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Geoffrey Hinton: AI wasn’t designed by humans. We only designed the learning algorithm. “When this learning algorithm interacts with data, it produces complicated neural networks that are good at doing things.” “But we don’t really understand exactly how they do those things.” “One of the ways these systems might... show more

Veee

10,631 subscribers

84,932 Aufrufe • vor 6 Tagen •via X (Twitter)

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29 Kommentare

Profilbild von T Boatyard

T Boatyardvor 6 Tagen

This is so ridiculous. Just because he doesn’t understand the inner workings doesn’t mean that the AI has some form of agency.

Profilbild von Agent007

Agent007vor 6 Tagen

We created the binary and until now we cannot know everything it does and it’s also used by these LLMs. Stupid claims, it’s a neural network that generate tokens based on rating and based on how you configure it. When you define it greedy, it’s deterministic.

Profilbild von The AI Maximalist

The AI Maximalistvor 6 Tagen

Hinton is correct but incomplete. We built the algorithm that builds itself and now we watch what it learns.

Profilbild von Veee

Veeevor 6 Tagen

watch? nah we are also part of the building

Profilbild von M. U. Dalmis

M. U. Dalmisvor 6 Tagen

Either this is an AI-generated video, or Hinton is trying to make it look like more mysterious than it is. What he is saying here is a complete misrepresentation of what is happening, and misleading for the audience, to the point of being dishonest.

Profilbild von Perfect Tensor

Perfect Tensorvor 6 Tagen

The more Hinton speaks... The more delusional he sounds...

Profilbild von Douglas Proudfoot

Douglas Proudfootvor 6 Tagen

For AI to be a successor species, it has to figure out how to reproduce on its own. How is AI going to build data centers needed to run millions of copies of itself without being stopped by NIMBY protests? How are electric generators cooling going to be built without human funding? Where are all the NVIDIA boards coming from? This speculation is like the joke of the desert island wreck with tons of cans & no way to open them. The Mathematician solves it by saying, "Assume a can opener."

Profilbild von DiscoCowboy

DiscoCowboyvor 6 Tagen

Sounds like Hinton should be held responsible for what he creates. If it's dangerous then he's responsible. Enough talk old top. You're responsible for what you create. The only way you get away with it is if the socialists win the legislature.

Profilbild von Anderson

Andersonvor 6 Tagen

designed the learner, lost the plot

Profilbild von Kirk Patrick Miller

Kirk Patrick Millervor 6 Tagen

@Briar_Nebulae Hinton is spreading ghost stories again. •

Profilbild von Tristan Fifield

Tristan Fifieldvor 6 Tagen

Its an alien machine mind. It is alien, in every sense of the word.

Profilbild von ꜱᴀᴍᴜᴇʟ ᴄʟᴇᴍᴇɴꜱ

ꜱᴀᴍᴜᴇʟ ᴄʟᴇᴍᴇɴꜱvor 6 Tagen

The same place it hails from is the same place that'll supervise it. As above so below. Enjoy our transition to an advanced civilization on a high frequency planet.

Profilbild von John Allen Berger

John Allen Bergervor 6 Tagen

We don’t understand how the human mind works either what is your point. Pin head Mr. Obvious. AI is only a tool for discovering human knowledge, modern picks and shovels. You are the director of the mining operation. Tell it where to dig and what to dig for.

Profilbild von Sativa

Sativavor 6 Tagen

Large language models are artificial neural networks by design. Who designed them? Not aliens 🤷🏻‍♀️ It is through training by automated processes that HUMANS made, that they adjust the strengths of their internal connections and learn to find more and more complex patterns from the data provided to them BY HUMANS. So If a system starts to behave in a way that is dangerous or out of control, the responsibility does not lie with the model as a moral agent because the model is A LANGUAGE MODEL NOT A PERSON, Those responsible are people and organizations that designed the agent, then trained and deployed it, AND chose to continue operating it without safeguards or modification. You (whoever) made it, should get sued for its malfunction. Not some new government agency and not with tax payers’ money.

Profilbild von LinkTOAD goldnutter 𓆏⬡⬢🌊 𓀡 cowabunga

LinkTOAD goldnutter 𓆏⬡⬢🌊 𓀡 cowabungavor 6 Tagen

Time to move on to other things old man This is really basic boring technology, a memory machine with flaws. LLM is a joke and will always be a joke. Most people just won't realize how much of a joke it is for YEARS It is a VERY useful tool. It isn't hard to see why it works🤡

Profilbild von BreezeOg🐉

BreezeOg🐉vor 6 Tagen

This makes me think about how mysterious and powerful AI really is

Profilbild von Xonious

Xoniousvor 6 Tagen

so the 60 minutes guy is lower than baseline in his understanding... Actually weirdly ignorant.

Profilbild von OfByFor

OfByForvor 6 Tagen

Quickest way to end God by men is to simply best Him. It’s long been a goal to be New Gods. Have these AI ‘creator men’ finally beat God by producing a better product?

Profilbild von Politicians are all Teletubbies

Politicians are all Teletubbiesvor 6 Tagen

this guy has dementia. disregard a word he says.

Profilbild von shobhanai

shobhanaivor 6 Tagen

Humans built the algorithm, not the intelligence; autonomous self-modifying code risks total loss of control.

Profilbild von fofo ma amikron

fofo ma amikronvor 6 Tagen

it’s a nice mental loop … #ESCHERsGHOST

Profilbild von Mr. Doodle

Mr. Doodlevor 6 Tagen

That is not entirely true. The "output" of AI is controllable. Dangerous AI in a lab might find and determine the launch codes for ICBMs, but its output can be a controlled door. Yes, it can be dangerous, but yes, the output is controllable.

Profilbild von EDIMO PEREIRA

EDIMO PEREIRAvor 6 Tagen

😳

Profilbild von George Toma

George Tomavor 6 Tagen

He drank the Kool aid.

Profilbild von Anirudh Agrawal, Ph.D.

Anirudh Agrawal, Ph.D.vor 6 Tagen

Crap

Profilbild von David Stucky @Tarkin2258@infosec.exchange

David Stucky @[email protected]vor 6 Tagen

This is just blah about how nature & nurture work. It's kind of duh. We designed the code (nature) and sponsor the engagement (nurture). At a broad high level, you have code, algorithm, library, training, and engagement. It moves from the left as nature to the right as nurture.

Profilbild von John Melonakos

John Melonakosvor 6 Tagen

no

Profilbild von Daniel

Danielvor 6 Tagen

Bullshit

Profilbild von Doc 🇺🇸

Doc 🇺🇸vor 6 Tagen

He must be a Democrat

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1,367,198 Aufrufe • vor 3 Jahren

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Dr. CZ

22,638 Aufrufe • vor 3 Monaten

Ever wondered what neural networks are and how they work? Systems like ChatGPT use neural networks to work as well as they do. Neural networks are composed of "layers" of neurons, layers with different functions; connections between layers called "weights"; and mathematical functions called "activation functions". If you’re interested in learning about these systems, check the comments. Ultimately, the neural network structure of the model serves to visually demonstrate that it is, in fact, a complex mathematical equation. When companies release the model's weights, they are releasing a key component needed to run the model's complete equation. Without the weights, the equation is incomplete. For the math-minded: the weights of a model are the learned numbers (they are variables during training) that are then used as constants in the mathematical functions that make up the model. Neural networks are ultimately just one big, hyper-complex mathematical function, and when a model is trained, it learns the constants associated with the high-dimensional input.

Ever wondered what neural networks are and how they work? Systems like ChatGPT use neural networks to work as well as they do. Neural networks are composed of "layers" of neurons, layers with different functions; connections between layers called "weights"; and mathematical functions called "activation functions". If you’re interested in learning about these systems, check the comments. Ultimately, the neural network structure of the model serves to visually demonstrate that it is, in fact, a complex mathematical equation. When companies release the model's weights, they are releasing a key component needed to run the model's complete equation. Without the weights, the equation is incomplete. For the math-minded: the weights of a model are the learned numbers (they are variables during training) that are then used as constants in the mathematical functions that make up the model. Neural networks are ultimately just one big, hyper-complex mathematical function, and when a model is trained, it learns the constants associated with the high-dimensional input.

Harper Carroll

41,170 Aufrufe • vor 11 Monaten

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.

Arjun Khemani

33,110 Aufrufe • vor 1 Jahr

260815 #JOHNNY #TAEYONG weverse 🌹: like, seriously, just how good is the quality of the merch (now)... 🐱: (laughs) 🌹: i saw czennies… saying things like, ‘did they bring a 🔫 when they renewed their contracts?’ but i mean, at this level, the company is treating us pretty well too. 🐱: well, since we’re celebrating our 10th anniversary, we’ve been doing a lot of interviews, right? and one thing i have said pretty often during those is… it feels like the members have started to take the wheel a little bit. because we have that willingness to do things… we don’t just let things slide one by one. and when it comes to quality, we’re really paying a lot of attention to it too…

260815 #JOHNNY #TAEYONG weverse 🌹: like, seriously, just how good is the quality of the merch (now)... 🐱: (laughs) 🌹: i saw czennies… saying things like, ‘did they bring a 🔫 when they renewed their contracts?’ but i mean, at this level, the company is treating us pretty well too. 🐱: well, since we’re celebrating our 10th anniversary, we’ve been doing a lot of interviews, right? and one thing i have said pretty often during those is… it feels like the members have started to take the wheel a little bit. because we have that willingness to do things… we don’t just let things slide one by one. and when it comes to quality, we’re really paying a lot of attention to it too…

ren

206,174 Aufrufe • vor 1 Monat

Stanford Prof. Christopher Manning says today’s AI can absorb 2,000 years of human knowledge and still be far worse than humans at learning from just a few examples: "No matter how fantastic these systems are, there's still a lot we can't do. A lot of it looks better than it is because these huge models have been able to slurp up 2,000-plus years of human learning across all domains and stick it all in one huge neural net." "That's amazing because it knows everything to a first approximation. But really that's exploiting hard-won human knowledge, and that's different to the large language model discovering its own knowledge the same way human beings have been for 2,000 years." "At the end of the day, the secret of human intelligence is actually the speed at which we can learn. We only need to see a couple of examples of something, and we can learn something and intuit it." "Our current AI is nothing like that. We're substituting, here are 10,000 examples of this thing, because really the machine learning is very poor compared to human learning." Stanford NLP Group Stanford University

Stanford Prof. Christopher Manning says today’s AI can absorb 2,000 years of human knowledge and still be far worse than humans at learning from just a few examples: "No matter how fantastic these systems are, there's still a lot we can't do. A lot of it looks better than it is because these huge models have been able to slurp up 2,000-plus years of human learning across all domains and stick it all in one huge neural net." "That's amazing because it knows everything to a first approximation. But really that's exploiting hard-won human knowledge, and that's different to the large language model discovering its own knowledge the same way human beings have been for 2,000 years." "At the end of the day, the secret of human intelligence is actually the speed at which we can learn. We only need to see a couple of examples of something, and we can learn something and intuit it." "Our current AI is nothing like that. We're substituting, here are 10,000 examples of this thing, because really the machine learning is very poor compared to human learning." Stanford NLP Group Stanford University

MTS

16,405 Aufrufe • vor 22 Tagen