It's amazing how many researchers underestimate the importance of UX and design.
Personally, seeing non-technical people using prompts for the first time -- and getting results that make sense -- is so incredible. Their eyes light up, they are surprised, and they want to keep playing with it. Amazing!
A sizeable part of the population just got access to an incredibly complex AI model and can play with it. What a collective experience.
Clarke's third law: "Any sufficiently advanced technology is indistinguishable from magic."
Most people don't understand that ChatGPT has no idea what they're talking about. It lacks it's own thought and heuristic patterns, and only gives you the most-likely response to your prompt. People don't know that though, so they think the Mechanical Turk is actually playing chess.
I mostly agree with the headline here. ChatGPT is hardly any more innovative than a Markov chain.
I don't think he's underestimating the UX/design piece, I think it's just outside of the scope of what he's talking about. It seems pretty clear to me that his statement is talking about the underlying AI technology (which makes sense given his background).
And in any case, I wouldn't call anything about the UX or design here innovative - it's obviously a huge improvement in terms of usability, but a chat UI is a pretty well-established thing.
Researchers point of view is based on their area of research and that's fair and expected.
Yann LeCun compares ChatGPT in the context of the related research.
Imagine a ChatGPT equivalent that memorizes many questions and does a brute force strategy for an answer. It may "look" magic, but there's nothing magic about it. We all accepted that this is the case with Blue Gene - https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
What's different here?
Productization and usability are difference concerns here and Yann LeCun is not a usability researcher. Granted, that doesn't mean usability/accessibility doesn't impact research outcomes.
It's quite fascinating how information retrieval and search engines have evolved..
From trying to teach people how to google via "I am feeling lucky", to using language models for ranking, to building LLMs to better adapt to user queries and move beyond keywords, to having arguably useful chatbots that can synthesize responses.
I am curious to see what the future holds. Maybe an AI that can anticipate our queries?
> It's amazing how many researchers underestimate the importance of UX and design.
Yes. Also the fact that ChatGPT's UX and design leaves much to be desired. They could add/improve the product in so many obvious ways. I wonder if they either 1.) don't care, or 2.) have plans to, but are holding off until they take on a subscription fee.
>> using prompts for the first time -- and getting results that make sense -- is so incredible.
Years ago it was possible to insert prompts into Google and get results that made sense, results that were meaningfully tied to the information you requested. The young people today who missed that time think it magic.
chatGPT is innovative in the way the iPhone was innovative. That, of course can be market winning, but it's dangerous, especially for businesses and investors, to get caught up in hype that there is some new "AI" out there that can do things we previously couldn't
This is very much people seeing the Apple Lisa(and/or Mac) for the first time. It had all been done better in the labs at Xerox over a decade earlier. No one got to use it though.
Show me the AI chat system from a decade ago that could hallucinate a git repo.
Edit: let me be more careful:
Show me the AI chat system from a decade ago that wasn't specifically designed to hallucinate git repos, but would hallucinate a git repo in response a detailed natural language prompt. It should have been good enough in its output to cause the user to write more prompts in an attempt to gauge whether the AI system is querying the internet in realtime to generate its response.
Google is the new Xerox - tons of cool tech that nobody gets to see besides white papers and articles. And the only real product that makes money is the search engine that may get disrupted very soon by newcomers using tech based on Google's own research.
He's not wrong. The key ingredient in ChatGPT is not brilliance but capital. It takes a lot of money not just to do the raw training but to get all the data together and process everything.
There's no break through insights that make ChatGPT work, just a lot of consolidated wealth.
The hacker part of me finds AI less and less interesting for this reason. We're seeing what the limits of pouring resources into the problem are. The results are cool, but I think we'll very soon see progress bound since we're using most of the data we can and it doesn't look like adding even more parameters to these models might not yield that much of result.
But this shift in AI means it's increasing something programmers consume rather than produce.
As far as interesting AI goes, I found the series of posts on stuff people were doing with Prolog that showed up during the last month much more interesting.
When language models run out of more trillions of words to train, there is one way ahead - we need to generate more. But wait, you might say, garbage in garbage out. It won't work.
Normally it wouldn't, but we add an extra ingredient here. We get a validation signal. This is problem specific, but for code it would mean to integrate the LM with a compiler and runtime so it can iterate until it solves the task, step by step. For other tasks it could mean hooking the AI to simulators, games and robots to solve tasks. It is also possible to use LLMs as simulators of text.
Basically doing Reinforcement Learning with a Language Model and not just for human preferences, but for problem solving on a grand scale. Collect data from problem solving, train on it, and iterate. It costs just electricity, but LLMs can make their own data. Anthropic's Constitutional AI which is RLAIF - reinforcement learning from AI feedback is proof it can be done.
There are a lot of things that require an amount of processing power that the average person doesn't yet possess. This doesn't mean that they aren't interesting.
50 years ago you would have said "these computer things aren't interesting, only big institutions and corporations have them".
I find it the opposite. There were a lot of projects I didn't have the time to work on and got bored to fast learning. So, now I can tell chatgpt to build it for me. and I only have to debug the 1 or 2 things it can't do. I built a blogging platform, a voice based chatbot using gpt3, something that takes blog article and trims out all the garbage html so only the basic html is left. created a python api, and called it from shared hosting using php. something that trims the times out of youtube captions so I can post it into chatgpt to give me an outline. I just tell it what I want the function to do and it does it.
It's helped me way up my own personal productivity and creativity.
I think what’s really being emphasized here is that ChatGPT is an first generation product without much of a novel technology moat compared to what others with capital can deliver.
Microsoft will soak this work up as a pillar of their own AI assistance tech, but the other big tech firms (and probably some startups) are positioned to try to leapfrog what we see here, now that the market has been demonstrated.
In the language of tech history, ChatGPT is the Lycos or Altavista to some not-yet-launched Google.
Sure, the ideas could be old (in ml research timeframes) but it works and they shipped it. It’s most people’s first exposure to a lot of innovative ideas that make up LLM at scale.
I agree with his assertion that the only reason other people haven’t seen this out of Meta or Google is that they “have a lot to lose” from putting out bots “that make stuff up” - that’s the root of Google
seemingly losing the narrative war I think we’re seeing here. Not sure how to get them to work through that fear.
>Google seemingly losing the narrative war I think we’re seeing here.
Google doesn't care about the narrative war. They care about getting sued by the EU.
They can put out a similar model whenever they want. The just don't want to, because the thing about AI research (vs., say, social media) is there's no first mover advantage. The best model wins and there's no network effect to bolster incumbents.
> they “have a lot to lose” from putting out bots “that make stuff up”
2023 should be the year of AI validation. For starters, we could extract facts from all the training examples (trillions) and organise them in a knowledge base. Where there is inconsistency or variation, the model should learn the distribution, so later it can confidently say a fact is not in its training data or is controversial. Note that I didn't say the model should find the truth, just the distribution of "facts".
We can add source reputation signals to this KB to improve its alignment with truth. And if it is controversies we want to know, we can look at news headlines, they usually reference facts being debated. So we can know a fact is contested and by who.
Another interesting approach - probing the model for "truth" by identifying a direction in the latent space that aligns with it. The logic being, even when the model is deceptive, it has an interest to know it is not telling the truth to keep the narrative consistent. So we need to just identify this signal. This is also part of work for AI alignment.
I hope this year we will see large investments in validation, because the unverified outputs of a generative model are worthless and they know it. At the very least hook the model up with a calculator and a way to query the web.
"In terms of underlying techniques, ChatGPT is not particularly innovative," said Yann LeCun, Meta's chief AI scientist, in a small gathering of press and executives on Zoom last week.
"It's nothing revolutionary, although that's the way it's perceived in the public," said LeCun. "It's just that, you know, it's well put together, it's nicely done."
It seems he s trying to say something that is technically correct, but exactly at the wrong time. It s going to be a lot of backlash if you go against the hype wave that we are riding. Better to explain in a more nuanced way how chatGPT managed to become so popular despite being an incremental improvement of fundamental findings of the past few years.
People love it because it understands language, every time and without fail. So what if it spits out lies, this thing makes people happy, which is the value at play here
At least it shows Yann LeCun and Gary Marcus can agree on some things. Thankfully someone has gone full circle and had ChatGPT construct a rap battle between those minds:
I mean, ChatGPT isn't even innovative for OpenAI's team. In terms of raw language capabilities it's not much more effective as GPT-3, which passed the HN Turing test almost a year before. Some of the credit goes to the fact that Dall-E 2 got everyone looking at OpenAI (and wrt Dall-E I'm personally not aware of any comparable work done anywhere else), a lot of the credit goes to the fact that OpenAI was willing to put it out there, and the work they did to make ChatGPT much safer really doesn't get acknowledged enough (on release, ChatGPT would refuse to produce potentially hateful/harmful text, and eventually a lot of the workarounds got patched as well).
It's probably not fair to characterize the discourse around ChatGPT as treating it as a revolutionary bit of tech, lay people are taking note because 1) people they know are actually using it and 2) the interface really is intuitive - literally anyone knows how to use a chatbot.
AI researchers tend to have blinders when new techniques are released that don't fit their previous experience. For instance, perhaps the specific architecture of ChatGPT isn't that innovative (although I think it is), but training such a large model on so much data is innovative. Clearly this is going to become the norm going forward.
Just as a personal anecdote. I worked on a ML research team at a FAANG in the early 2010s, just as NNs were becoming popular. Everyone on the ML research team had come to prominence before NNs, so they all specialized in different areas of ML. Nearly every researchers there treated NNs like some fad that would pass and thought they wouldn't be much better than the current techniques. How wrong they were... Of course, they're all working on NNs now.
LeCun confuses the idea invention with that of innovation. Revolutions don't happen because suddenly there's a new idea, but because of one that catches on.
Yann's tweets on this are pretty funny. I would flippantly summarize them as: "Don't get me wrong, the openai people are great. All I'm doing is educating the media, who have the mistaken belief that chatGPT is good"
That seems to be a lot of news articles these days.
I'm not particularly excited by ChatGPT, but the mercenary in me is impressed at the investment from MS it generated. From a business perspective, it accomplished "and profit!". Whether it pays dividends or reaches a richer/more-sophisticated milestone is to be seen.
Personally, I think it'll muddy and flood the "bullshit" & truthiness layer pervading news, social media, auto-generated Youtube content and blogs, our digitally mediated life, etc. It's essentially the next level "bot", and those aren't held in high regard, as effective as they are. Ultimately, they're noise.
I'll go a step further: ChatGPT feels more like VR & crypto than it does.. say, cloud.
[+] [-] cl42|3 years ago|reply
Personally, seeing non-technical people using prompts for the first time -- and getting results that make sense -- is so incredible. Their eyes light up, they are surprised, and they want to keep playing with it. Amazing!
A sizeable part of the population just got access to an incredibly complex AI model and can play with it. What a collective experience.
[+] [-] smoldesu|3 years ago|reply
Most people don't understand that ChatGPT has no idea what they're talking about. It lacks it's own thought and heuristic patterns, and only gives you the most-likely response to your prompt. People don't know that though, so they think the Mechanical Turk is actually playing chess.
I mostly agree with the headline here. ChatGPT is hardly any more innovative than a Markov chain.
[+] [-] idopmstuff|3 years ago|reply
And in any case, I wouldn't call anything about the UX or design here innovative - it's obviously a huge improvement in terms of usability, but a chat UI is a pretty well-established thing.
[+] [-] random3|3 years ago|reply
Yann LeCun compares ChatGPT in the context of the related research. Imagine a ChatGPT equivalent that memorizes many questions and does a brute force strategy for an answer. It may "look" magic, but there's nothing magic about it. We all accepted that this is the case with Blue Gene - https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
What's different here?
Productization and usability are difference concerns here and Yann LeCun is not a usability researcher. Granted, that doesn't mean usability/accessibility doesn't impact research outcomes.
[+] [-] PartiallyTyped|3 years ago|reply
From trying to teach people how to google via "I am feeling lucky", to using language models for ranking, to building LLMs to better adapt to user queries and move beyond keywords, to having arguably useful chatbots that can synthesize responses.
I am curious to see what the future holds. Maybe an AI that can anticipate our queries?
[+] [-] ryanSrich|3 years ago|reply
Yes. Also the fact that ChatGPT's UX and design leaves much to be desired. They could add/improve the product in so many obvious ways. I wonder if they either 1.) don't care, or 2.) have plans to, but are holding off until they take on a subscription fee.
[+] [-] scifibestfi|3 years ago|reply
[+] [-] sandworm101|3 years ago|reply
Years ago it was possible to insert prompts into Google and get results that made sense, results that were meaningfully tied to the information you requested. The young people today who missed that time think it magic.
[+] [-] version_five|3 years ago|reply
[+] [-] Turing_Machine|3 years ago|reply
Sometimes "making the first X that doesn't suck" is a lot more important than "making the first X".
[+] [-] zwieback|3 years ago|reply
[+] [-] soperj|3 years ago|reply
[+] [-] jancsika|3 years ago|reply
Edit: let me be more careful:
Show me the AI chat system from a decade ago that wasn't specifically designed to hallucinate git repos, but would hallucinate a git repo in response a detailed natural language prompt. It should have been good enough in its output to cause the user to write more prompts in an attempt to gauge whether the AI system is querying the internet in realtime to generate its response.
[+] [-] nightski|3 years ago|reply
[+] [-] msikora|3 years ago|reply
[+] [-] babypuncher|3 years ago|reply
[+] [-] time_to_smile|3 years ago|reply
There's no break through insights that make ChatGPT work, just a lot of consolidated wealth.
The hacker part of me finds AI less and less interesting for this reason. We're seeing what the limits of pouring resources into the problem are. The results are cool, but I think we'll very soon see progress bound since we're using most of the data we can and it doesn't look like adding even more parameters to these models might not yield that much of result.
But this shift in AI means it's increasing something programmers consume rather than produce.
As far as interesting AI goes, I found the series of posts on stuff people were doing with Prolog that showed up during the last month much more interesting.
[+] [-] visarga|3 years ago|reply
Normally it wouldn't, but we add an extra ingredient here. We get a validation signal. This is problem specific, but for code it would mean to integrate the LM with a compiler and runtime so it can iterate until it solves the task, step by step. For other tasks it could mean hooking the AI to simulators, games and robots to solve tasks. It is also possible to use LLMs as simulators of text.
Basically doing Reinforcement Learning with a Language Model and not just for human preferences, but for problem solving on a grand scale. Collect data from problem solving, train on it, and iterate. It costs just electricity, but LLMs can make their own data. Anthropic's Constitutional AI which is RLAIF - reinforcement learning from AI feedback is proof it can be done.
[+] [-] woah|3 years ago|reply
50 years ago you would have said "these computer things aren't interesting, only big institutions and corporations have them".
[+] [-] sharemywin|3 years ago|reply
It's helped me way up my own personal productivity and creativity.
[+] [-] swatcoder|3 years ago|reply
Microsoft will soak this work up as a pillar of their own AI assistance tech, but the other big tech firms (and probably some startups) are positioned to try to leapfrog what we see here, now that the market has been demonstrated.
In the language of tech history, ChatGPT is the Lycos or Altavista to some not-yet-launched Google.
[+] [-] mattnewton|3 years ago|reply
I agree with his assertion that the only reason other people haven’t seen this out of Meta or Google is that they “have a lot to lose” from putting out bots “that make stuff up” - that’s the root of Google seemingly losing the narrative war I think we’re seeing here. Not sure how to get them to work through that fear.
[+] [-] password11|3 years ago|reply
Google doesn't care about the narrative war. They care about getting sued by the EU.
They can put out a similar model whenever they want. The just don't want to, because the thing about AI research (vs., say, social media) is there's no first mover advantage. The best model wins and there's no network effect to bolster incumbents.
[+] [-] visarga|3 years ago|reply
2023 should be the year of AI validation. For starters, we could extract facts from all the training examples (trillions) and organise them in a knowledge base. Where there is inconsistency or variation, the model should learn the distribution, so later it can confidently say a fact is not in its training data or is controversial. Note that I didn't say the model should find the truth, just the distribution of "facts".
We can add source reputation signals to this KB to improve its alignment with truth. And if it is controversies we want to know, we can look at news headlines, they usually reference facts being debated. So we can know a fact is contested and by who.
Another interesting approach - probing the model for "truth" by identifying a direction in the latent space that aligns with it. The logic being, even when the model is deceptive, it has an interest to know it is not telling the truth to keep the narrative consistent. So we need to just identify this signal. This is also part of work for AI alignment.
I hope this year we will see large investments in validation, because the unverified outputs of a generative model are worthless and they know it. At the very least hook the model up with a calculator and a way to query the web.
[+] [-] password11|3 years ago|reply
"In terms of underlying techniques, ChatGPT is not particularly innovative," said Yann LeCun, Meta's chief AI scientist, in a small gathering of press and executives on Zoom last week.
"It's nothing revolutionary, although that's the way it's perceived in the public," said LeCun. "It's just that, you know, it's well put together, it's nicely done."
[+] [-] november84|3 years ago|reply
It's not free from error but what it's able to put together is pretty great...
[+] [-] seydor|3 years ago|reply
People love it because it understands language, every time and without fail. So what if it spits out lies, this thing makes people happy, which is the value at play here
[+] [-] visarga|3 years ago|reply
[+] [-] unknown|3 years ago|reply
[deleted]
[+] [-] KrazyButTrue|3 years ago|reply
[+] [-] robga|3 years ago|reply
https://mobile.twitter.com/k_saifullaah/status/1602073411077...
[+] [-] lofatdairy|3 years ago|reply
It's probably not fair to characterize the discourse around ChatGPT as treating it as a revolutionary bit of tech, lay people are taking note because 1) people they know are actually using it and 2) the interface really is intuitive - literally anyone knows how to use a chatbot.
[+] [-] lostmsu|3 years ago|reply
No, its ability to follow instructions is much better than GPT-3.
[+] [-] sharemywin|3 years ago|reply
[+] [-] rcme|3 years ago|reply
Just as a personal anecdote. I worked on a ML research team at a FAANG in the early 2010s, just as NNs were becoming popular. Everyone on the ML research team had come to prominence before NNs, so they all specialized in different areas of ML. Nearly every researchers there treated NNs like some fad that would pass and thought they wouldn't be much better than the current techniques. How wrong they were... Of course, they're all working on NNs now.
[+] [-] drewcoo|3 years ago|reply
https://www.wired.com/insights/2015/01/innovation-vs-inventi...
I think he also confuses his jealousy with insight.
[+] [-] mshake2|3 years ago|reply
[+] [-] SillyUsername|3 years ago|reply
"First they ignore you, then they laugh at you, then they fight you, then you win"
[+] [-] uptownfunk|3 years ago|reply
[+] [-] drcode|3 years ago|reply
https://twitter.com/ylecun/status/1617921903934726144?t=2wZZ...
[+] [-] lostmsu|3 years ago|reply
[+] [-] rockemsockem|3 years ago|reply
[+] [-] browningstreet|3 years ago|reply
I'm not particularly excited by ChatGPT, but the mercenary in me is impressed at the investment from MS it generated. From a business perspective, it accomplished "and profit!". Whether it pays dividends or reaches a richer/more-sophisticated milestone is to be seen.
Personally, I think it'll muddy and flood the "bullshit" & truthiness layer pervading news, social media, auto-generated Youtube content and blogs, our digitally mediated life, etc. It's essentially the next level "bot", and those aren't held in high regard, as effective as they are. Ultimately, they're noise.
I'll go a step further: ChatGPT feels more like VR & crypto than it does.. say, cloud.
[+] [-] __seka__|3 years ago|reply
"Not particularly innovative" is not really of concern. Besides OpenAI most likely received the media attention they wanted.
Looking forward to the products Meta is going to release soon.
[+] [-] unknown|3 years ago|reply
[deleted]
[+] [-] unknown|3 years ago|reply
[deleted]