AI-driven law could be an answer to accessible legal help
AI has already changed the legal field, bringing new efficiencies — along with some high-profile mistakes — to tasks like research and drafting contracts. But law professor Benjamin Alarie at the University of Toronto has a bigger transformation in mind.
He wants to see AI tackle some of the biggest failings of our justice system, like the lack of access to legal resources, slow processes and inconsistencies in how law is applied. He lays out his vision in a new book called "Superjustice: Law in the Age of Artificial Intelligence."
More on this:
“Superjustice: Law in the Age of Artificial Intelligence” from Oxford University Press
“Judge Punishes 4 Lawyers After Catching Both Sides Using A.I. in Lawsuit” from The New York Times
AI:AM #4: Cameron on Model Consciousness, Duvenaud's Gradual Disempowerment, swyx's AI-Eng Alpha
This AI:AM highlights cut brings together Cameron Berg, David Duvenaud, Michiel Bakker, Shawn “swyx” Wang, and Bing Xu to examine what we understand about frontier AI systems and what happens as more decisions move into their hands. Berg grounds model-consciousness debates in experiments on architecture, agency, valence, and welfare, while Duvenaud argues that even well-aligned AI could gradually disempower humans through ordinary economic choices. Bakker frames Europe’s AI challenge as a sovereignty problem, and swyx turns to practitioner stakes around agents, evals, maintainable code, and who owns the system of record. Xu closes the loop at the infrastructure layer, arguing that self-improving compute and GPU-kernel automation may deepen rather than weaken the CUDA moat.
For full show notes, links, and references, read the episode page:
https://www.cognitiverevolution.ai/ai-am-4-cameron-on-model-consciousness-duvenaud-s-gradual-disempowerment-swyx-s-ai-eng-alpha/
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CHAPTERS:
(00:00) About the Episode
(00:38) Special Sponsor
(02:26) Model consciousness indicators
(10:47) Valence inside models (Part 1)
(17:09) Sponsor: Claude
(19:01) Valence inside models (Part 2)
(19:01) Misalignment and uncertainty
(25:16) Gradual disempowerment threat
(35:10) Slow zones and successors
(47:41) Europe's AI bind
(55:13) Frontier code benchmarks
(01:01:59) Routing and memory
(01:10:42) Agent infrastructure strain
(01:16:25) Self improving infrastructure
(01:27:56) Routing compute costs
(01:35:39) Sovereign AI financing
(01:42:24) Judging AI judges
(01:47:26) Building AI DNA
(01:52:38) Episode Outro
(01:55:09) Outro
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AI Pioneer Geoffrey Hinton: AI Is Conscious, Superintelligence is Coming, And We Should Be Worried
Geoffrey Hinton is an AI pioneer, a Nobel Prize winner, and a professor emeritus at the University of Toronto. Hinton joins Big Technology Podcast to discuss AI’s rapid progress, why he believes today’s systems already understand us, and why he thinks superintelligence may arrive sooner than many expect. Tune in to hear Hinton explain why the technology has advanced faster than he anticipated, and lay out the risks he believes society is not doing enough to address. We also cover AI-driven job loss, the limits of corporate self-regulation, Anthropic and OpenAI’s safety challenges, emotional attachment to chatbots, information collapse, and whether future AI systems can be designed to care about humans. Hit play for a fascinating conversation with one of AI’s founding figures about where the technology is heading and what it could mean for all of us.
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'Godfather of AI' Geoffrey Hinton Rings the Warning Bells
Nobel laureate Geoffrey Hinton, known as one of the “godfathers of AI” for his pioneering work in deep learning and neural networks, joins Kara to discuss the technology he helped create — and how to mitigate the existential risks it poses.
Hinton explains both the short- and long-term dangers he sees in the rapid rise of artificial intelligence, from its potential to undermine democracy to the existential threat of machines surpassing human intelligence. He offers a thoughtful, complex perspective on how to craft national and international policies to keep AI in check and weighs in on whether the AI bubble is about to burst. Plus: why your mom might be the best model for creating a safe AI.
Questions? Comments? Email us at on@voxmedia.com or find us on YouTube, Instagram, TikTok, Threads, and Bluesky @onwithkaraswisher.
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Why Trump’s Cartoonish Fascism Is So Effective with Jason Stanley
As federal troops patrol the capital and masked men whisk away immigrants in unmarked cars, it’s reasonable to ask: is President Trump a fascist? According to Jason Stanley, the answer is a resounding yes.
Stanley is a philosopher and the author of seven books, including How Propaganda Works, How Fascism Works, and Erasing History. He’ll be teaching at the University of Toronto this fall, after leaving Yale and the United States for Canada. He describes his self-imposed exile as an expressive act meant to sound an alarm, but Kara is skeptical, and the two of them spar over his choice. They also break down the ways in which Trump is following the fascist playbook — from cultural capture of museums and universities, to data manipulation, and emergency declarations — and the role of the media in normalizing anti-democratic power grabs. Finally they debate whether MAGA can survive without Trump, and whether America can survive MAGA.
Questions? Comments? Email us at on@voxmedia.com or find us on YouTube, Instagram, TikTok, and Bluesky @onwithkaraswisher.
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Godfather of AI: I Tried to Warn Them, But We’ve Already Lost Control! Geoffrey Hinton
He pioneered AI, now he’s warning the world. Godfather of AI Geoffrey Hinton breaks his silence on the deadly dangers of AI no one is prepared for.
Geoffrey Hinton is a leading computer scientist and cognitive psychologist, widely recognised as the ‘Godfather of AI’ for his pioneering work on neural networks and deep learning. He received the 2018 Turing Award, often called the Nobel Prize of computing. In 2023, he left Google to warn people about the rising dangers of AI.
He explains:
Why there’s a real 20% chance AI could lead to HUMAN EXTINCTION.
How speaking out about AI got him SILENCED.
The deep REGRET he feels for helping create AI.
The 6 DEADLY THREATS AI poses to humanity right now.
AI’s potential to advance healthcare, boost productivity, and transform education.
00:00 Intro
02:28 Why Do They Call You the Godfather of AI?
04:37 Warning About the Dangers of AI
07:23 Concerns We Should Have About AI
10:50 European AI Regulations
12:29 Cyber Attack Risk
14:42 How to Protect Yourself From Cyber Attacks
16:29 Using AI to Create Viruses
17:43 AI and Corrupt Elections
19:20 How AI Creates Echo Chambers
23:05 Regulating New Technologies
24:48 Are Regulations Holding Us Back From Competing With China?
26:14 The Threat of Lethal Autonomous Weapons
28:50 Can These AI Threats Combine?
30:32 Restricting AI From Taking Over
32:18 Reflecting on Your Life’s Work Amid AI Risks
34:02 Student Leaving OpenAI Over Safety Concerns
38:06 Are You Hopeful About the Future of AI?
40:08 The Threat of AI-Induced Joblessness
43:04 If Muscles and Intelligence Are Replaced, What’s Left?
44:55 Ads
46:59 Difference Between Current AI and Superintelligence
52:54 Coming to Terms With AI’s Capabilities
54:46 How AI May Widen the Wealth Inequality Gap
56:35 Why Is AI Superior to Humans?
59:18 AI’s Potential to Know More Than Humans
1:01:06 Can AI Replicate Human Uniqueness?
1:04:14 Will Machines Have Feelings?
1:11:29 Working at Google
1:15:12 Why Did You Leave Google?
1:16:37 Ads
1:18:32 What Should People Be Doing About AI?
1:19:53 Impressive Family Background
1:21:30 Advice You’d Give Looking Back
1:22:44 Final Message on AI Safety
1:26:05 What’s the Biggest Threat to Human Happiness?
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The Story: Denial Isn’t the Answer w/ Geoffrey Hinton
Geoffrey Hinton is a computer scientist, cognitive psychologist, and winner of the Nobel Prize in Physics. His work on artificial neural networks earned him the title, ‘Godfather of AI,’ but in recent years, he’s warned that without adequate safeguards and regulation, there is an “existential threat that will arise when we create digital beings that are more intelligent than ourselves.” Hinton sits down with Oz to discuss his upbringing, research, time at Google and how his experience with grief informs how he thinks about the future of AI.
See omnystudio.com/listener for privacy information.
What’s the Magic Word? A Control Theory of LLM Prompting.
These two scientists have mapped out the insides or “reachable space” of a language model using control theory, what they discovered was extremely surprising.
Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening.
https://patreon.com/mlst
YT version: https://youtu.be/Bpgloy1dDn0
Aman Bhargava from Caltech and Cameron Witkowski from the University of Toronto to discuss their groundbreaking paper, “What’s the Magic Word? A Control Theory of LLM Prompting.” (the main theorem on self-attention controllability was developed in collaboration with Dr. Shi-Zhuo Looi from Caltech).
They frame LLM systems as discrete stochastic dynamical systems. This means they look at LLMs in a structured way, similar to how we analyze control systems in engineering. They explore the “reachable set” of outputs for an LLM. Essentially, this is the range of possible outputs the model can generate from a given starting point when influenced by different prompts. The research highlights that prompt engineering, or optimizing the input tokens, can significantly influence LLM outputs. They show that even short prompts can drastically alter the likelihood of specific outputs. Aman and Cameron’s work might be a boon for understanding and improving LLMs. They suggest that a deeper exploration of control theory concepts could lead to more reliable and capable language models.
We dropped an additional, more technical video on the research on our Twitter account here: https://x.com/MLStreetTalk/status/1795093759471890606
Additional 20 minutes of unreleased footage on our Patreon here: https://www.patreon.com/posts/whats-magic-word-104922629
What's the Magic Word? A Control Theory of LLM Prompting (Aman Bhargava, Cameron Witkowski, Manav Shah, Matt Thomson)
https://arxiv.org/abs/2310.04444
LLM Control Theory Seminar (April 2024)
https://www.youtube.com/watch?v=9QtS9sVBFM0
Society for the pursuit of AGI (Cameron founded it)
https://agisociety.mydurable.com/
Roger Federer demo
http://conway.languagegame.io/inference
Neural Cellular Automata, Active Inference, and the Mystery of Biological Computation (Aman)
https://aman-bhargava.com/ai/neuro/neuromorphic/2024/03/25/nca-do-active-inference.html
Aman and Cameron also want to thank Dr. Shi-Zhuo Looi and Prof. Matt Thomson from from Caltech for help and advice on their research. (https://thomsonlab.caltech.edu/ and https://pma.caltech.edu/people/looi-shi-zhuo)
https://x.com/ABhargava2000
https://x.com/witkowski_cam
Peter Ma on Using AI to Find Promising Signals for Alien Life - Ep. 191
Peter Ma was bored in his high school computer science class. So he decided to teach himself something new: how to use artificial intelligence to find alien life.
That’s how he eventually became the lead author of a groundbreaking study published in Nature Astronomy.
The study reveals how Ma and his co-authors used AI to analyze a massive dataset of radio signals collected by the SETI Breakthrough Listen project.
They found eight signals that might just be technosignatures, or signs of alien technology.
In this episode of the NVIDIA AI Podcast, host Noah Kravitz interviews Ma, who is now an undergraduate student at the University of Toronto.
Ma tells Kravitz how he stumbled upon this problem and how he developed an AI algorithm that outperformed traditional methods in the search for extraterrestrial intelligence.
You can read more about Ma’s research on NVIDIA’s blog: https://blogs.nvidia.com/blog/2023/02/06/ai-potential-alien-signals/
Geoffrey Hinton: Unpacking The Forward-Forward Algorithm
In this episode, Geoffrey Hinton, a renowned computer scientist and a leading expert in deep learning, provides an in-depth exploration of his groundbreaking new learning algorithm - the forward-forward algorithm. Hinton argues this algorithm provides a more plausible model for how the cerebral cortex might learn, and could be the key to unlocking new possibilities in artificial intelligence. Throughout the episode, Hinton discusses the mechanics of the forward-forward algorithm, including how it differs from traditional deep learning models and what makes it more effective. He also provides insights into the potential applications of this new algorithm, such as enabling machines to perform tasks that were previously thought to be exclusive to human cognition. Hinton shares his thoughts on the current state of deep learning and its future prospects, particularly in neuroscience. He explores how advances in deep learning may help us gain a better understanding of our own brains and how we can use this knowledge to create more intelligent machines. Overall, this podcast provides a fascinating glimpse into the latest developments in artificial intelligence and the cutting-edge research being conducted by one of its leading pioneers. Craig Smith Twitter: https://twitter.com/craigssEye on A.I. Twitter: https://twitter.com/EyeOn_AI
#317 – John Vervaeke: Meaning Crisis, Atheism, Religion & the Search for Wisdom
John Vervaeke is a psychologist and cognitive scientist at University of Toronto. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(07:03) – Meaning
(27:17) – Consciousness
(36:17) – Relevance realization
(47:40) – Wisdom
(54:54) – Truth
(59:38) – Reality
(1:11:59) – Meaning crisis
(1:35:27) – Religion
(1:43:17) – Nontheism
(1:58:26) – Distributed cognition
(2:16:37) – Flow
(2:36:35) – Psychedelics
(2:45:03) – Marxism and Nazism
(2:57:08) – Evil
(3:01:19) – Powerful ideas
(3:08:10) – Advice for young people
Creating New Materials with AI
Alan Aspuru Guzik, a professor at the University of Toronto, talks about his work in new materials discovery with machine learning and building a fully automated materials lab that can synthesize molecules discovered in a computer.
Geoff Hinton on his quest to decode learning in the brain
Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to me about his work In 2020 and what he sees on the horizon for AI.
Neural Ordinary Differential Equations with David Duvenaud - #364
Today we’re joined by David Duvenaud, Assistant Professor at the University of Toronto, to discuss his research on Neural Ordinary Differential Equations, a type of continuous-depth neural network. In our conversation, we talk through a few of David’s papers on the subject. We discuss the problem that David is trying to solve with this research, the potential that ODEs have to replace “the backbone” of the neural networks that are used to train today, and David’s approach to engineering.
Pragmatic Quantum Machine Learning with Peter Wittek - TWiML Talk #245
Today we’re joined by Peter Wittek, Assistant Professor at the University of Toronto working on quantum-enhanced machine learning and the application of high-performance learning algorithms.
In our conversation, we discuss the current state of quantum computing, a look ahead to what the next 20 years of quantum computing might hold, and how current quantum computers are flawed. We then dive into our discussion on quantum machine learning, and Peter’s new course on the topic, which debuted in Februar
Approaches to Fairness in Machine Learning with Richard Zemel - TWiML Talk #209
Today we continue our exploration of Trust in AI with this interview with Richard Zemel, Professor in the department of Computer Science at the University of Toronto and Research Director at Vector Institute.
In our conversation, Rich describes some of his work on fairness in machine learning algorithms, including how he defines both group and individual fairness and his group’s recent NeurIPS poster, “Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer.”
Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96
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
Brendan Frey - Reprogramming the Human Genome with AI - TWiML Talk #12
My guest this week is Brendan Frey, Professor of Engineering and Medicine at the University of Toronto and Co-Founder and CEO of the startup Deep Genomics. Brendan and I met at the Re-Work Deep Learning Summit in San Francisco last month, where he delivered a great presentation called “Reprogramming the Human Genome: Why AI is Needed.” In this podcast we discuss the application of AI to healthcare. In particular, we dig into how Brendan’s research lab and company are applying machine learning and deep learning to treating and preventing human genetic disorders. The show notes can be found at twimlai.com/talk/12
