Terence Tao – Kepler, Newton, and the true nature of mathematical discovery
We begin the episode with the absolutely ingenious and surprising way in which Kepler discovered the laws of planetary motion.
People sometimes say that AI will make especially fast progress at scientific discovery because of tight verification loops.
But the story of how we discovered the shape of our solar system shows how the verification loop for correct ideas can be decades (or even millennia) long.
During this time, what we know today as the better theory can actually make worse predictions.
And the reasons it survives this epistemic hell is some mixture of judgment and heuristics that we don’t even understand well enough to actually articulate, much less codify into an RL loop. Hope you enjoy!
Watch on YouTube; read the transcript.
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Timestamps
(00:00:00) – Kepler was a high temperature LLM
(00:11:44) – How would we know if there’s a new unifying concept within heaps of AI slop?
(00:26:10) – The deductive overhang
(00:30:31) – Selection bias in reported AI discoveries
(00:46:43) – AI makes papers richer and broader, but not deeper
(00:53:00) – If AI solves a problem, can humans get understanding out of it?
(00:59:20) – We need a semi-formal language for the way that scientists actually talk to each other
(01:09:48) – How Terry uses his time
(01:17:05) – Human-AI hybrids will dominate math for a lot longer
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#453 — AI and the New Face of Antisemitism
Sam Harris speaks with Judea Pearl about causality, AI, and antisemitism. They discuss why LLMs won't spawn AGI, alignment concerns in the race for AGI, Pearl's public life after the murder of his son Daniel, the post-October 7th shift toward open anti-Zionism, the overlap between anti-Zionism and antisemitism, the misuse of "Islamophobia," Israel's fracture under Netanyahu, confronting anti-Zionism in universities, and other topics.
If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.
#142 Arjun Subramonian: The Intersection of AI, Diversity, and Ethics
This episode is sponsored by Celonis, the global leader in process mining. AI has landed and enterprises are adapting. To give customers slick experiences and teams the technology to deliver. The road is long, but you're closer than you think.
Your business processes run through systems. Creating data at every step. Celonis recontrusts this data to generate Process Intelligence. A common business language. So AI knows how your business flows. Across every department, every system and every process. With AI solutions powered by Celonis enterprises get faster, more accurate insights. A new level of automation potential. And a step change in productivity, performance and customer satisfaction Process Intelligence is the missing piece in the AI Enabled tech stack.
Go to https:/celonis.com/eyeonai to find out more. On episode #141 of Eye on AI, Craig Smith sits down with Arjun Subramonian, a PhD student hailing from UCLA, with a focus on AI fairness and ethics. Arjun's work encompasses the intricate intersections of machine learning, algorithmic fairness, social justice, and ethics, all while ensuring inclusivity and equity in the realm of technology.
In this episode, we embark on a journey through the power of technology, exploring the potential biases in tech development. Arjun shares his personal journey from Silicon Valley to academia, shedding light on the challenges faced as a transgender researcher.
Arjun guides us through the ways technology can have a positive impact, from using neo-pronouns to fostering inclusive environments for the queer community. We also venture into the complexities of democratizing AI research, the risks of AI misuse, and the limitations of the scientific method.
Our conversation wraps up by emphasizing the importance of inclusivity and equity in the global context, underlining the need for cross-globe communication in building a more inclusive, equitable technology landscape.
Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI
00:00 Preview and Celonis Ad
03:12 Promoting LGBTQ+ Inclusion in AI
15:29 Decentralized Organizations
22:29 Pitfalls of AI and Academia
30:59 AI Evolution and Challenges of Inclusion
38:26 Intersectionality and Inclusivity in AI
45:20 Celonis
Judea Pearl: Causal Reasoning, Counterfactuals, Bayesian Networks, and the Path to AGI
Judea Pearl is a professor at UCLA and a winner of the Turing Award, that’s generally recognized as the Nobel Prize of computing. He is one of the seminal figures in the field of artificial intelligence, computer science, and statistics. He has developed and championed probabilistic approaches to AI, including Bayesian Networks and profound ideas in causality in general. These ideas are important not just for AI, but to our understanding and practice of science. But in the field of AI, the idea of causality, cause and effect, to many, lies at the core of what is currently missing and what must be developed in order to build truly intelligent systems. For this reason, and many others, his work is worth returning to often.
This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts or support it on Patreon.
This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.
Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
00:00 – Introduction
03:18 – Descartes and analytic geometry
06:25 – Good way to teach math
07:10 – From math to engineering
09:14 – Does God play dice?
10:47 – Free will
11:59 – Probability
22:21 – Machine learning
23:13 – Causal Networks
27:48 – Intelligent systems that reason with causation
29:29 – Do(x) operator
36:57 – Counterfactuals
44:12 – Reasoning by Metaphor
51:15 – Machine learning and causal reasoning
53:28 – Temporal aspect of causation
56:21 – Machine learning (continued)
59:15 – Human-level artificial intelligence
1:04:08 – Consciousness
1:04:31 – Concerns about AGI
1:09:53 – Religion and robotics
1:12:07 – Daniel Pearl
1:19:09 – Advice for students
1:21:00 – Legacy
#164 — Cause & Effect
Sam Harris speaks with Judea Pearl about his work on the mathematics of causality and artificial intelligence. They discuss how science has generally failed to understand causation, different levels of causal inference, counterfactuals, the foundations of knowledge, the nature of possibility, the illusion of free will, artificial intelligence, the nature of consciousness, and other topics.
If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.
Why big companies will never get content management right, with UCLA’s Sarah T Roberts
Behind the screen: content moderation in the shadows of social media author Sarah T Roberts joins Verge editor-in-chief Nilay Patel to discuss the business and dark side of content moderation while pondering future solutions.
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Deep Learning in Optics with Aydogan Ozcan - TWiML Talk #237
Today we’re joined by Aydogan Ozcan, Professor of Electrical and Computer Engineering at UCLA, exploring his group's research into the intersection of deep learning and optics, holography and computational imaging. We specifically look at a really interesting project to create all-optical neural networks which work based on diffraction, where the printed pixels of the network are analogous to neurons. We also explore practical applications for their research and other areas of interest.