Rank #1: Anticipating Superintelligence with Nick Bostrom - TWiML Talk #181
In this episode, we’re joined by Nick Bostrom, professor in the faculty of philosophy at the University of Oxford, where he also heads the Future of Humanity Institute, a multidisciplinary institute focused on answering big-picture questions for humanity with regards to AI safety and ethics.
Nick is of course also author of the book “Superintelligence: Paths, Dangers, Strategies.” In our conversation, we discuss the risks associated with Artificial General Intelligence and the more advanced AI systems Nick refers to as superintelligence. We also discuss Nick’s writings on the topic of openness in AI development, and the advantages and costs of open and closed development on the part of nations and AI research organizations. Finally, we take a look at what good safety precautions might look like, and how we can create an effective ethics framework for superintelligent systems.
The notes for this episode can be found at https://twimlai.com/talk/181.
Rank #2: Trust and AI with Parinaz Sobhani - TWiML Talk #208
In today’s episode we’re joined by Parinaz Sobhani, Director of Machine Learning at Georgian Partners.
In our conversation, Parinaz and I discuss some of the main issues falling under the “trust” umbrella, such as transparency, fairness and accountability. We also explore some of the trust-related projects she and her team at Georgian are working on, as well as some of the interesting trust and privacy papers coming out of the NeurIPS conference.
This week’s series is sponsored by our friends at Georgian Partners. Georgian recently published Building Conversational AI Teams, a comprehensive guide to lead you through sourcing, acquiring and nurturing a successful conversational AI team. Download at: https://gptrs.vc/convoai.
For this episode's complete show notes, visit twimlai.com/talk/208.
Rank #3: LSTMs, Plus a Deep Learning History Lesson with Jürgen Schmidhuber - TWiML Talk #44
Rank #4: Machine Learning Platforms at Uber with Mike Del Balso - TWiML Talk #115
Rank #5: How ML Keeps Shelves Stocked at Home Depot with Pat Woowong - TWiML Talk #175
Today we’re joined by Pat Woowong, principal engineer in the applied machine intelligence group at The Home Depot.
We discuss a project that Pat recently presented at the Google Cloud Next conference which used machine learning to predict shelf-out scenarios within stores. We dig into the motivation for this system and how the team went about building it, including what type of models ended up working best, how they collected their data, their use of kubernetes to support future growth in the platform, and much more.
For the complete show notes, visit twimlai.com/talk/175.
Rank #6: Reinforcement Learning Deep Dive with Pieter Abbeel - TWiML Talk #28
Rank #7: Training Data for Autonomous Vehicles - Daryn Nakhuda - TWiML Talk #57
Rank #8: Contextual Modeling for Language and Vision with Nasrin Mostafazadeh - TWiML Talk #174
Today we’re joined by Nasrin Mostafazadeh, Senior AI Research Scientist at New York-based Elemental Cognition.
Our conversation focuses on Nasrin’s work in event-centric contextual modeling in language and vision, which she sees as a means of giving AI systems a bit of “common sense.” We discuss Nasrin’s work on the Story Cloze Test, which is a reasoning framework for evaluating story understanding and generation. We explore the details of this task--including what constitutes a “story”--and some of the challenges it presents and approaches for solving it. We also discuss how you model what a computer understands, building semantic representation algorithms, different ways to approach “explainability,” and multimodal extensions to her contextual modeling work.
The notes for this episode can be found at https://twimlai.com/talk/174.
Rank #9: Evolutionary Algorithms in Machine Learning with Risto Miikkulainen - TWiML Talk #47
Rank #10: Trends in Computer Vision with Siddha Ganju - TWiML Talk #218
In the final episode of our AI Rewind series, we’re excited to have Siddha Ganju back on the show.
Siddha, who is now an autonomous vehicles solutions architect at Nvidia shares her thoughts on trends in Computer Vision in 2018 and beyond. We cover her favorite CV papers of the year in areas such as neural architecture search, learning from simulation, application of CV to augmented reality, and more, as well as a bevy of tools and open source projects.
The complete show notes for this episode can be found at https://twimlai.com/talk/218.
For more information on our AI Rewind series, visit https://twimlai.com/rewind18.
Rank #11: Practical Deep Learning with Rachel Thomas - TWiML Talk #138
Rank #12: Understanding Deep Neural Nets with Dr. James McCaffrey - TWiML Talk #13
Rank #13: Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang
Rank #14: ML for Understanding Satellite Imagery at Scale with Kyle Story - TWiML Talk #173
Today we’re joined by Kyle Story, computer vision engineer at Descartes Labs.
Kyle and I caught up after his recent talk at the Google Cloud Next Conference titled “How Computers See the Earth: A Machine Learning Approach to Understanding Satellite Imagery at Scale.” We discuss some of the interesting computer vision problems he’s worked on at Descartes, including custom object detectors and the company’s geovisual search engine, covering everything from the models they’ve developed and platform they’ve built, to the key challenges they’ve had to overcome in scaling them.
For the complete show notes, visit twimlai.com/talk/173.
Rank #15: Trends in Natural Language Processing with Sebastian Ruder - TWiML Talk #216
In this episode of our AI Rewind series, we’ve brought back recent guest Sebastian Ruder, PhD Student at the National University of Ireland and Research Scientist at Aylien, to discuss trends in Natural Language Processing in 2018 and beyond.
In our conversation we cover a bunch of interesting papers spanning topics such as pre-trained language models, common sense inference datasets and large document reasoning and more, and talk through Sebastian’s predictions for the new year.
The complete show notes for this episode can be found at twimlai.com/talk/216.
For more information on the AI Rewind 2018 series, visit twimlai.com/rewind18.
Rank #16: Learning to Learn, and other Opportunities in Machine Learning with Graham Taylor - TWiML Talk #62
Rank #17: Evaluating Model Explainability Methods with Sara Hooker - TWiML Talk #189
In this, the first episode of the Deep Learning Indaba series, we’re joined by Sara Hooker, AI Resident at Google Brain.
I had the pleasure of speaking with Sara in the run-up to the Indaba about her work on interpretability in deep neural networks. We discuss what interpretability means and when it’s important, and explore some nuances like the distinction between interpreting model decisions vs model function. We also dig into her paper Evaluating Feature Importance Estimates and look at the relationship between this work and interpretability approaches like LIME.
We also talk a bit about Google, in particular, the relationship between Brain and the rest of the Google AI landscape and the significance of the recently announced Google AI Lab in Accra, Ghana, being led by friend of the show Moustapha Cisse. And, of course, we chat a bit about the Indaba as well.
For the complete show notes for this episode, visit twimlai.com/talk/189.
For more information on the Deep Learning Indaba series, visit twimlai.com/indaba2018.
Rank #18: Growth Hacking Sports w/ Machine Learning with Noah Gift - TWiML Talk #158
In this episode of our AI in Sports series I'm joined by Noah Gift, Founder and Consulting CTO at Pragmatic Labs and professor at UC Davis.
Noah previously worked for a startup called Score Sports, which used machine learning to uncover athlete influence on social media and internet platforms. We look into some of his findings in that role, including how to predict the impact of athletes’ social media engagement. We also discuss some of his more recent work in using social media to predict which players hold the most on-court value, and how this work could lead to more complete approaches to player valuation. Finally, we spend some time discussing some areas that Noah sees as ripe for new research and experimentation across sports, and we take a look at his upcoming book Pragmatic AI, An Introduction to Cloud-Based Machine Learning. For those interested in pre-ordering the book, be sure to check out the link in the show notes for a nice discount code.
The notes for this show can be found at twimlai.com/talk/158.
For more on our AI in Sports series visit twimlai.com/aiinsports.
Rank #19: Trends in Reinforcement Learning with Simon Osindero - TWiML Talk #217
In this episode of our AI Rewind series, we introduce a new friend of the show, Simon Osindero, Staff Research Scientist at DeepMind.
We discuss trends in Deep Reinforcement Learning in 2018 and beyond. We’ve packed a bunch into this show, as Simon walks us through many of the important papers and developments seen last year in areas like Imitation Learning, Unsupervised RL, Meta-learning, and more.
The complete show notes for this episode can be found at https://twimlai.com/talk/217.
For more information on our 2018 AI Rewind series, visit https://twimlai.com/rewind2018.
Rank #20: Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295
Today we are joined by Lukas Biewald, CEO and Co-Founder of Weights & Biases. Lukas, previously CEO and Founder of Figure Eight (CrowdFlower), has a straightforward goal: provide researchers with SaaS that is easy to install, simple to operate, and always accessible. Seeing a need for reproducibility in deep learning experiments, Lukas founded Weights & Biases. In this episode we discuss:
- The experiment tracking tool, how it works, and the components that make it unique in the ML marketplace
- The open, collaborative culture that Lukas promotes
- How Lukas got his start in deep learning experiments, what his experiment tracking used to look like,
- The current Weights & Biases business success strategy and what his team is working on today
The complete show notes for this episode can be found at twimlai.com/talk/295.
Thanks to our friends at Weights & Biases for their support of the show, their sponsorship of this episode, and our upcoming event, TWIMLcon: AI Platforms.
Registration for TWIMLcon is still open! Visit twimlcon.com/register today!