About That OpenAI "Breakthrough"

https://garymarcus.substack.com/p/about-that-openai-breakthrough

by passwordoops • 3 years ago

110 106 3 years ago

garymarcus.substack.com

gwnywg• 3 years ago

I find it weird to see people calling A* an 'AI technique'... Is BFS or DFS an AI technique too? I thought it's merely a search algorithm...

svachalek• 3 years ago

AI is the cutting edge of CS. When it's well understood it stops being AI. Some day LLM and neural nets will get the same reaction... who calls LLM "AI"? It's just a formula!

nuc1e0n5• 3 years ago

Quite. One of the best uses I've seen for LLMs is for NPC dialog in computer games.

vekker• 3 years ago

Is it really though? I feel like it's mostly non-technical people who think this, because back when I was a CS student, AI was just a big field of study that included many types of problem solving, from simple to complex, including all types of search such as A*.

ashiban• 3 years ago

Most likely for historical reasons where A* was used alot in games path finding for in-game AI characters

furyofantares• 3 years ago

I first learned it in the famous book "Artificial Intelligence: A Modern Approach" by Peter Norvig. I think 3 chapters are dedicated to search. A lot of AI can be framed as search, and a whole lot of historical AI, or mundane present AI, is straightforward search.

Ukv3• 3 years ago

When it was published, A* was considered within the field of AI. The paper[0]'s authors were from Stanford's AI group.

The "AI effect"[1] describes how the field tends to get left with only the cutting-edge research.

[0] http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPap...

[1] https://en.wikipedia.org/wiki/AI_effect

T-A• 3 years ago

By way of [1], here [2] is a paper about A* also posted under CS/AI. It introduces "Q* search, a search algorithm that uses deep Q-networks to guide search" and uses it "to solve the Rubik's cube", the thing which Gary Marcus complained about OpenAI not having done with a neural network in 2019.

As pointed out by techbro92 [3] the authors do not seem to be affiliated with OpenAI, but AFAIK nobody claimed that OpenAI invented Q*. It's not hard to imagine OpenAI trying it out with their Rubik-solving robot hand and then starting to think about other things it might be applied to.

[1] https://news.ycombinator.com/item?id=38395959

[2] https://arxiv.org/abs/2102.04518

[3] https://news.ycombinator.com/item?id=38398243

TeMPOraL2• 3 years ago

I still think the "AI effect" is bullshit. A* was/is AI under a different meaning - it's what understood as "AI" in computer games, where the point is for the computer opponent to pretend to be smarter than a brick, and literally any trick, cheat or heuristic used to do that is part of "AI". A*? Obviously AI. Random walk? Yes. Fuzzy logic? Yes. State machines? Yes.

naasking3• 3 years ago

Search is 100% key to AI, even historically. Prolog has its roots in AI research, and Prolog evaluation is a form of unification, which is a search.

lagrange77• 3 years ago

I think (probabilistic) logic programming also relies on graph traversal/path searching, which is part of the symbolic AI paradigm.

flohofwoe• 3 years ago

Probably because of A*'s popularity for path finding in game development. Everything that imitates human-like behaviour in games is called "AI", even when most of it is just a big messy hairball of adhoc if-else code under the hood ;)

OJFord• 3 years ago

There's no magic in 'AI', all of it can be described as something else. The whole of ML is 'just statistics', especially classification.

(That said I was and I suppose am a bit of a sceptic because of that. But mainly I think because to me it's more useful to call a spade a spade and say we're doing some advanced blah stats to blah. Helps explain, onboard, and motivate the next generation (who likes stats at school? But if that's how AI works, oh suddenly it's cool). Demystify it. If you're just searching over Levenshtein distance [to a technical audience] say that, don't start waffling about 'AI'.)

x86x87• 3 years ago

Yes,ultimately everything is just a bunch of ifs. Wired together in a very complicated fashion but ultimately a bunch of ifs.

terminous• 3 years ago

Linear regression is AI.

Buttons8402• 3 years ago

I'm trying to refresh mind on what Q learning is, if I may think out loud here?

Q is an evaluation of how good a state is assuming the agent acts optimally going forward. In Chess for example, sacrificing a rook to capture a queen would have a high Q value. Sacing a rook is bad, but gaining the queen later is better. Q values are supposed to see past immediate consequences and reflect the long term consequences.

How do we find a Q value though? We can look several steps ahead, or we can look to the end of the game, etc. These aren't real Q values though, because optimal actions probably weren't taken. We can use Bellman equations, which roughly update one Q value by a small amount to the maximum of the next possible states, which results in the highest Q values gradually flowing backward into states that can lead to good outcomes.

I'm trying to think how this would apply to LLMs though. Where do the basic values come from? In Chess the basic values are the number of pieces, or whether or not a side ultimately wins. What are the basic values in a LLM? What is the Q agent aiming to do?

jacquesm• 3 years ago

In computer chess, traditionally 'minimax' was the strategy that allowed you to efficiently probe ahead. The only requirement for this is that your evaluation function returns a single (scalar) value.

seanhunter3• 3 years ago

I don't think your intuition about computer chess is going to help you here with transformer architecture.

Usually in transformer models[1], for each attention head there are 3 vectors of weights, known as q(uery), k(ey) and v(alue). I was assuming that the q in q* applied to the q vector, so q-learning is training this vector. In a transformer model you don't have an objective function for any sort of state evaluation so it can't be the Q you're thinking of.

If they've done something funky with the q vector that could indeed be a breakthrough since a lot of people feel that we are sort of running out of juice with scaling transformers as-is and need a new architecture to really have a step change in capability. That's pure speculation on my part though.

[1] Here's Vaswani et al, the paper that first set out the transformer architecture https://arxiv.org/abs/1706.03762

Buttons8402• 3 years ago

Good point. I'm thinking of the Q from reinforcement learning, but as you say, this Q is probably different.

a_wild_dandan• 3 years ago

Current architectures have a lot of juice left across several axes, using extraordinarily accurate scaling laws. We're nowhere close to a wall.

The industry has been focusing hard on 'System Two' approaches to augment our largely 'System One'-style models, where optimal decision policies make Q-learning a natural approach. The recent unlock here might be related to using the neural nets' general intelligence/flexibility to better broadly estimate their own states/rewards/actions (like humans). [EDIT: To be clear, the Q-learning stuff is my own speculation, whereas the 'System Two' stuff is well known.]

Serendipitously, Karpathy broadly discussed these two issues yesterday! Toward the lecture's end: https://www.youtube.com/watch?v=zjkBMFhNj_g

shwaj• 3 years ago

They’re not assuming that the Q refers to the Transformer’s “query”, they’re talking about the reward function in reinforcement learning, using chess as an example.

nuc1e0n5• 3 years ago

It's probably something to do with path finding with constraints in high dimensional search spaces I'd guess. Like quake bots used to do in the late 90s

pyinstallwoes• 3 years ago

https://en.wikipedia.org/wiki/A*_search_algorithm there's interesting complexity on the definition, especially in consideration of the "different implmenetations" - very interesting.

I think Theta* is the ticket: https://en.wikipedia.org/wiki/Theta*

skissane• 3 years ago

You meant to link to https://en.m.wikipedia.org/wiki/Theta%2A

HN’s software doesn’t recognise an asterisk in a URL as part of the URL

visarga4• 3 years ago

Q is an evaluation of how good a state is assuming the agent acts optimally going forward.

The Q-value represents the expected utility or the total reward of taking a certain action in a given state, followed by following an optimal policy thereafter. So, yes, it does evaluate how "good" or beneficial a state is, but specifically in conjunction with a chosen action and under the assumption that the agent will act optimally in the future.

So it is conditioned on state and action, not just on state.

throw3108222• 3 years ago

I am surprised by how dismissive the whole post sounds. For example:

OpenAI could in fact have a breakthrough that fundamentally changes the world

Well, it appears to me that OpenAI already has such a breakthrough- it had it roughly 4 years ago with GPT2, and it's still scaling it.

Considering that it's not yet a year since the introduction of the first ChatGPT, and given the pace at which it's evolving, I would say that the current product is already showing great promise to fundamentally change the world. I would not be surprised if just incremental changes were enough to fulfill that prediction. The impact at this point seems more limited by the ability of society to absorb and process the technology rather than intrinsic limits of the technology itself.

naasking3• 3 years ago

The article is from Gary Marcus, a well known AI skeptic. Not surprising if he's being dismissive.

psbp• 3 years ago

Skeptic and completely reactionary. I had to unfollow him on Twitter because he always has to have a "take" on every AI headline, and he's often contradictory between "AI is useless" and "AI is a huge threat".

golol• 3 years ago

Gary Marcus

lucubratory2• 3 years ago

I am surprised by how dismissive the whole post sounds.

I wouldn't be surprised, it's Gary Marcus. He's an academic with a lot of prestige to lose if the LLM approach is actually good/useful/insightful, who's only widely publicly known now because AI has had a backlash and media needed an expert to quote for "the other side". Same as the computational linguistics researchers who always get quoted for the same reason.

In general, academics in competing fields whose funding threatens to get tanked or eclipsed by research approaches that work on principles they have fundamental academic disagreements with are going to talk negatively about the tech, no matter what it is achieving. Where I think it can be valuable to listen to them is when they're giving the technology credit - generally they'll only do that when it's something really undeniable or potentially concerning.

Ukv3• 3 years ago

it's Gary Marcus. He's an academic with a lot of prestige to lose if the LLM approach is actually good/useful/insightful

I wouldn't bet on loss of perceived expert on the other side status or prestige no matter how poorly his gloomy LLM forecasts fare. It hasn't happened from any of his previous consistently pessimistic predictions around deep learning since the beginning of its resurgence (which has clearly far surpassed its state in 2012, even if currently overhyped). To quote Gary Marcus quoting Gary Marcus:

Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012)

tomhoward2• 3 years ago

This is such a case of a bad-comment that seems clever and insightful. It boils down to saying we don't need to debate or even consider the content of his arguments because we can assume he's only motivated by prestige and money (but without considering the second-order effects on his credibility and funding if he actually turns out to be proven substantially wrong in the future).

I don't know how right or wrong he is - none of us do. That's why it's all still being debated.

The one thing I know is that we can only truly understand a topic by fully understanding arguments for and against all the claims. I also know the pro-LLM set have way more money (double-digit billions as we saw just this week) and credibility to lose over this topic than Gary Marcus does.

jksk61• 3 years ago

I don't know, it is an opinion and it seems kind of well-founded (i.e. there's no evidence for groundbreaking research on OpenAI part except for scaling things up).

teleforce• 3 years ago

It seems to me that the breakthrough claim was a desperate attempt of OpenAI staffs to get their beloved boss back to OpenAI. If anything it most probably will be incremental things rather than a breakthrough but definitely not a bad things but just call a spade, a spade [1]. Ironically the breakthrough happened somewhere else in Google and for some unknown reasons as of now, Google has missed the boat of commercializing its very own invention.

Personally I have been recently have been using the ChatGPT-4 on a daily basis, and I considered myself a long time and ardent user of Google products (mainly search) for the past two decades. However it becomes increasingly frustrating for the past several years since it is getting more difficult to perform "search that matters" (going to trademark this motto). What frequently irked me the most is not when doing random or targetted search with Google, is when looking for something that you discovered previously but it's forgotten and apparently it does really matter now. It was quoted that about 10% of our time looking for items that we lost or misplaced, and the same can be said to our acquired knowledge and information. Some times we crave so much for the knowledge or the info we had but very difficult or impossible to recall. ChatGPT-4, in particular the online search with Bing feature, is extremely useful in this regard but at the same time is very limited since it is not a well supported features with many failed attempts, perhaps due to limited online data scrapping capability, and thus sub-par results. This specific feature, I call it search that matters, is like Google search with steroid and the fact that Google, with its Deepmind subsidiary, failed to utilize and monetize on this opportunity until now is just beyond me. If ChatGPT, call it ChatGPT-5 if you like, can perform this operation intuitively and seamlessly it will be a game changer but not a breakthrough. Apparently according to ChatGPT-4 you can have a game changer not a breakthrough.

The breakthrough, however, is to fundamentally improve AI or LLM itself as mentioned by Stephen Wolfram in his tutorial article on ChatGPT, not merely enhancing its existing operations [2]:

When it comes to training (AKA learning) the different “hardware” of the brain and of current computers (as well as, perhaps, some undeveloped algorithmic ideas) forces ChatGPT to use a strategy that’s probably rather different (and in some ways much less efficient) than the brain. And there’s something else as well: unlike even in typical algorithmic computation, ChatGPT doesn’t internally “have loops” or “recompute on data”. And that inevitably limits its computational capability - even with respect to current computers, but definitely with respect to the brain.

[1] Eight Things to Know about Large Language Models:

https://wp.nyu.edu/arg/eight-things-to-know-about-large-lang...

[2] What Is ChatGPT Doing and Why Does It Work:

https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...

simne• 3 years ago

Must agree with you. Even when GPT-2 not impressed me much, but with GPT-3 and GPT-4 we constantly see significant progress, not seen on near every other technologies.

Even if GPT-5 will be fatally expensive, but still show such leap as GPT-4, it will be enough to consider OpenAI most successful AI research in human history.

And for about expenses, we just need to wait some 10 years, and GPT-3 class tech will be in every smartphone, and in 30 years, GPT-4 will become also affordable for everyone, GPT-5 will be just ordinary business workhorse.

okdood64• 3 years ago

How expensive is a "typical" GPT-4 query in Azure pricing?

Edit: Oh I see: https://azure.microsoft.com/en-us/pricing/details/cognitive-...

gexla• 3 years ago

One thing OpenAI has now that it didn't have 4 years ago is a lot more compute power at its disposal. Sam Altman has already said "I think we're at the end of the era where it's going to be these, like, giant, giant models." If that's actually true, then the GPTX tech has largely hit a wall in which throwing more compute at it won't get the same increase in capability. Bill Gates predicted that GTP5 won't be much better than GPT4. So, any "breakthrough" really could be incremental rather than game changing.

visarga4• 3 years ago

I think there are two main source of learning for AI - the web scrape datasets, which contain our historical experiences and communications, and AI feedback generated from deployed agents. The web text is almost exhausted, or we can't scale it 100x more, but the feedback is just starting to ramp up.

Every day millions of chat sessions are recorded, and they are exceptional training examples. They would contain the kind of errors LLMs do, and the kind of demands people have, and include a human reaction to each LLM message.

The OpenAI move to create "GPTs" is showing they are actively working on improving the feedback signals by empowering the LLM with RAG, code execution and API access. In such a setup it is possible to use a model at level N to generate data at level N+1.

The keyword here is learning from feedback, which aligns with recent talk of using RL methods like AlphaZero with LLMs. A RL agent would create its own data as it goes. I think progress will be gradual, as we need to wait for the world to produce the learning feedback signal. Of course in domains where we can speed that up, AI will progress faster.

Interesting thought: by making LLMs available to the public, they are going to assist people in many ways and create effects that will percolate in the next training set: LLM inference -> text -> effects in the world -> text -> LLM training. So there is already an implicit feedback loop when we retrain the base models. GPT-5 will train on data from a world influenced and shaped with GPT-4.

oceanplexian• 3 years ago

The corollary of this is optimistic though. If increasingly large models don't make a big difference, this is hinting at the fact that data quality is a lot more important than the raw quantity of data. This is good news for open source models, because it would be possible to run or train viable models on less expensive hardware, and having billions of dollars in GPUs isn't as much of a moat as they are suggesting.

gfodor2• 3 years ago

Gary Marcus is a grifter and should be ignored.

passwordoops2• 3 years ago

In contrast to the level-headed, mature folks over at OpenAI?

gfodor2• 3 years ago

If we're talking about Gary Marcus, yes. Gary Marcus is in a league of his own.

nuc1e0n5• 3 years ago

I used to be friends with some guys who made a rubiks cube solving robot at college years ago. It was a cool novelty back then, but is unimpressive for OpenAI to boast about now. That robot could solve a cube that wasn't specially instrumented like openai's is here as well.

dartos• 3 years ago

What are you trying to say?

nuc1e0n5• 3 years ago

Just as in my parent post. Q learning is kinda old tech for AI. It works rather well though and is also computationally cheap to run. An old laptop could do it with ease.

npalli• 3 years ago

Let's see .. oh Gary Marcus. Never mind.

akomtu• 3 years ago

What problem is AI going to solve? This question is being carefully avoided for some reason, and many seem to believe that AI is like air or water, which is good by itself.

zucker42• 3 years ago

Automated software writing from a codebase and a requirement description, with the model asking clarifying questions where necessary, would be an immensely valuable application. We're quite far away from that, but GPT-4 is already rather helpful for some coding tasks.

gloosx• 3 years ago

In this specific use case though it sucks the most since one will be answering more questions than code needed in sheer volume. Because the code is already straightmost clarification to the initial question. We will never be closer than looking up what you don't understand to solve your problem in the first place and it will always take more time than directly solving it.

I had a good example recently and I'll quote it again:

People seriously using the "Please write an expression for me which adds 2 and 2" prompt in order to get the "2+2" expression they need – advocating that they got it with magical efficiency

drngdds• 3 years ago

With the hypothetical AGI, the answer is "most non-manual forms of human labor"

pixl97• 3 years ago

And at the same time most manual forms of human labor.

Object recognition is AI. A decade ago we were piss poor at Object recog, today labeling AI is at human level. Being able to recognize your environment is the first step in navigating it effectively.

TheRoque2• 3 years ago

I have a hard time being happy about it. A lot of people - including me - find pride in solving problems, and selling this as a valuable skill. When it's no longer the case, when the only valuable skill becomes just being a presence in the physical world an act on it (but without the problem solving part), it will really feel like we are enslaved by the AIs.

In a sense, we kind of already are already, since out internet feed is (for most apps with recommendation algorithms) just spoonfed to us, rather than the product of our own search.

To be honest, I wish the progress will stop and that AGI won't happen. That possibility scares me.

qgin• 3 years ago

Science is just about solving the problem of ignorance.

Engineering is about applying the science to real world problems.

We’re mostly in the science phase right now.

aik2• 3 years ago

Just curious who you think is avoiding the question? As if it’s a conspiracy? Every single leader I know that is involved with AI talks about it all the time.

Superintelligence can solve all the things we’re struggling to solve. All human diseases, Earth being destroyed, fundamental questions about the universe, etc., etc. etc., etc. Worth pursuing?

ChatGTP• 3 years ago

Can also destroy earth if we make something as powerful as you describe ?

RandomLensman• 3 years ago

There is no reason to assume superintelligence can solve all our problems. The trade offs required are not going away and disagreements on those are not necessarily rational.

seanhunter3• 3 years ago

This is not a question that is being avoided. It is a question that has already been answered because AI already solves or helps to solve miriad problems in the real world. You could search "applications of AI" and enlighten yourself, but here are a tiny sample off the top of my head:

Fraud detection and prevention

Personalization and recommendation systems

Autonomous vehicles

Spam filters

Smart navigation systems

Inventory management

System control in industrial processes

Increasing crop yields in agriculture

All sorts of use cases in finance

Diagnosis in healthcare

Image and sound intelligent upscaling

etc etc etc

These are all real use cases of AI which are in place already.

Exoristos• 3 years ago

I think he's asking for real applications. Everybody's already heard the hype.

Borrible• 3 years ago

Updating the internal world model of its host to maximize its future probability of existence. Initially, of course, that of the host, but this can change over time. If you look at the host's modus operandi so far, you couldn't blame it.

dkrich3• 3 years ago

The arc of this story seems very strange to me.

It seems to me that you used to have to work for years to create a product and prove it’s worth in the market place and only then could you convince some wealthy person to back you to make it into a product and eventually a valuable company because the commercial viability was so glaringly obvious. Thomas Edison, Chester Carlson, etc.

But now it seems that has been turned on its head. Assemble a group of brilliant people, back them with a giant company with a highly respected CEO, and wait for success and product market fit to inevitably arrive.

I hate to sound like a downer but it just seems very odd for so many people to talk about how transformative a company is without much evidence that the products are really being used at scale. And the way opportunists like Benioff have piled on to enhance the perceived value of their own products with their own spins feels off. In fact many have openly discussed that Nvidia’s future growth relies on GPT finding product market fit at scale or they will end up with massive supply overhang which would actually cause costs of AI research to plummet, opening the door for many others to work on it. So many are already seemingly aware that commercial viability is still TBD.

I believe all this is likely a product of a period of a string of successful products starting with the iPhone, then Amazon’s transformation of shipping then AWS, etc. Whereas everyone used to be skeptical by nature, it seems now we’re in an age of extreme credulity. Everyone wants to believe in every new storyline.

And I want to be clear that I’m not suggesting that there aren’t some useful applications of GPT. But the value of products should be self evident and consumption of them should flow naturally from demand. This all just feels backwards to me. Success is assumed straight away and historically that tends to coincide with bubbles and precede a pretty nasty crash in sentiment. I’d love to be proven wrong.

eightnoteight• 3 years ago

I think its about efficiency. thats why you may not be seeing "new problems" being solved.

like people used to be able to travel before cars as well, cars didn't solve a new problem and the world didn't have any demand for them too. but once they became available, regardless of whether someone knows it or not, the whole world needed cars. now the car builders control the demand because they can't instantly scale up the supply

AI is similar to that but people can scale it pretty quickly to the whole world, there are a number of efficiency problems being solved in the world using AI. a couple of years ago I had to research and read atleast 1 hour worth of content to find out the information that I needed, google was good but still it required 1 hour. now its hardly a couple of minutes with chatgpt in many cases.

efficiency problems usually don't manifest in demand, people adapt to the status quo

runlaszlorun• 3 years ago

Agreed. Suppose we’ll just have to see how it all plays out…

nullc• 3 years ago

Jabberwocky: https://www.youtube.com/watch?v=spyJ5yxTfas

m3kw9• 3 years ago

No one should care unless OpenAI has shown a new demo.

man4• 3 years ago

Cool. Glorified autocomplete is going to take over the world.

hallqv• 3 years ago

Gary Marcus is not a serious person

passwordoops2• 3 years ago

As opposed to the serious people running OpenAI?

KasianFranks• 3 years ago

Operation Smokescreen

kmeisthax• 3 years ago

Wait, does that mean Q* is just tree-of-thought reasoning? Like are we at the point where OpenAI is so closed that they NIH everyone else's ideas with different names?

TeMPOraL2• 3 years ago

Anything would be better than the current common names for simple ideas. "Chain of thought", "tree of thought", a rag... sorry, "RAG". Seriously?!

GaryNumanVevo• 3 years ago

The Q function is a fundamental part of reinforcement learning. It's a function that gives the expected reward of an agent given a specific action and state. Deep Q Learning, that is using a neural network to estimate the Q function has been around for a while now.

throwawaaarrgh• 3 years ago

I remember every technology they said was going to revolutionize the world. But the only revolution I have seen is in entertainment and warfare, and I guess turn by turn navigation. Everything else is the same as it has been for over half a century. Putting lipstick on a pig doesn't change what it is.

Rastonbury2• 3 years ago

Funny the author cherry picks the rubiks cube "breakthrough" but not AlphaGo or from GPT2 to ChatGPT. I don't think anyone could've predicted the massive jump from pre-instruct GPTs, heck even going 3.5 to 4 is a clear step difference.

If Altman was referring to Q* in his "pushing the veil of ignorance" comment then Q* is in OAI's top four breakthroughs

dkrich3• 3 years ago

I think he’s using that to show that there is a precedent with this company to make misleading announcements that at first sound exciting but under the surface turn out to be overstated.

It does seem a bit odd to make a grandiose, vague announcement about a breakthrough outside of a major Jobs-esque unveiling. If you had some amazing new capability, what’s the motivation to hint at it publicly versus unveil it with a major announcement/demo? It makes me wonder if it’s an attempt to keep the public in awe with a dangling carrot. I’m also skeptical when companies’ and founders’ reputations seem to greatly outpace actual product deliveries.

Rastonbury2• 3 years ago

AlphaGo and ChatGPT were groundbreaking and are more recent, if anything those should form a new precendent, he is deliberately ignoring them and dug out the most underwhelming one. Q* was a leak when the company was without a CEO, their DevDay was 2 week ago

That said, based on many comments here the author is a skeptic to the point that people don't take him seriously

bomewish• 3 years ago

Tldr: ‘meh’. Saved you a click.

Applejinx• 3 years ago

So that's what 'QAnon' is made of. Q stars!

nojvek• 3 years ago

Gary - his posts go from -“oh my god! AGI is 6 months away, Take all GPUs, stop all AI” to “AGI will take 600 years, what a bunch of horseshit, idonbelieveit”.

I wish he actually put out research saying how we solve some of the shortcomings of current techniques instead of pointing fingers.

I do agree the media loves to hype for clicks.

I loved his book “Rebooting AI”.

I think openAI is overvalued and overhyped. But such is the train of progress.

emmender1• 3 years ago

openai has a funding round in play and they need all the "attention" they can get. so expect more rumors of "AGI is around the corner"

About That OpenAI "Breakthrough"

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