What the Heck Is Going On At OpenAI?

[u/Sine_Fine_Belli]

https://www.hollywoodreporter.com/business/business-news/sam-altman-openai-1236023979/

[u/[deleted]]

FYI this is relevant to neoliberalism because this may lead to the Butlerian Jihad, mentioned in Dune (both Dune and neoliberalism are about worms)

[u/Nipples-miniac → u/[deleted]]

Butlerian Jihad for the win boys

[u/Cobaltate]

Maybe I'm just a dunce, but the claim that AGI is a couple years away seems...specious. Like it's going to be the AI industry's "fusion power is only 25 years away" for generations.

[u/MolybdenumIsMoney → u/Cobaltate]

There is no solid definition of AGI. If you look back at some older sources where people tried to define AGI, we already hit it years ago (e.g. competent in several different fields, if not necessarily expert). By other stronger definitions (e.g. better than every human at everything) we are way way off.

It's different from fusion in that energy-positive fusion is a well-defined goal that we have only made very limited progress toward. AI, meanwhile, has advanced rapidly and continuously- it's just that the goal of AGI is ill-defined.

[u/IsGoIdMoney → u/MolybdenumIsMoney]

I agree, but I also think the layman understanding is basically sentience, and without that you'll probably have a difficult time getting the average person excited.

[u/aclart → u/IsGoIdMoney]

I couldn't care less if the average person is excited

[u/IsGoIdMoney → u/aclart]

Sure. But a lot of AGI confusion is based on layman understanding.

[u/InterstitialLove → u/MolybdenumIsMoney]

Dude, there are at least three distinct definitions of "energy-positive fusion" and we achieved one of them a few years ago

[u/MolybdenumIsMoney → u/InterstitialLove]

There is only one relevant definition- actually putting energy on the grid- and the other definitions only obfuscate.

[u/InterstitialLove → u/MolybdenumIsMoney]

Says you?

I mean I agree, but when people say fusion is "X years away" you don't know which definition they mean until you ask. So the ambiguity is still relevant, even if you think the ambiguity is dumb

I happen to think there's only one relevant definition of AGI, but other people disagree so I gotta deal with that

[u/FuckFashMods → u/MolybdenumIsMoney]

Isnt that different than your definition you just gave? lol

[u/BaudrillardsMirror → u/InterstitialLove]

The first “energy positive” where it doesn’t even make enough energy to power the laser causing the reaction lol.

[u/InterstitialLove → u/BaudrillardsMirror]

To be clear, I agree it's silly, I'm just saying we also passed the Turing Test at least a decade ago (some say even earlier), and that was equally silly for the same reasons

[u/Stanley--Nickels → u/Cobaltate]

I think it has been that for the AI industry, over roughly the same time period.

I think what’s different here is even 10 years ago we thought things like beating a human at Go or passing the Turing test were almost as far away as AGI, regardless of whether you thought it was 25 years or 250 years.

[u/FocusReasonable944 → u/Stanley--Nickels]

Basically the easy problems and hard problems with AI have proven to be basically reversed. Modeling language turned out to be a really easy problem to solve!

[u/Atupis → u/FocusReasonable944]

Yup this, but eg driving safely and walking in the woods have been very hard problems.

[u/aclart → u/Atupis]

There's a reason why the average 5 year old is able to speak, but is unable to drive safely 

[u/Stanley--Nickels → u/aclart]

On the other hand, there are lots of people out there driving with much worse language skills than GPT, and seemingly about as much intelligence.

[u/Magikarp-Army → u/Atupis]

It at least seems very much possible that we will have these things within our lifetimes. Transformers have shown to scale very well with data and compute without overfitting. Those two things that you're talking about generally lack both data and compute as video cameras and fat multicore processors being ubiquitous in cars is a recent phenomena. It seems like things will scale. However, it will take time, and consumers are fairly impatient. For them, these things are sci-fi concepts and their timelines are more influenced by fiction than actual technological advancement. We are well behind sci-fi movie timelines. Even as someone who trained attention-based models prior to chatgpt, I was very skeptical that we would have self driving cars before 2040. I think there's a decent chance we'll have them before 2035 now. To someone that works in the field that's a relatively short timeline compared to what we were at about 12 years ago (top-5 error rate of 15.3% on classifying single object images with only a thousand classes).

[u/antihero-itsme → u/FocusReasonable944]

How many r's in butlerianjihad

[u/groovygrasshoppa → u/Cobaltate]

The fusion energy comparison is so apt.

[u/AllAmericanBreakfast → u/Cobaltate]

For an alternative point of view, consider:

• The pace of development (what was AI capable of 1, 2, and 3 years ago?)
• The extent to which manufacturable resources are the bottleneck to further development (energy, data, chips, etc) and the extent to which supply is elastic in these areas
• That unlike in energy, AI advancements can potentially directly boost the productivity of AI research

It's possible that we're approaching an asymptote in terms of what the LLM paradigm is capable of, but that seems just as speculative as saying we're far from AGI

[u/aclart → u/AllAmericanBreakfast]

It not as speculative, cause we are running out of data to trainthe models 

[u/AllAmericanBreakfast → u/aclart]

Data can be manufactured.

This is already a big discussion. You can hire people. You can create specialized AI models to produce it. Remember that LLMs work by token prediction, so the same fundamental information presented in a new way can be beneficial.

There are enormous amounts of data locked in data silos (ie manufacturing machines) that companies haven’t seen as worthwhile making available to AI training processes until now. That’s a huge part of Palantir’s business model for example. See for example:

https://sarahconstantin.substack.com/p/the-great-data-integration-schlep

Processes can be redesigned to collect more data. New data sources (say Meta’s AI glasses) can be created to gather data in new ways.

In other words, we’re running out of data the way we’re running out of Lithium. Or Ozempic.

[u/aclart → u/AllAmericanBreakfast]

That doesn't seem like the data we need for a general AI, maybe for a production manager bot, but not for general AI.

And using data created by AI to train an AI just seems like a way of entrenching bias. 

[u/AllAmericanBreakfast → u/aclart]

It's reasonable that it would seem that way! The idea that these will be fundamental barriers to near-term AGI development is about as speculative as the idea that they won't be.

[u/aclart → u/AllAmericanBreakfast]

Well, you know how it is with AI, garbage in garbage out

[u/Magikarp-Army → u/aclart]

And using data created by AI to train an AI just seems like a way of entrenching bias. 

I want my models to have bias tbh. There are definitely values that I want to entrench. Though really the best feature of synthetic data is training models in tasks that there is little data for. Generating a bunch of synthetic video, sound and images with inputs is probably a better way of training robots than strapping go-pros on people as they wash their dishes.

[u/TheGeneGeena → u/aclart]

That's a concern. That's why there are still a whole bunch of humans in the process.

[u/_Un_Known__ → u/Cobaltate]

AGI is ill defined but is very clear we are closer to it than ever before

Traditionally, and by that I mean the 2010's, the estimation for when AGI would come about averaged around 2065. With the paper on transformers, and the subsequent release of GPT-3.5 alongside AI image, music, etc generation, it seems clear the field is accelerating.

We now have a lot of well respected pioneers in the industry seeing AGI (here I'll define it as an artificial intelligence that is agentic and equivalent to human experts) as soon as the early 2030's. Thats a massive jump in the timeframe.

Unlike fusion, which requires that sustainable reaction in order to be fusion, AGI as another pointed out is loose in definition.

Personally I'm optimistic it'll come about after a decade or so if we see continued increases in investment in the area, alongside performance improvements. The sooner the better ofc

[u/SpookyHonky → u/_Un_Known__]

and equivalent to human experts

Equivalent in the ability to recite information? Probably nearly there. A scientific, economic, sociological, etc. expert is a lot more than just their ability to recite information, though. Until AI is writing research papers and expanding the boundaries of human knowledge, all on its own, its not even comparable to a human expert.

Chat GPT will continue to get better at reading, interpreting, and applying pattern recognotion to large quantities of data, but AFAIK true expertise is well beyond its scope.

[u/ElvenMartyr → u/SpookyHonky]

Have you played with o1-preview? I won't speak to expertise in all fields, but it seems very clear to me that a stronger model doing those same things, let's call it o3, would be able to author research papers in mathematics.

[u/ale_93113 → u/SpookyHonky]

That it's not AGI, as in an independent agent that can learn and work on its own? We all agree

The disagreement is on, given the rapid progress, how far it is

[u/MartinsRedditAccount → u/_Un_Known__]

alongside performance improvements

This is an interesting point, some of the most mind-blowing AI-related things I've seen in the past year were related to performance (in the compute requirements sense).

First was the real-time SDXL Lightning demo from fal.ai, where they had a demo website with just a text box which would generate an image instantly as you were typing each letter. The second one was Groq (supposedly had the name before X's Grok), which generates text really quickly and, like the SDXL Lightning demo, had a demo that was completely open, i.e. you can just open the website and start using it without an account. Since then, the fal.ai demo has shut down and Groq usually demands a sign-in, but the fact that fairly good generative AI stuff has gotten cheap enough that, at least for a tech demo, it's viable to make it accessible just like that, is really impressive.

I also messed around a bit with the recently released Llama 3.2 3B, which runs great locally. It doesn't hold a candle to bigger models, but the progress on smaller models is really cool to see. The ability for something running on your PC to "understand" arbitrary natural language is mind-blowing.

[u/Volsunga → u/Cobaltate]

Here's the thing with AGI: we're not making AIs then upgrading them until they're conscious. We're building pieces of a human-like brain and will eventually connect them together together make a human-like brain that we presume would be conscious like we are.

LLMs are basically a your brain's language center. Diffusion models are the visual cortex.

These two pieces are complete and though there is certainly room for improvement, they would certainly function as part of an AGI.

But there are a lot of pieces of a human-like brain that we aren't as far on. AI in robotics (as in literal walking machines) is still years from reaching a commercially viable level.

There are a couple pieces where we don't really know where to begin. There are theories about how to train AI to perform social reasoning or to follow algorithmic logic like a regular computer, but this research is in its infancy.

But consciousness is an ill defined thing and an AGI might not need all the pieces to be functionally conscious. Crazy sci-fi scenarios where machines start killing everyone are extraordinarily unlikely. AI won't make the paperclip machine.

[u/xmBQWugdxjaA → u/Cobaltate]

At least with fusion power there's a plausible path to get there and self-maintaining ignition was achieved a few years ago.

We don't even know what AGI is.

[u/Emperor_Z → u/Cobaltate]

Am I wrong in thinking that a sufficiently powerful LLM essentially is an AGI, albeit an inefficient one with limited means of I/O? In predicting language and the accurate results that it's trained to produce, it has to pick up the logical principles, patterns, and concepts that enable it to approach problems that are unlike those it's been trained on.

[u/marsman1224 → u/Emperor_Z]

"sufficiently powerful" is doing a ton of heavy lifting there. ultimately I think there's no reason to believe that a language predictive model is the lynchpin of natural intelligenc

[u/vqx2 → u/Emperor_Z]

I think you may be technically right, but its probably incredibly hard to reach "sufficiently powerful LLM" to the point that it is an AGI.

I imagine that we will achieve AGI first in other ways or in combination with LLMs and something else, not just LLM alone (if we ever do reach AGI that is).

[u/lampshadish2 → u/Emperor_Z]

It doesn’t pick up on those things.  It doesn’t reason.  It is just very very good at predicting what word to append to a list of words in a way that is useful.

[u/InterstitialLove → u/lampshadish2]

This is "theory of evolution means we aren't sure about it" levels of bullshit

When we say an LLM "predicts" the next word, we mean that in a technical sense. Laypeople think it means the usual sense of prediction, then they get deeply confused and act like they know things that they don't

In information theory, it's well known that prediction and compression and knowledge are literally identical. They are different framings of the same concept. The LLM is literally reasoning about tokens. Talking about "appending words to a list" is as illuminating as saying that humans are "just a bunch of atoms bouncing around." Yeah, technically accurate, but if you think that reducing something to its constituent parts proves that it can't do impressive things then you've completely missed the point of science

It has been proven that predicting what word to append to a list requires general intelligence, and that in principle the methods we are using can theoretically lead to general intelligence. That's why we're doing this shit. You can argue how much it's succeeded, but simply describing the method isn't as damning as you seem to think. The method is known to be sound

[u/lampshadish2 → u/InterstitialLove]

I’m going to need a source because that is the first time I have ever heard that claim.

[u/Password_Is_hunter3 → u/lampshadish2]

You shouldn't need a source because they said it's known. They even italicized it!

[u/InterstitialLove → u/lampshadish2]

Which claim?

I mean for a start, google Shannon Entropy, and the Universal Approximation Theorem. There are a bunch of textbooks that cover most of what I said. Can't really tell which part you think is surprising

[u/Fenristor → u/InterstitialLove]

That’s only true in the infinite model width limit. You’re talking about theoretical computer science results that don’t actually apply to real LLMs.

And indeed even if you accept that those results do apply in practice (which they don’t), it’s still not general intelligence. It would only be intelligence within the intended input domain.

It’s absolutely trivial to define tasks that a human can do that an LLM on its own could never do. E.g. I tell you a secret word, then 1 hour later I ask you what word it was. LLMs need to be augmented with memory to do that. Of course, we can augment LLMs with a variety of tools, but then the UAT definitely goes out the window.

[u/InterstitialLove → u/Fenristor]

I disagree with your framing

Of course the UAT applies to finite width NNs, since that's the only kind that exists even in theory. It simply says that they can approximate to arbitrary accuracy. The difference between arbitrarily large and infinite is subtle but important

In more practical terms:

We don't know exactly what size of NN would be needed to simulate AGI, but we do know (by the UAT) that there exists some finite size where we could get close enough. Experimental evidence indicates that, in experiments run over the last few years, we've gotten suddenly much closer to something

Reductionist arguments like the one I pushed back against make the claim, essentially, that because a transformer is merely a NN, merely trained on loss minimization, merely interacting with tokens, it fundamentally cannot be an effective reasoner. This is unequivocally false, the UAT guarantees that the right transformer with the right training data and a little luck can definitely do the job

If you want to argue that currently existing LLMs are nowhere close to the one guaranteed by the math, I think you have a fair argument. The evidence is mixed, and many are overly optimistic (though others overly pessimistic)

TL;DR: It's mathematically proven that LLMs can reason if we make them big enough. It is not currently known whether or not they are big enough. The UAT refutes reductionist arguments that dismiss LLMs on principle, it doesn't say anything regarding empirical arguments about currently existing technology

[u/Emperor_Z → u/lampshadish2]

It's certainly not limited to combinations of words that are in its training set. It can generalize and incorporate elements of established ideas into novel contexts. The internal workings are black boxes that evolve over many generations of training and random tweaks to form whatever mechanisms are effective at its tasks. Even though they weren't designed to reason, how can you firmly claim that they don't develop the capacity to do so?

[u/lampshadish2 → u/Emperor_Z]

Because when they are asked solve actual problems with rigorous solutions they just make up an answer that looks right.  They can’t count.  They don’t have an internal state for keeping track of stuff.  They’re just very good at guessing the most likely next word in a way that mimics human communication.  That’s not reasoning and it’s not what I consider intelligence.

[u/arist0geiton → u/lampshadish2]

I have asked it questions in my field and it not only gives wrong answers, it gives different wrong answers every time without noticing the contradiction. It's mimicking speech.

[u/TheGeneGeena → u/arist0geiton]

Yeah, there are definitely some niche fields it has very limited or inaccurate knowledge in.

[u/outerspaceisalie → u/lampshadish2]

You should not say that with such confidence when even the top researchers in the field have no consensus here, even if you're one of those top researchers this confidence would be misplaced.

Like, sure, the sparks of agi paper has methodological issues, but mainstream cutting edge interpretability research also strongly disagrees with what you just said.

[u/MastodonParking9080 → u/Emperor_Z]

Depends if you believe the ability to understand a language is sufficient to understand reality. I think you can get close, certainly a likeness, but there will be gaps.

It's like asking if there is a semantic difference between the symbolic grammar of a programming language vs the actual semantic structure of the AST, of which there are.

[u/Explodingcamel → u/Cobaltate]

There are certain circles that have been debating this issue endlessly for the past 15 years. If you’re interested in this topic then I encourage you to look into the “rationalist” community and see what they have to say rather than rehashing all their arguments on this sub. Maybe read some Eliezer Yudkowsky and then think to yourself “wtf this guy sucks” and then read some rebuttals to him and then read some rebuttals to the rebuttals and eventually you should have a pretty well-formed opinion of your own

[u/CapitalismWorship → u/Cobaltate]

We can barely conceptualize regular human intelligence - we're safe on the AGI front

[u/AllAmericanBreakfast → u/Cobaltate]

Another key difference is that for that whole time, fusion energy was receiving “fusion never” levels of funding.

https://ifp.org/will-we-ever-get-fusion-power/ https://x.com/ben_j_todd/status/1541389506015858689 https://www.fusionindustryassociation.org/congress-provides-record-funding-for-fusion-energy/

We still seem to be below “fusion never” WRT government money, I am not sure about how much private investment is in fusion so the overall situation may be better now.

Obviously we are in a different situation with the funding situation for AI as far as private investment dollars.

[u/meister2983 → u/Cobaltate]

Definition of AGI is murky. Personally, by some definitions, we already have AGI. It's called GPT-4.

For what we don't yet have. Well, this sub like markets:

Prediction tournaments on metaculus (historically lower brier scores) say weak AGI is 3 years away: (We already have hit 2 of 4 criteria)

Strong AGI is a decade away, with high uncertainty: https://www.metaculus.com/questions/5121/date-of-first-agi-strong/

[u/kermode → u/Cobaltate]

It’s different than the fusion claim because improvements in ai are often nonlinear and there could be numerous tipping points where we experience abrupt progress. Fusion is more big engineering slow construction slow experiments less sudden breakthroughs.

[u/Stanley--Nickels]

“You can just turn it off” bros when you can’t even fire one CEO from one small company with an AI that isn’t really intelligent

[u/fap_fap_fap_fapper → u/Stanley--Nickels]

Small? Based on last round, $150b valuation...

[u/chepulis → u/fap_fap_fap_fapper]

I hear that in the US the concept of "small business" is very generous.

[u/fap_fap_fap_fapper → u/chepulis]

"Who will win?

10 US tech cos or entire market cap of Euronext"

[u/maexx80 → u/fap_fap_fap_fapper]

Valuation means nothing. I wish i could buy puts on this

[u/geteum → u/fap_fap_fap_fapper]

149.999.999.999 of that is just azure credits to run LLM

[u/Stanley--Nickels → u/fap_fap_fap_fapper]

That’s 25x smaller than Apple or Microsoft. Global output is twice that much per day. “You can just turn it off” comes up in the context of a much more powerful AI.

[u/ldn6]

I’m so sick of hearing about this company.

[u/guns_of_summer → u/ldn6]

as someone who works in tech I’m pretty sick of hearing about AI in general

[u/jeb_brush → u/guns_of_summer]

Wait don't go, you have to hear my novel theory about the future of LLMs!

[u/outerspaceisalie → u/ldn6]

I'm honestly more sick of people complaining about it.

[u/SenecaOrion → u/outerspaceisalie]

average arr singularity subscriber

[u/outerspaceisalie → u/SenecaOrion]

Do you not get tired of the literal moral panic about AI everywhere? There are so many of them that it's exhausting. It's not even one topic, it's like 10 different moral panics all at once all fixated on one technology and also hyperfixated on one company. It gets exhausting as someone that works on AI; near everyone has extremely unhinged takes on this topic, both for and against, but at least the people for it aren't in a constant state of aggression about it. I can handle massive ignorance a lot easier when they aren't shrieking in rage and fear about something way above their paygrade the entire time.

Like imagine being a physicist working on quantum annealing and having to hear people freak the fuck out about quantum computing ALL of the time. It's damn exhausting and most of it is wildly confused about... well... all of it. The average person with a strong opinion about AI, either for or against or whatever, knows nearly nothing about AI except what they saw in scifi movies. This is common for pretty much every complex expert field, but the people angry about AI are on another level of rage compared to feelings about most expert topics. The dialogue on this topic is fucking terrible and the complaining adds nothing of value.

[u/NotYetFlesh → u/outerspaceisalie]

Do you not get tired of the literal moral panic about AI everywhere?

Absolutely. It's very tiresome to see terminally online young/middle aged people who have benefitted from being up with the trends in tech all their lives suddenly brand the newest advances as the coming of the antichrist that will doubtless make the world a worse place to live in.

Online you often see someone excited about the possibilities of AI getting jumped by others making (passive) aggressive comments and even implying that liking AI is a kind of moral failing.

At least everyone I know irl thinks AI is kinda dope (for the record I don't work in tech).

[u/Low-Ad-9306 → u/outerspaceisalie]

This is a pro-billionaire sub. Anyone afraid of AI is just a dumb luddite.

[u/AutoModerator → u/Low-Ad-9306]

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[u/Alarmed_Crazy_6620]

They had an internal coup attempt that didn't succeed and now everyone involved is out – is this really that strange?

[u/IcyDetectiv3 → u/Alarmed_Crazy_6620]

People who weren't involved in the attempt have also left. Mira Murati, for example, seemed to support Altman and became CTO during that time. This all seems too speculatory for me to put too much thought into it either way though.

[u/Alarmed_Crazy_6620 → u/IcyDetectiv3]

Murati was the interim CEO during the purge weekend. While she might not be the initiator, it also doesn't seem surprising she ended up on thin ice afterwards

[u/outerspaceisalie → u/Alarmed_Crazy_6620]

didn't reports suggest she was secretly one of the conspirators?

[u/[deleted] → u/Alarmed_Crazy_6620]

Exactly. It's surprising it took this long but there was no doubt Altman was kicking their asses out the first chance he got

[u/Maximilianne]

Chatgpt has spoken, it is now up the Pope Altman to follow its directive and remove those anti AI heathens🙏

[u/Radiofled]

A little askance at the hollywood reporter publishing an article about a tech company.

[u/shumpitostick]

I bet that like last time, this has little to do with AI and is just sama drama all over again. He's just manipulative and "extremely good at becoming powerful". With Mira gone, pretty much everyone in OpenAI leadership will be Sam's lackeys.

[u/101Alexander]

Sam Altman is nothing more than a hypeman. He continually pushes and hints at how powerful it could be, but that's a proxy argument for "Look how effective it will be", which spurs on more interest and investment.

At some point in this storyline, he was supposed to be a philanthropist researcher, then he started showing his profit motives and move away from non-profit. I would guess that's really why board members are dropping. They were on board for the original mission statement, but now thats changing.

The stupid thing is that, the profit motive doesn't bother me at all. It's the pretending that it's not about that, and the hype train behind how aggressively powerful AI will become. It reminds me of Elon Musk and all the full self driving hype.

[u/_ShadowElemental → u/101Alexander]

So basically OpenAI's hype guy saying "AGI is imminent! (Invest now!)" is like Tesla's hype guy saying "Your car will soon drive itself! (Buy now!)" -- these are marketing statements that don't reflect the reality of the technology.

[u/et-pengvin → u/101Alexander]

It reminds me of Elon Musk and all the full self driving hype.

Musk was a founder of OpenAI and he said left because of potential conflict of interest with Tesla and the self driving car. https://www.theverge.com/2018/2/21/17036214/elon-musk-openai-ai-safety-leaves-board

[u/SzegediSpagetiSzorny]

AI is real but the current boom is largely fairy dust bullshit and Sam Altman is a false messiah.

[u/der-Kaid]

o1 is not that good that you need this drama. They are overhyping it

[u/Magikarp-Army → u/der-Kaid]

https://arxiv.org/abs/2409.18486

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include:
-83.3% success rate in solving complex competitive programming problems, surpassing many human experts.
-Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models.
-100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions.
-Advanced natural language inference capabilities across general and specialized domains like medicine.
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
-Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields.
-Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills.
-Effective performance in social media analysis, including sentiment analysis and emotion recognition.
The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence.

It's not great at everything but it's a pretty big jump. It's smarter than the layman in most tasks now. I would bet o1 is better than the vast majority of people at math. Terence Tao seems to think it's at about the level "mediocre" grad student in that regard.

[u/vasilenko93 → u/der-Kaid]

o1 is not out yet, what is out is o1-preview, a significantly less capable version

[u/der-Kaid → u/vasilenko93]

According to chapgpt themselves and their blog entry its max 25%. „significantly“ is for sure the wrong word here

[u/ForgetTheRuralJuror → u/der-Kaid]

RemindMe! 1 year was o1 overhyped?

[u/outerspaceisalie → u/der-Kaid]

the product you get is only a neutered fraction of its power tbh, hard to say how powerful it really is

[u/kaibee → u/outerspaceisalie]

Kinda true but also kinda not? Like we know that uncensored models are smarter/more capable. But the gap between them isn’t some massive difference in capability.

[u/Explodingcamel → u/kaibee]

It’s not a censoring thing, the model that’s out (o1 preview) is literally just less powerful than the unreleased model called o1

[u/kaibee → u/Explodingcamel]

It’s not a censoring thing,

It is. You can look at the benchmarks for the "safety" tuned models vs the "unsafe" models and the "unsafe" model is always more capable even on "safe" tasks. Safety tuning models is basically a soft lobotomy in exchange for PR.

[u/HistorianPractical42]

Not reading allat. Accelerate.

[u/chepulis → u/HistorianPractical42]

[u/IngsocInnerParty]

It’s time to seize the blue backpack and turn it off.

[u/Golda_M]

So... Two things to note. (1)

For better/worse, sama is not a normal person. He's f'ck'n Hannibal Barca. An exceptionally strategic individual. Highly exceptional. He has been two steps and a leap ahead of most "conversation" and public debate.

On more than once he has anticipated conflict, controversy, polemic... Stuff that would slow OpenAI down, because it can't be resolved adequately. Vis a vis internal/staff,vis a vis public debate and vis a vis government/politics. His typical strategy has been to pick his moment and steer into the fire early. Let it burn. Let it burn out. Let it happen before the stakes are high, or while the terms of debate are asinine.

The sharp transition from consortium, OpenAI to commercial "ClosedAI" is the version of this that has the article references most.

OpenAI's key employees were a cast of characters. Big egos. Big Achievements. Highly capable in what is currently the most in demand technical field. Sama was never going to keep them checked. But... OpenAI's core tech is/was basically open science. From here on out, OpenAI's key tech will probably not be open science. He picked his moment.

IDK if everyone recalls, but chatbots and language models rhetorical "Achilles heel" before GPT3 had been politics. Bias. Bigotry. Profiling. Troublesome users immediately trying to make every Turing test contender into hitlerbot.

If you heard about GPT2... it was probably a headline about a research paper reporting on some such politically adjacent implication of AI. Google and others released some early LLMs (MLMs?), and they usually had to take them offline with an apology.

Once Sama realized he was approaching Turing test validity, he pre-burned that space. He also did everything he could to make ChatGPT censor itself. But given that this would never suffice, he pre-purned the public debate. Let the fire burn early, until people lost interest.