, the Netflix Prize, a frequently cited example. In support of a competition to see if someone could build a better recommendation engine, Netflix made an anonymized movie rating dataset available to the public. A group of researchers, however, discovered a linkage attack that allowed large portions of the data to be de-anonymized by cross-referencing it with publicly available IMDB user data. But what if we don’t want to publish data, but rather use it to create machine learning models that we allow others to query or incorporate into products? It turns out that machine learning models are vulnerable to privacy leakage as well. For example, consider a membership inference attack against a machine learning model. In this kind of attack, patterns in the model’s output are used to extract the data the model was trained on. These types of attacks, powered by ‘shadow’ machine learning models, have been shown to be effective against black-box models trained in the cloud with Google Prediction API and Amazon ML. In another example, an attack called model inversion [pdf] was used to extract recognizable training images (i.e. faces) from cloud-based image recognition APIs. Because these APIs return a confidence score alongside the label of a face submitted for recognition, an adversary could systematically construct an input face that maximizes the APIs confidence in a given label.   Differential privacy is an approach that provides mathematically guaranteed privacy bounds–it’s not a specific algorithm. For any given problem, there can be many algorithms that provide differential privacy. Aaron Roth provided a great example of a simple differential privacy algorithm in our interview. In his example, a polling company wants to collect data about who will vote for Trump in the upcoming election, but are concerned about the privacy of the people they poll. Roth explains that they could use a simple yet differentially private method of collecting the data. Instead of simply asking for the pollees voting data, the company could instruct the individuals to first flip a coin and if the coin is heads, answer the question honestly, but if the coin is tails, give a random answer decided by another coin flip. Because the statistical characteristics of the coin flip are known, you can still make inferences about the wider population even though your data collection has been partially corrupted. At the same time, this method ensures that the individuals in your study are protected by plausible deniability. That is to say, if the data were to be exposed there’s no way of knowing if a given answer was honest or part of the injected noise. Some tech companies are already starting to reap the benefits of differential privacy. For example: Apple. Apple uses differentially private methods of capturing user data to gain insights about user behavior on a large scale. They’re currently using this method for applications as diverse as QuickType and Emoji suggestions, Lookup Hints in Notes, crashing and energy draining domains in Safari, autoplay intent in Safari, and more. Google. In addition to using differential privacy to help understand the effectiveness of search query suggestions in its Gboard keyboard, Google, along with other cloud providers, has a huge incentive to explore these methods due to the public nature of many of the machine learning models they offer. Google has published several papers on the topic so far, including RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response and Deep Learning with Differential Privacy. Bluecore. Bluecore offers software and services to help marketers find and retain their best customers through targeted email marketing. The company uses differential privacy techniques to pool data across companies to improve customer outcomes while preventing any individual customer from being able to gain any insights into competitors’ data. Be sure to check out my interview with Bluecore director of data science Zahi Karam. Uber. Uber uses differential privacy to protect sensitive data against internal and external privacy risks. When company data analysts explore average trip distances in a city, for example, their queries go through an internal differential privacy system called Chorus, which rewrites their queries to ensure differential privacy. Open-source tools for differential privacy are beginning to emerge from both academic and commercial sources. A few examples include: Epsilon is a new differential privacy software system offered by Georgian Partners. Epsilon currently works for logistic regression and SVM models. At this time it’s only offered to the firm’s partners and portfolio companies, but the team behind that project plans to continue expanding the tool’s capabilities and availability. For more check out my interview with Georgian’s Chang Liu. SQL Elastic Privacy is an open source tool from Uber that can be used in an analytics pipeline to determine the level of privacy required by a given SQL query. This becomes a parameter that allows them to fine-tune their differential privacy algorithm. Diffpriv is an R package that aims to make differential privacy easy for data scientists. Diffpriv replaces theoretical sensitivity analysis with sensitivity sampling, helping to automate the creation of privacy assured statistics, models, and other structures. ARX is a more comprehensive open-source offering comprising a GUI-based tool and a Java library implementing a variety of approaches privacy-preserving data analysis, including differential privacy. As you might imagine, differential privacy continues to be an active research topic. According to Roth, hot research areas include the use of differential privacy to create and publish synthetic datasets, especially for medical use cases, as well as better understanding the ‘local method’ of differentially private data collection, in which the noise is injected at the time of collection as opposed to after collection. Differential privacy isn’t a silver bullet capable of fixing all of our privacy concerns, but it’s an important emerging tool for helping organizations work with and publish sensitive data and data products in a privacy-preserving manner. I really enjoyed producing this series and learned a ton. I’m eager to hear about what readers and listeners think about it, so please email or tweet over any comments or comment below. Sign up for our Newsletter to receive this weekly to your inbox.
Bits and Bytes This week news from Google I/O and Microsoft Build have dominated the news. Here are the highlights: Google introduces ML developer kit. The SDK supports text recognition, face detection, barcode scanning, image labeling, and landmark recognition for developers integrating AI tech into iOS and Android. It also boasts the ability to run its models offline. Google demos lifelike voice bot with Google Duplex. The technology is capable of conducting natural conversations to carry out real-world tasks–such as booking an appointment–over the phone. It works with Google Assistant, which also got 6 new voices and the ability to perform complex tasks via voice command. Microsoft and Qualcomm partner for vision at the edge. The partnership allows for Qualcomm’s latest AI hardware accelerators to deliver real-time AI at the edge. The developer kit, which uses Microsoft’s Azure IoT Edge and Azure Machine Learning, can be used to create camera-based IoT solutions. This is part of Microsoft's larger expansion of Azure IoT Edge which received updates at the recent Microsoft Build conference. Microsoft announces Cognitive Services updates. Microsoft's suite of AI services received updates including a unified Speech service and Bing Visual Search, custom object detection, added features for Bing Custom Search, new speech features with custom translator, custom voice, custom speech, and more. Microsoft announces new ML.NET platform. ML.NET is a cross-platform, open source machine learning framework. It will allow .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing ML models. Dollars & Sense Notable, a voice-powered healthcare startup, raises $3M Learning Machine, a software startup making blockchain-based credentials at scale, raises $3M Xnor.ai, a startup focusing on AI edge computations, raises $12M conDati, a digital marketing analytics company, raises $4.75M Gamalon, a startup working on AI capable of creating summaries or explanations of speech or text, raises $20M Innovaccer, a healthcare data platform company, raises $25M Unisound, a Chinese AI solutions company, raises $100M Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
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 & 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 Nest returns to the Google nest in AI push. Google is bringing device maker Nest back under its control as it fights Amazon and Apple for a foothold in the AI-enabled smart home market. Your smart watch could one day detect diabetes. A recent clinical study demonstrated that the Apple Watch, using its heart rate sensor paired with a machine learning algorithm, could detect diabetes with 85 percent accuracy. Photonic AI chip battle brewing. Last week I noted the $10 million fundraising by startup Lightelligence. Not to be outdone, Lightmatter, another startup with MIT roots going after the same light-powered AI chip market, announced its $11 million funding round. Facebook DensePose tools to re-skin videos in real time. The new Facebook AI Research system consists of DensePose-COCO, a human-annotated dataset which maps 2D images to 3D surfaces on the human body, and DensePose-RCNN, a new network architecture for classifying and localizing body surface parts from video frames. Version 0.2 of AWS Model Server for MXNet adds ONNX support. AWS updated its open source Model Server for Apache MXNet (MMS) library, which packages and serves deep learning models. New features include support for Open Neural Network Exchange (ONNX) models and the ability to publish operational metrics directly to Amazon CloudWatch. Dollars & Sense Paige.ai raises $25 million for cancer detection powered by computer vision Loris.ai, a Crisis Text Line spin-out, raises $2 million AdHive closes $5.5M ICO presale in 36 minutes for community-driven video advertising platform Boost AI raises $5M from Alliance Venture for a virtual AI-based custom agents
Bits and Bytes 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.
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