92. Daniel Filan - Peering into neural nets for AI safety

92. Daniel Filan - Peering into neural nets for AI safety

Author: The TDS team July 14, 2021 Duration: 1:06:02

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 the podcast that the field of AI alignment has emerged to tackle this problem, but a related effort is also being directed at a separate dimension of the safety problem: AI interpretability.

Our ability to interpret how AI systems process information and make decisions will likely become an important factor in assuring the reliability of AIs in the future. And my guest for this episode of the podcast has focused his research on exactly that topic. Daniel Filan is an AI safety researcher at Berkeley, where he’s supervised by AI pioneer Stuart Russell. Daniel also runs AXRP, a podcast dedicated to technical AI alignment research.


While the active production of Towards Data Science has concluded, its archive remains a vital resource. Created by The TDS team, this collection captures a specific moment in the rapid evolution of data science and artificial intelligence. Each conversation pulls you directly into the room with leading researchers and practitioners who were shaping the tools and theories of their time. The discussions are not abstract lectures; they are grounded explorations of real-world problems, ethical dilemmas, and technical challenges that defined the field's trajectory. You'll hear experts dissect the implications of their work, from algorithmic fairness to the practicalities of deploying models at scale. This podcast served as a forum for nuanced debate, where complex ideas were unpacked with clarity and depth. Listening now offers a unique historical perspective, a chance to understand the foundational conversations that continue to influence where technology is headed next. The archive of Towards Data Science stands as a substantive record of insight, preserving the voices and questions from the forefront of a digital revolution.
Author: Language: en-us Episodes: 50

Towards Data Science
Podcast Episodes
100. Max Jaderberg - Open-ended learning at DeepMind [not-audio_url] [/not-audio_url]

Duration: 45:25
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 actions until it masters a task. And by last year, RL had achieved some…
99. Margaret Mitchell - (Practical) AI ethics [not-audio_url] [/not-audio_url]

Duration: 45:43
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 picture of a cat to a label that says “cat”, for example. Machine lear…
98. Mike Tung - Are knowledge graphs AI’s next big thing? [not-audio_url] [/not-audio_url]

Duration: 48:56
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 writing. And human writing is often wrong, biased, or both, which means…
97. Anthony Habayeb - The present and future of AI regulation [not-audio_url] [/not-audio_url]

Duration: 49:31
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 on machine learning models to deliver value for their customers. More…
96. Jan Leike - AI alignment at OpenAI [not-audio_url] [/not-audio_url]

Duration: 1:05:17
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 dangerously creative solutions that have side-effects that could be unde…
95. Francesca Rossi - Thinking, fast and slow: AI edition [not-audio_url] [/not-audio_url]

Duration: 46:42
The recent success of large transformer models in AI raises new questions about the limits of current strategies: can we expect deep learning, reinforcement learning and other prosaic AI techniques to get us all the way…
94. Divya Siddarth - Are we thinking about AI wrong? [not-audio_url] [/not-audio_url]

Duration: 1:02:44
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 — human beings by artificial superintelligences that have their own sense…
91. Peter Gao - Self-driving cars: Past, present and future [not-audio_url] [/not-audio_url]

Duration: 1:01:21
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 yet, just three years later, the company was acquired by GM for over a b…
90. Jeffrey Ding - China’s AI ambitions and why they matter [not-audio_url] [/not-audio_url]

Duration: 49:07
There are a lot of reasons to pay attention to China’s AI initiatives. Some are purely technological: Chinese companies are producing increasingly high-quality AI research, and they’re poised to become even more importan…