We could not locate the page you were looking for.

Below we have generated a list of search results based on the page you were trying to reach.

404 Error
Earlier this year, we rolled out a major update to the TWIML website designed to make it easier for you to discover, use, and share podcast episodes and other TWIML content. Here are our top 10 favorite features of the new site: /*! elementor - v3.6.7 - 03-07-2022 */ .elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px} All of TWIML’s content together at last .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} More FREE content With the release of the new site, you can now access all content from past TWIMLcon and TWIMLfest events with one free login!   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Events Speaking of events, our various conference sites have all been brought under the new site, so no more jumping to different URLs or managing different logins to find and access this content from our awesome events. You'll also find info for upcoming events, like last week's webcast The Evolution of Machine Learning Platforms at Facebook and TWIMLcon: AI Platforms 2022.   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Solutions We now surface content from our Solutions Guide on the homepage of the new site, making it easier to discover TWIML blog posts, industry research, and vendor solution profiles that can help you do your job better. Expanded podcast functionality   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Discoverable The Podcast section of the new site provides quick and easy access to all 584 episodes and allows you to discover new episodes you might like based on series, topics, and our curated playlists.   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Video first We’ve recorded all episodes in both audio and video since early last year and this new version of the site presents a video-first experience to desktop and tablet users.   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Shareable We’ve made it easier to follow and share the podcast via your podcast listening and social media platforms of choice right from the episode page. Fresh and functional   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Beautiful While the theme I personally hand-crafted nearly six years ago took us a long way, it was definitely time to spruce up the site. The new design is cleaner, easier to read and use, and more consistent overall.   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Searchable The new site features both cross-site searches that spans all the site’s content and a dynamic episode search feature on the podcast page that makes it easy to filter through episodes by topic, guest, or episode number.   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Connected The new TWIML Network feature allows you to navigate from any show or session to that item’s guest or speaker, and from there see all the other things we’ve done with them. You can also, for the first time, directly access a list of past podcast guests and conference speakers.   .cls-1{fill:#fff;}.cls-2{isolation:isolate;}.cls-3{fill:url(#linear-gradient);}.cls-4{fill:url(#_Áåçûìÿííûé_ãðàäèåíò_1364);mix-blend-mode:multiply;opacity:.5;} Responsive The new site was designed from the ground up to be more easily accessed from mobile devices, so you can get your TWIML fix on the go. In time, I’d like to see us offer more frequent and practical written content to complement our existing offerings. If you’d like to contribute as either an author or editor of articles and other content, I’d love to hear from you. Please reach out.
Bits & Bytes Google announces TensorFlow 2.0 Alpha, TensorFlow Federated, TensorFlow Privacy. At the 3rd annual TensorFlow Developer Summit, Google announced the first alpha release of TensorFlow 2.0 and several other new releases such as: TensorFlow Federated – a new open-source framework that allows developers to use all the ML-training features from TF while keeping the data local; TensorFlow Privacy – which uses differential privacy to process data in a private manner; extensions to TensorFlow Extended (TFX), a platform for end-to-end machine learning; and Activation Atlases – which attempts to visualize and explain how neural networks process images. Google open sources GPipe, a library for parallel training of large-scale neural networks. GPipe, which is based on the Lingvo (a TensorFlow framework for sequence modeling), is applicable to any network consisting of multiple sequential layers and allows researchers to “easily” scale performance. [Paper] Facebook AI researchers create a text-based adventure to study how AI speak and act. Researchers from Facebook and University College London specifically investigated the impact of grounding dialogue – a collection of mutual knowledge, beliefs, and assumptions essential for communication between two people–on AI agents. Google announces Coral platform for building IoT hardware with on-device AI. Coral targets developers creating IoT hardware from prototyping to production. It is powered by a TPU that is specifically designed to run at the edge and is available in beta. Google and DeepMind are using AI to predict the energy output of wind farms. Google announced that it has made energy produced by wind farms more viable using DeepMind’s ML algorithms to better predict the wind output. Ben-Gurion U. develops new AI platform for ALS care. Researchers at Ben-Gurion University have used ML models to develop a new method of monitoring and predicting the progression of neurodegenerative and help identify markers for personalized patient care and improve drug development. Google rolls out AI grammar checker for G Suite users. Google applies ML techniques to understand complex grammar rules and identify “tricky” grammatical errors by G Suite users. Dollars & Sense PolyAI, a London, UK-based platform for conversational AI, raised $12M in Series A funding Wade & Wendy, a NYC-based AI recruitment platform, closed a $7.6M Series A funding Brodmann17, a Tel Aviv, based provider of vision-first technology for automated driving, raised $11M in Series A funding Paradox.ai, a Scottsdale-based assistive intelligence platform raised $13.34M series A funding Apple acquires patents from AI security camera maker Lighthouse Horizon Robotics, China-based AI chip maker raises $600M ELSA, US-based AI language learning app, raised $7M Modulate, a Cambridge-based ML startup raised $2M in seed funding Zone7, which uses AI to predict injuries in sports, has secured $2.5M DataRobot acquires a data collaboration platform company, Cursor Splice Machine announced that it has raised $16M for unified ML platform Senseon has raised $6.4M to tackle cybersecurity threats with an AI ‘triangulation’ approach Ctrl-labs, a New York startup announced that it has raised $28M in a funding round led by GV, Google’s venture capital arm Armorblox, a Sunnyvale, CA-based provider of a natural language understanding platform for cybersecurity, raised $16.5M Series A funding ViSenze, Singapore-based AI startup, has raised $20M in series C funding BlackBerry announces the acquisition of Cylance, a cybersecurity and AI firm To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.
Sam Charrington: Today we're excited to continue the AI for the Benefit of Society series that we've partnered with Microsoft to bring you. In this episode. We're joined by Peter Lee, Corporate Vice President at Microsoft Research responsible for the company's healthcare initiatives. Peter and I met a few months ago at the Microsoft ignite conference where he gave me some really interesting takes on AI development in China. We reference those in the conversation and you can find more on that topic in the show notes. This conversation centers on three impact areas that Peter sees for AI and healthcare, namely diagnostics and therapeutics, tools and the future of precision medicine. We dig into some examples in each area and Peter details the realities of applying machine learning and some of the impediments to rapid scale. Before diving in I'd like to thank Microsoft for their support of the show and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with this intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more about their plan at Microsoft.ai. Enjoy. Sam Charrington: [00:02:18] All right, everyone. I am on the line with Peter Lee. Peter is a corporate vice president at Microsoft responsible for the company's healthcare initiatives. Peter, it is so great to speak with you again. Welcome to This Week in Machine Learning and AI. Peter Lee: [00:00:14] Sam, it's great to be here. Sam Charrington: [00:00:17] Peter, you gave a really interesting presentation to a group that I was at at Ignite about what some of Microsoft was working on, at Microsoft Research as well as a really interesting take on AI development in China. That kind of piqued my interest, and we ended up sitting down to chat about that in a little bit more detail. While I did cover that for my blog and newsletter, and I'll be linking to it in the show notes, we won't be diving into that today. It was a really, really interesting take that I reflect on often, and I think it's an interesting setup for diving into your background, because you do have a very interesting background and an interesting perspective and set of responsibilities at Microsoft. On that note, can you share with our audience a little bit about your background? Peter Lee: [00:01:11] Sure, Sam. I'd love to do that. I agree it is a little bit unusual, although I think the common thread throughout has been about research and trying to bring research into the real world. I'm a computer scientist by training. I was a professor of computer science at Carnegie Mellon for a long time, actually for 24 years, and at the end of my time there was the head of the Computer Science Department. Then I went to Washington, D.C, to serve at an agency called DARPA, which is the Defense Advanced Research Projects Agency. That's kind of the storied research agency that built the Saturn V booster technology, invented the ARPANET, which became the Internet, developed robotics, lots and lots of other things. I learned a lot about bringing research to life there. Then, after a couple of years there, I was recruited to Microsoft and joined Microsoft Research. Started managing the mothership lab in Redmond, in the headquarters in Redmond, and then a little bit later all of the U.S. research labs and then ultimately, all of Microsoft's 13 labs around the world. Right about that time, Steve Ballmer announced his retirement. Satya Nadella took over as the CEO. Harry Shum took over all of AI and research at Microsoft and became my boss. They asked me to start a new type of research organization internally. It's called NExT, which stands for New Experiences in Technologies, and we've been trying to grow and incubate new research-powered businesses ever since, and most recently in healthcare. Sam Charrington: [00:03:04] I think when I think about AI and healthcare, there's certainly a ton of ground to cover there, but I think one of the areas that gets a lot of attention of late is all the progress that's being made around applying neural nets, CNNs in particular, to imagery. I'm wondering from your perspective, how do you tend to think about AI applied to the healthcare space and where the big opportunities are? Peter Lee: [00:03:37] Yeah. When I think about AI and healthcare, I'm really optimistic about the future. Not that there aren't huge, difficult problems and sometimes things always seem to go slower than you expect. It's a little bit like watching grass grow. It does grow and things do happen, but sometimes it's hard to see it. But over the last 15 years, the thing that I think is underappreciated is the entire healthcare industry has gone digital. It was only 15 years ago that, for example, in the United States, less that 10% of physicians were recording your health history in a digital electronic health record. Now, we're up over 95%, and that's just an amazing transformation over 15 years. It's not like we don't still have problems, data is siloed, it's not in standard formats. There's all sorts of problems, but the fact that it's gone digital just opens up huge, huge amounts of potential. I kind of look at the potential for AI in three areas. One is the thing that you pointed out, which are AI technologies that actually lead to better diagnostics and therapeutics, things that actually advance medical science and medical technology. A second area for AI is in the area of tools, tools that actually make doctors better at what they do, make them happier while they're doing it, and also improve the experience for you and me as patients or consumers of healthcare. Then the third area is in this wonderful future of precision medicine that's taking new sources of information, digital information, your genome, your proteome, your immunome, data from your fitness wearables and so on and integrating all of that together to give you a complete picture of what's going on with your body. Those are sort of three broad areas, and they're all incredibly exciting right now. Sam Charrington: [00:05:51] When you think about the first two of those categories, better diagnostics and therapeutics and tools, how do you distinguish them? It strikes me that giving doctors a better way to analyze medical imagery, for example, or to use that example again, is a tool that they can use, but when you say tools, what do you specifically mean? Peter Lee: [00:06:14] Yeah. You're absolutely right. There's an overlap. It's not like the boundaries between these things are all that hardened, but if you think about one problem that doctors have today is by some estimates in the United States, doctors are spending 40 to 50% of their workdays entering documentation, entering notes that record what happened in their encounters with patients. That's sometimes called an encounter note. That documentation is actually required now by various rules and regulations. It's an incredible source of burden. In fact, I'm guessing you've had this experience, most people have. You go to your doctor, I go to mine, and I like her very much, but while I'm being examined by her, she's not looking at me. She's actually sitting at a PC, typing in the encounter notes. The reason she's doing that is if she doesn't do it while she's examining me, she'll have to do it for a couple of hours maybe in the evening, taking time away from her own family. That burden is credited or blamed for a rise in physician burnout. Well, AI technologies today are rapidly approaching the point where ambient intelligence can just observe and listen to a doctor-patient encounter and automate the vast majority of the burden of that required clinical note-taking. That's an example of the kind of technology that could in a really material way just improve the lives and the workday satisfaction of doctors and nurses. I put that in a different category than technologies that actually give you more precise diagnosis of what's ailing you or ability to target therapies that might actually attack the very specific genetic makeup, let's say, of a cancer that's inhabiting your body right now. Sam Charrington: [00:08:17] Got it. Got it. Maybe let's take each of these categories in turn. I'd love to get a perspective from you on where you see the important developments coming from, from a research perspective, and where you see the opportunities and where you see things heading in each. Peter Lee: [00:08:42] Sure. Well, why don't we start with your example of imaging, because computer vision based on deep neural nets has just been progressing at this stunning rate. It seems like every week you see another company, another startup, or another university research group showing off their latest advances in using deep neural net-based computer vision technologies to do various kinds of medical image diagnosis or segmentation. Here at Microsoft, we've been working pretty hard on those as well. We have this wonderful program based primarily in India that's been trained on the health records and eye images of over 200,000 patients. That idea of taking all that data, you get the signal of which of those patients have, let's say, suffered from, say, diabetic retinopathy or a progression of refractive error leading to blindness. From that signal in the electronic health record, coupled with the images, we are able to train a computer vision-based thing to make a prediction about whether a child whose eye image has been taken is in danger of losing eyesight. That is in deployment right now in India, and, of course, for other parts of the world like the United States and Europe, which are more regulated, these things are in various states of clinical validation so they can be more broadly deployed. Another example is a project that we have called InnerEye that is trying to just reduce the incredible, kind of boring and mundane problem of just pixel-by-pixel outlining the parts of your body that are tumor and should be attacked with the radiation beam as opposed to healthy tissue. That problem with radiation therapy planning has to be done really perfectly, which is why it's this sort of pixel-by-pixel process. But there is maybe five or 15 minutes of real black magic that's drawing on all of the intuition and experience and wisdom of a radiologist and then two to three hours of complete drudgery, and much of that complete drudgery can just be eliminated with modern computer vision technologies. These things are really developing so rapidly and coming online. They tend not to replace completely what doctors and radiologists can do, because there is always some judgment and intuition involved in these things, but when done right, they can integrate into the workflow to really enable, to kind of liberate clinicians from a lot of drudgery and to reduce mistakes. I think one other thing that's sometimes not fully appreciated is you also, when you get these tools, you can take these measurements over and over and over again. When they become cheap, you can take them every day, if necessary, which allows you to track progression of a disease or its treatment over time much more precisely. These sorts of applications, I think, in medical imaging, I think are really promising. One thing I ... it's a hobby horse of mine ... before I pause, is in 2015 here in Microsoft Research we invented something called deep residual networks, which are now commonly called ResNets. ResNet has become part of an industry standard and research standard in computer vision using deep neural nets. We ourselves have refrained from using ResNets for doing things like imaging of 3D images for the purposes of radiation therapy planning, and there are various technical reasons for that. Sometimes we have a mixture of being proud seeing the rest of the world use our invention for interesting medical imaging, but we also sometimes get worried that people don't quite understand the failure modes in these things. But, still, the progress has just been spectacular. Sam Charrington: [00:13:14] That's kind of an interesting prompt. Maybe let's take a moment to explore the failure modes, and why don't you ... It sounds like you don't advise folks to apply ResNets to the types of images that we tend to see in medical imaging. What's that about? Peter Lee: [00:13:32] Yeah. It's not advising or warning people against it. If you think about, let's say, take the problem of radiation therapy planning, it's a 3D problem. You have a tumor that is a 3D mass in your body and you're trying to come up with the plan for that radiation beam to attack ideally as much of that tumor while preserving as much healthy tissue as possible. Of course, your picture into that 3D tumor is as a series of two-dimensional slices, at least with current medical imaging. One very basic question is, as you examine slice-by-slice that tumor with respect to the healthy tissue, is each slice being properly and logically registered with the next one? A simple or naïve application of a convolutional neural network, like a ResNet, doesn't automatically do that. The other problem is it's unclear to what extent a bad training sample or set of training samples will do to one of these deep neural nets. In fact, just in the last few weeks and months, there have been more and more interesting academic research studies showing some interesting failure modes from a surprisingly small number of bad training samples. I think that these things are changing all the time. Our algorithms and our algorithmic understanding are improving all the time, but at least within our research groups, we've taken pains to understand that this application of computer vision isn't like others. It's more in the realm of, say, driverless cars where safety is of paramount concern, and we just have to have absolute certainty that we understand the possible failure modes of these things. Sometimes with just an off-the-shelf application of ResNets or any similar deep neural net algorithm, we and now more and more other researchers at universities are finding that we don't yet fully understand the failure modes. Sam Charrington: [00:16:02] In some ways, there's an opportunity beyond kind of naïve application of an algorithm that performs very well on ImageNet. Today, you can get data sets that include kind of these 2D representations of what are fundamentally 3D applications or 3D images and apply the regular 2D algorithms to them and find interesting things. But you're saying that a) we can do better and b) we may not even be doing the right things in many cases because of these safety issues. I'm wondering, on the first of those two points, the doing better, is there either a standard approach that's better than ResNet for these 3D images that you've developed at Microsoft or have seen otherwise? Or where are we in terms of taking advantage of the 3D nature of medical images and deep learning? Peter Lee: [00:17:06] Yeah. That's a good question. For our InnerEye project, which is really run by a great set of researchers based mostly in our Cambridge, U.K. research lab and led by Antonio Criminisi. He's really one of the preeminent authorities in computer vision. In fact, he led an effort some years ago to work out the 3D computer vision for Kinect, and so he's really specialized in 3D. The InnerEye project, which is really for us an effort to really understand completely the workflow of radiation therapy planning, that system actually doesn't use residual network. What it does is it uses kind of an architecture of layered what are called decision forests. That gives not only some benefits in terms of more compact representations of machine-learned models and, therefore, some performance improvements, but it allows us to kind of capture a kind of logical registration of the images as they go slice-by-slice. In other words, you're inferring not just the segmentation of each 2D image slice, but you're actually trying to infer the voxel, the 3D voxel volume of the tumor that you're trying to attack. Then on top of that, there's a process involved when you're dealing with medical technologies. You don't just put it out there and start applying it on people. You get it peer-reviewed. You get it peer-reviewed, in this case, in computer science journals and in medical journals, and you go through a clinical validation, and if you're in the United States, for example, through an FDA approval process. For us, as we're learning about what does our cloud, what do our AI services, what do our tools have to be in order to support this future of AI-powered healthcare, InnerEye is an example of us going end-to-end to try to build it all out and to understand all those components and to understand what has to be done to really do it right. It's been a great learning experience. We're now in the process not only of working with various companies who might want to integrate this InnerEye technology into their medical devices, but we're starting to now pull apart the kind of bricks and mortar that we used in the technical architecture for InnerEye in order to expose those as APIs for other developers to use. Our intent is not to get into the radiation therapy business. Our intent is not to get into radiology. But we do want our cloud and our AI services and our algorithms to be a great place for any other company or any other startup or innovator who wants to do that and ideally do it on our cloud, using our tools. Sam Charrington: [00:20:29] An interesting point in there. You mention that the decision forests that you developed to address this problem ... I guess we often think of there being this tradeoff between factors like explainability or safety, as you related that second point, and performance, which we think of as the neural net is delivering kind of the ultimate in performance in many cases. But in this case, this decision forest algorithm is outperforming at least your classic 2D ResNets, and I'm imagining also providing benefits in terms of explainability/safety. Is that correct? Peter Lee: [00:21:21] Well, we feel very strongly that it provides benefits in terms of safety. Explainability is really another very interesting question and problem. There's a potential for greater explainability. One of the lessons that we learned when we were working on AI for sales intelligence ... We had really developed tremendous amount of AI that would ingest large amounts of data from the world as well as from customer relationship management databases, emails and so on for our sales teams and used that through various AI algorithms to do things like synthesize new offers to specific customers or to surface new prospective customers or to suggest new discount pricing for specific customers. One of the things we learned is that no self-respecting sales executive is going to offer a 20% discount to a customer just because his algorithm says so. Typically- Sam Charrington: [00:22:35] Doctors are probably similar? Peter Lee: [00:22:37] That's right. In that situation, we also moved away from, in that specific case, moved away from the pure deep neural net architecture to having a kind of layered architecture of Bayesian graphical models. The reason for that was so that we could synthesize an explanation in plain English of not only offer a 20% discount, but why. As we get into, away from more point solutions that are kind of machine learning or AI-powered to more of that digital assistant that is the companion to a clinician and gives that clinician a second opinion or advice on a first opinion, those sorts of explanations undoubtedly are going to become important, especially at the beginning when we're trying to establish trust in these things. As we've been experimenting even with the kind of ambient intelligence to just listen in on a doctor-patient encounter and try to automate a note, one thing we've found is that doctors will look at the synthesized note and not trust everything in it because they don't quite yet have the understanding of why did the note come out this way. It became important to provide tools so that when you, say, click on a specific entry in the note, that it could be mapped back to a running transcript and to the right spot in the running transcript that was recorded. These sorts of things I think are part of maybe the human-computer interaction or the human-AI interaction that we're having to think about pretty hard as we try to integrate these things into clinical workflow. Sam Charrington: [00:24:30] Before we move on beyond diagnostics and therapeutics, all of the examples that you gave fell into the domain of computer vision. Are there interesting things happening in diagnostics beyond the kind of onslaught of these new computer vision-based approaches? Peter Lee: [00:24:51] Yeah. I think actually some of the most interesting things are not in computer vision, and this maybe crosses over into the precision medicine thing. One of the projects I'm so excited about is something that we're doing jointly with a Seattle biotech startup, Adaptive Biotechnologies. The setup is this: If you take a small blood sample from your body, in that sample, in that one-mL sample, you'll end up capturing on the order of one million T cells. The T cells are one of the primary agents in your adaptive immune system. About two and a half years ago, there was a major scientific breakthrough that got published that showed that the receptor ... There's a receptor on the surface of your T cells, and in that receptor, there's a small snippet of DNA. There was strong evidence two and a half years ago that that snippet of DNA completely determines what pathogen or infectious disease agent or cancer that T cell has been programmed to seek out and destroy. That paper was very interesting because it used a simple linear regression in order to identify from a read of that little snippet of DNA on the T cell receptor whether you had CMV, cytomegalovirus, or not. It was really just an impressive paper and just very recent. Well, the thing that was interesting about Adaptive Biotechnologies is Adaptive Biotechnologies was in the business of giving you a printout of that specific snippet of DNA in all the T cell receptors in a blood sample. They had a business model that would help some cancer centers titrate the amount of specific chemotherapy you were getting based on a reading of that DNA. That raised the question, would it be possible to take that printout of those T cell receptor DNA sequences and, in essence, think of that as a language and translate it into the language of antigens? Then, if you can do that, can you take those antigens and do a kind of topic identification problem to figure out what infectious diseases, what cancers, and what autoimmune disorders your body is currently coping with right now? It turned into this very interesting new business opportunity for Adaptive Biotechnologies that if machine learning could be used to solve those two problems, then they would have a technology that would be very similar to a universal diagnostic, a simple blood test powered by machine learning that could do early diagnosis of any infectious disease, any cancer, and any autoimmune disorder. Microsoft found that interesting enough that we actually took an investment position in Adaptive Biotechnologies and agreed to work with them on the machine learning. And Adaptive, for their part, agreed to build a bigger production pipeline in order to generate training data to power that machine learning that we're developing at Microsoft. What has transpired since then has been an amazing amount of progress where we've added tremendous amount of sophistication actually using deep neural nets and started to feed it with billions of points of training data. In fact, this year, the production facility at Adaptive will be able to generate up to a trillion points of training data. We're now targeting five specific diseases, ovarian cancer, pancreatic cancer, type I diabetes, celiac disease, and Lyme disease. That's two cancers, two autoimmune disorders, and one infectious disease with the same machine learning pipeline. It's still an experiment, but it kind of shows you the potential power of these advances in immunology, in genomics, and AI all being bound together to give the possibility. We know the science now is valid, and if we can now build the technology that ties those things together, we get the potential for a universal diagnostic, but as close a thing that we could imagine getting to the Star Trek tricorder as anything. Sam Charrington: [00:29:31] Mm-hmm (affirmative). That was the thing that popped immediately to mind for me, the tricorder. That example, I think, captures for me really plainly both the promise of applying machine learning and AI to this healthcare domain, but also maybe a little bit of the frustration in thinking through, okay, collecting a trillion samples and you've got this pipeline, why does it take so long? There's certainly regulatory and political types of reasons that maybe we'll get into. I'm wondering if you can elaborate on with that much training data and kind of the science in place and a pipeline in place, what are the realities of applying machine learning in this type of context that impede kind of rapid scale? Why just five diseases and not 25, for example? Peter Lee: [00:30:43] Yeah. That's such a great question. Yeah, human biology is just so complicated. I will say there are three ways, maybe, to take a cut at that. If we took a look at the very basic science, just consider the human genome, something that geneticists at several universities have taught me which was really eye-opening, is if you look at the human genome and then look at all the possible variants, the number of variants in the human genome that would still be considered homo sapiens is just astronomically large. Yet, the total number of people on the planet relative to that number is really tiny, only, what, seven and a half billion people. In fact, if we had somehow DNA samples from every human that has ever existed, I think most estimates say there are fewer than 106 billion people that have ever existed since Adam and Eve. If we are using modern machine learning, which is basically looking at statistical patterns and correlations, we have an immediate problem for a lot of basic problems in genomics, because we basically don't have a source of enough training data. The complexity of human beings, the complexity of cancer, the genetic complexity of disease, is just vastly larger than the number of people that have ever existed. Sam Charrington: [00:32:21] Meaning relative to the possible combinations of genes- Peter Lee: [00:32:28] That's right. Sam Charrington: [00:32:28] ... every human is ... I guess it shouldn't be surprising that every human is unique, but even given ... It's a little counterintuitive. You'd think there's only these four letters that were thrown together to figure all this stuff out. Right? Peter Lee: [00:32:43] Yes. What that means is that, yes, we will and we have been making ... We, meaning the scientific community and the technology community, have been making stunning advances and making really meaningful improvements for neonatal intensive care, for cancer treatments, for immunology, but fundamentally, scientifically, we still need something beyond just machine learning. We really need something that gets into the basic biology. That's kind of one reason why this is hard. Another reason is these are just big problems. In the project with Adaptive Biotechnologies, there are between 10 to the 15th and 10 to the 16th different T cell receptors that your body can produce and on the order of maybe 10 to the 7th known antigens. Imagine we're trying to do is trying to fill out a gigantic Excel spreadsheet with 10 to the 16th columns and 10 to the 7th rows. That's just a heck of a big table, and so you end up needing a large amount of training data to discern enough structure, find enough patterns in order to have a shot at filling in at least useful parts of that table. The good news is everybody has T cells, and so we can take blood samples from anybody, from just ordinary, healthy people, and then we can go to research laboratories around the world that have stored libraries of antigens and start correlating those stored libraries of antigens against those what are called naïve blood samples. That's exactly what Adaptive Biotechnologies is doing in order to generate the very large amount of training data. It's a little bit of a good news situation there that we don't need to find thousands or millions of sick people. We can generate the data from just ordinary samples. But it's still a very large amount of data that we need. Then the third kind of way that I think about this is it gets back to the safety issue. We do things a certain way because ultimately, medicine and medical science is based on causal relationships. In other words, we want to know that A causes B, but what we typically get out of machine learning is just A is correlated with B. We get those inferences, and then it takes more work and more testing under controlled circumstances to know that there's a causal relationship. All three of those things kind of create challenges. It does take time, but I think the good thing is as the regulatory organizations like the FDA have gotten smarter and smarter about what is machine learning, what is it good for, what are its limitations, that whole process has gotten, I think, faster and more efficient over time. Then there's a second element, which is, of course, companies are in it to make money. At a minimum, even if they have purely humanitarian intentions, at a minimum they have to be sustained over time. That means that insurance companies and Medicare and Medicaid, they have to be willing to reimburse doctors and nurses when they actually use or prescribe these diagnostics and therapeutics. All of that takes time. Sam Charrington: [00:36:37] At least on the second of your three points, in thinking about scaling, solving problems like this, specifically training data, do you have a rule of thumb, a chart that says, okay, one trillion training samples will get us these five diseases, but we'll need 10 trillion to get to 10 diseases? I realize that that's almost an asinine question and it's much more complex than that, but does it make sense at all to think of it like that? And think of, I guess, the impact of collecting training data and what the trajectory looks like that over time, kind of like the way we thought of as we drive the cost of sequencing down, the downstream effects that that'll have? Peter Lee: [00:37:27] Yeah. Well, when you find the answer to that question, please tell me. In my experience, I've seen this go two ways. One of the wonderful things about modern machine learning algorithms today is that they're far less susceptible to problems of over-fitting. They come very close to this wonderful property that the more data, the more better. But it does happen that sometimes you hit a wall, that you start to see a trail-off in improvement. We really don't know. The kind of early results that we've gotten with admittedly simpler diseases like CMV, and then CMV is actually not that interesting from a medical perspective, they give us tremendous hope. Then other internal, more technical validations, give us supreme confidence that the basic science, the biological science is well-understood now. Once you start really attacking much more complex diseases, like any cancer, it's really hard. I would be unwilling personally to make a prediction about what will happen. But there's every reason today for optimism, and I think the only unknown is whether there is a what if we fall off a cliff at some point and stop finding improvements. Or if we're going to just get to a viable FDA-approved diagnostic in the near term that will be constantly improving as more and more people are diagnosed. It could really go in either way. I'm really unable and actually unwilling to make a prediction about which way it will go, but we are feeling pretty confident. Incidentally, I should say last month Adaptive Biotechnologies closed a deal with Genentech for applications of this T cell receptor antigen map in the therapeutic space, in the area of cellular therapies for targeted cancer treatments. That deal has a value of over $2 billion, so there's also some ... When you're dealing with commercial relationships like that, there's a tremendous amount of due diligence. These are big bets and big pharma is accustomed to making large, risky bets like this, but I think it's another sign that at least leading scientists at one of the larger pharmaceutical organizations is also increasingly confident that we can fill out this map. Sam Charrington: [00:40:38] We've talked about diagnostics. We've talked about precision medicine. What do you see happening on the tooling side, both from the doctor's perspective as well as the patient experience perspective? Peter Lee: [00:40:52] Yeah. One thing, it's a simple thing, but it's been surprising how useful it has turned out to be. We've been piloting chatbot technology that we call the Microsoft Health Bot. This has been sort of in a beta program with a few dozen healthcare organizations. What it does is, we've sort of advanced our cognitive services for language processing, for natural language processing, for conversational understanding and the tooling to provide a drag-and-drop interface so that ordinary people can program these chatbots, at least for medical settings, and then we've improved the models, the language models, so they understand medical and healthcare concepts and terms. We've been surprised at the kinds of applications that people use. One example is there are organizations that have made prescription bots. The idea is this. Maybe you get a prescription from your doctor or from the hospital and you go to the pharmacy, you get your prescription filled, and then a day or two later, you get a message from this intelligent chatbot that's asking, "How's it going? Do you have any questions? Or have you had any issues with your medication?" It invites you proactively to get into a conversation that gives the healthcare provider tremendous insight into whether you're adhering to your prescription. That's a huge problem. Something like 35% of people actually don't follow through with their prescription medications. It's just there to answer questions. Maybe you have some stomach upsets or some people who are on a lot of medications hate having all those bottles and they put them all, dump all the pills into a baggy and then they can't remember which pills are which. The health bot is able to converse with you and say, "Oh, well, why don't you point your phone camera at a bunch of pills and I'll remind you what they are." It uses modern computer vision ResNets, actually, to remind you what these pills are. The kind of engagement that the healthcare providers get, the improvements in engagement and the satisfaction that people like you and me have is really improved. Or just asking simple benefits questions or medical triage of various sorts, these kinds of ideas have been surprisingly interesting. In fact, so surprising for us that later this week, we'll be making that product generally available for sale. You'll be able to use the Microsoft Health Bot technology without any restriction, except for payment, of course. That is something that has gone extremely well. That technology now is being baked into more and more of, I think, of what people will be seeing. We have a collaboration hub application in Office 365 called Teams, and Teams has been this just wonderful technology for improving collaboration in all sorts of workplace settings. Well, we've made Teams healthcare compliant and able to connect to electronic health record systems, and then by integrating great kind of collaboration intelligence tools, to just parse records or a newer way to go to find certain bits of information or just to be able to ask an intelligent agent that is part of your team, "Did so-and-so check the sutures last night?" and be able to get a smart answer whether people are awake or not. There are all these little ways that I think AI can be used in the workflow of healthcare delivery. One of the things that is, I think, underappreciated about healthcare delivery today, especially in acute care settings, is it's a super collaborative environment. Sometimes there can be as many as 20 people that are working together as a team delivering care to multiple patients at a time. How to keep that team of 20 people all on the same page and all coordinated is getting to be a really difficult problem, typically done with Post It notes and half-erased whiteboards now transitioning to pretty insecure consumer messaging apps. But the idea of having real enterprise-grade collaboration support with AI, I think just can make all of that much better and then provide much more security and privacy for people. A lot of these applications of AI end up being less flashy than doing some automatic radiation therapy planning of a medical image, but they really kind of help people, those people on the front lines of healthcare delivery do their jobs better. Sam Charrington: [00:46:34] I tend to find myself having really kind of mixed feelings about conversational applications, at least from the perspective of talking about them on the podcast. There's no question that conversational experiences and interfaces will be a huge part of the way we interact with computers in the future and that there's tons of work that needs to happen there because of the reasons that you mentioned, like less flashy. I wonder if there's still interesting research. At least my question to you is are there still interesting research challenges there? Or is it all, do we have all the pieces and it's just kind of rolling up the sleeves and building enterprise software, which we know is hard and takes time? Peter Lee: [00:47:21] Yeah. It's a good question. It feels like research to me. Sam Charrington: [00:47:27]. (laughter) Elaborate. Peter Lee: [00:47:28] Some of the problems, if anything, feel little difficult, honestly. If we just, say, take the problem of listening to a doctor-patient conversation and from that, understanding what should go into the standard form of a clinical encounter note. Here's a typical thing. There could be an exchange. Let's say, Sam, you're my doctor and I'm your patient, you might be asking me how I'm doing and I might complain about the pain in my left knee hasn't gone away. We can have an exchange about how that goes, and ultimately, what goes into the note by you is a note about my continued lack of weight loss and that my being overweight is contributing to the lack of healing with my knee problem. That may or may not have been a part of our conversation. While it's important that the weight loss element be in that clinical note ... In fact, it might even mean revenue for that doctor because there might be a weight loss program that gets prescribed and so on. That's important and it's important not to miss that. The human exchange here and the things that are implicit in those conversations, let alone the fact that I'll say kneecap and you'll say patella, are things that are as close to general artificial intelligence style problems as anything. Sam Charrington: [00:49:15] Yeah. Peter Lee: [00:49:18] Look, we don't kid ourselves that we're anywhere close to solving those types of problems, but those are the kinds of problems we think about, even when we just look at the kind of day-to-day, minute-by-minute work that people do to deal with their healthcare. Sam Charrington: [00:49:33] Right, right. Peter Lee: [00:49:34] There's another one that's interesting. To really unlock the power of AI, what we would want to do is to just open up huge databases to great researchers and innovators everywhere, but, of course, we need to do that without violating anyone's privacy. There's one problem, something called de-identification. It would be great to be able to take a treasure trove of what's in electronic health records and "de-identify" it. Well, some parts of those electronic health records are easy to do because there might be a field called Social Security Number, another field called Name, another one called Address, and so on, so you can just scrub those out. But large amounts of clinical data involve just unstructured notes, and to really have a deep understanding of what's in those notes and in order to scrub those in a way that won't inadvertently reveal somebody's identity or their medical condition, again, is something that in the ultimate, ends up being a very general AI problem. Sam Charrington: [00:50:41] That's a great reframing of the way to think about this is I guess most chatbots are boring because they're boring. Kind of the entity intent framework that most chatbots are built on is kind of like table stakes relative to what we're really trying to do with conversational experiences. That really requires a level of sophistication and our ability to use and work with and manipulate natural language that is very much at the research frontier now. And that's why most current in-production chatbots are kind of boring. Peter Lee: [00:51:27] Yeah. We've taken a step forward of trying to think of these things almost in terms of being able to play a game of 20 questions. One of the most inspiring applications of health bots that we dream about is in matching people to clinical trials. At any point, there are thousands of clinical trials. You can go to a website called clinicaltrials.gov and there's a search bar there, and you can type in something like breast cancer. When you do that, you get this gigantic dump of every registered clinical trial going on that might be pertinent to breast cancer. While that's useful, the problem with that is it's hard to know which ones of those ... If you are, say, someone who's desperate to find a clinical trial to enroll in because you've run out of other viable options for whatever is ailing you, it's just almost impossible to go through all of that technical information and try to understand this. Would it be possible to use an AI to read through all that technical information and then to synthesize what amounts to a game of 20 questions, something that'll converse with you and ask you questions in order to narrow down to just that one or two or three clinical trials that might be a match for you. It's that kind of thing where it's not fully general conversation of the sort that I think you and I were talking about just a minute ago, but is slightly more structured than that in order to help you more intelligently, more efficiently find the right medical or healthcare solution for you. That kind of application is something that we're really putting a lot of kind of heart and mind into, along with many others around the world. It's exciting that we're starting to see these things actually make it into clinical use today. I kind of agree with you. I do roll my eyes sometimes at the overheated hype around intelligent agents and chatbots as well, just like anybody else, but it's really getting somewhere in these more limited domains. Sam Charrington: [00:53:56] I think it also says why the interesting work in domains like this is going to be ... It's not generic. You're solving a specific problem and there's a lot of investment in getting the machine running AI right for this particular problem as opposed to implementing a generic framework. Peter Lee: [00:54:16] That's right. Sam Charrington: [00:54:17] Awesome. Well, Peter, thank you so much for taking the time to chat with me about the stuff you're seeing and working on in the healthcare space. A ton of really interesting examples in there and I'm looking forward to following all this work and digging deeper. Thank you. Peter Lee: [00:54:37] And we didn't even talk about China once. That's great. Sam Charrington: [00:54:41] Well, you mentioned ResNet a few times kind of taunting me to dive into that conversation, but I'll refer folks to the article and we'll put the link in the show notes. Peter Lee: [00:54:52] Sounds great. It was really a pleasure chatting.
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.
Last week on the podcast I interviewed Clare Gollnick, CTO of Terbium Labs, on the reproducibility crisis in science and its implications for data scientists. We also got into an interesting conversation about the philosophy of data, a topic I hadn’t previously thought much about. The interview seemed to really resonate with listeners, judging by the number of comments we’ve received via the show notes page and Twitter. I think there are several reasons for this. I’d recommend listening to the interview if you haven't already. It’s incredibly informative and Clare does an excellent job explaining some of the main points of the reproducibility crisis. The short of it though is that many researchers in the natural and social sciences report not being able to reproduce each other’s findings. A 2016 “Nature” survey demonstrated that more than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments. This concerning finding has far-reaching implications for the way scientific studies are performed. Gollnick suggests that one contributing factor is the idea of “p-hacking”–that is, examining one’s experimental data until patterns are found that meet the criteria for statistical significance, before determining a specific hypothesis about the underlying causal relationship. P-hacking is also known as “data fishing” for a reason: You’re working backward from your data to a pattern, which breaks the assumptions upon which statistical significance is determined in the first place. Clare points out that data fishing is exactly what machine learning algorithms do though–they work backward from data to patterns or relationships. Data scientists can thus fall victim to the same errors made by natural scientists. P-hacking in the sciences, in particular, is similar to developing overfitted machine learning models. Fortunately for data scientists, it is well understood that cross-validation, by which a hypothesis is generated on a training dataset and then tested on a validation dataset, is a necessary practice. As Gollnick points out, testing on the validation set is a lot like making a very specific prediction that’s unlikely to occur unless your hypothesis is true, which is essentially the scientific method at its purest. Beyond the sciences, there’s growing concern about a reproducibility crisis in machine learning as well. A recent blog post by Pete Warden speaks to some of the core reproducibility challenges faced by data scientists and other practitioners. Warden refers to the iterative nature of current approaches to machine and deep learning and the fact that data scientists are not easily able to record their steps through each iteration. Furthermore, the data science stack for deep learning has a lot of moving parts, and changes in any of these layers–the deep learning framework, GPU drivers, or training or validation datasets–can all impact results. Finally, with opaque models like deep neural networks, it’s difficult to understand the root cause of differences between expected and observed results. These problems are further compounded by the fact that many published papers fail to explicitly mention many of their simplifying assumptions or implementation details, making it harder for others to reproduce their work. Efforts to reproduce deep learning results are further confounded by the fact that we really don’t know why, when or to what extent deep learning works. During an award acceptance speech at the 2017 NIPS conference, Google’s Ali Rahimi likened modern machine learning to alchemy for this reason. He explained that while alchemy gave us metallurgy, modern glass making, and medications, alchemists also believed they could cure illnesses with leeches and transmute base metals into gold. Similarly, while deep learning has given us incredible new ways to process data, Rahimi called for the systems responsible for critical decisions in healthcare and public policy to be “built on top of verifiable, rigorous, thorough knowledge.” Gollnick and Rahimi are united in advocating for a deeper understanding of how and why the models we use work. Doing so might mean a trip back to basics–as far back as the foundations of the scientific method. Gollnick mentioned in our conversation that she’s been fascinated recently with the “philosophy of data,” that is, the philosophical exploration of scientific knowledge, what it means to certain of something, and how data can support these. It stands to reason that any thought exercise that forces us to face tough questions about issues like explainability, causation, and certainty, could be of great value as we broaden our application of modern machine learning methods. Guided by the work of science philosophers like Karl Popper, Thomas Kuhn, and as far back as David Hume, this type of deep introspection into our methods could prove useful for the field of AI as a whole. What do you think? Does AI have a reproducibility crisis? Should we bother philosophizing about the new tools we’ve made, or just get to building with them? Sign up for our Newsletter to receive this weekly to your inbox.
Bits & Bytes Google AI to predict heart disease with eye scans. The tech is being developed by Google’s health subsidiary Verily. It works by scanning the back of a patient’s eye, it then uses that image to deduce patient age, blood pressure, smoking status, and their risk of heart attack. It’s still in its early stages, though, and is not ready for clinical use. Google debuts ‘auto ads’ for intelligently ad placement. While Google has long used machine learning to determine the best ads to show on a web page, this new feature reads the target page and selects the best ad placement on the page. Google claims that participating publishers saw ad revenue increases of 10-15%, however, some beta users were not happy about the number of ads being placed on their pages. IBM partners with game dev platform Unity to create IBM Watson Unity SDK. I’ve had my eye on Unity since my interview with Danny Lange, their VP for ML and AI. The new SDK is being launchedon the Unity Asset Store and will allow developers to integrate visual recognition, speech text, and language classification features into their games and AR/VR applications more easily. Qualcomm adds AI engine to Snapdragon mobile platform. The Qualcomm AI Engine consists of software, hardware and APIs meant to support efficient neural network inference on client devicesrunning Snapdragon processors. Accenture launches AI testing service. Accenture’s taking a “Teach and Test” approach to the service, with the former focused on the choice of data, models, and algorithms used to train ML models, and the latter on up-front and ongoing evaluation of model performance, explainability and bias. MindBridge adds NLP to its AI-powered auditing software. The update allows audit professionals to naturally ask query transactional data and gain insight into potential errors and risky transactions. Dollars & Sense Vectra, a cybersecurity startup, raises $36M for global expansion of its AI-Based Security Platform SparkCognition, an AI solutions startup, raises $56.5 million Series B For International Expansion StatusToday, an employee productivity startup, raises $3.91 million to improve employee productivity with AI Prophesee, a machine vision startup, raises $19 million for its machine vision technology Agent IQ, an AI customer service bot startup, raises $6.3M Benevolentai acquires Cambridge research facility to accelerate AI-enabled drug development Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Amazon to design its own AI chips for Alexa, Echo devices. This announcement follows similar moves made by rivals Apple and Google, both of which have developed custom AI silicon. Amazon, which reportedly has nearly 450 people on staff with chip expertise, sees custom AI chips as a way to make it's AI devices faster and more efficient. Google’s Cloud TPU AI accelerators now available to the public. Cloud TPUs are custom chips optimized for accelerating ML workloads in Tensorflow. Each boasts up to 180 teraflops of computing power and 64 gigabytes of high-bandwidth memory. Last week Google announced their beta availability via the Google Cloud. Cloud TPUs are available in limited quantities today and cost $6.50 / TPU-hour. At this cost, users can train a ResNet-50 neural network on ImageNet in less than a day for under $200. Finding pixie dust unavailable, Oracle sprinkles AI buzzword on cloud press release. The company applied "AI" to its Cloud Autonomous Services, including its Autonomous PaaS, and its Autonomous Database and Autonomous Data Warehouse products to make them "self-driving, self-securing and self-repairing" software. Oh boy! In other news, the company ran the same play for a suite of AI-powered finance applications. LG to introduce new AI tech for its smartphones. Following the launch of its ThinQ and DeepThinQ platforms earlier this year, as previously noted in this newsletter, LG will introduce new Voice AI and Vision AI features for its flagship V30 smartphone at the gigantic Mobile World Congress event next week. Applitools updates AI-powered visual software testing platform. I hadn't heard of this company before, but it's a pretty cool use case. The company released an update to its Applitools Eyes product, which is a tool for software development and test groups that allows them to ensure a visually consistent user experience as the application evolves. The company uses AI and computer vision techniques to detect changes to rendered web pages and applications, and report the ones that shouldn't be there. Dollars & Sense OWKIN, a company using transfer learning to accelerate drug discovery and development, closes $11m Series A financing. Ditto, a UK AI startup, raises £4 million to bring the expert system back via "software advisor" bots which aim to replicate human expertise and accountability. Palo Alto-based Uncomnon.co raises $18M in Series A funding for Uncommon IQ, its AI-powered talent marketplace. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
#MyAI, Your AI A few weeks back, following my visit to CES, I asked you to share your thoughts on AI in our personal lives. We've seen some insightful responses so far and, as the contest comes to an end, I thought I’d share some of them. As a reminder, we asked listeners to record short video responses to the questions: How has AI impacted your personal and home life? And how do you see it impacting you in the future? A common theme in many of the responses was an appreciation for how AI facilitates small tasks in new and more efficient ways. Sometimes this new level of utility makes it hard to think how you would've done it before. For example, for those who use it, features like photos search and text prediction offer fundamental shifts in usability. This calls to mind the idea that AI only seems like “AI” when it's magic; everything else recedes into the background and becomes expected. Some respondents agreed that the tech still had a long way to go with more complex tasks. At times, AI-powered actions can feel clunky and inefficient, forcing you to go out of your way to overcome its limitations. Having to reiterate spoken commands or correct recommendations are a couple of examples. So again, not always magic, but still a lot of goodness to be had. Let’s take a quick look at a few of AI products which folks said were most useful: Google Photos. The ubiquity of smartphones means we all have tons of photos and making sense of them all is non-trivial. According to our community, AI has made a big difference in making photo libraries more accessible. From the Google Photos app, you can easily search your gallery with a myriad of phrases and get astoundingly accurate results. Want to pull up those vacation pictures you took a couple of years ago? Just search “beach.” Looking for that picture of your neighbor’s dog? Just search for “dog,” and the pic is there. The integration is seamless, efficient, and intuitive--everything you’d want an AI to be. Peeling back the veil a bit, one presumes that Google Photos uses a combination of computer vision (CV) and natural language processing (NLP) techniques to label images so that they can be easily searched. Similar functionality is available via the Google Cloud Vision API, for you to embed into your own applications, or via the Cloud AutoML Vision API we discussed last month, should you need to train with your own images. The app also uses AI to pick and enhance your best pictures. Duolingo. One listener, Chandana, mentioned Duolingo, one of my favorite apps, in her submission. Duolingo is a popular language learning app that I’ve used a ton in my own endeavors. Like the other apps mentioned here, Duolingo uses machine learning so seamlessly that it’s not immediately appreciable just how much is involved.At the core of Duolingo is a model that tracks statistics about every word they’ve ever taught you--it’s a database with billions of entries that’s updated 3,000 times per second! The app uses an approach they call Half-LIfe Regression (HLR) to optimize when words are presented to users. HRL combines machine learning and data science with the psycholinguistic theory of “forgetting curves.” After implementing this method, Duolingo saw ten percent increases in user retention and activity. You can read more about HLR and even check out the source code at Duolingo’s blog post on the topic. Gboard. Typing on a glass surface is not quite as intuitive as having actual keys, so developing predictive text was essential to the rise of the modern smartphone. The first apps used predictive models based on dictionaries, but now products like Google’s Gboard rely on neural nets.Gboard initially used a Gaussian model to quantify the probability of tapping neighboring keys, coupled with a rule-based model to represent cognitive and motor errors. More recently these were replaced with a single long short-term memory (LSTM) model trained with a connectionist temporal classification (CTC) criterion. Google shares a bunch of detail about the AI behind Gboard here on their research blog. These are but a few of the many great examples of simple, seamless, every-day AI. I have enjoyed hearing your thoughts on AI through your entries, and I’d love to hear from more of you. Don’t be intimidated by the video format—we’ve made it super easy to record and upload your video right from the TWIML web page. Join the conversation by giving us your thoughts in 2 minutes or less. As a bonus, there are some pretty cool AI-powered prized for folks who get the most likes on their video. Sign up for our Newsletter to receive this weekly to your inbox.
One of the papers I’ve been meaning to look into is the Wide and Deep Learning paper published by Google Research a couple of weeks ago. It turns out that the paper is both short and very much on the applied side of the spectrum, so it’s relatively easy reading. There’s also a lot of supporting material, between the Google Research blog, the TensorFlow docs and the video they created, though I found that reading the paper helped me understand the video, as opposed to the other way around! The background here is that a team from Google Research developed a recommender model that combines the best aspects of logistic regression and neural nets and found than it outperformed either approach individually by a small but significant percentage. The basic idea is that linear models are easy to use, easy to scale and easy to understand. They’re also pretty good at “memorizing” the relationships between individual features when you use some simple feature engineering to capture the relationship between individual features. This feature engineering, which is very commonly used, results in a lot of derived features and so the linear models that uses it is called “wide” learning in this paper. What the linear models aren’t really good at are “generalizing” across different features because they can’t really see those relationships unless you feed in a set of higher order derived features that capture this, and doing so is labor intensive. This is where neural nets, or so called “deep” models, come into play. They are better at generalizing and rooting out unexpected feature combinations that have predictive value. But they’re also prone to over-generalization and don’t do a good job at “memorizing” specific feature combinations that are infrequently seen in the training data. So this paper proposes a jointly trained model that combines both wide and deep learning. By jointly trained we mean that this isn’t an ensemble model, where we train a linear model and a neural net separately and then choose the best prediction among the two. That doesn’t help us here because for ensemble to work, we need both models to be independently accurate. That would mean we would need to do all the feature engineering we’re trying to avoid for the linear model. Rather, by training the wide and deep models together, they can each do what they’re best at while keeping the overall model complexity low. It’s actually pretty surprising how much system-level implementation detail this paper packs into 4 pages. I was left feeling like I have a pretty good understanding of how the recommendation system for the Google Play store was designed so as to make recommendations against a 1 million item app catalog using over 500 billion training examples to serve each request in about 10 ms under a peak load of 10 million app scoring requests per second. In addition to publishing the paper, Google also open sourced their TensorFlow implementation of the model with a high level API for Wide & Deep models called a “DNN Linear Combined Classifier”. Alright, I hope you enjoyed learning about this paper as much as I enjoyed reading it. Before we jump over to Projects, a few quick notes: In recent weeks we’ve talked about the ICML and CVPR conferences. This week Leo Tam posted a blog calling out his impressions from both and his top 10 posts from each. Check it out for a concise look into what you missed at these conferences. Next, this week was the IJCAI conference, the International Joint Conference on AI. I haven’t seen much by way of summaries or highlight posts so I don’t have much to say about it, but if you see anything good send it my way to share. Finally, if you’re looking for a contextualized view into a bunch of interesting and important research papers including bot, and how they all fit together, you’ll like Xavier Amatriain’s presentation from last week’s Data Science Summit. The focus of the talk is providing the audience with a reminder of all the problems for which traditional ML is still state of the art relative to the new hotness deep learning, and he cites the relevant papers for each area. The slides are up on SlideShare and are highly recommended.
This week’s show covers the International Conference on Machine Learning (ICML 2016), “dueling architectures” for reinforcement learning, AI safety goals for robots, plus top AI business deals, tech announcement, projects and more. ICML 2016 –Accepted Papers | ICML New York City – Which companies had accepted papers at #icml2016 ? Best Paper Awards – [1511.06581] Dueling Network Architectures for Deep Reinforcement Learning – [1601.06759] Pixel Recurrent Neural Networks – [1602.07415] Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling – My winner in the best name category: Extended and Unscented Kitchen Sinks – Demystifying Deep Reinforcement Learning Research Google Research Blog: Bringing Precision to the AI Safety Discussion OpenAI Blog: Concrete AI safety problems Paper: 1606.06565.pdf OpenAI technical goals Artificial intelligence achieves near-human performance in diagnosing breast cancer — ScienceDaily Paper: 1606.05718.pdf Business Twitter pays up to $150M for Magic Pony Technology, which uses neural networks to improve images | TechCrunch Increasing our Investment in Machine Learning | Twitter Blogs Artificial Intelligence Explodes: New Deal Activity Record For AI DARPA is looking to make huge strides in machine learning | PCWorld Data-Driven Discovery of Models (D3M) – Federal Business Opportunities: Opportunities AI Culture Wars in Silicon Valley How Siri Started — and Lost — the Assistant Race How Google is Remaking Itself as a “Machine Learning First” Company — Backchannel AI, Apple and Google Technology Lighting the way to deep machine learning | Engineering Blog | Facebook Code Intel Launches ‘Knights Landing’ Phi Family for HPC, Machine Learning The Toronto Raptors Are Using IBM’s Watson to Draft A Winning Team | Motherboard Projects Hello, TensorFlow! How to read: Character level deep learning GitXiv: Collaborative Open Computer Science Machine Learning Yearning Mastering Feature Engineering – O’Reilly Media Bonus I didn’t have time to cover: The Stanford Question Answering Dataset
This week’s show looks at Facebooks’ new DeepText engine, creating art with deep learning and Google Magenta, how to build artificial assistants and bots, and applying economics to machine learning models. Here are the notes for this week’s show: DeepText: Facebook’s Text Understanding Engine Introducting DeepText: Facebook’s Text Understanding Engine FBLearner Flow Research: Text Understanding from Scratch Natural Language Processing (almost) from Scratch Machine Learning and Art Google Magenta Neural Art A Neural Algorithm of Artistic Style Neural Art in TensorFlow Autoencoding Blade Runner Courses: NYU’s Machine Learning for Artists Goldsmith’s University of London The Latest TensorFlow Paper TensorFlow: A system for large-scale machine learning Business of ML & AI Microsoft Confirms Microsoft Ventures VC Arm Intel Acquires Computer Vision for IOT, Automotive Lumiata Closes $10 Million Series B Financing with Intel Capital Findo raises $3M to help you find files and documents through natural language queries More Bots, and How to Build Artificial Assistants Motion AI lets anyone easily build a bot Sequel lets you create a ‘Me’ bot, beats Google to the punch Hybrid Intelligence: How Artificial Assistants Work The Economics of Machine Learning models The preoccupation with test error in applied machine learning Towards Cost-Optimized Artificial Intelligence More Cool Deep Learning posts Deep Reinforcement Learning: Pong from Pixels A Survey of Deep Learning Techniques Applied to Trading Just for Fun Building an IoT Magic Mirror Magic Mirror on GitHub Image Credit: Microsoft