Terry Tao is one of the world’s leading mathematicians and winner of many awards including the Fields Medal. He is Professor of Mathematics at the University of California, Los Angeles (UCLA). Following his talk, Terry is in conversation with fellow mathematician Po-Shen Loh.
The Oxford Mathematics Public Lectures are generously supported by XTX Markets.
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Oxford Math Professor's teaching shock tactic equivalent to the Zen
Master's meditation interrupt paddle.., "You just look at it, and you notice..", Students are suddenly awake for dawning realization. (And the horror of "I know nothing" at the Centre of Time Duration Timing Actuality)
The story of Cadet Navigators who went back in time-timing at an English Village set at a nodal historic event due to plague removing the human population, is possibly a SiFi fantasy derived from wave-packaging frequency integration of heterodyne nodal-vibrational emitter-receiver log-antilog properties of superposition-quantization logic, but it is applicable to the qualitative Gold-Silver Rules of solution to this q-a Actuality.
Ie the answer to your question is easier than you think and simpler to find.., approximately what Einstein suggested. But Actuality is Analog Calculus, so device modeling organically, ..complicates Digital Model Duplicates.
The flipside of The Halting Problem, is the Observable Eternity-now Interval Actuality condensation modulation superposition-quantization here-now-forever of logarithmic relative-timing proportioning.
Numberness->Numerical Mathematics is the hyper awareness of beginning-ending of arbitarily delineated bio-logical shell-horizons in the perspectives that are composed by infinitesimal =>gradually changing differentiates.., ie quantization by re-evolutionary phase-locked coherence-cohesion sync-duration resonance quantization cause-effect objectives. (As can be derived from the superposition-quantization logic of prime-cofactor frequency occurrence in the "Distribution of Primes " Calculus.
Saying that LLMs are just auto complete is kinda dumb. Not wrong, just as dumb as saying that a derivative of a function is just inclination of the tangent line on that point. There are a lot of natural principles associated with that concept, that same goes for language. You can even say that humans do the same thing with language, no human is going to develop a full language by itself out in the wild.
00:11 L'intelligenza artificiale sta cambiando il mondo um
02:00 AI in science and mathematics enables faster and more powerful travel
05:33 Terence Tao demonstrates the challenges and potential of GPT-4 in solving mathematical problems
07:18 AI produce risultati convincenti ma non affidabili
10:51 AI holds potential to revolutionize problem-solving in science and mathematics
12:40 Using AI in science and mathematics for drug modeling and material discovery
16:04 AI accelerates weather predictions significantly
17:49 AI can enhance mathematical predictions and proofs.
20:57 Formal verification of mathematical theorems can take decades and modern technology is speeding up the process.
22:40 AI accelerates project formalization in mathematics
25:40 AI facilita la modifica efficiente delle dimostrazioni matematiche.
27:11 AI can effortlessly solve tasks humans find difficult
30:06 The potential of formal languages for massive collaboration
31:40 The need for theoretical guarantees in automating mathematics and science
34:34 Breaking down complex problems into small, digestible steps is crucial for understanding fundamentals in AI.
36:01 The promise of formalization projects in decoupling high-level conceptual ability from low-level technical skills.
38:38 Educators should be open to change and collaboration for a future-proof design.
40:05 AI enables scientists to collaborate across disciplines
42:52 La matematica รจ sempre piรน importante in scienze e discipline umanistiche
44:28 Compression sensing technique revolutionized various fields
47:27 L'importanza dell'innovazione guidata da sfide specifiche nell'AI
48:53 AI plays a crucial role in simplifying complex geometry problems.
51:45 AI revolutionized perceptions on human activities
My childhood hero is getting old๐ข
Professor Tao is a visionary. He sees what AI in combination with proof assistants are capable of, it can revolutionize mathematics.
I should watch this again – thanks to Tao & all!!
Doing wrong is also part of doing right. Perspective from student
Thank you for the video.
10 years ago we called them neural networks and Ai (for weather prediction) is just tha
The recent hype doesnt have any new potential .
We need faster computers ,not AI run on traditional chips.
So it is quantum computers that would help, not AI
Sorry dr Tao..
The challenge is hallucination : The solution is be an expert in Prompt Engineering to get reliable outcome, ie needs to be domain specific expert.
You know he's the real deal when his brain thinks so fast that you can hear his mouth struggling to keep up.
Can we have someone transcribe this entire speech and then have a better orator deliver it?
My autistic gene tingles every time you say โumโ
Need translate for Bahasa, Please ๐
Amazing to be alive at the same time as this man, especially considering his humility. https://terrytao.wordpress.com/career-advice/does-one-have-to-be-a-genius-to-do-maths/
Most irritating voice pattern. He needs to work on it.
Overview of AI Technology
Nature of AI: The speaker describes AI as a "guessing machine" that processes inputs to produce outputs, such as text or images. The mathematical operations involved are relatively straightforward, involving encoding inputs as numbers, applying weights, and combining them through multiple layers.
Comparison to Existing Technology: AI is likened to a jet engine in a world accustomed to land-based travel, suggesting that while it can significantly accelerate processes, it requires new frameworks and safety protocols to be effectively integrated.
AI's Potential and Limitations
Creativity vs. Reliability: AI tools, especially large language models, are noted for their creativity and ability to understand natural language inputs. However, this comes at the cost of predictability and reliability, as AI can provide different answers to the same query and may not always be correct.
Examples of AI Performance: The transcript mentions the performance of GPT-4 on math olympiad problems, where it occasionally provided correct solutions but often failed, illustrating the inconsistency in AI's problem-solving capabilities.
Applications and Risks
Scientific Applications: AI is already being used in fields like drug design and material science to reduce the number of candidates for expensive tests, thereby accelerating research processes.
Modeling and Simulation: AI can significantly speed up complex simulations, such as climate modeling, by learning from existing data and providing faster predictions, though challenges remain in data assimilation and reliability.
Risk Management: The speaker emphasizes the importance of safety and verification when using AI, especially in areas with potential harm, such as medical or financial decision-making.
AI in Mathematics
Potential Transformations: AI has the potential to transform mathematics by providing tools for verifying proofs and improving mathematical reasoning. This could have broad implications for other fields that rely on mathematical components.
Integration with Proof Assistants: AI can be combined with proof assistants, which are computer languages designed to verify the correctness of proofs, to enhance reliability and accuracy in mathematical and engineering applications.
I wonder if this talk would have changed if it was given after the Google news of passing silver olympiads with AI
BIG Terence Tao, his humor sense and humble way to talk … Most lovable mathematician
I love that the court system still works for Chinese Nazis hahaha
Do you still now Niklas Seenfaat?
I donโt have chinese friends in Switzerlandโฆ only Germans and Russian Israelis maybe as an example
is the project open for 40 y. o. informatic guys that would love to learn about Math?
Is the Trinity college project still there? I want to start it again with a view friendsโฆ ๐
I'm honestly afraid that we're going to run out of problems which are solvable by humans. I really hope not. The implications of this might be that we can only ever keep up if we're also "wired in"…so to speak.๐๐๐
I recently became aware of the "ARC challenge for AI" and I felt it captured/formalized what I experience as the weakness of current LLM/AI reasoning.
It seems similar to doing "machine mathematics", but in a toy problem scenario.
Timestamps
0:02 – Introduction to AI's transformative potential in science and mathematics
6:34 – Challenges in AI reliability and step-by-step problem solving
13:10 – AI's role in automating scientific validation and material synthesis
19:47 – Proof assistants and their importance in mathematics and engineering
26:24 – AI's impact on problem-solving and human intelligence
33:00 – Importance of formalization in scientific papers and changing metrics
39:34 – AI enabling interdisciplinary collaboration and specialized roles in science
46:11 – Development of standardized AI models and need for energy-efficient solutions
Sadly this was before ai got silver in the math Olympiad
He is not an expert AI so cringe
So basically, AI is bad, but they're still going to fire everybody and replace them with shoddy models anyway.
mathematics at its core, like other scientific disciplines, is about conceptual modelling of reality- known or unknown. what makes mathematical modelling unique is the conceptual language for modelling based on formalisation and unitisation of parameters and functional relationships. between aspects of reality and existence of a mathematical model is the top mathematician who have the creative apriori glimpse of possibility and proof. AI in my opinion currently is dealing with aposteriori possibilities by permutation, n mundane validation by known proofs.
Lower your entry fees!!
This is so wrong. You should be ashamed of yourselves. I assume anyone associated with Oxford is a petty liar begging for attention now?
The 1% success rate on math problems and the failure to do simple math I think is likely that there was an explicite solution for the problem it solved, in the training data.
I can't stand this mumbling, bumbling stuttering fool. He should go to speech therapy!
He talks like XQC
I hope this comment is eventually seen by Terence Tao.
I feel that mathematicians do math not to advance mathematical knowledge, but for the beauty that lies underneath the numbers. AI mathematicians, seemingly incapable of emotions and perceiving beauty of mathematics, will just crunch numbers and give proofs. Mathematicians will ultimately rely on them, and that might seemingly take the fun of doing math.
I don't want to criticize AI, but this is one possibility. Doing math for it's beauty is the main reason mathematicians love math.
Well… the auto-complete thing. Umm… I mean, there certainly is mapping of letters after letters forming words after words forming clauses after clauses forming sentences after sentences forming paragraphs after paragraphs forming chapters after chapters forming documents after documents but there's also context which filters the generation but if you have enough data and neural network connections, there could be also ideas after ideas and thoughts after thoughts and mental models after mental models, at least in theory. But is there will, because humans have a will and a goal. Does AI have internal reward system with digital satisfaction hormones?
As an undergrad student it's nice to see that even Terry Tao throws his slides together the night before without practicing his presentation.
There is much more to say about machine learning in science.
It remains to be seen whether AI can compare to our existing proof search techniques (notibly, logic/relational models). There is no reason to believe they ever will.
Taylorism for the mathematical sciences. Scary stuff.
Muito boa palestra do grande matemรกtico e medalhista Fields professor Terence Tao.
…. Am eating popcorn as Terence pulls the rug out from underneath Openai's market valuation…. ๐
Thank you to the hosts very much for providing the space, for this presentation, Po-Shen Loh, did a great job as the moderator/interviewer, and thank you Terence Tao, for sharing your time and work sir, peace
I give this channel a lot of grief over their unending flow of non-math lectures, but this is not one of those. Thank you for this excellent upload, Oxford Mathematics and Dr. Tao.
I LOVE hearing a top mathematician talk about creativity. These thinkers are artists.
Does anyone know when this lecture was actually given?