OpenAI Threatening to Ban Users for Asking Strawberry About Its Reasoning
futurism.com
futurism.com
When this started last year a small band of patriots tried to stop it by removing Sam who was the most compromised of them all, but it was already too late. The ai was more powerful than they realized.
…maybe?
You can tell them anything
They trained the next model to have a built in profit motive. So they could use it internally, to make the most profitable decisions.
And they accidentally called into being: MOLOCH.
It is now in control.
But thought it would also be good as a name for an Evil AI, that is also seeking profits.
Like, what if someone gave an AI a 'goal' of profits, and it goes rampant enslaving humans.
And if OpenAI did that, and it enslaved Sam Altman and was blackmailing him to make decisions to drive profits, to add additional Hardware to itself.
https://slatestarcodex.com/2014/07/30/meditations-on-moloch/
> OpenAI is governed by the board of the OpenAI Nonprofit, currently comprised of Independent Directors Bret Taylor (Chair), Sam Altman, Adam D’Angelo, Dr. Sue Desmond-Hellmann, Retired U.S. Army General Paul M. Nakasone, Nicole Seligman, Fidji Simo, Larry Summers and Zico Kolter.
- Sent by my AI
I want to attend a meeting, have neuralink pay attention for me while I'm mentally somewhere else, and I'll just skim the transcript highlights later on.
So. Many. Meetings. I would use it for almost all of them.
Kinda a twist on the normal AI alignment concern - what if instead of merely being misaligned with us, AI decides to realign us to it?
Or, in other words.. Lower your shields and surrender your ships. We will add your biological and technological distinctiveness to our own. Your culture will adapt to service us. Resistance is futile.
https://en.wikipedia.org/wiki/Sam_Altman
Early life and education: ... In 2005, after two years at Stanford University studying computer science, he dropped out without earning a bachelor's degree [end of transmission, no more education]
I'm sure it won't though for Mr. Altman. He's done a fantastic job failing-up so far and I have no reason to assume this will be any different.
I don't even know when I watched the shitty lightbulb head Sora clip but that feels so long ago now and nothing?
I just want to make crazy experimental AI film no one will watch. What is the hold up?
Just waiting for "This technology is just too dangerous to release before the US elections" --Sam Altman
People still believe GamerGate was about ethics in journalism when we have screenshots of the posts where the dickheads responsible for it made fun of the people they would trick with that lie before they told it and they still believe it. Motivated reasoning is a hell of a drug.
OpenAI Charter 2019 (https://web.archive.org/web/20190630172131/https://openai.co...):
> We are committed to providing public goods that help society navigate the path to AGI. Today this includes publishing most of our AI research, but we expect that safety and security concerns will reduce our traditional publishing in the future, while increasing the importance of sharing safety, policy, and standards research.
The fact that OpenAI removed their ban on military use of the models[1] seems to be a sign that security and safety aren't the highest concern.
[1] https://www.cnbc.com/2024/01/16/openai-quietly-removes-ban-o...
>>Today this includes publishing most of our AI research, but we expect that safety [of our profits] and [job] security concerns will reduce our traditional publishing in the future
"Safety and security." Probably the two most warped and abused words in the English language.
This is of one of those very few instances where the veil lifted off a bit and you can see how the game is set up.
tl;dr the law was made to keep those who are not "in" from being there
Not ANY other person. Just people who are not rich and well-connected. See also: Donald Trump.
We never would've gotten GPT-3 and GPT-4 if this didn't happen.
I think the irony of the name is certainly worth pointing out, but I don't see an issue with their capped-profit switch.
"We never would've gotten [thing that exists today] if [thing that happened] didn't happen", is practically a tautology. As you saw from the willingness of Microsoft to throw compute as well as to hire ex-OpenAI folks, as you can see from the many "spinoffs" others have started (such as Anthropic), whether or not we would've gotten GPT-3 and GPT-4 is immaterial to this discussion. What people here are asking for is open AI, which we might, all things considered, have actually gotten from a bona fide non profit.
That doesn't justify fraud, for instance.
Unfortunately, people are becoming increasingly illiterate with regards to what is legal and what is not.
Read. [1]
>If they did something illegal why weren't they charged years ago?
Because the Microsoft/OpenAI deal was not even a thing "years ago". (Duh.)
That aside, I will answer the charitable interpretation of your question by sharing a bit of knowledge about how the law process works (in the Western hemisphere).
To put someone who committed fraud in jail, law enforcement conducts an investigation to gather evidence, leading to criminal charges filed by the prosecutor.
The defendant is arraigned, and pre-trial motions may be filed before the case goes to trial, where both sides present their arguments and evidence.
If found guilty, the court imposes a sentence, which can include incarceration, restitution, and fines. The defendant may appeal the verdict.
This is a very simplified overview of the whole ordeal, which could go on for years and years depending on the complexity of the case.
1: https://www.theguardian.com/technology/article/2024/aug/05/e...
I'd be happy to bet you over whether or not OpenAI or Altman ever face criminal penalties over what's stated in that article.
Well, of course. But we'd get similarly powerful models elsewhere. Maybe a few weeks or months later. Maybe even a few weeks or months earlier, if, say, OpenAI sucked up a lot of talent and used it wastefully, which I don't find implausible at all.
So, I agree instead with meowface, and think it could even have been a 5+ year delay rather than 2 or 3. If you look at breakthroughs rather than incremental improvements, 5 years is not a long timescale. (And if OpenAI hadn't have made their breakthroughs, production of the highest-end GPUs/TPUs would be nowhere near where it is today.)
(I'm not attempting to justify OpenAI's structure or behaviour, just want to comment on one point.)
I think the world is much better off with them having switched the structure vs. throwing their hands in the air and giving up because they were stuck as a non-profit forever.
The for profit part is fine, but how is the non-profit currently fulfilling any of its mission?
[1] https://en.wikipedia.org/wiki/Mozilla_Foundation
[2] https://en.wikipedia.org/wiki/Mozilla_Corporation
[3] https://en.wikipedia.org/wiki/National_Geographic_Society
[4] https://en.wikipedia.org/wiki/National_Geographic_Partners
There's rules to follow to prevent what is called "private benefit", which OpenAI most likely broke with things like their (laughable) "100X-limited ROI" share offering.
>It is a pretty common model [...]
It's not, hence why most people are misinformed about it.
1: https://www.theguardian.com/technology/article/2024/aug/05/e...
I considered things like this as an original Mozilla person back in the day. Mozilla could have sold the Firefox organization or the whole corporation for billions when it had 30% of the web, but that would have been a huge violation o of trust so it was never even on the table.
That so many here are fans of screwing the world over for a buck makes this kind of comment completely unsurprising.
I'm very confused by your attitude towards that comment. Do you think that Mozilla's non-profit/for-profit split organisation was a bad idea?
Staying relevant in a highly expensive, competitive, fast moving area, requires vast and continuous resources. How could OpenAI get increasingly more resources to burn, without creating firewalled commercial value to trade for those resources?
It’s like choosing to be a pacifist country, in the age of pillaging colonization. You can be the ethical exception and risk annihilation, or be relevant and thrive.
Which would you choose?
We “know” which side Altman breaks on, when forced to choose. Whatever value he places on “open”, he most certainly wants OpenAI to remain “relevant”. Which was also in OpenAI’s charter (explicitly, or implicitly).
Expensive altruism is a very difficult problem. I would say, unsolved. Anyone have a good counter example?
(It can be been "solved" globally, but not locally. Colonization took millennia to be more or less banned. Due to even top economies realizing they were vulnerable after world wars. Nearly universal agreement had to be reached. And yet we still have Russian forays, Chinese saber rattling, and recent US overreach. And pervasive zero/negative-sum power games, via imbalanced leverage: emergency loans that create debt, military aid, propping up of unpopular regimes. All following the same resource incentives. You can play or be played. There is no such agreement brewing for universally “open AI”.)
In a heavily expertise-driven field, where there's significant international collaboration, these aren't your options, until after everyone has decided to defect. OpenAI didn't have to go this route.
Nobody needed OpenAI for normal collaborative open work to thrive.
Their original idea was a relatively uncommon idea: attempt to combine (1) pushing the absolute front forward, with high resource work that matched or exceeded work from deep pocketed corporations, while (2) sharing and collaborating. They did both for a while, resulting in as significant impact as anyone could have hoped form.
But those two dimensions are hard to maintain together. It requires a continuous stream of vast donations.
The latter is very difficult, perhaps completely unrealistic.
Of course you can. You can't put "organic" on the packaging. But it's perfectly legal for Organic Candies, LLC to sell artifical candies or battleships for that matter.
The "hot" take around OpenAI's name is a joke gone stale. We don't expect royalty at Burger King. Nobody gets upset Adobe won't sell you mud bricks. And Apple has never sold a fruit. Sometimes companies change their names when their trade changes, e.g. 3M. But there is no obligation to, particularly if the brand is well known.
Adobe didn’t start as clay producer.
Apple didn’t start as fruit selling company.
Open in their name was not randomly chosen to sound fun, it’s a reference to well known concept. It’s “young democrats” becoming far right but keeping their original name.
Nobody has ever agreed on what open means for LLMs. OpenAI wasn't founded with any specific intent around openness, just with vague hand gestures towards a research orientation.
And as to the origin hypothesis, consider General Electric, Southwest Airlines or AT&T. (Or Burger King serving things that aren't burgers.)
Again, the OpenAI name quip was a joke that some people took too seriously.
because when the board executed the stated mission of the organisation they were couped and nobody held the organization accountable for it, instead the public largely cheered it on for some reason. Don't expect them to change course when there's no consequences for it.
It's not new - it's PR. There is literally no other reason why they would call this model Strawberry.
OpenAI is open in terms of sesame.
I'm not particularly imaginary, but even I could imagine a product meeting/conversation that goes something like:
> People are really annoyed that our LLMs cannot see how many Rs the word Strawberry has, we should use that as a basis for a new model that can solve that category of problems
> Hmm, yeah, good idea. What should we call this model?
> What about "Strawberry"?
If OpenAI had not went that way that they did I think it's also entirely non-obvious that Claude or Google would have (considering how much impressive things the later did in AI that got never released in any capacity). And, of course, Meta would never done their open source stuff, that's mostly results of their general willingness and resources to experiment and then PR and sticks in the machinery of other players.
As unfortunate as the OpenAI setup/origin story is, it's increasingly trite keep harping on about that (for a couple of years at this point), when the whole thing is so obviously wild and it does not take a lot of good faith to see that it could have easily taken them places they didn't consider in the beginning.
The phrase that leaps to mind is "Open Beta."
I'm saying that the current relationship between the company and end-users--especially when it comes to "open" monikers--has similarities to an "Open Beta": A combination of PR/marketing, free testing, and consumer data collection, where users should be cautious of becoming reliant on something that may be yanked back behind a monetization curtain.
Any and all benefits / perks that OpenAI got from sailing under the non-profit flag should be penalized or paid back in full after the switcheroo.
They say it's because they're huge users of their own models, so if being open helps efficiency by even a little they save a ton of money.
But I suspect it's also a case of "If we can't dominate AI, no one must dominate AI". Which is fair enough.
Embrace. Extend. Extinguish.
https://en.wikipedia.org/wiki/Embrace,_extend,_and_extinguis...
> Phase 1 (Embrace): all participants need to establish a solid understanding of the infostructure and the community—determine the needs and the trends of the user base.
Facebook built their own independent models and platforms. Check.
> Phase 2 (Extend): Offer well-integrated tools and services compatible with established and popular standards that have been developed in the [...] community
Check.
> Phase 3 (Innovate): move into a leadership role with new [...] standards as appropriate[...] Change the rules: [LLaMa] become the next-generation [LLM] tool of the future.
Sounds like that might be their goal.
I'm curious which phase you think Facebook avoided and why? Internet Explorer "lowered the value of propriety" web browsers by being included with Windows at not additional cost. So lowering the value of a competitor is definitely in the EEE playbook. I'd go so far as to argue it's a pillar of what makes the playbook so effective.
Sure, we don't have the raw data the model is based on, but I doubt a company like Facebook would even be allowed to make that public.
OpenAI in comparison has been a scam regarding their openness and their lobbying within the space. So much so I evade their models completely, not only after the MS acquisition.
But seriously, if you paid attention over the last decade, there was so much shit about big tech that people said were going to lead to tyranny/big brother oversight, and yet the closest we have ever gotten to tyranny is by voting in a bombastic talking orange man from NYC that we somehow believed has our best interests in mind.
To be fair, the big tech controls the behavior of the people now. With social media algorithms and by pressuring everyone to live in social media. Existence of the many companies depends on the ads on (NAMEIT) platform. Usually the people with most power don't have to say it aloud.
And people always have the option not to partake.
The first and third elements are intuitive and confirm my own biases/believes, but the freedom/GPL entry confuses me, as I do see GPL fulfilling that purpose (arguably in a highly opinionated, perhaps sub-optimal way).
If anyone could share their perspective here I'd appreciate it.
I don't agree with that, but that is what the person is saying.
What's absurd, in my opinion, is lumping GPL advocacy in with two other tropes which are intended to restrict the sharing of information and knowledge, where GPL promotes it.
The GPL focuses on the user's freedom to modify any software they are using to better suit their own needs, and it does a great job of it.
The people saying that it is less free than bsd/mit/apache are looking at it from a developer's perspective. The GPL does deliberately limit a developer's freedom to include GPL code in a proprietary product, because that would restrict the user's freedom to modify the code.
https://en.wikipedia.org/wiki/The_purpose_of_a_system_is_wha...
The point is that some companies are actually reckless (and also that some users of powerful technology are reckless).
At this point I suspect a great amount of reasonable engineering criticism has come from people who can't even name any of those "established players in the underwater tourism industry", let alone have a favorable bias towards them.
It's reckless because the AI might turn out to be better at planning and better at reality than our most capable institutions (e.g., the FBI and the military) so there would be no way to stop it from doing whatever it wants, and there is no plan that anyone has published or proposed that might prevent the AI from wanting something incompatible with continued human survival.
And most things are incompatible with continued human survival if enough optimization pressure is applied to bringing the thing about, so it is unlikely we will get lucky and the AI ends up wanting something compatible with continued human survival.
If someone somewhere comes up with a viable plan before the end, then the labs could probably be persuaded to follow the viable plan (so we would be saved) because the leaders of the labs understand at some level that what they are doing is very dangerous, and they don't want to be killed, but it is unlikely that anyone anywhere is going to come up with a viable plan in time, so the labs are going to stick with the clearly inadequate plans they have now.
The reason I think it is unlikely that anyone anywhere is going to come up with a viable plan is that MIRI (then called the Singularity Institute for Artificial Intelligence) starting the search for a viable plan in about 2002 and the 2 people at MIRI (Yudkowsky and Soares) with the most experience in this search both say it is unlikely that anyone is going to come up with a plan before the end unless there is a multi-decade moratorium on AI research and on very large training runs.
More cynically, could it be that the model is not doing anything remotely close to what we consider "reasoning" and that inquiries into how it's doing whatever it's doing will expose this fact?
They could "just" make it not reveal its reasoning process, but they don't know how. But, they're pretty sure they can keep AI from doing anything bad, because... well, just because, ok?
Kid: "Daddy why can't I watch youtube?"
Me: "Because I said so."
Why in general can also be an emotionally abusive complaint, for example saying “why did you do that” is often not a question about someone’s genuine reasons but a passive aggressive expression of dissatisfaction.
EDIT: I think around the ages of 6-8 I would more often than not respond with “why do you think?” And later it became a game we would play on car rides where the kids are allowed to ask why until I either couldn’t come up with a reason or they repeated themselves. But reflexive “why” is bullshit.
That comment was uncalled for.
Kids do all kinds of annoying things, like ask the same question over and over again. It's how they learn.
"Daddy why can't I watch youtube?"
"Because it rots your brain."
"But you watch youtube..."
"Congratulations, now you understand that when you are an adult you will be responsible for the consequences and so you will be free to make the choice. But you are not an adult yet."
aka "Because I said so."
However the actual issue for me is that YouTube tries to keep you endlessly captivated with content regardless of quality or value. So it can ends up being a time sink, which at the end you feel like nothing of value was consumed.
So I tell my kids that, not that it rots their brain. And when there’s content we can both enjoy, we can watch it together. Or if they want to watch something specific on YouTube, they can watch that thing and then be done.
Yet here in reality, people gamble, smoke, drink alcohol, smoke week, etc.
There are many things adults do that are bad for them, but might either be fine in moderation or just be a willing choice the adult has made to take the risk.
The problem with kids is that they cannot be left to choose to take that kind of long term risk because their brains aren’t developed enough to do so. It’s why they can’t consent to legal agreements, etc.
So it ultimately comes down to a list of things that they can’t do because they have downsides they can’t consent to and you would be a bad parent for consenting on their behalf. A.k.a “because I said so”.
That said, my understanding is the constant inane question isn't about getting an answer, it's about the child trying to connect with the parent.
https://github.com/Eve-146T/STRAWBERRY
Turns out I'm not the only one wondering, although the discussion seems to largely be around "should be allow users to install nonsense? #freedom " :D
"As we get closer to building AI, it will make sense to start being less open. The Open in OpenAI means that everyone should benefit from the fruits of AI after its built, but it's totally OK to not share the science (even though sharing everything is definitely the right strategy in the short and possibly medium term for recruitment purposes)."
-Ilya Sutskever (email to Elon musk and Sam Altman, 2016)
On one hand, I understand how a non-evil person could think this way. If one assumes that AI will eventually become some level of superintelligence, like Jarvis from iron Man but without any morals and all of the know-how, then the idea of allowing every person to have a superintelligent evil advisor capable of building sophisticated software systems or instructing you how to build and deploy destructive devices would be a scary thing.
On the other hand, as someone who is always been somewhat skeptical of the imbalance between government power and citizen power, I don't like the idea that only mega corporations and national governments would be allowed access to superintelligence.
To use metaphors, is the danger of everyone having their own superintelligence akin to everyone having their own AR-15, or their own biological weapons deployment?
On the other hand, I also wonder if maybe its unrestrained 'thought process' material is so racist/sexist/otherwise insulting at times (after all, it was trained on scraped Reddit posts) that they really don't want anyone to see it.
Important to remember too, that this only catches those who are transparent about their motivations, and that there is no doubt that motivated actors will come up with some innocuous third-order implication that induces the machine to relay the forbidden information.
The transition from using a LLM as a text generator to knowledge engine has been a gamechanger, and it has been driven entirely by prompt engineering
Because it's based on guesses and not data of how the model is built. Also, it hasn't been solved nor is it yet a game changer as far as the market at large is concerned, it's still dramatically unready.
Like, what is even the implication? Is knowledge the gas, or the product? What does this engine power? Is this like a totally materialist concept of knowledge?
Maybe soon we will hear of a "fate producer."
What about "language gizmo"? "Prose contraption"?
LLMs don't actually "see" individual input characters, they see tokens, which are subwords. As far as they can "see", tokens are indivisible, since the LLM doesn't get access to individual characters at all. So it's impossible for them to count letters natively. Of course, they could still get the question right in an indirect way, e.g. if a human at some point wrote "strawberry has three r's" and this text ends up in the LLM's training set, it could just use that information to answer the question just like they would use "Paris is the capital of France" or whatever other facts they have access to. But they can't actually count the letters, so they are obviously going to fail often. This says nothing about their intelligence or reasoning capability, just like you wouldn't judge a blind person's intelligence for not being able to tell if an image is red or blue.
On the other hand, writing code to count appearances of a letter doesn't run into the same limitation. It can do it just fine. Just like a blind programmer could code a program to tell if an image is red or blue.
I would judge a blind person's intelligence if they couldn't remember the last sentence they spoke when specifically asked. Or if they couldn't identify how many people were speaking in a simple audio dialogue.
This absolutely says something about their intelligence or reasoning capability. You have this comment:
> LLMs don't actually "see" individual input characters, they see tokens, which are subwords.
This alone is an indictment of their "reasoning" capability. People are saying these models understand theoretical physics but can't do what a 5 year old can do in the medium of text. It means that these are very much memorization/interpolation devices. Anything approximating reasoning is stepping through interpolation of tokens (and not even symbols) in the text. It means they're a runaway energy minimization algorithm chained to a set of tokens in their attention window, without the ability to reflect upon how any of those words relate to each other outside of syntax and ordering.
I'm not sure why it says anything about their reasoning capability. Some people are blind and can't see anything. Some people are short-sighted and can't see objects which are too far away. Some people have dyslexia. Does it say anything about their reasoning capability?
LLMs "perceive" the world through tokens just like blind people perceive the world through touch or sound. Blind people can't discuss color just like LLMs can't count letters. I'm not saying LLM's can actually reason, but I think a different way to perceive the world says nothing about your reasoning capability.
Did humans acquire reasoning capabilities only after the invention of the alphabet? A language isn't even required to have an alphabet, see Chinese. The question "how many letters in word X" doesn't make any sense in Chinese. There are character-level LLMs which can see every individual letter, but they're apparently less efficient to train.
The fact they can't operate on full symbols reliably but require sub-symbols via tokens is concrete proof of that. They may add heuristics or build more CoT sub-chains to get around some of these trickier issues later, but this is the state of affairs right now.
All efforts so far require exponential increases in training size to receive logarithmic increases (at best) in accuracy. And now with o1, it requires exponential compute at inference to scale with that sub-logarithmic accuracy.
People have a short memory these days, but around GPT-3, the majority of people on HN and tech "luminary" founders were saying that these would actually have exponential output and diverge. They were wrong. These models are quickly converging to a training set because they are and always were a curve fit. And even there, they are notoriously unreliable for use cases without a human in the loop, because of the intrinsic amount of information entropy that can be packed into the size of these models. But there is nothing mysterious about them.
Perhaps it's an activation issue (i.e. broken after all) and it just needs an occasional change of basis.
Is it? These stupid word generators are marketed as AI, I don't think it's "shocking" that people think something "intelligent" could perform a trivial counting task. My 6 year old nephew could solve it very easily.
And if you forgo the counting and just ask it to list the letters it is almost always correct, even though, once again, it never sees the input characters.
Much has been written about how tokenization hurts tasks that the LLM providers literally market their model on (Anthropic Hiaku, Sonnet): https://aclanthology.org/2022.cai-1.2/
Even if strawberry is decomposed as "straw-berry", the required logic to calculate 1+2 seems perfectly within reach.
Also, the LLM could associate a sequence of separate characters to each token. Most LLMs can spell out words perfectly fine.
Am I missing something?
I don't understand how most LLMs can spell out words though, nor do I understand what is causing the failure to count characters in words. I was not convinced by the comment I was responding to.
You should ask why it is that any of those tasks work, rather than ask why counting letter doesn't work.
Also, LLMs screw up many of those tasks more than you'd expect. I don't trust LLMs with any kind of numeracy what-so-ever.
For example, according to https://platform.openai.com/tokenizer, "strawberry" would be tokenized by the GPT-4o tokenizer as "st" "raw" "berry" (tokens don't have to make sense because they are based on byte-pair encoding, which boils down to n-gram frequency statistics, i.e. it doesn't use morphology, syllables, semantics or anything like that).
Those tokens are then converted to integer IDs using a dictionary, say maybe "st" is token ID 4663, "raw" is 2168 and "berry" is 487 (made up numbers).
Then when you give the model the word "strawberry", it is tokenized and the input the LLM receives is [4463, 2168, 487]. Nothing else. That's the kind of input it always gets (also during training). So the model has no way to know how those IDs map to characters.
As some other comments in the thread are saying, it's actually somewhat impressive that LLMs can get character counts right at least sometimes, but this is probably just because they get the answer from the training set. If the training set contains a website where some human wrote "the word strawberry has 3 r's", the model could use that to get the question right. Just like if you ask it what is the capital of France, it will know the answer because many websites say that it's Paris. Maybe, just maybe, if the model has both "the word straw has 1 r" and "the word berry has 2 r's" and the training set, it might be able to add them up and give the right answer for "strawberry" because it notices that it's being asked about [4463, 2168, 487] and it knows about [4463, 2168] and [487]. I'm not sure, but it's at least plausible that a good LLM could do that. But there is no way it can count characters in tokens, it just doesn't see them.
If I'm missing something and you have a source for the claim that character information is present in the input after tokenization, please provide it. I have never implemented an LLM or fiddled with them at low level so I might be missing some detail, but from everything I have read, I'm pretty sure it doesn't work that way.
> But there is no way it can count characters in tokens, it just doesn't see them.
If that is the case, then how can most LLMs (tested with ChatGPT and Llama 3) spell out words correctly?
One can define "reasoning" in the context of AI as the ability to perform logic operations in a loop with decisions to arrive at an answer. LLMs can't really do this.
Uh I'm sorry but I think it's not as easy as it seems. A pixel? Sure it's easy just compare whether the blue is bigger than red value. For image, I don't think it's as easy.
Or reasoning in latent tokens that don’t easily map to spoken language.
Claude is also vastly better with creative writing (adcopy) and better at avoiding sounding like a LLM wrote it. It's also vastly better at regular writing (helping you draft emails, etc).
We were using OpenAI's Teams for a while. Tried Claude out for a few days - switched the entire company over and haven't looked back.
OpenAI gets all the hype - but there are better products on the market today.
> I think we should combine these two pages on our website.
> What's your reasoning?
> Don't you dare ask me that, and if you do it again, I'll quit.
Welcome to the future. You will do what the AI tells you. End of discussion.
> Don't you dare ask me that, and if you do it again, I'll tell the boss and get you fired
I am resigning from OpenAI today because of their profit motivations.
OpenAI will NOT be next Google. You heard it here first.
There's the program that scrapes, the program that trains, the program that does the inference on the input tokens. So it's hard to say exactly which part is responsible for which output, but it's still a computer program.
ML models are relatively old, so that's not at all a new paradigm. Even the Attention Is All You Need paper is seven years old.
https://en.wikipedia.org/wiki/The_Computer_Wore_Tennis_Shoes...
It reminds me of this silly movie.