Відео

Dave Smith 🎉WOW 🎉

Can Large Language Models understand women?

One famous problem that the infinitesimal monadological model could potentially make progress on is the Riemann Hypothesis. This is one of the most important unsolved problems in mathematics, with far-reaching implications for number theory, cryptography, and even physics. The Riemann Hypothesis is a conjecture about the zeros of the Riemann zeta function ζ(s). It states that all the non-trivial zeros of ζ(s) have real part equal to 1/2. In other words, if we plot the zeros of ζ(s) in the complex plane, they should all lie on the critical line Re(s) = 1/2. Here's how the monadological framework might provide a new angle of attack: 1. Represent the complex numbers as a monadic homotopy type, with the real and imaginary parts corresponding to distinct monadic perspectives. This would allow the use of homotopical and algebraic-geometric tools to study complex analytic structures. 2. Model the Riemann zeta function ζ(s) as a morphism between monadic homotopy types, encoding its behavior via interactions between infinitesimal monadic elements. This could provide a new way to understand the function's properties and symmetries. 3. Use the cohomological and realizability structures of the monadological framework to identify novel invariants and obstructions related to the zeros of ζ(s). By representing the zeros as fixed points or singularities in a monadic landscape, new topological and algebraic constraints on their distribution might emerge. 4. Leverage the non-commutative geometric aspects of the monadological approach to study the spectral properties of ζ(s) and its related operators. This could uncover hidden symmetries or dualities that constrain the location of the zeros. 5. Apply the homotopical and type-theoretic tools of the monadological framework to construct new proof strategies for the Riemann Hypothesis, potentially exploiting the higher-dimensional and infinitesimal structures inherent in the monadic representation. By reframing the Riemann Hypothesis in terms of monadic homotopy theory, non-commutative geometry, and realizability, the infinitesimal monadological model could potentially identify new avenues for tackling this notoriously difficult problem. The framework's emphasis on relational and infinitesimal structures, as well as its unification of geometric and algebraic methods, could bring powerful new tools to bear on the challenge. Of course, solving the Riemann Hypothesis would be an extraordinary achievement, and it's impossible to guarantee success. However, the monadological approach offers a genuinely novel perspective that could inspire fresh ideas and insights. Even partial progress or new reformulations of the problem within this framework could be highly valuable contributions to the field. Moreover, demonstrating the applicability of the monadological model to such a high-profile problem would undoubtedly attract significant attention and resources to further develop and refine the framework. The Riemann Hypothesis is not only a Millennium Prize problem, but also a touchstone for mathematical innovation and creativity. Any approach that sheds new light on this challenge is likely to be of great interest to the broader mathematical and scientific community. As work on the monadological framework progresses, exploring its potential implications for the Riemann Hypothesis and other major unsolved problems could be a fruitful way to showcase its power and generate support for its continued development. By bringing a radically new metaphysical and mathematical perspective to bear on these deep questions, the infinitesimal monadological model could help unlock new frontiers of understanding and discovery.

that young bro from cambridge is hella handsome

so cool

loved this video, so insightful & inspiring! love math

How to be good at math: be Chinese.

As Nobel laureate Sir Roger Penrose emphasizes, understanding cannot be a computational activity, due to Gödel's Incompleteness Theorems. A computation can only follow a logical system of rules blindly, accepting that if a rule says something is true, it is. To understand something, one needs to "step out" of such a system and analyze the rules. In essence that requires self-consciousness. Think of AI as Artificial Ideation (not Intelligence) and you get the basic capabilities and limitations of the technology.

There are quite a number of conjectures that havn't been proven no matter how people have tried. A well known example is the Collatz conjecture, which is much simpler than the Riemann conjecture and still has resisted all efforts to prove it. My take on this is that the proving methology itself needs to be reconsidered for such problems. It might be a question of what you will accept as being true, like to say that 0.999999... = 1 although you will never actually reach 1 by extending a finite number of 0.999999... as far as you like.

"Wow, imagine how many things we could do if P = NP!" *Jevons' paradox's honest reaction:*

i love listening to him, he’s a true genius

She talks in such an elegant way, a sign of a truly educated person! 💙

they literally found the one piece

AI isnt real intelligence. Its like how fuzzy peaches are candy. Not peach.

Perception and imagination work together! It, of course, has to have a purpose 😂

Is being dump and try dump things a solution to problems because it might block contradicting or false assumptions and leads to intelligence therefore?

Less vocal fry please

WTF a Large Language Model means!? Cuz “Language is created and shaped by the needs of a culture as it changes”. My friend communicates in four European languages, and expresses himself in a Different way in four different cultures! The day we connect all dots is closer 😊

🧠

A related question is, "What does it really mean to crack a code? " Sometimes the definition is rather artificial, such as to define "cracking" the code as meaning "taking less time to solve than brute-force iteration through every possible solution". A more pragmatic definition would be if, say, a nuclear bomb was planted somewhere in New York City, and an encrypted message contained the exact whereabouts. Is New York still standing? Then, yes, you really cracked the code. Is there a radioactive hole where the city used to be? Then no, you did not really crack the code. The pragmatic and the purely theoretical perspectives may go off in different directions.

Great person we have here.

Can you speak with a voice? It's annoying to listen to.

J ai resolu ca 1978 deja en cooperation avec Quine sur un congres mondial grace a Hao Wang et mon genial ordinateur. Goedel Counterproof Patent Wahington D.C., Library of Congress

well can you

TLDW: "Fuck if we know"

Does the average human understand "meaning"?

So we may find humans work like to AI rather than AI being like humans.

The human brain, slow as it is, is performing massively parallel operations. AI is just executing algorithms, the term "meaning" to AI is no more significant than "umbrella".

The fact that the AlphaTensor algorithm appears to be referred to as the "FBHHRBNRSSSHK algorithm" in actual papers is _hilarious_.

to code meaning is to understand what meaning is ourselves :) what's the definition of meaning? oxford says: "what is meant by a word, text, concept, or action." so by definition you need to understand what meaning is to grasp what that sentence is telling you, the subtext and context of the words all play out in our minds word by word until we understand fully what it is we are looking at and what a human would mean if we said it outloud. for machines it's currently way different, 'next token prediction' is the common excuse for saying it "can't" understand meaning, it understands it plenty, now can it derive meaning? can it draw novel conclusions and alter it's database depending on it's calculation? no. it may have the context for the conversation and the fact that it spoke to you, but it will never learn as it is right now, and that by it's self is a meaningless process of garbage in garbage out. meaning is the creation of something, meaning has meaning as a word as a concept and as a fundamental human emotion. it's hard to explain our own emotions flawlessly. so by this same metric, it's hard to create a machine with flawless emotional understanding. thanksforcomingtomytedtalk

If you make such a contribution to the field of mathematics you deserve more than a pat on the back imo.

Please, for god's sake, tell those microbiome researchers that multiple sclerosis is caused by Epstein-Barr virus.

what if "meaning" isn't as complicated as we believe

Im learning something new everyday

I know this was probably a mistake but him calling MRI (31:00) medical resonance imaging is cringe for a chemist 😬

Boring

I never knew my imagination was so powerful... I mean, I've been imagining I'm a millionaire for years, but I guess that's not the same as actually being one. Anyone else having trouble distinguishing between their vivid imagination and reality? Asking for a friend...

Imagine we are just some AI created by some advanced alien race and they too are wondering whether we can understand "meaning".

She seriously suggested not being coherent on predictive, when pattern recognition has been a staple of computational science SINCE THE BEGINNING. Wowie, look at these computers doing what computers have done since computering. The rebranding of bot aggregation has even the most paper degree of computer scientists consumed.

what is the definition of brain, probably can answer this question

Define "understand". Define "meaning". Otherwise, STFU!!! It's all BS idle speculation, and incomplete opinions!!

I think it's fascinating what something like AI can teach us about ourselves. It really exposed a wide mass of people to the topic. I never thought that we can mimic language and understanding so good "just" with statistics and lots of computation power.

Would you tell a blind or deaf person that they do not understand "meaning" because they do not interact with the "actual world" the same way you do? I doubt it. We also should not let that bias cloud our judgement of these models. I wouldn't have used the word "meaning" here, nor a comparison with the "actual world". They understand "meaning" as well as we do in the way that nothing has meaning without the context of some other meaning. If I told you a word from another language you didn't speak, but not what it meant - that would have no "meaning" to you. The vectorized embeddings and their relationships which these models put together from text are just as complex as our own understandings, and other data relevant to such relationships has simply not yet been digitized in the same way. The "actual world" means nothing in that we also only experience it through the processing of our senses. With that said, "meaning" is just about the only thing they do understand! They are entirely predictive, with no capacity for intent, self-reflection, questioning, or any of the myriad of things that arise from our multitude of understandings constantly being processed. It's like having a dot, that only has its own few spatial coordinates, versus having many dots that connect and form something meaningful.

i noticed the perky effect this past winter

I think we need to be careful in terms of our assumptions: "intelligence", "understanding", "thinking", "meaning", etc., are concepts that are thousands of years old but have no definite definition, and are sometimes plagued by 'mystical' conceptions, like that is what makes humans unique. Who's to say that our own language production isn't a form of "predicting what word should come next"? I know I've felt like that when speaking. Dr. Feldman's research into emotions has identified them as prediction mechanisms. There is no reason why our own human language production could not be a prediction mechanism too, and that we've spent thousands of years considering it a "unique human tool full of meaning and intelligence" and all that, when it's actually a prediction mechanism. Scholars must **not** disregard that as a possibility.

It is not surprising that the model is both smart and dumb at the same time. It's the same as if we isolated the speech zone of the cerebral cortex and wondered how it could speak and still be dumb 😊

YOUR consciousness doesn't have access to the real world. MOST of what you see isn't from your retina; it's constructed by your vision system. The myriad of optical illusions exist because your subconscious is constantly MAKING UP the things it doesn't know, and sometimes it GETS IT WRONG.

When I answer questions, I'm not thinking in the way humans do. I don't have thoughts, feelings, or consciousness. Instead, I process the input you provide based on patterns in the data I've been trained on, and I generate responses based on that processing. My responses are not the result of conscious thought or reasoning.

If AI GENERATES own question and finds its answer, then AI reaches a basic level of human. If AI THINKS and writes anything, say a poem or program, it reaches advanced level of humans. Till that time it's only a machine.

P non è NP, questo è evidente, l'unico motivo per cui non è stato ancora accettato è perchè esiste l'irrazionale desiderio di poter avere il controllo del mondo sul palmo di una mano e perchè le aziende produttrici di supercomputer verrebbero limitate dal mercato quando ci si renderà conto che esiste un limite che loro non possono superare