ChatGPT is a great way to delve into new topics, and for that alone it’s worth it.
Like bears in space? ))) seems like a great way to delve into known topics so that you are able to catch it when it starts to confidently and plausibly hallucinate
If you're not an expert in your field, GPT will successfully lie to you. There's no second opinion.
Rather than a feelings filter, it needs an integrity filter.
LLMs don't lie, that implies they have intent and is giving too much credibility to the idea that AGI may happen. They get things wrong because it's trying to use the wrong tool for the task at hand. It's a next word prediction engine, and that's not very useful for most things. By their very nature, they "hallucinate", but they don't really hallucinate, they just give the wrong output because it is just an LLM.
Pathological liars don't intend to deceive. They can't seem to stop themselves. They literally hallucinate events even when presented with evidence to the contrary and we still call them liars.
A "hallucination engine" presented as a source of truth is fundamentally dishonest. We don't go to fortune-tellers when we need legal advice. We don't learn law from reading every Matlock script ever written.
A human hallucinating behind the wheel and plowing into traffic gets jail and a license suspension. An AI doing the same gets a press release about FSD being right around the corner.
This tech is literally replacing humans (by abstraction of the responsibilities they assume). It needs to be held accountable as one even if it's not technically AGI (or wherever the goalposts are being moved to next).
But our continued faith in tech as a growth industry depends on it not being perceived as a lie, so we must keep up the illusion that your car is just suffering from cyberpsychosis when it hallucinates and drives you off a cliff.
google, while far from perfection, gives me a selection of links that i can scan and see for myself which one of them makes more sense.
even looking for something stupid and potentially spammy like "chili soup recipe", i learned there are different opinions on how to make it, and i learned that apparently in Texas using beans is considered somewhat of a blasphemy. GPT did not mention any of that, just authoritatively barfed out one random recipe without any nuance or even any sources at all.
If you already know what you want "Chili soup" then doing a classic search and looking up all the options is probably the best bet. If you don't know what you want (i have all these ingredients make me a recipe or I kind of liked this but want it to be <different>) then the aggregation that LLM's do is more suited.
Or you combine the two. Ask ChatGPT a question to figure out what you want, what are some areas to dive into more. The do the classic search research and primary sources to fill in the details. This is what I mean by blindly trusting the output, the best description I've heard is that it's a digital intern.
It's the Socratic method on steroids, where the student is probing a possibly fallible professor with the library of Alexandria at their hands.
It is by no means perfect, but it allows me to identify what general field I am going into, what I should read about it, what core concepts are important, how to expand my knowledge, and most importantly, helps me better formulate the problem by asking questions or failing to understand what I want to convey.
By all means, HN has lost it.
I think there's value here, but I also think people are way overestimating the quality of the "knowledge" they think they're receiving from it on novel topics.
I think ChatGPT and the likes should be thought of as cartographers who when probed can provide an outline of regions of the knowledge space and give hints as to what could be useful.
Given then a high level but also imprecise map, I can ask the GPS for clarity as to where I am going.
The problem is that I often don't know where that is and thus I don't know what to ask from the GPS, but I can describe it to the cartographer, and they can draw a map for me.
In this analogy, the GPS is search engines / books / papers.
Yes, because people think that LLMs are almost AGI based on the social media reactions and can't imagine they still have unknown/unsolved problems. But if we take a look at the 14 years of self driving car development, it becomes clear how AI can be both amazing and not good enough at the same time.
Self-driving is very possible in many situations and if there was a "Manhattan Project" for self-driving to be up and running by 2025 I think we could do it... But there are so many vested interests that this won't happen.
... and then everyone is disappointed.
BTW, I'm not saying this is all bad... Everyone asking for a 6-month AI research moratorium gets it indirectly via societal inertia and regulatory muddle!
Surprise, surprise... this has happened before:
> Lay responses to ELIZA were disturbing to Weizenbaum and motivated him to write his book Computer Power and Human Reason: From Judgment to Calculation, in which he explains the limits of computers, as he wants to make clear his opinion that the anthropomorphic views of computers are just a reduction of the human being and any life form for that matter.[29] In the independent documentary film Plug & Pray (2010) Weizenbaum said that only people who misunderstood ELIZA called it a sensation.[30]
https://en.wikipedia.org/wiki/ELIZA#Response_and_legacy
And, it's easy to see why. You can talk the damn thing, and it talks back! People love to anthropomorphize things, anyway, but if you can talk to it and it talks back, people think there's got to be something to it.
This time, though, is a little different. GPT-3 and GPT-4 actually do behave like they understand natural language to a great extent. That makes them directly analogous to Searle's Chinese room construct, and suggests that they could actually pass the Turing test (if suitably fine-tuned).
This is great, because, as you say, it's amazing. But I also think it's not good enough, because the fact that GPT-4 may be able to pass the Turing test really says more to me about the limitations of the Turing test than anything else. Likewise with the Chinese room analogy: we know what's in the box, and we know it shouldn't be trusted.
But, you're not going to get that kind of analysis from the general public.
So it was even worse than I remember.
Ironically, that was largely because it let you more easily use sites built for desktop, due to the larger screen space and ease of pinch to zoom. Those older phones would have been somewhat more useful if mobile first/responsive sites had been a thing then, but it took the popularity of the iPhone for that to happen.
Then people started sideloading and basically showed Apple they needed a store which they quickly came up with. Getting an application on the other two platforms at the time was mind numbingly bad (activesync was to put it mildly awful to use). In some cases you needed to get the carrier involved (better have a few months to validate and a few hundred thousand dollars to pay for it).
Also that screen they used was way better than what any other phone out there had at the time. Most of the top end phones needed a stylus and itty bitty keyboard to be any sort of useful.
I would say it was not until the droidx came out that anyone had anything that approached how cool the iphone was.
I think Blackberry was the only one that did both OK enough to take seriously. I know people loved the Sidekick, but I never used it and don't recall if people used a web browser on it, or just text messaging.
The first iPhone was more impressive than a Blackberry.
So it did even less than I gave it credit for.
That's part of my point. There was a tremendous amount of hype for something that was incredibly limited and objectively bad. However eventually it became very good and took over the world.
That's why it barely got over six million units sold.
The iPod had a somewhat similar trajectory. The first gen version was pretty much just another MP3 player and iTunes didn't even run on Windows at first.
This is pretty much what I've seen. ChatGPT (that's 3.5, right? GPT3 was interesting but still pretty laughable) was a massive step forward, and incredibly exciting to witness and interact with. But it still does have limitations, especially if you try to separate hype (which comes from an ecosystem of people who have incentives to hype it) from reality.
> The first steam engines were also written off as being less powerful than a horse.
> The first electric motors were written off as being less powerful than steam engines
And the first Segways were written off, the first NFTs were written off, etc.
(in fact, we've seen this same argument for blockchains changing everything real soon now :)
The first NFTs were written off... by you and other writter-offers. If you didn't write them off back then and minted some you'd have made a pretty penny by now.
I don't know, I'm not sure those examples strengthen your case.
And that some people made money on NFTs doesn't make them useful. For a brief moment, a lot of people got scammed so others could prosper. Hardly a world changing technology(at least not for the better)
Interesting bit of historical trivia: In the US, the main truck driver's union is the International Brotherhood of Teamsters. What is a teamster? Historically, it was a person who wrangled a team of horses or oxen to pull a wagon. That profession was effectively eliminated by the creation of the internal combustion engine.
Two of the other main trades that were involved in the rise of unions are longshoremen and stevedores. The former pull cargo off ships and get it onto land. The latter organize cargo on the ships.
The creation of shipping containers dramatically reduced the need for those jobs, leading to some of the longest strikes in the US. While the unions technically "won", the strikes mostly incentivizing shipping companies to push even farther into mechanization so that they were less reliant on labor. There are far fewer dockworkers and longshoremen today than there were before containerization.
Change is hard.
They are quite skilled, but so are many other skilled tradespeople. Electricians and machinists for example do not make that kind of money despite their skill. So why do crane operators make so much?
When containerization, and subsequently other types of automation, hit ports, the union resisted fiercely but ultimately had to work out a deal with the port operators.
That deal was to reward the most tenured union tradespeople with much larger pay packages, at the cost of the less experienced tradespeople, who would have to find different work.
This agreement became agreeable to both sides, since the operators could still massively reduce the workforce, and the only price they'd have to pay would be high salaries for the union workers who remained. And the union was able to reward their longest tenured members.
It's hard to fault the union for this: the alternative was likely both huge reduction in workforce and less attractive pay, so they at least got good pay out of it for the remaining workers. But it made a lot of the less tenured union workers resentful because they felt the union sacrificed them in favor of the union 'insiders'.
The book 'The Box' by Marc Levinson is a great coverage of this topic, if steel shipping containers are the sort of thing that get you going.
Not the sort of data we'll have crop up in CommonCrawl.
And this is all before we even bring up the topic of prompt injection, a problem so intrinsic to the technology that OpenAI doesn't even take bug reports on it because bug reports "are for problems that can be fixed".
In my understanding, LLMs already hit a big wall. We can't increase the size of models mainly because it's too expensive, but also doing so may not be as effective as before. We've also run out of data. The free lunch is likely already over, for now. It's unlikely that we'll see huge improvements in the direction we've seen during recent years.
Instead, what I see is that the first letter 'L' is getting smaller. People are working on (relatively) smaller specialized models. But it means these models are unlikely outperform larger LLMs (in the direction mentioned above).
This may not be the best example considering that steam engines were around since at least 20BCE[0] but the first successful application wasn't till almost 1700.
Which steam engine do you mean and do you have a citation for this comment? The first industrial steam engines were based on the Newcomen design and were used to pump water out of mines. Their big drawback was efficiency, not power. They were only economical in coal mines, which had fuel immediately available at near zero cost.
[0] https://en.wikipedia.org/wiki/Newcomen_atmospheric_engine
The first electric motors were written off as being less powerful than steam engines.
So it goes.
I think both of these views can be true at the same time: ChatGPT (or, LLMs really) are revolutionary and they won't revolutionize the world the way technologists/researchers say.
Early adopters will use the technology and do amazing things with it. Unions are already pushing back on AI (truckers, federal employees in Canada, writers in Hollywood) and maybe rightly so. At the same time, dismissing these technologies because they don't meet your high standards yet is probably foolish.