Towards Data Science
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Towards Data Science
Note: The TDS podcast's current run has ended. Researchers and business leaders at the forefront of the field unpack the most pressing questions around data science and AI.
Episodi recenti
130 episodi130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default*
Progress in AI has been accelerating dramatically in recent years, and even months. It seems like every other day, there’s a new, previously-believed-...
129. Amber Teng - Building apps with a new generation of language models
It’s no secret that a new generation of powerful and highly scaled language models is taking the world by storm. Companies like OpenAI, AI21Labs, and...
128. David Hirko - AI observability and data as a cybersecurity weakness
Imagine you’re a big hedge fund, and you want to go out and buy yourself some data. Data is really valuable for you — it’s literally going to shape yo...
127. Matthew Stewart - The emerging world of ML sensors
Today, we live in the era of AI scaling. It seems like everywhere you look people are pushing to make large language models larger, or more multi-moda...
126. JR King - Does the brain run on deep learning?
Deep learning models — transformers in particular — are defining the cutting edge of AI today. They’re based on an architecture called an artificial n...
125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?
It’s no secret that the US and China are geopolitical rivals. And it’s also no secret that that rivalry extends into AI — an area both countries consi...
124. Alex Watson - Synthetic data could change everything
There’s a website called thispersondoesnotexist.com. When you visit it, you’re confronted by a high-resolution, photorealistic AI-generated picture of...
123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)
Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people — myself included — “A...
122. Sadie St. Lawrence - Trends in data science
As you might know if you follow the podcast, we usually talk about the world of cutting-edge AI capabilities, and some of the emerging safety risks an...
121. Alexei Baevski - data2vec and the future of multimodal learning
If the name data2vec sounds familiar, that’s probably because it made quite a splash on social and even traditional media when it came out, about two...
120. Liam Fedus and Barrett Zoph - AI scaling with mixture of expert models
AI scaling has really taken off. Ever since GPT-3 came out, it’s become clear that one of the things we’ll need to do to move beyond narrow AI and tow...
119. Jaime Sevilla - Projecting AI progress from compute trends
There’s an idea in machine learning that most of the progress we see in AI doesn’t come from new algorithms of model architectures. instead, some argu...
118. Angela Fan - Generating Wikipedia articles with AI
Generating well-referenced and accurate Wikipedia articles has always been an important problem: Wikipedia has essentially become the Internet's encyc...
117. Beena Ammanath - Defining trustworthy AI
Trustworthy AI is one of today’s most popular buzzwords. But although everyone seems to agree that we want AI to be trustworthy, definitions of trustw...
116. Katya Sedova - AI-powered disinformation, present and future
Until recently, very few people were paying attention to the potential malicious applications of AI. And that made some sense: in an era where AIs wer...
115. Irina Rish - Out-of-distribution generalization
Imagine, for example, an AI that’s trained to identify cows in images. Ideally, we’d want it to learn to detect cows based on their shape and colour....
114. Sam Bowman - Are we *under-hyping* AI?
Google the phrase “AI over-hyped”, and you’ll find literally dozens of articles from the likes of Forbes, Wired, and Scientific American, all arguing...
113. Yaron Singer - Catching edge cases in AI
It’s no secret that AI systems are being used in more and more high-stakes applications. As AI eats the world, it’s becoming critical to ensure that A...
112. Tali Raveh - AI, single cell genomics, and the new era of computational biology
Until very recently, the study of human disease involved looking at big things — like organs or macroscopic systems — and figuring out when and how th...
111. Mo Gawdat - Scary Smart: A former Google exec’s perspective on AI risk
If you were scrolling through your newsfeed in late September 2021, you may have caught this splashy headline from The Times of London that read, “Can...
110. Alex Turner - Will powerful AIs tend to seek power?
Today’s episode is somewhat special, because we’re going to be talking about what might be the first solid quantitative study of the power-seeking ten...
109. Danijar Hafner - Gaming our way to AGI
Until recently, AI systems have been narrow — they’ve only been able to perform the specific tasks that they were explicitly trained for. And while na...
108. Last Week In AI — 2021: The (full) year in review
2021 has been a wild ride in many ways, but its wildest features might actually be AI-related. We’ve seen major advances in everything from language m...
107. Kevin Hu - Data observability and why it matters
Imagine for a minute that you’re running a profitable business, and that part of your sales strategy is to send the occasional mass email to people wh...
106. Yang Gao - Sample-efficient AI
Historically, AI systems have been slow learners. For example, a computer vision model often needs to see tens of thousands of hand-written digits bef...
105. Yannic Kilcher - A 10,000-foot view of AI
There once was a time when AI researchers could expect to read every new paper published in the field on the arXiv, but today, that’s no longer the ca...
104. Ken Stanley - AI without objectives
Today, most machine learning algorithms use the same paradigm: set an objective, and train an agent, a neural net, or a classical model to perform wel...
103. Gillian Hadfield - How to create explainable AI regulations that actually make sense
It’s no secret that governments around the world are struggling to come up with effective policies to address the risks and opportunities that AI pres...
102. Wendy Foster - AI ethics as a user experience challenge
AI ethics is often treated as a dry, abstract academic subject. It doesn’t have the kinds of consistent, unifying principles that you might expect fro...
101. Ayanna Howard - AI and the trust problem
Over the last two years, the capabilities of AI systems have exploded. AlphaFold2, MuZero, CLIP, DALLE, GPT-3 and many other models have extended the...
100. Max Jaderberg - Open-ended learning at DeepMind
On the face of it, there’s no obvious limit to the reinforcement learning paradigm: you put an agent in an environment and reward it for taking good a...
99. Margaret Mitchell - (Practical) AI ethics
Bias gets a bad rap in machine learning. And yet, the whole point of a machine learning model is that it biases certain inputs to certain outputs — a...
98. Mike Tung - Are knowledge graphs AI’s next big thing?
As impressive as they are, language models like GPT-3 and BERT all have the same problem: they’re trained on reams of internet data to imitate human w...
97. Anthony Habayeb - The present and future of AI regulation
Corporate governance of AI doesn’t sound like a sexy topic, but it’s rapidly becoming one of the most important challenges for big companies that rely...
96. Jan Leike - AI alignment at OpenAI
The more powerful our AIs become, the more we’ll have to ensure that they’re doing exactly what we want. If we don’t, we risk building AIs that use da...
95. Francesca Rossi - Thinking, fast and slow: AI edition
The recent success of large transformer models in AI raises new questions about the limits of current strategies: can we expect deep learning, reinfor...
94. Divya Siddarth - Are we thinking about AI wrong?
AI research is often framed as a kind of human-versus-machine rivalry that will inevitably lead to the defeat — and even wholesale replacement of — hu...
93. 2021: A year in AI (so far) - Reviewing the biggest AI stories of 2021 with our friends at the Let’s Talk AI podcast
2020 was an incredible year for AI. We saw powerful hints of the potential of large language models for the first time thanks to OpenAI’s GPT-3, DeepM...
92. Daniel Filan - Peering into neural nets for AI safety
Many AI researchers think it’s going to be hard to design AI systems that continue to remain safe as AI capabilities increase. We’ve seen already on t...
91. Peter Gao - Self-driving cars: Past, present and future
Cruise is a self-driving car startup founded in 2013 — at a time when most people thought of self-driving cars as the stuff of science fiction. And ye...