The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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799 afleveringen, van nieuw naar oud
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Scalable Differential Privacy for Deep Learning with Nicolas Papernot - TWiML Talk #134
3 mei 2018 · 59 min
In this episode of our Differential Privacy series, I'm joined by Nicolas Papernot, Google PhD Fellow in Security and graduate student in the department of computer science at Penn State University. Nicolas and I continue this week’s look into differential privacy with a discussion of his recent paper, Semi-supervised Knowledge Transfer for Deep Learning From Private Training Data. In our conversation, Nicolas describes the Private Aggregation of Teacher Ensembles model proposed in this paper, and how it ensures differential privacy in a scalable manner that can be applied to Deep Neural Networks. We also explore one of the interesting side effects of applying differential privacy to machine learning, namely that it inherently resists overfitting, leading to more generalized models. The notes for this show can be found at twimlai.com/talk/134. Lees meer
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Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133
1 mei 2018 · 38 min
In this episode of our Differential Privacy series, I'm joined by Zahi Karam, Director of Data Science at Bluecore, whose retail marketing platform specializes in personalized email marketing. I sat down with Zahi at the Georgian Partners portfolio conference last year, where he gave me my initial exposure to the field of differential privacy, ultimately leading to this series. Zahi shared his insights into how differential privacy can be deployed in the real world and some of the technical and cultural challenges to doing so. We discuss the Bluecore use case in depth, including why and for whom they build differentially private machine learning models. The notes for this show can be found at twimlai.com/talk/133 Lees meer
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Differential Privacy Theory & Practice with Aaron Roth - TWiML Talk #132
30 april 2018 · 43 min
In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its inception. We explore the application of differential privacy to machine learning systems, including the costs and challenges of doing so. Aaron discusses as well quite a few examples of differential privacy in action, including work being done at Google, Apple and the US Census Bureau, along with some of the major research directions currently being explored in the field. The notes for this show can be found at twimlai.com/talk/132. Lees meer
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Optimal Transport and Machine Learning with Marco Cuturi - TWiML Talk #131
26 april 2018 · 33 min
In this episode, i’m joined by Marco Cuturi, professor of statistics at Université Paris-Saclay. Marco and I spent some time discussing his work on Optimal Transport Theory at NIPS last year. In our discussion, Marco explains Optimal Transport, which provides a way for us to compare probability measures. We look at ways Optimal Transport can be used across machine learning applications, including graphical, NLP, and image examples. We also touch on GANs, or generative adversarial networks, and some of the challenges they present to the research community. The notes for this show can be found at twimlai.com/talk/131. Lees meer
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Collecting and Annotating Data for AI with Kiran Vajapey - TWiML Talk #130
23 april 2018 · 40 min
In this episode, I’m joined by Kiran Vajapey, a human-computer interaction developer at Figure Eight. In this interview, Kiran shares some of what he’s has learned through his work developing applications for data collection and annotation at Figure Eight and earlier in his career. We explore techniques like data augmentation, domain adaptation, and active and transfer learning for enhancing and enriching training datasets. We also touch on the use of Imagenet and other public datasets for real-world AI applications. If you like what you hear in this interview, Kiran will be speaking at my AI Summit April 30th and May 1st in Las Vegas and I’ll be joining Kiran at the upcoming Figure Eight TrainAI conference, May 9th&10th in San Francisco. The notes for this show can be found at twimlai.com/talk/130 Lees meer
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Autonomous Aerial Guidance, Navigation and Control Systems with Christopher Lum - TWiML Talk #129
19 april 2018 · 53 min
Ok, In this episode, I'm joined by Christopher Lum, Research Assistant Professor in the University of Washington’s Department of Aeronautics and Astronautics. Chris also co-heads the University’s Autonomous Flight Systems Lab, where he and his students are working on the guidance, navigation, and control of unmanned systems. In our conversation, we discuss some of the technical and regulatory challenges of building and deploying Unmanned Autonomous Systems. We also talk about some interesting work he’s doing on evolutionary path planning systems as well as an Precision Agriculture use case. Finally, Chris shares some great starting places for those looking to begin a journey into autonomous systems research. The notes for this show can be found at twimlai.com/talk/129. Lees meer
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Infrastructure for Autonomous Vehicles with Missy Cummings - TWiML Talk #128
16 april 2018 · 44 min
In this episode, I’m joined by Missy Cummings, head of Duke University’s Humans and Autonomy Lab and professor in the department of mechanical engineering. In addition to being an accomplished researcher, Missy also became one of the first female fighter pilots in the US Navy following the repeal of the Combat Exclusion Policy in 1993. We discuss Missy’s research into the infrastructural and operational challenges presented by autonomous vehicles, including cars, drones and unmanned aircraft. We also cover trust, explainability, and interactions between humans and AV systems. This was an awesome interview and i'm glad we’re able to bring it to you! The notes for this show can be found at twimlai.com/talk/128. Lees meer
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Hyper-Personalizing the Customer Experience w/ AI with Rob Walker - TWiML Talk #127
13 april 2018 · 42 min
In this episode, we're joined by Rob Walker, Vice President of decision management and analytics at Pegasystems, a leading provider of software for customer engagement and operational excellence. Rob and I discuss what’s required for enterprises to fully realize the vision of providing a hyper-personalized customer experience, and how machine learning and AI can be used to determine the next best action an organization should take to optimize sales, service, retention, and risk at every step in the customer relationship. Along the way we dig into a couple of key areas, specifically some of the techniques his organization uses to allow customers to manage the tradeoff between model performance and transparency, particularly in light of new laws like GDPR, and how all this ties to an enterprise’s ability to manage bias and ethical issues when deploying ML. We cover a lot of ground in this one and I think you’ll find Rob’s perspective really interesting. The notes for this show can be found at twimlai.com/talk/127. Lees meer
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Information Extraction from Natural Document Formats with David Rosenberg - TWiML Talk #126
9 april 2018 · 46 min
In this episode, I’m joined by David Rosenberg, data scientist in the office of the CTO at financial publisher Bloomberg, to discuss his work on “Extracting Data from Tables and Charts in Natural Document Formats.” Bloomberg is dealing with tons of financial and company data in pdfs and other unstructured document formats on a daily basis. To make meaning from this information more efficiently, David and his team have implemented a deep learning pipeline for extracting data from the documents. In our conversation, we dig into the information extraction process, including how it was built, how they sourced their training data, why they used LaTeX as an intermediate representation and how and why they optimize on pixel-perfect accuracy. There’s a lot of interesting info in this show and I think you’re going to enjoy it. The notes for this show can be found at twimlai.com/talk/126. Lees meer
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Human-in-the-Loop AI for Emergency Response & More w/ Robert Munro - TWiML Talk #125
5 april 2018 · 48 min
In this episode, I chat with Rob Munro, CTO of the newly branded Figure Eight, formerly known as CrowdFlower. Figure Eight’s Human-in-the-Loop AI platform supports data science & machine learning teams working on autonomous vehicles, consumer product identification, natural language processing, search relevance, intelligent chatbots, and more. Rob and I had a really interesting discussion covering some of the work he’s previously done applying machine learning to disaster response and epidemiology, including a use case involving text translation in the wake of the catastrophic 2010 Haiti earthquake. We also dig into some of the technical challenges that he’s encountered in trying to scale the human-in-the-loop side of machine learning since joining Figure Eight, including identifying more efficient approaches to image annotation as well as the use of zero shot machine learning to minimize training data requirements. Finally, we briefly discuss Figure Eight’s upcoming TrainAI conference, which takes place on May 9th & 10th in San Francisco. Train AI you can join me and Rob, along with a host of amazing speakers like Garry Kasparov, Andrej Karpathy, Marti Hearst and many more and receive hands-on AI, machine learning and deep learning training through real-world case studies on practical machine learning applications. For more information on TrainAI, head over to figure-eight.com/train-ai, and be sure to use code TWIMLAI for 30% off your registration! For those of you listening to this on or before April 6th, Figure Eight is offering an even better deal on event registration. Use the code figure-eight to register for only 88 dollars. The notes for this show can be found at twimlai.com/talk/125. Lees meer
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Systems and Software for Machine Learning at Scale with Jeff Dean - TWiML Talk #124
2 april 2018 · 55 min
In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View. As you’ll hear, I was very excited for this interview, because so many of Jeff’s contributions since he started at Google in ‘99 have touched my life and work. In our conversation, Jeff and I dig into a bunch of the core machine learning innovations we’ve seen from Google. Of course we discuss TensorFlow, and its origins and evolution at Google. We also explore AI acceleration hardware, including TPU v1, v2 and future directions from Google and the broader market in this area. We talk through the machine learning toolchain, including some things that Googlers might take for granted, and where the recently announced Cloud AutoML fits in. We also discuss Google’s process for mapping problems across a variety of domains to deep learning, and much, much more. This was definitely one of my favorite conversations, and I'm pumped to be able to share it with you. The notes for this show can be found at twimlai.com/talk/124. Lees meer
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Semantic Segmentation of 3D Point Clouds with Lyne Tchapmi - TWiML Talk #123
29 maart 2018 · 36 min
In this episode I’m joined by Lyne Tchapmi, PhD student in the Stanford Computational Vision and Geometry Lab, to discuss her paper, “SEGCloud: Semantic Segmentation of 3D Point Clouds.” SEGCloud is an end-to-end framework that performs 3D point-level segmentation combining the advantages of neural networks, trilinear interpolation and fully connected conditional random fields. In our conversation, Lyne and I cover the ins and outs of semantic segmentation, starting from the sensor data that we’re trying to segment, 2d vs 3d representations of that data, and how we go about automatically identifying classes. Along the way we dig into some of the details, including how she obtained a more fine grain labeling of points from sensor data and the transition from point clouds to voxels. The notes for this show can be found at twimlai.com/talk/123. Lees meer
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Predicting Cardiovascular Risk Factors from Eye Images with Ryan Poplin - TWiML Talk #122
26 maart 2018 · 43 min
In this episode, I'm joined by Google Research Scientist Ryan Poplin, who recently co-authored the paper “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.” In our conversation, Ryan details his work training a deep learning model to predict various patient risk factors for heart disease, including some surprising ones like age and gender. We also dive into some interesting findings he discovered with regards to multi-task learning, as well as his use of an attention mechanisms to provide explainability. This was a really interesting discussion that I think you’ll really enjoy! The notes for this show can be found at twimlai.com/talk/122. Lees meer
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Reproducibility and the Philosophy of Data with Clare Gollnick - TWiML Talk #121
22 maart 2018 · 38 min
In this episode, i'm joined by Clare Gollnick, CTO of Terbium Labs, to discuss her thoughts on the “reproducibility crisis” currently haunting the scientific landscape. For a little background, a “Nature” survey in 2016 showed 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." Clare gives us her take on the situation, and how it applies to data science, along with some great nuggets about the philosophy of data and a few interesting use cases as well. We also cover her thoughts on Bayesian vs Frequentist techniques and while we’re at it, the Vim vs Emacs debate. No, actually I’m just kidding on that last one. But this was indeed a very fun conversation that I think you’ll enjoy! For the complete show notes, visit twimlai.com/talk/121. Lees meer
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Surveying the Connected Car Landscape with GK Senthil - TWiML Talk #120
19 maart 2018 · 30 min
In this episode, I’m joined by GK Senthil, director & chief product owner for innovation at Toyota Connected. GK and I spoke about some of the potential opportunities and challenges for smart cars. We discussed Toyota’s recently announced partnership with Amazon to embed Alexa in vehicles, and more generally the approach they’re taking to get connected car technology up to par with smartphones and other intelligent devices we use on a daily basis. We cover in-car voice recognition and touch on the ways ML & AI need to be developed to be useful in vehicles, as well as the approaches to getting there. The notes for this show can be found at twimlai.com/talk/120 Lees meer
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Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang
15 maart 2018 · 47 min
In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! For complete show notes, head over to twimlai.com/talk/119 Lees meer
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Towards Abstract Robotic Understanding with Raja Chatila - TWiML Talk #118
12 maart 2018 · 48 min
In this episode, we're joined by Raja Chatila, director of Intelligent Systems and Robotics at Pierre and Marie Curie University in Paris, and executive committee chair of the IEEE global initiative on ethics of intelligent and autonomous systems. Raja and I had a great chat about his research, which deals with robotic perception and discovery. We discuss the relationship between learning and discovery, particularly as it applies to robots and their environments, and the connection between robotic perception and action. We also dig into the concepts of affordances, abstract teachings, meta-reasoning and self-awareness as they apply to intelligent systems. Finally, we touch on the issue of values and ethics of these systems. The notes for this show can be found at twimlai.com/talk/118. Lees meer
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Discovering Exoplanets w/ Deep Learning with Chris Shallue - TWiML Talk #117
8 maart 2018 · 45 min
Earlier this week, I had a chance to speak with Chris Shallue, Senior Software Engineer on the Google Brain Team, about his project and paper on “Exploring Exoplanets with Deep Learning.” This is a great story. Chris, inspired by a book he was reading, reached out on a whim to a Harvard astrophysics researcher, kicking off a collaboration and side project eventually leading to the discovery of two new planets outside our solar system. In our conversation, we walk through the entire process Chris followed to find these two exoplanets, including how he researched the domain as an outsider, how he sourced and processed his dataset, and how he built and evolved his models. Finally, we discuss the results of his project and his plans for future work in this area. This podcast is being published in parallel with Google’s release of the source code and data that Chris developed and used, which we’ll link to below, so if what you hear inspires you to dig into this area, you’ve got a nice head start. This was a really interesting conversation, and I'm excited to share it with you! The notes for this show can be found at twimlai.com/talk/117 The corresponding blog post for this project can be found at https://research.googleblog.com/2018/03/open-sourcing-hunt-for-exoplanets.html Lees meer
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Learning Active Learning with Ksenia Konyushkova - TWiML Talk #116
5 maart 2018 · 32 min
In this episode, I speak with Ksenia Konyushkova, Ph.D. student in the CVLab at Ecole Polytechnique Federale de Lausanne in Switzerland. Ksenia and I connected at NIPS in December to discuss her interesting research into ways we might apply machine learning to ease the challenge of creating labeled datasets for machine learning. The first paper we discuss is “Learning Active Learning from Data,” which suggests a data-driven approach to active learning that trains a secondary model to identify the unlabeled data points which, when labeled, would likely have the greatest impact on our primary model’s performance. We also discuss her paper “Learning Intelligent Dialogs for Bounding Box Annotation,” in which she trains an agent to guide the actions of a human annotator to more quickly produce bounding boxes. TWiML Online Meetup Update Join us Tuesday, March 13th for the March edition of the Online Meetup! Sean Devlin will be doing an in-depth review of reinforcement learning and presenting the Google DeepMind paper, "Playing Atari with Deep Reinforcement Learning." Head over to twimlai.com/meetup to learn more or register. Conference Update Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Early price ends February 2! The notes for this show can be found at https://twimlai.com/talk/116. Lees meer
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Machine Learning Platforms at Uber with Mike Del Balso - TWiML Talk #115
1 maart 2018 · 49 min
In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.” In our discussion, Mike shares some great advice for organizations looking to get value out of machine learning. He also details some of the pitfalls companies run into, such as not have proper infrastructure in place for maintenance and monitoring, not managing their expectations, and not putting the right tools in place for data science and development teams. On this last point, we touch on the Michelangelo platform, which Uber uses internally to build, deploy and maintain ML systems at scale, and the open source distributed TensorFlow system they’ve created, Horovod. This was a very insightful interview, so get your notepad ready! Vote on our #MyAI Contest! Over the past few weeks, you’ve heard us talk quite a bit about our #MyAI Contest, which explores the role we see for AI in our personal lives! We received some outstanding entries, and now it’s your turn to check them out and vote for a winner. Do this by visiting our contest page at https://twimlai.com/myai. Voting remains open until Sunday, March 4th at 11:59 PM Eastern time. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/115. Lees meer
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Inverse Programming for Deeper AI with Zenna Tavares - TWiML Talk #114
26 februari 2018 · 28 min
For today’s show, the final episode of our Black in AI Series, I’m joined by Zenna Tavares, a PhD student in the both the department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Lab at MIT. I spent some time with Zenna after his talk at the Strange Loop conference titled “Running Programs in Reverse for Deeper AI.” Zenna shares some great insight into his work on program inversion, an idea which lies at the intersection of Bayesian modeling, deep-learning, and computational logic. We set the stage with a discussion of inverse graphics and the similarities between graphic inversion and vision inversion. We then discuss the application of these techniques to intelligent systems, including the idea of parametric inversion. Last but not least, zenna details how these techniques might be implemented, and discusses his work on ReverseFlow, a library to execute tensorflow programs backwards, and Sigma.jl a probabilistic programming environment implemented in the dynamic programming language Julia. This talk packs a punch, and I’m glad to share it with you. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/114. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018 Lees meer
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Statistical Relational Artificial Intelligence with Sriraam Natarajan - TWiML Talk #113
23 februari 2018 · 48 min
In this episode, I speak with Sriraam Natarajan, Associate Professor in the Department of Computer Science at UT Dallas. While at NIPS a few months back, Sriraam and I sat down to discuss his work on Statistical Relational Artificial Intelligence. StarAI is the combination of probabilistic & statistical machine learning techniques with relational databases. We cover systems learning on top of relational databases and making predictions with relational data, with quite a few examples from the healthcare field. Sriraam and his collaborators have also developed BoostSRL, a gradient-boosting based approach to learning different types of statistical relational models. We briefly touch on this, along with other implementation approaches. Join the #MyAI Discussion! As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/113. For complete contest details, visit twimlai.com/myai. Lees meer
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Classical Machine Learning for Infant Medical Diagnosis with Charles Onu - TWiML Talk #112
20 februari 2018 · 48 min
In this episode, part 4 in our Black in AI series, i'm joined by Charles Onu, Phd Student at McGill University in Montreal & Founder of Ubenwa, a startup tackling the problem of infant mortality due to asphyxia. Using SVMs and other techniques from the field of automatic speech recognition, Charles and his team have built a model that detects asphyxia based on the audible noises the child makes upon birth. We go into the process he used to collect his training data, including the specific methods they used to record samples, and how their samples will be used to maximize accuracy in the field. We also take a deep dive into some of the challenges of building and deploying the platform and mobile application. This is a really interesting use case, which I think you’ll enjoy. Join the #MyAI Discussion! As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/112. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018. Lees meer
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Learning "Common Sense" and Physical Concepts with Roland Memisevic - TWiML Talk #111
15 februari 2018 · 33 min
In today’s episode, I’m joined by Roland Memisevic, co-founder, CEO, and chief scientist at Twenty Billion Neurons. Roland joined me at the RE•WORK Deep Learning Summit in Montreal to discuss the work his company is doing to train deep neural networks to understand physical actions. In our conversation, we dig into video analysis and understanding, including how data-rich video can help us develop what Roland calls comparative understanding, or AI “common sense”. We briefly touch on the implications of AI/ML systems having comparative understanding, and how Roland and his team are addressing problems like getting properly labeled training data. Enter Our #MyAI Contest! Are you looking forward to the role AI will play in your life, or in your children’s lives? Or, are you afraid of what’s to come, and the changes AI will bring? Or, maybe you’re skeptical, and don’t think we’ll ever really achieve enough with AI to make a difference? In any case, if you’re a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. The notes for this show can be found at twimlai.com/talk/111. Lees meer
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Trust in Human-Robot/AI Interactions with Ayanna Howard - TWiML Talk #110
13 februari 2018 · 47 min
In this episode, the third in our Black in AI series, I speak with Ayanna Howard, Chair of the Interactive School of Computing at Georgia Tech. Ayanna joined me for a lively discussion about her work in the field of human-robot interaction. We dig deep into a couple of major areas she’s active in that have significant implications for the way we design and use artificial intelligence, namly pediatric robotics and human-robot trust. That latter bit is particularly interesting, and Ayanna provides a really interesting overview of a few of her experiments, including a simulation of an emergency situation, where, well, I don’t want to spoil it, but let’s just say as the actual intelligent beings, we need to make some better decisions. Enjoy! Are you looking forward to the role AI will play in your life, or in your children’s lives? Or, are you afraid of what’s to come, and the changes AI will bring? Or, maybe you’re skeptical, and don’t think we’ll ever really achieve enough with AI to make a difference? As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/110. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018. Lees meer
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Data Science for Poaching Prevention and Disease Treatment with Nyalleng Moorosi - TWiML Talk #109
8 februari 2018 · 53 min
For today’s show, I'm joined by Nyalleng Moorosi, Senior Data Science Researcher at The Council for Scientific & Industrial Research or CSIR, in Pretoria, South Africa. In our discussion, we discuss two major projects that Nyalleng is apart of at the CSIR, one, a predictive policing use case, which focused on understanding and preventing rhino poaching in Kruger National Park, and the other, a healthcare use case which focuses on understanding the effects of a drug treatment that was causing pancreatic cancer in South Africans. Along the way we talk about the challenges of data collection, data pipelines and overcoming sparsity. This was a really interesting conversation that I’m sure you’ll enjoy. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/109. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/blackinai2018. Lees meer
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Security and Safety in AI: Adversarial Examples, Bias and Trust w/ Moustapha Cissé - TWiML Talk #108
6 februari 2018 · 50 min
In this episode I’m joined by Moustapha Cissé, Research Scientist at Facebook AI Research Lab (or FAIR) Paris. Moustapha’s broad research interests include the security and safety of AI systems, and we spend some time discussing his work on adversarial examples and systems that are robust to adversarial attacks. More broadly, we discuss the role of bias in datasets, and explore his vision for models that can identify these biases and adjust the way they train themselves in order to avoid taking on those biases. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/108. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/blackinai2018. Lees meer
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Peering into the Home w/ Aerial.ai's Wifi Motion Analytics - TWiML Talk #107
2 februari 2018 · 41 min
In this episode I’m joined by Michel Allegue and Negar Ghourchian of Aerial.ai. Aerial is doing some really interesting things in the home automation space, by using wifi signal statistics to identify and understand what’s happening in our homes and office environments. Michel, the CTO, describes some of the capabilities of their platform, including its ability to detect not only people and pets within the home, but surprising characteristics like breathing rates and patterns. He also gives us a look into the data collection process, including the types of data needed, how they obtain it, and how it is parsed. Negar, a senior data scientist with Aerial, describes the types of models used, including semi-supervised, unsupervised and signal processing based models, and how they’ve scaled their platform, and provides us with some real-world use cases. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/107. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome. Lees meer
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Physiology-Based Models for Fitness and Training w/ Firstbeat with Ilkka Korhonen - TWiML Talk #106
2 februari 2018 · 36 min
In this episode i'm joined by Ilkka Korhonen, Vice President of Technology at Firstbeat, a company whose algorithms are embedded in fitness watches from companies like Garmin and Suunto and which use your heartbeat data to offer personalized insights into stress, fitness, recovery and sleep patterns. We cover a ton about Firstbeat in the conversation, including how they transform the sensor readings into more actionable data, their use of a digital physiological model of the human body, how they use sensor data to identify and predict physiological changes within the body, and some of the opportunities that Firstbeat has to further apply ML in the future. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/106. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome. Lees meer
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Machine Learning for Signal Processing Applications w/ Stuart Feffer & Brady Tsai - TWiML Talk #105
1 februari 2018 · 36 min
In this episode, I'm joined by Stuart Feffer, co-founder and CEO of Reality AI, which provides tools and services for engineers working with sensors and signals, and Brady Tsai, Business Development Manager at Koito, which develops automotive lighting solutions for car manufacturers. Stuart and Brady joined me at CES a few weeks ago after they announced a partnership to bring Adaptive Driving Beam, or ADB, headlights to North America. Brady explains what exactly ADB technology is and how it works, while Stuart walks me through the technical aspects of not only this partnership, but of the reality AI platform as a whole. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/105. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome. Lees meer
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Personalizing the Ferrari Challenge Experience w/ Intel AI - TWiML Talk #104
31 januari 2018 · 38 min
In this episode, I'm joined by Andy Keller and Emile Chin-Dickey to discuss Intel's partnership with the Ferrari Challenge North American Series. Andy is a Deep Learning Data Scientist at Intel and Emile is Senior Manager of Marketing Partnerships at the company. In this show, Emile gives us a high-level overview of the Ferrari Challenge partnership and the goals of the collaboration. Andy & I then dive into the AI aspects of the project, including how the training data was collected, the techniques they used to perform fine-grained object detection in the video streams, how they built the analytics platform, some of the remaining challenges with this project, and more! Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/104. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome. Lees meer
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Deep Learning for 3D Sensors and Cameras in Lighthouse with Alex Teichman - TWiML Talk #103
30 januari 2018 · 42 min
In this episode, I sit down with Alex Teichman, CEO and Co-Founder of Lighthouse, a company taking a new approach to the in-home smart camera. Alex and I dig into what exactly the Lighthouse product is, and all the interesting stuff inside, including its combination of 3D sensing, computer vision, and NLP. We also talk about Alex’s process for building the Lighthouse network architecture, they tech stack the product is based on, and some things that surprised him in their efforts to get AI into a consumer product. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/103. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome. Lees meer
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Computer Vision for Cozmo, the Cutest Toy Robot Everrrrr! with Andrew Stein - TWiML Talk #102
30 januari 2018 · 44 min
In this episode, I'm joined by Andrew Stein, computer vision engineer at consumer robotics company Anki, and his partner in crime Cozmo, a toy robot with tons of personality. Andrew joined me during the hustle and bustle of CES a few weeks ago to give me some insight into how Cozmo works, plays, and learns, and how he’s different from other consumer robots you may know, such as the Roomba. We discuss the types of algorithms that help power Cozmo, such as facial detection and recognition, 3D pose recognition, reasoning, and even some simple emotional AI. We also cover Cozmo’s functionality and programmability, including a cool feature called Code Lab. This was a really fun interview, and you’ll be happy to know there’s a companion video starring Cozmo himself right here: https://youtu.be/jUkacU1I0QI. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/102. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome. Lees meer
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Expectation Maximization, Gaussian Mixtures & Belief Propagation, OH MY! w/ Inmar Givoni - Talk #101
26 januari 2018 · 49 min
In this episode i'm joined by Inmar Givoni, Autonomy Engineering Manager at Uber ATG, to discuss her work on the paper Min-Max Propagation, which was presented at NIPS last month in Long Beach. Inmar and I get into a meaty discussion about graphical models, including what they are and how they’re used, some of the challenges they present for both training and inference, and how and where they can be best applied. Then we jump into an in-depth look at the key ideas behind the Min-Max Propagation paper itself, including the relationship to the broader domain of belief propagation and ideas like affinity propagation, and how all these can be applied to a use case example like the makespan problem. This was a really fun conversation! Enjoy! Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Visit twimlai.com/ainy2018 for registration details. Early price ends February 2! Lees meer
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A Linear-Time Kernel Goodness-of-Fit Test - NIPS Best Paper '17 - TWiML Talk #100
24 januari 2018 · 22 min
In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodness-of-Fit Test.” In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/100. Lees meer
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Solving Imperfect-Information Games with Tuomas Sandholm - NIPS ’17 Best Paper - TWiML Talk #99
22 januari 2018 · 28 min
In this episode I speak with Tuomas Sandholm, Carnegie Mellon University Professor and Founder and CEO of startups Optimized Markets and Strategic Machine. Tuomas, along with his PhD student Noam Brown, won a 2017 NIPS Best Paper award for their paper “Safe and Nested Subgame Solving for Imperfect-Information Games.” Tuomas and I dig into the significance of the paper, including a breakdown of perfect vs imperfect information games, the role of abstractions in game solving, and how the concept of safety applies to gameplay. We discuss how all these elements and techniques are applied to poker, and how the algorithm described in this paper was used by Noam and Tuomas to create Libratus, the first AI to beat top human pros in No Limit Texas Hold’em, a particularly difficult game to beat due to its large state space. This was a fascinating interview that I'm really excited to share with you all. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/99 Lees meer
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Separating Vocals in Recorded Music at Spotify with Eric Humphrey - TWiML Talk #98
19 januari 2018 · 27 min
In today’s show, I sit down with Eric Humphrey, Research Scientist in the music understanding group at Spotify. Eric was at the Deep Learning Summit to give a talk on Advances in Deep Architectures and Methods for Separating Vocals in Recorded Music. We discuss his talk, including how Spotify's large music catalog enables such an experiment to even take place, the methods they use to train algorithms to isolate and remove vocals from music, and how architectures like U-Net and Pix2Pix come into play when building his algorithms. We also hit on the idea of “creative AI,” Spotify’s attempt at understanding music content at scale, optical music recognition, and more. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/98 Lees meer
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Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos - TWiML Talk #97
17 januari 2018 · 39 min
In this show I speak with Greg Diamos, senior computer systems researcher at Baidu. Greg joined me before his talk at the Deep Learning Summit, where he spoke on “The Next Generation of AI Chips.” Greg’s talk focused on some work his team was involved in that accelerates deep learning training by using mixed 16-bit and 32-bit floating point arithmetic. We cover a ton of interesting ground in this conversation, and if you’re interested in systems level thinking around scaling and accelerating deep learning, you’re really going to like this one. And of course, if you like this one, you’re also going to like TWiML Talk #14 with Greg’s former colleague, Shubho Sengupta, which covers a bunch of related topics. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. Lees meer
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Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96
16 januari 2018 · 35 min
In this episode, we hear from David Duvenaud, assistant professor in the Computer Science and Statistics departments at the University of Toronto. David joined me after his talk at the Deep Learning Summit on “Composing Graphical Models With Neural Networks for Structured Representations and Fast Inference.” In our conversation, we discuss the generalized modeling and inference framework that David and his team have created, which combines the strengths of both probabilistic graphical models and deep learning methods. He gives us a walkthrough of his use case which is to automatically segment and categorize mouse behavior from raw video, and we discuss how the framework is applied here and for other use cases. We also discuss some of the differences between the frequentist and bayesian statistical approaches. The notes for this show can be found at twimlai.com/talk/96 Lees meer
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Embedded Deep Learning at Deep Vision with Siddha Ganju - TWiML Talk #95
12 januari 2018 · 34 min
In this episode we hear from Siddha Ganju, data scientist at computer vision startup Deep Vision. Siddha joined me at the AI Conference a while back to chat about the challenges of developing deep learning applications “at the edge,” i.e. those targeting compute- and power-constrained environments.In our conversation, Siddha provides an overview of Deep Vision’s embedded processor, which is optimized for ultra-low power requirements, and we dig into the data processing pipeline and network architecture process she uses to support sophisticated models in embedded devices. We dig into the specific the hardware and software capabilities and restrictions typical of edge devices and how she utilizes techniques like model pruning and compression to create embedded models that deliver needed performance levels in resource constrained environments, and discuss use cases such as facial recognition, scene description and activity recognition. Siddha's research interests also include natural language processing and visual question answering, and we spend some time discussing the latter as well. Lees meer
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Neuroevolution: Evolving Novel Neural Network Architectures with Kenneth Stanley - TWiML Talk #94
11 januari 2018 · 46 min
Today, I'm joined by Kenneth Stanley, Professor in the Department of Computer Science at the University of Central Florida and senior research scientist at Uber AI Labs. Kenneth studied under TWiML Talk #47 guest Risto Miikkulainen at UT Austin, and joined Uber AI Labs after Geometric Intelligence, the company he co-founded with Gary Marcus and others, was acquired in late 2016. Kenneth’s research focus is what he calls Neuroevolution, applies the idea of genetic algorithms to the challenge of evolving neural network architectures. In this conversation, we discuss the Neuroevolution of Augmenting Topologies (or NEAT) paper that Kenneth authored along with Risto, which won the 2017 International Society for Artificial Life’s Award for Outstanding Paper of the Decade 2002 - 2012. We also cover some of the extensions to that approach he’s created since, including, HyperNEAT, which can efficiently evolve very large networks with connectivity patterns that look more like those of the human and that are generally much larger than what prior approaches to neural learning could produce, and novelty search, an approach which unlike most evolutionary algorithms has no defined objective, but rather simply searches for novel behaviors. We also cover concepts like “Complexification” and “Deception”, biology vs computation including differences and similarities, and some of his other work including his book, and NERO, a video game complete with Real-time Neuroevolution. This is a meaty “Nerd Alert” interview that I think you’ll really enjoy. Lees meer
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A Quantum Computing Primer and Implications for AI with Davide Venturelli - TWiML Talk #93
8 januari 2018 · 34 min
Today, I'm joined by Davide Venturelli, science operations manager and quantum computing team lead for the Universities Space Research Association’s Institute for Advanced Computer Science at NASA Ames. Davide joined me backstage at the NYU Future Labs AI Summit a while back to give me some insight into a topic that I’ve been curious about for some time now, quantum computing. We kick off our discussion about the core ideas behind quantum computing, including what it is, how it’s applied and the ways it relates to computing as we know it today. We discuss the practical state of quantum computers and what their capabilities are, and the kinds of things you can do with them. And of course, we explore the intersection between AI and quantum computing, how quantum computing may one day accelerate machine learning, and how interested listeners can get started down the quantum rabbit hole. The notes for this show can be found at twimlai.com/talk/93 Lees meer
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Learning State Representations with Yael Niv - TWiML Talk #92
22 december 2017 · 47 min
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode I speak with Yael Niv, professor of neuroscience and psychology at Princeton University. Yael joined me after her invited talk on “Learning State Representations.” In this interview Yael and I explore the relationship between neuroscience and machine learning. In particular, we discusses the importance of state representations in human learning, some of her experimental results in this area, and how a better understanding of representation learning can lead to insights into machine learning problems such as reinforcement and transfer learning. Did I mention this was a nerd alert show? I really enjoyed this interview and I know you will too. Be sure to send over any thoughts or feedback via the show notes page at twimlai.com/talk/92. Lees meer
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Philosophy of Intelligence with Matthew Crosby - TWiML Talk #91
21 december 2017 · 30 min
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests.This time around i'm joined by Matthew Crosby, a researcher at Imperial College London, working on the Kinds of Intelligence Project. Matthew joined me after the NIPS Symposium of the same name, an event that brought researchers from a variety of disciplines together towards three aims: a broader perspective of the possible types of intelligence beyond human intelligence, better measurements of intelligence, and a more purposeful analysis of where progress should be made in AI to best benefit society. Matthew’s research explores intelligence from a philosophical perspective, exploring ideas like predictive processing and controlled hallucination, and how these theories of intelligence impact the way we approach creating artificial intelligence. This was a very interesting conversation, i'm sure you’ll enjoy. Lees meer
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Geometric Deep Learning with Joan Bruna & Michael Bronstein - TWiML Talk #90
20 december 2017 · 40 min
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. This time around I'm joined by Joan Bruna, Assistant Professor at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU, and Michael Bronstein, associate professor at Università della Svizzera italiana (Switzerland) and Tel Aviv University. Joan and Michael join me after their tutorial on Geometric Deep Learning on Graphs and Manifolds. In our conversation we dig pretty deeply into the ideas behind geometric deep learning and how we can use it in applications like 3D vision, sensor networks, drug design, biomedicine, and recommendation systems. This is definitely a Nerd Alert show, and one that will get your multi-dimensional neurons firing. Enjoy! Lees meer
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AI at the NASA Frontier Development Lab with Sara Jennings, Timothy Seabrook and Andres Rodriguez
19 december 2017 · 37 min
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode i'm joined by Sara Jennings, Timothy Seabrook and Andres Rodriguez to discuss NASA’s Frontier Development Lab or FDL. The FDL is an intense 8-week applied AI research accelerator, focused on tackling knowledge gaps useful to the space program. In our discussion, Sara, producer at the FDL, provides some insight into its goals and structure. Timothy, a researcher at FDL, describes his involvement with the program, including some of the projects he worked on while on-site. He also provides a look into some of this year’s FDL projects, including Planetary Defense, Solar Storm Prediction, and Lunar Water Location. Last but not least, Andres, Sr. Principal Engineer at Intel's AIPG, joins us to detail Intel’s support of the FDL, and how the various elements of the Intel AI stack supported the FDL research. This is a jam packed conversation, so be sure to check the show notes page at twimlai.com/talk/89 for all the links and tidbits from this episode. Lees meer
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Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru
19 december 2017 · 32 min
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode I sit down with Timnit Gebru, postdoctoral researcher at Microsoft Research in the Fairness, Accountability, Transparency and Ethics in AI, or FATE, group. Timnit is also one of the organizers behind the Black in AI group, which held a very interesting symposium and poster session at NIPS. I’ll link to the group’s page in the show notes. I’ve been following Timnit’s work for a while now and was really excited to get a chance to sit down with her and pick her brain. We packed a ton into this conversation, especially keying in on her recently released paper “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US”. Timnit describes the pipeline she developed for this research, and some of the challenges she faced building and end-to-end model based on google street view images, census data and commercial car vendor data. We also discuss the role of social awareness in her work, including an explanation of how domain adaptation and fairness are related and her view of the major research directions in the domain of fairness. The notes for this show can be found at twimlai.com/talk/88 For series information, visit twimlai.com/nips2017 Lees meer
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Integrative Learning for Robotic Systems with Aaron Ames - TWiML Talk #87
15 december 2017 · 47 min
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. Today we’re joined by Aaron Ames, Professor of Mechanical & Civil Engineering at Caltech. Aaron joined me before his talk at the Deep Learning Summit “Eye, Robot: Computer Vision and Autonomous Robotics” and I had a ton of questions for him. While he considers himself a “hardware guy”, we got into a great discussion centered around the intersection of Robotics and ML Inference. We cover a range of topics, including Boston Dynamics backflipping robot (If you haven't seen it, check out the show notes), Humanoid Robotics, His work on motion primitives and transitions and he even gives us a few predictions on the future of robotics. Lees meer
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Visual Recognition in the Cloud for Law Enforcement with Chris Adzima - TWiML Talk #86
14 december 2017 · 36 min
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. In this episode we’re joined by Chris Adzima, Senior Information Analyst for the Washington County Sheriff’s Department. While Chris is not a traditional data scientist, he comes to us with a very interesting use case using AWS’s Rekognition. Chris is using Rekognition to identify suspects in the Portland area by running their mugshots through the software. In our conversation, he details how he is using Rekognition, while giving us few use cases along the way. We discuss how bias affects the work he is doing, and how they try to remove it from their process, not only from a software developer standpoint, but from a law enforcement standpoint and what his next steps are with the Rekognition software. This was a pretty interesting discussion, i’m sure you’ll enjoy it! Lees meer
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Embodied Visual Learning with Kristen Grauman - TWiML Talk #85
13 december 2017 · 39 min
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. This time around we’re joined by Kristen Grauman, a professor in the department of computer science at UT Austin. Kristen specializes in Computer Vision and joined me leading up to her talk at the Deep Learning Summit “Learning where to look in video”. Kristen & I cover the details from her talk, like exploring how a vision system can learn how to move and where to look. Kristen considers how an embodied vision system can internalize the link between “how I move” and “what I see”, explore policies for learning to look around actively, and learn to mimic human videographer tendencies, automatically deciding where to look in unedited 360 degree video. The notes for this show can be found at twimlai.com/talk/85. For series details, visit twimlai.com/reinvent. Lees meer