ODSC Presentation "Putting Deep Learning to Work" by Alex Ermolaev, Nvidia
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We will look at the best practices for using deep learning as well as most popular use cases across several horizontal and vertical domains. Open Data Science Conference West, San Francisco, November 2-4, 2017
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ODSC Presentation "Putting Deep Learning to Work" by Alex Ermolaev, Nvidia
- 1. Alex Ermolaev PUTTING DEEP LEARNING TO WORK OPEN DATA SCIENCE CONFERENCE San Francisco, November 2-4, 2017
- 4. 4 AI SYSTEMSTRADITIONAL ‘AI SYSTEMS’ = ‘LEARNING SYSTEMS’ Changing its code to improve results Probabilistic Potential for more general purpose Pre-programmed to do same thing every time Deterministic One time use and limited purpose
- 5. 5 AI TECHNOLOGIES OVER TIME 1960s 1980s 1990s 2000s 2010s Cybernetics, Control Systems Expert Systems, AT&T, IBM Data Mining, OCR Page rank Machine learning, Netflix prize, PayPal Deep Learning
- 6. 6 AI ERROR RATES COLLAPSED AFTER 2012 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 1990 1992 1995 1997 2000 2002 2005 2007 2010 2012 2015 2017 Source: Microsoft Corp Speech Recognition Accuracy
- 7. 7 DEEP LEARNING BIG BANG NIPS (2012) ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto Ilya Sutskever University of Toronto Geoffrey e. Hinton University of Toronto Launched from Alex Krizhevsky’s bedroom
- 8. 8 GPU COMPUTING AT THE HEART OF AI Performance Beyond Moore’s Law
- 9. 9 GPU DEEP LEARNING IS A NEW COMPUTING MODEL Training Device Datacenter TRAINING Billions of Trillions of Operations GPU train larger models, accelerate time to market 10s of billions of image, voice, video queries per day GPU inference for fast response, maximize datacenter throughput DATACENTER INFERENCING Billions of intelligent devices & machines Recognition, reasoning, problem solving GPU inference: real-time accurate response DEVICE INFERENCING
- 10. 10 MORE DATA + BIGGER MODEL + MORE COMPUTE = BETTER RESULT 2012 AlexNet 8 Layers 1.4 GFLOP ~16% Error 152 Layers 22.6 GFLOP ~3.5% Error 2015 ResNet 16X Model IMAGE RECOGNITION SPEECH RECOGNITION 2014 Deep Speech 1 80 GFLOP 7,000 hrs of Data ~8% Error 465 GFLOP 12,000 hrs of Data ~5% Error 2015 Deep Speech 2 10X Training Ops
- 11. 11 “ Methods that scale with computation are the future of AI.” Richard Sutton, University of Alberta/Google DeepMind
- 12. 12 THE ECONOMICS OF AI Source: Kindred
- 13. 13 “Mobile computing, inexpensive sensors collecting terabytes of data and the rise of machine learning that can use that data will fundamentally change the way the global economy is organized.” Cloud Services Manufacturing Transportation Healthcare AI IS TRANSFORMING EVERY INDUSTRY Deep Learning Innovation at Unprecedented Pace
- 14. 14 EVERY INDUSTRY HAS AWOKEN TO AI 2014 2016 1,549 19,439 Higher Ed Internet Healthcare Finance Automotive Others Government Developer Tools Organizations engaged with NVIDIA on Deep Learning
- 15. 15 WHEN TO APPLY DEEP LEARNING? Requirements for Successful Project PROBLEM: big, core, expensive PATTERN: existing, complex DATA: labeled, more data = better model TEAM: data science lead, mixed skills INFRASTRUCTURE: efficient compute
- 16. 16 Vision Title hereSound NLP Cyber Title hereFraud Robotics Prediction Title hereRiskRecommender DEEP LEARNING USE CASES Where to apply DL?
- 17. 17 DEEP LEARNING FOR VISION Beyond Cats eCommerce Satellite Agriculture Visual Search for eCommerce Carbon monitoring from satellite images LettuceBot only spray weeds
- 18. 18 DEEP LEARNING FOR HEALTHCARE Saving Lives Electronic health records Pathology Radiology If >$5000 Charges in time < 1 week in > 5 zip codes Then fraud Electronic health record – predicting future patients Recognizing cancer patterns Bone age assessment in pediatrics
- 19. 19 CYBER: MALWARE DETECTION The Unsolved Problem Malware is #1 cyber problem Why DL for Malware? Malware Detection Accuracy If >$5000 Charges in time < 1 week in > 5 zip codes Then fraud WannaCry created by NSA - stolen – becomes ransomware - 300,000 computers locked Old/Known Malware Deep Learning Older ML Signiture -based New/Unknown Malware Most vendors . . . . . . . . . . 99% New malware samples reported to VirusTotal: 1M per Day New computer vulnerabilities found: Few per Year
- 20. 20 Body/bullet text no longer has a bullet icon Use 20 pt font No sub-bullets allowed No more than five bullets; one idea per bullet Example of highlighted text Subtitle: 24 pt, one line maximum
- 21. 21 FRAUD DETECTION The Evolution of AI Algorithms Rule-based Analytics Machine Learning Deep Learning If >$5000 Charges in time < 1 week in > 5 zip codes Then fraud Low Accuracy High False Alarms 70-85% Accuracy Acceptable False Positives Promises >90% Accuracy Lowest False Positives Automated Feature Extraction
- 22. 22 TEACHING A ROBOT TO STAND UP FOR ITSELF New approaches to AI promise to help scientists build machines with greater autonomy. Researchers at UC Berkeley are tapping into the processing power and integrated software of NVIDIA’s DGX-1 to advance robotics using reinforcement learning. DGX-1 will allow them to iterate faster and ultimately build robots that are able to understand and navigate a diverse and changing world on their own.
- 23. 23 ACCELERATING DISCOVERIES WITH AI New drugs typically take 12-14 years and $2.6 billion to bring to market. BenevolentAI is using GPU deep learning for NLP to bring new therapies to market quickly and more affordably. They’ve automated the process of identifying patterns within large amounts of research literature, enabling scientists to form hypotheses and draw conclusions quicker than any human researcher could. And using the NVIDIA DGX-1 AI supercomputer, they identified two potential drug targets for Alzheimer’s in less than one month.
- 24. 24 DEEP LEARNING FOR TEXT/NLP INFORMATION EXTRACTION SENTIMENT ANALYSIS AUTOMATIC SUMMARIZATION QUESTION ANSWERING SYSTEMS SEQUENCE LABELING Very Recent Developments
- 25. 25 DEEP AUTOENCODERS FOR RECOMMENDATIONS Just Published
- 26. 26 Vision Title hereSound NLP Cyber Title hereFraud Robotics Prediction Title hereRiskRecommender DEEP LEARNING USE CASES Where to apply DL?
- 27. 27
Notes de l'éditeur
- First, lets define AI? There are many ways to define AI. One of the simplest way I like is to say that AI systems are learning systems. This might not entirely obvious, but AI systems are dramatically different from traditional programming paradigm, as shown on this slide. Traditional programming works extremely good for adding numbers. There is no wonder that early mainframe computers found a lot of application in accounting domain first and then moved to other numerical areas. But the real world is a lot more then just adding numbers. A lot more. If we want to get out of numbers adding, we have to construct our computing systems in a dramatically different way: Instead of pre-programmed set of instructions,, we need computing systems which are able to learn. Instead of deterministic systems, we need scholastic or probabilistic systems. Instead of one program for each task, we need systems applicable for large number of individual tasks. So, AI systems are fundamentally different. There are several implications of those differences: First, AI systems need to learn from something. They need to learn from DATA. So, data becomes a huge deal. Second, there is always a probability that this a cat – never 100% certain answer. Not every use case can deal with it. Third, constructing a learning system is a lot harder then basic code. So, the complexity and costs are higher. Finally, since AI systems are so different from traditional programming, it could be quite difficult for traditional software developers to make the switch.
- AI has been known under many names. Most theoretical AI concepts were developed in the 1960s. This includes many of the concepts used today. However, the computing power available back then was generally not sufficient to implement those ideas. As computer capabilities improved, different AI models became popular under different names. I had a privilege to work on AT&T bell Labs Expert systems in the 1990s, which worked surprisingly well for very narrow specific scenarios. 1990s data mining boom brought us Optical Character Recognition/Handwriting Recognition industry, which is still going strong today. 1990s also gave us Google PageRank and other algorithms. The wave called “machine learning is more recent and includes many things you use daily such as Netflix movie recommendation, Paypal fraud detection or Amazon product recommendations. Unfortunately, all of AI use cases were extremely narrow and, while AI was/is successful in those specific areas, widespread adoption never been possible. The challenge of AI is the challenge of finding new markets, just like any other technology. Note: we should not confuse science fiction “AI” with the technology called “AI”. Those are two very different concepts.
- While AI been around for a while, its level of accuracy was always way below average human capabilities… until now This graph come Microsoft Speech team, which is a world leader in this area. The graph shows that we had encouraging improvements in the 1990s, but then we reached a plateau because existing at the time algorithms can no longer offer incremental improvement. The plateau lasted for several years. Then, suddenly, AI error rates collapsed after 2012. What happened? Deep learning happened. The picture is pretty much the same in many other areas. We get approximately the same 20% AI error rate reduction in other areas as well. The little red line represent average human capabilities. AI systems accuracy went right throw it. There was nothing particular awesome about human capabilities – evolution gave us enough to survive. The most remarkable thing is that this new set of algorithms are not only more accurate, but also general purpose. We have exactly the same algorithms and exactly the same architectures applied to many different areas and producing exactly the same results. This is the first in AI history.
- The modern AI revolution started in 2012 when University of Toronto PhD student Alex Krizhevsky created software that automatically learned to classify images from 1 million set of images. Alex got an idea for this model when he was reading a paper on how to do matrix multiplications on Nvidia GPUs. So, Alex adopted one of the older handwriting recognition models and trained his software on 2 NVIDIA GTX 580 GPUs in a few days and won the ImageNet competition. The modern era of AI was born We call this revolution deep learning. To be more precise, the more precise definition is something like “supervised backpropagation on artificial deep neural networks on parallel architecture”. Each word has specific meaning. We can talk about details later, but what we have a specific set of components which, taken together, produce remarkable accurate results for very broad of scenarios – for the first time in history. There is plenty of debate in academic community whether this is the best algorithm. There are many branches of AI research community and they have different ideas and believes. We are very excited about it and Nvidia supports research in many areas. The important thing for this audience is to remember that we have working AI now, for the first time in history. Since AI now works, it cannot be ignored. If you want accurate results – you use deep learning.
- One of the reason GPU parallel computing dominates supercomputing now: Serial CPU computing relies on increased clock speed and transistor count, And about 10 years ago that stopped scaling GPU parallel compute will scale well into the future In 2012 a breakthrough occurred: Big Bang Image detection record smashed; and then a whole series of other records smashed voice, speech, robotics, strategy …all on unstructured data big data with GPU compute and new algorithms…. TRANSITION: With these new techniques as a toolkit, they started finding applications in every industry….
- Deep learning requires a new computing model with an end-to-end platform. As a new computing model, GPU deep learning is changing how software is developed and how it runs. In the past, software engineers crafted programs and meticulously coded algorithms. Now, algorithms learn from tons of real-world examples — software writes itself. Programming is about coding instruction. Deep learning is about creating and training neural networks. The network can then be deployed in a data center to infer, predict and classify from new data presented to it. Networks can also be deployed into intelligent devices like cameras, cars and robots to understand the world. With new experiences, new data is collected to further train and refine the neural network. Learnings from billions of devices make all the devices on the network more intelligent. Neural networks will reap the benefits of both the exponential advance of GPU processing and large network effects — that is, they will get smarter at a pace way faster than Moore’s Law. Whereas the old computing model is “instruction processing” intensive, this new computing model requires massive “data processing.” To advance every aspect of AI, we’re building an end-to-end AI computing platform — one architecture that spans training, inference and the billions of intelligent devices that are coming our way.
- What is the secret behind such a high accuracy algorithms? Deep learning improves with "more data + bigger models + more computation" Microsoft ResNet increased network complexity, ~16x more complex model than AlexNet Baidu's DeepSpeech increased 4x in model size, 2x in data, and 10x in computation in just 1 year! I have a question for this audience. Do you have any data? Any data at all? Who has a lot of data? In the age of AI, data becomes a huge asset. The more data you have, the better are you potential results.
- Another interesting question is whether one can ignore that latest developments? Below is a slide from our friends at Kindred which is focused specifically on robotics use case. As you move from human operated machinery to AI-agmented humans operation and human
- At NVIDIA we are seeing it first hand. Deep learning is fueling all areas of business. Preventing disease. Building smart cities. Revolutionizing analytics. These are just a few things happening today with AI and, specifically, deep learning. Already, organizations are using deep learning to transform moonshots into real results. More than 19,000 companies are currently using deep learning to advance their respective industries, solving what was once unsolvable.
- Another interesting question is whether one can ignore that latest developments? Below is a slide from our friends at Kindred which is focused specifically on robotics use case. As you move from human operated machinery to AI-agmented humans operation and human
- Deep learning seems to have no bounds. Whether is it is looking at noisy satellite imagery and classifying crop yield, vegetation, and change in landscapes to understand the impact of carbon or visual search in e-commerce to framing and agriculture where lettuce bot model deployed on a tractor only sprays the weeds and saves 90% of chemical and keeps our food safer. NASA Carbon Monitoring System Program - DIGITS Problem: Need to study satellite images to understand crop yield changes, vegetation, landscapes, and impact of carbon. But image classification is a challenging problem due to the high variability inherent in satellite data Solution: DeepSat: Labeled datasets of satellite images + framework for the classification of images Training: NVIDIA® DIGITS™ DevBox Classification: Tesla-Accelerated NASA Pleiades Supercomputer (#13 LINPACK rating on TOP500 list) Impact: Classifying land cover from satellite images to understand crop yields, climate (emissions from forest), surface vegetation type, urban towns and landscapes https://news.developer.nvidia.com/nasa-using-deep-belief-networks-for-image-classification/ Pinterest Problem: Want to more easily search for similar pins and search by visual similarities Solution: The team used GPU-accelerated deep learning to teach their system how to recognize image features using a richly annotated dataset of billions of Pins curated by Pinterest users. The features can then be used to compute a similarity score between any two images and identify the best matches. Impact: A visual search tool that lets you zoom in on a specific object in a pinned image (or “Pin”) and discover visually similar objects, colors, patterns and more https://news.developer.nvidia.com/pinterest-sharpens-its-visual-search-skills/ ---- How Pinterest harnesses the power of A.I. to transform the Buy It Now experience // VentureBeat  SPONSORED: This sponsored post is part of our Mobile Commerce Explosion series. Produced in partnership with Braintree, the series looks at the explosive growth in mobile and how it’s disrupting industries and impacting brands. See the whole series here. The refrain of “mobile first” seems anachronistic now, but 2016 may actually be the year mobile commerce has taken hold aided by AI and machine learning. Braintree’s GM of Mobile, Aunkur Arya, sat down with Michael Yamartino, Pinterest’s Head of Commerce, to break it down on stage at MobileBeat 2016. Pinterest has long marketed itself as the world’s catalog of ideas—a place for users to find and save digital inspiration for real-life questions and ambitions. But with innovations in machine learning and AI the company has leveled up by adding visual search. Leveraging AI and machine learning technology has allowed them to implement sophisticated image recognition into the platform, broadening a user’s opportunity to discover more projects and ideas across the site by using the camera on their mobile phone. If a user spots something out there in the world they’d love to have, there’s a good chance Pinterest will be able to match it to a pin—and a retailer who has it. “There’s a shift in behavior from traditional, intent-based model of searching for something on the web to interact with a merchant directly,” says Arya. Pinterest embodies the shift to discovery- and experience-based commerce but closing the new buying loop had been the final challenge. “It’s really evolved into the taste graph,” Arya notes. “Pinterest started as a place where users were putting their preferences for things they like and their inspiration. It became a collection of that data, but evolved into a place where people are buying things now.” The seamless buying experience “Because of that we launched buyable pins,” agrees Yamartino. “It was a way to take all of that user interest on Pinterest and turn it into purchasing power by connecting users with merchants.” With the help of Braintree, Pinterest was able to create that seamless experience, which allowed consumers to go from pinning and searching to discovering and shopping. And that’s Pinterest’s strength, Yamartino says. When the buttons became the new merchandising trend a year ago, too much of the focus was on the button itself. “There was a Buy Button craze, and I think that missed the mark,” he says. “The focus was on the button when really it’s about the whole shopping experience—how you help someone discover something.” Personalization matters. A lot. That’s where their other big use for AI comes in: personalization. As a user moves through Pinterest, they reveal a tremendous amount of information about themselves. Searches, pins, board names, and more get fed into algorithms that update the user’s home feed to deliver a more and more personalized experience. “We’re constantly refining that model and building up a sense of what you’re interested in not just overall, but in the moment,” explains Yamartino. Getting it right requires machine learning. It takes a lot of computing power and a great algorithm—but those are just table stakes. “The two things you need to make a really good experience are a lot of data and a feedback loop that tells you whether your algorithm is progressing and going in the right direction,” Yamartino says. And with over 75 billion pins on Pinterest, collected onto 2 billion boards and 100 million users interacting with them, the company has an incredibly powerful dataset and feedback loop. Referring back to visual search, Yamartino explains, “Over 100 million times every month someone uses this visual search technology and that helps us refine it every time, so the accuracy just gets better and better.” “Then they close the loop and convert to purchase,” adds Arya. Stakes are even higher with mobile In a mobile-first world, personalization becomes even more important as retailers deal with consumers using small screens in short chunks of time. “The better you are at limiting the set of things people need to consider to only the ones that are actually interesting to them,” Yamartino says, “the more successful you’re going to be.” He notes that on mobile, you can only see roughly four items at a time—so retailers better make sure those are the right four items for that individual. “The same is true if you’re showing the search results inside a retailer app or you’re building a marketplace,” Arya adds. “The stakes are just higher. A Best Buy, a Walmart—they’ve got to figure out how to merchandise on mobile and how to make it simple. They’re ultimately going to be competing with this kind of experience.” And while large retailers have tapped into Pinterest, the platform is leveling the playing field, enabling many smaller companies with unique products to reach an audience directly. “For a lot of small companies, this is really the first time that they’ve done a lot of exploration into omnichannel,” says Yamartino. “And they’ve seen some tremendous growth right away.” Sponsored posts are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact sales@venturebeat.com. Connecterra Problem: Need to help farmers be more efficient and productive. Solution: GPU-accelerated deep learning solution that consists of a wearable device that monitors each animal in the herd and transmits the data to a cloud platform for analysis and prediction of behavioral patterns. Impact: Created a way to bring usable technology to farmers, helping them increase productivity and keep the herd healthier, while reducing the impact on the environment. https://news.developer.nvidia.com/fitbit-for-cows-uses-deep-learning-to-provide-insights-for-dairy-farmers/
- Deep learning has proven very effective for many healthcare applications Featured in ‘Nature’ - Mt Sinia put 700k patient records to work to build predictive models called deepPatient. The early results showed it more effective at predicting disease such as diabetes, schizophrenia and certain cancers then hand written features. The us naval medical center used deep learning for recognizing cancer in pathology images And mass general has built and effective model to do bone age assessment for growth disorders in children. This speeds up diagnosis and could prove a great tool for clinical trials in children using chemo. I’ve given you some great examples of training a model for incredibly impactful applications. Lets talk about deployment of the model and the computing model considerations EHR: “Deep Patient” at Mt. Sinai Challenge/Background - Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. - Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Solution - Unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. - Aggregated EHRs of 700,000 patients from the Mount Sinai data warehouse. Three-layer stack of denoising autoencoders used to capture hierarchical regularities and dependencies.. - Result: The result is a representation named “deep patient”. - Evaluated Deep Patient as broadly predictive of health states by assessing the probability of patients to develop various diseases. - Performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. - Results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. - Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. Impact - Findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems. Paper: http://www.nature.com/articles/srep26094 US Navy Naval Medical Center (San Diego) & Integrity Applications Inc. : Pathology / Recognizing cancer patterns using Deep Learning Background / Challenge - U.S. military’s hospitals care for disproportionately more male patients - Prostate cancer is second‐leading cause of cancer death in American men; Approximately 220,000 new cases per year Traditional approach is very labor-intensive - Prostate biopsy is conducted by taking “core samples” using a hollow needle - After processing, 5 micron sections of these samples are placed on glass slides, stained, and manually interpreted by a pathologist under a microscope. - Digital scans are opened with custom viewing software from the microscope vendor - Pathologist will annotate cancerous regions with polygons drawn by hand with a mouse - Process requires careful judgment and is susceptible to fatigue and stress factors. Polygons cannot be edited once drawn (e.g., at higher magnification). Biopsy analysis is challenging - Tissues can be difficult to differentiate - Cancerous region may be only partially sampled by the needle - This is an image classification problem Solution - Enter Deep Learning — GPU-accelerated convolutional neural networks excel at image recognition - 90% accuracy; Whereas one study of biopsy concordance found 75% agreement among expert pathologists - Automated image classification is 50% faster than a pathologist Imagery - 202 annotated full‐size color SVS images, average full size image ~ 845 MB, annotated by Navy pathologists System - GTX980, DIGITS, CUDA 7.5, cuDNN v4, NVCaffe 0.14 Impact - Faster, more accurate diagnosis - Another example where Deep Learning can amplify human intelligence in an area where machines naturally excel GTC 16 paper: http://on-demand.gputechconf.com/gtc/2016/presentation/s6442-ted-hromadka-cancerous-cells-histology-imagery-deep-learning.pdf MGH: Bone Age Assessments Background / Challenge - Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAA) are compared to chronologic age in the evaluation of endocrine and metabolic disorders. - While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. Solution - Deep convolutional neural networks to perform BAA. - BAA is ideal for machine learning approaches as there are a low number of images in a study and relatively standardized reports, avoiding tedious preprocessing steps while building the large cohort of images needed for image analysis. - Massachusetts General Hospital PACS system: - After a series of automated image preprocessing steps, including standardizing images to have white bones and black backgrounds, hand segmentation, and bone enhancement, images were analyzed using a deep convolutional neural network. - CCNN then provided automated bone age assessments, which were then compared to the radiologist’s report (ground truth). - Implemented algorithm on NVIDIA DevBox, which is built for a parallel machine learning computation and the high throughput data transfer using four Graphic Processing Units (GPUs). - The network was trained for 500 training epochs using stochastic gradient descent (SGD) algorithms with 0.01 of the base learning rate decreased by three steps based on convergence to loss function. - 8,677 BAA examinations were used - When compared to previously published data using our augmented dataset, the CNN was able to provide an age within 1 year of ground truth in 91-93% of cases. - By comparison, Greulich and Pyle’s original atlas provided standard deviations that ranged from 8 to 11 months for a given chronological age, reflecting the inherent variation in the study population. Impact - This has important implications to advance the state of the art of BAA determinations, an important piece of clinical data in the evaluation of endocrine and metabolic disorders. - By providing algorithmic BAA, a given practice or institution could augment their BAAs using a single rater, reducing interobserver variability. - This is also important in clinical trials which use BAA for monitoring of skeletal side effects in patients receiving experimental chemotherapeutics. - Deep CNNs are a promising technique to provide automated BAAs. - While we work to improve the performance of our algorithm, we can implement this algorithm in the clinical environment now
- More than 300,000 computers got their data locked – and going… Hospitals and large companies hit the hardest. WannaCry is a worm, exploits SMB on Windows OS. It was created by NSA, then stolen and converted into Ransomware. Attack unleashed on May 12, 2017, 2 month after initial discovery, but most people were still unprepared. There are many traditional ways to deal with this ransomware: close SMB ports (unless you can’t because you need them open), patch Windows (unless you can’t because its old) or update malware signatures (unless you can’t because, for example, holiday). There is also an alternative approach: do nothing at all if you have deep learning-based malware detection. Every company using deep learning prevented WannaCry. How effective is deep learning for cyber?
- There are many other use cases of deep learning in cyber security scenarios. One of our partners identified 35 different use cases for AI in cyber security. The slide above shows several of the use cases based on whether they require supervised or unsupervised learning and whether they work on continuous or batch data. Different cyber security expert might have interest in different scenarios. We already have ISV partners working on many of those scenarios. Unfortunately, those are projects in development and we can’t talk about them publicly at this time.
- UC Berkeley – Berkeley AI Research Lab (BAIR) Products NVIDIA DGX-1 Summary New approaches to AI, such as reinforcement learning, promise to help scientists build more useful machines. Researchers at UC Berkeley’s AI Research (BAIR) Lab are leveraging the speed and integrated software of NVIDIA’s DGX-1 to explore reinforcement learning faster than ever to help robots understand and navigate complex environments. Building machines that can not only learn from their environment, but judge the risks they’re taking is key to building smarter robots. Problem The team at BAIR is working on a dazzling array of artificial intelligence problems across a huge array of fields — from autonomous driving to robotics in manufacturing to — and they’re eager to experiment with as many different approaches as possible. They’re using dl techniques to advance accuracy and performance of applications of machine learning and deep learning, especially at scale. – For robotics in manufacturing: robots go through very repetitive motions so the big challenge is how to build AI that allows these robots to understand situations they've never seen before and still do the right thing. Simulated robots learn to run - they're told 'go as far north as possible' and from that they invent running from scratch as a way to achieve this. Now they're looking at how to get this onto a real robot (Darwin) Solution Applied deep learning and the NVIDIA DGX-1 with its larger memory. The team anticipates 2-3 orders of magnitude beyond what the they’re using now for training data and 2 orders of magnitude bigger in terms of the models themselves. For robotics: Reinforcement learning is about trying to make decisions. The human user doesn't tell the robot how to do a task, but provides the objective. The robot watches the human, tries to guess using the knowledge that the human is performing a task to figure out the goal of the human which gives the robot the objective and then can go and figure out how to fulfill that objective on its own. Darwin, a small robot (in the photo) leaverage / impulse learning - learning a reflex or instinct; it takes an image, processes it, and outputs the neural command. Result(s) TBD with the DGX-1 as researchers at BAIR have only recently acquired it. In their robotics research the team taught Darwin, a small robot (in the photo) impulse learning - learning a reflex or instinct; it takes an image, processes it, and outputs the neural command. They were able to get Darwin to make fluid full body motions. Impact With the 10x fold speedup of the DGX-1 that additional compute power will translate into more ideas being investigated. Now, they're starting to explore model based methods -- eg, your instinct to move your hand away from a hot stove tells you how to avoid hot things but it doesn't tell help you with other things that involve other temperatures, but once you have a mental model you can hone lots of different skills. Quote(s) “More compute power directly translates into more ideas being investigated, tried out, tuned to actually get them to work. So right now, an experiment might typically maybe take anywhere from a few hours to a couple of days, and if we can get something like a 10-fold speed-up, that would narrow it down from that time to much shorter times -- then we could right away try the next thing.“ Pieter Abbeel, Associate Professor, UC Berkeley, EECS “Building machines that can not only learn from their environment, but judge the risks that they’re taking is key to building smarter robots.” Sergey Levine, Assistant Professor, UC Berkeley, EECS. About The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, and robotics. BAIR includes over two dozen faculty and more than a hundred graduate students pursuing research on fundamental advances in the above areas as well as cross-cutting themes including multi-modal deep learning, human-compatible AI, and connecting AI with other scientific disciplines and the humanities. Additional Information https://blogs.nvidia.com/blog/2016/01/15/deep-learning-robot-walk/ http://bair.berkeley.edu/
- BenevolentAI Products NVIDIA DGX-1 Summary New drugs typically take 12-14 years and $2.6 billion to bring to market. BenevolentAI is using GPU deep learning to bring new therapies to market quickly and more affordably. They’ve automated the process of identifying patterns within large amounts of research literature, enabling scientists to form hypotheses and draw conclusions quicker than any human researcher could. And using the NVIDIA DGX-1 AI supercomputer, they identified two potential drug targets for Alzheimer’s in less than one month. Problem Identifying patterns and relationships in the information within biomedical literature, life science journals and online books is a key step in the process of drug discovery. But researchers face an impossible challenge to keep up with the rate at which new scientific knowledge is produced. There are 10,000 updates per day on PubMed alone. That’s one of the reasons new drugs typically take 12-14 years and cost $2.6B to bring to market. Solution UK startup BenevolentAI is using GPU-accelerated deep learning to automatically analyse millions of scientific articles and hundreds of medical databases. Its technology uses natural language processing, machine learning and artificial intelligence to formulate new, usable knowledge from complex scientific information. Result(s) BenevolentAI’s drug development scientists are able to form hypotheses and draw conclusions faster than any human researcher could alone, facilitating faster breakthroughs and delivering the potential for more precise and targeted medicines. Impact Since receiving Europe’s first DGX-1 in September 2016, BenevolentAI has identified two potential drug targets for Alzheimer’s that have attracted the attention of pharmaceutical companies. Quote(s) “The NVIDIA DGX-1 has boosted our processing power and accelerated the creation of new relationships amongst disparate information sources to yield faster scientific innovation. For the current application of our AI technology in the bioscience space, it will mean that new drug discoveries can be made faster and more efficiently than ever before.” Ken Mulvany, Co-founder and Director of BenevolentAI About BenevolentAI is a UK startup headquartered in London and founded in 2013. Its advanced and ambitious technology enhances the way bright minds discover, connect and apply knowledge to solve issues that affect us all by using vast neural networks - machine networks that simulate the web of neurons in the human brain. More information http://benevolent.ai/news-and-events/news/benevolentai-first-in-europe-to-use-advanced-deep-learning-supercomputer/ http://www.huffingtonpost.com/adi-gaskell/using-machine-learning-to_b_12049046.html https://blogs.nvidia.com/blog/2016/09/28/sap-benevolentai-dgx-1/
- Another interesting question is whether one can ignore that latest developments? Below is a slide from our friends at Kindred which is focused specifically on robotics use case. As you move from human operated machinery to AI-agmented humans operation and human