A bear case: My predictions regarding AI progress

https://www.lesswrong.com/posts/oKAFFvaouKKEhbBPm/a-bear-case-my-predictions-regarding-ai-progress

by suryao • 1 year ago

204 215 1 year ago

stego-tech2• 1 year ago

At some point there might be massive layoffs due to ostensibly competent AI labor coming onto the scene, perhaps because OpenAI will start heavily propagandizing that these mass layoffs must happen. It will be an overreaction/mistake. The companies that act on that will crash and burn, and will be outcompeted by companies that didn't do the stupid.

We're already seeing this with tech doing RIFs and not backfilling domestically for developer roles (the whole, "we're not hiring devs in 202X" schtick), though the not-so-quiet secret is that a lot of those roles just got sent overseas to save on labor costs. The word from my developer friends is that they are sick and tired of having to force a (often junior/outsourced) colleague to explain their PR or code, only to be told "it works" and for management to overrule their concerns; this is embedding AI slopcode into products, which I'm sure won't have any lasting consequences.

My bet is that software devs who've been keeping up with their skills will have another year or two of tough times, then back into a cushy Aeron chair with a sparkling new laptop to do what they do best: write readable, functional, maintainable code, albeit in more targeted ways since - and I hate to be that dinosaur - LLMs produce passable code, provided a competent human is there to smooth out its rougher edges and rewrite it to suit the codebase and style guidelines (if any).

dartharva2• 1 year ago

One could argue that's not strictly "AI labor", just cheap (but real) labor using shortcuts because they're not paid enough to give a damn.

stego-tech2• 1 year ago

Oh, no, you’re 100% right. One of these days I will pen my essay on the realities of outsourced labor.

Spoiler alert: they are giving just barely enough to not get prematurely fired, because they know if you’re cheap enough to outsource in the first place, you’ll give the contract to whoever is cheapest at renewal anyway.

carlosdp2• 1 year ago

I'll take that bet, easily.

There's absolutely no way that we're not going to see a massive reduction in the need for "humans writing code" moving forward, given how good LLMs are getting at writing code.

That doesn't mean people won't need devs! I think there's a real case where increased capabilities from LLMs leads to bigger demand for people that know how to direct the tools effectively, of which most would probably be devs. But thinking we're going back to humans "writing readable, functional, maintainable code" in two years is cope.

rahimnathwani• 1 year ago

  increased capabilities from LLMs leads to bigger demand for people that know how to direct the tools effectively

This is the key thing.

crote• 1 year ago

There's absolutely no way that we're not going to see a massive reduction in the need for "humans writing code" moving forward, given how good LLMs are getting at writing code.

Sure, but in the same way that Squarespace and Wix killed web development. LLMs are going to replace a decent bunch of low-hanging fruit, but those jobs were always at risk of being outsourced to the lowest bidder over in India anyways.

The real question is, what's going to happen to the interns and the junior developers? If 10 juniors can create the same output as a single average developer equipped with a LLM, who's going to hire the juniors? And if nobody is hiring juniors, how are we supposed to get the next generation of seniors?

Similarly, what's going to happen to outsourcing? Will it be able to compete on quality and price? Will it secretly turn into nothing more than a proxy to some LLM?

torginus• 1 year ago

Hate to be the guy to bring it up but Jevons paradox - in my experience, people are much more eager to build software in the LLM age, and projects are getting started (and done!) that were considered 'too expensive to build' or people didn't have the necessary subject matter expertise to build them.

Just a simple crud-ish project needs frontend, backend, infra, cloud, ci/cd experience, and people who could build that as one man shows were like unicorns - a lot of people had a general how most of this stuff worked, but lacked the hands on familiarity with them. LLMs made that knowledge easy and accessible. They certainly did for me.

I've shipped more software in the past 1-2 years than the 5 years before that. And gained tons of experience doing it. LLMs helped me figure out the necessary software, and helped me gain a ton of experience, I gained all those skills, and I feel quite confident in that I could rebuild all these apps, but this time without the help of these LLMs, so even the fearmongering that LLMs will ;make people forget how to code' doesn't seem to ring true.

fragmede12• 1 year ago

What lasting consequences? Crowdstrike and the 2017 Equifax hack that leaked all our data didn't stop them. The shares of crowdstrike after it happened I bought are up more than the SP500. Elon went through Twitter and fired everybody but it hasn't collapsed. A carpenter has a lot of opinions about the woodworking used on cheap IKEA cabinets, but mass manufacturing and plastic means that building a good solid high quality chair is no longer the craft it used to be.

bloomingkales• 1 year ago

Let's imagine that we all had a trillion dollars. Then we would all sit around and go "well dang, we have everything, what should we do?". I think you'll find that just about everyone would agree, "we oughta see how far that LLM thing can go". We could be in nuclear fallout shelters for decades, and I think you'll still see us trying to push the LLM thing underground, through duress. We dream of this, so the bear case is wrong in spirit. There's no bear case when the spirit of the thing is that strong.

mola• 1 year ago

Wdym all of us? I certainly would find much better usages for the money.

What about reforming democracy? Use the corrupt system to buy the votes, then abolish all laws allowing these kind of donations that allow buying votes.

I'll litigate the hell out of all the oligarchs now that they can't out pay justice.

This would pay off more than a moon shot. I would give a bit of money for the moon shot, why not, but not all of it.

jayemar• 1 year ago

"So, after Rome's all yours you just give it back to the people? Tell me why."

bookofjoe• 1 year ago

leave dang out of this

liuliu3• 1 year ago

I think all these articles begging the question: what's author's credential to claim these things.

Be careful about consuming information from chatters, not doers. There is only knowledge from doing, not from pondering.

wavemode• 1 year ago

I'm generally more skeptical when reading takes and predictions from people working at AI companies, who have a financial interest in making sure the hype train continues.

To make an analogy - most people who will tell you not to invest in cryptocurrency are not blockchain engineers. But does that make their opinion invalid?

JackYoustra• 1 year ago

The crypto people have no coherent story about why crypto is fundamentally earth-shaking more than a story about either gambling or regulatory avoidance, whereas the story for AI, if you believe it, is a second industrial revolution and labor automation where, to at least some small extent, it is undeniable.

liuliu3• 1 year ago

Of course I trust people who working on L2 chains to tell me how to scale Bitcoin and people who working on cryptography to walk me through the ETH PoS algorithms.

You cannot lead to truth by learning from people who don't know. People who know can be biased, sure, so the best way to learn is to learn the knowledge, not the "hot-takes" or "predictions".

friendzis• 1 year ago

Be careful about consuming information from chatters, not doers

The doers produce a new javascript framework every week, claiming it finally solves all the pains of previous frameworks, whereas the chatters pinpoint all the deficiencies and pain points.

One group has an immensely better track record than the other.

liuliu3• 1 year ago

I would listen to people who used the previous frameworks about the deficiencies and pain points, not people who just casually browse the documentation about their high-flying ideas why these have deficiencies and pain points.

One group has an immensely more convincing power to me.

usaar3334• 1 year ago

LW isn't a place that cares about credentialism.

He has tons of links for the objective statements. You either accept the interpretation or you don't.

NitpickLawyer• 1 year ago

He has tons of links for the objective statements.

I stopped at this quote

LLMs still seem as terrible at this as they'd been in the GPT-3.5 age.

This is so plainly, objectively and quantitatively wrong that I need not bother. I get hyperbole, but this isn't it. This shows a doubling-down on biases that the author has, and no amount of proof will change their mind. Not an article / source for me, then.

viccis2• 1 year ago

Regarding "AGI", is there any evidence of true synthetic a priori knowledge from an LLM?

cheevly3• 1 year ago

Produce true synthetic a priori knowledge of your own, and ill show you an automated LLM workflow that can arrive at the same outcome without hints.

viccis2• 1 year ago

Build an LLM on a corpus with all documents containing mathematical ideas removed. Not a single one about numbers, geometry, etc. Now figure out how to get it to tell you what the shortest path between two points in space is.

dartharva2• 1 year ago

At some point there might be massive layoffs due to ostensibly competent AI labor coming onto the scene, perhaps because OpenAI will start heavily propagandizing that these mass layoffs must happen. It will be an overreaction/mistake. The companies that act on that will crash and burn, and will be outcompeted by companies that didn't do the stupid.

(IMO) Apart from programmer assistance (which is already happening), AI agents will find the most use in secretarial, ghostwriting and customer support roles, which generally have a large labor surplus and won't immediately "crash and burn" companies even if there are failures. Perhaps if it's a new startup or a small, unstable business on shaky grounds this could become a "last straw" kind of a factor, but for traditional corporations with good leeway I don't think just a few mistakes about AI deployment can do too much harm. The potential benefits, on the other hand, far outmatch the risk taken.

hattmall10• 1 year ago

I see engineering, not software, but the other technical areas that have the biggest threat. High paid, knowledge based fields, but not reliant on interpersonal communication. Secretarial and customer support less so, they aren't terribly high paid and anything that relies on interacting with people is going to meet a lot of pushback. US based call centers is already a big selling point for a lot of companies and chat bots have been around for years in customer support and people hate them and there's a long way to go to change that perception.

readthenotes1• 1 year ago

LLMs seem less hyped than block chains were back in the day

kfarr• 1 year ago

Agreed and unlike blockchain people actually use this product

randomNumber75• 1 year ago

Some people use blockchain to buy drugs...

parliament32• 1 year ago

Because with blockchain/crypto there was actual money to be made (at the expense of users or not). LLMs are just a money furnace.

n_ary• 1 year ago

Hmm, I didn’t read the article but from the gist of other comments, we seem to have bought into Sama’s “agents so good, you don’t need developers/engineers/support/secretaries/whatever anymore”. Issue is, it is almost same as claiming, pocket calculators so good, we don’t need accountants anymore, even computers so good, we don’t need accountants anymore. This AI seems to claim to be that motor car moment when horse cart got replaced. But a horse cart got replaced with a Taxi(and they also have unions protecting them!). With AI, all these “to be replaced” people are like accountants, more productive, same as with higher level languages compared to assembly, many new devs are productive. Despite cars replacing the horse carts of the long past, we still fail to have self driving cars and still someone needs to learn to drive that massive hunk of metal, same as whoever plans to deploy LLM to layoff devs must learn to drive those LLMs and know what it is doing.

I believe it is high time we come out this madness and reveal the lies of the marketers and grifters of AI for what it is. If AI can replace anyone, it should begin with doctors, they work with rote knowledge and service based on explicit(though ambiguous) inputs, same as an LLM needs, but I still have doctors and wait for hours on end in the waiting room to get prescribed a cough hard candy only to later comeback again because it was actually covid and my doctor had a brain fart.

a-dub• 1 year ago

LLMs are not good in some domains and bad in others. Rather, they are incredibly good at some specific tasks and bad at other tasks. Even if both tasks are in the same domain, even if tasks A and B are very similar, even if any human that can do A will be able to do B.

i think this is true of ai/ml systems in general. we tend to anthropomorphise their capability curves to match the cumulative nature of human capabilities, where often times the capability curve of the machine is discontinuous and has surprising gaps.

worik• 1 year ago

It blows Google out of the water at being Google

That is enough for me.

mandevil• 1 year ago

I sincerely wonder how long that will be true. Google was amazing and didn't have more than small, easily ignorable ads in 1999, and they weren't really tracking you the way they are today, just an all-around better experience than Google delivers today.

I'm not sure that it's a technology difference that makes LLM a better experience than search today, it's that the VC's are still willing to subsidize user experience today, and won't start looking for return on their investment for a few more years. Give OpenAI 10 years to pull all the levers to pay back the VC investment and what will it be like?

timmy-turner• 1 year ago

They will sell "training data slots". So that when I'm looking for a butter cookie recipe, ChatGPT says I'll have to use 100g of "Brand (TM) Butter" instead of just "Butter".

Ask it how to deploy an app to the cloud and it will insist you need to deploy it to Azure.

These ads would be easily visible though. You can probably sell far more malicious things.

andsoitis• 1 year ago

This poetic statement by the author sums it up for me:

”People are extending LLMs a hand, hoping to pull them up to our level. But there's nothing reaching back.”

blitzar• 1 year ago

When you (attempt to) save a person from drowning there is ridiculously high chance of them drowning you.

nakedneuron• 1 year ago

Haha.

Shame on you for making me laugh. That was very inappropriate.

csomar5• 1 year ago

LLMs still seem as terrible at this as they'd been in the GPT-3.5 age. Software agents break down once the codebase becomes complex enough, game-playing agents get stuck in loops out of which they break out only by accident, etc.

This has been my observation. I got into Github Copilot as early as it launched back when GPT-3 was the model. By that time (late 2021) copilot can already write tests for my Rust functions, and simple documentation. This was revolutionary. We didn't have another similar moment since then.

The Github copilot vim plugin is always on. As you keep typing, it keeps suggesting in faded text the rest of the context. Because it is always on, I kind of can read into the AI "mind". The more I coded, the more I realized it's just search with structured results. The results got better with 3.5/4 but after that only slightly and sometimes not quite (ie: 4o or o1).

I don't care what anyone says, as yesterday I made a comment that truth has essentially died: https://news.ycombinator.com/item?id=43308513 If you have a revolutionary intelligence product, why is it not working for me?

kiratp2• 1 year ago

You’re not using the best tools.

Claude Code, Cline, Cursor… all of them with Claude 3.7.

csomar5• 1 year ago

Nope. I try the latest models as they come and I have a self-made custom setup (as in a custom lua plugin) in Neovim. What I am not, is selling AI or AI-driven solutions.

roncesvalles3• 1 year ago

The last line has been my experience as well. I only trust what I've verified firsthand now because the Internet is just so rife with people trying to influence your thoughts in a way that benefits them, over a good faith sharing of the truth.

I just recently heard this quote from a clip of Jeff Bezos: "When the data and the anecdotes disagree, the anecdotes are usually right.", and I was like... wow. That quote is the zeitgeist.

If it's so revolutionary, it should be immediately obvious to me. I knew Uber, Netflix, Spotify were revolutionary the first time I used them. With LLMs for coding, it's like I'm groping in the dark trying to find what others are seeing, and it's just not there.

roenxi2• 1 year ago

I knew Uber, Netflix, Spotify were revolutionary the first time I used them.

Maybe re-tune your revolution sensor. None of those are revolutionary companies. Profitable and well executed, sure, but those turn up all the time.

Uber's entire business model was running over the legal system so quickly that taxi licenses didn't have time to catch up. Other than that it was a pretty obvious idea. It is a taxi service. The innovations they made were almost completely legal ones; figuring out how to skirt employment and taxi law.

Netflix was anticipated online by and is probably inferior to YouTube except for the fact that they have a pretty traditional content creator lab tacked on the side to do their own programs. And torrenting had been a thing for a long time already showing how to do online distribution of video content.

fragmede12• 1 year ago

it's just not there

Build the much maligned Todo app with Aider and Claude for yourself. give it one sentence and have it spit out working, if imperfect code. iterate. add a graph for completion or something and watch it pick and find a library without you having to know the details of that library. fine, sure, it's just a Todo app, and it'll never work for a "real" codebase, whatever that means, but holy shit, just how much programming did you need to get down and dirty with to build that "simple" Todo app? Obviously building a Todo app before LLMs was possible, but abstracted out, the fact that it can be generated like that's not a game changer?

mlsu• 1 year ago

Revolutionary things are things that change how society actually works at a fundamental level. I can think of four technologies of the past 40 years that fit that bill:

the personal computer

the internet

the internet connected phone

social media

those technologies are revolutionary, because they caused fundamental changes to how people behave. People who behaved differently in the "old world" were forced to adapt to a "new world" with those technologies, whether they wanted to or not. A newer more convenient way of ordering a taxicab or watching a movie or music are great consumer product stories, and certainly big money makers. They don't cause complex and not fully understood changes to way people work, play, interact, self-identify, etc. the way that revolutionary technologies do.

Language models feel like they have the potential to be a full blown sociotechnological phenomenon like the above four. They don't have a convenient consumer product story beyond ChatGPT today. But they are slowly seeping into the fabric of things, especially on social media, and changing the way people apply to jobs, draft emails, do homework, maybe eventually communicate and self-identify at a basic level.

I'd almost say that the lack of a smash bang consumer product story is even more evidence that the technology is diffusing all over the place.

grumbel• 1 year ago

While I don't disagree with that observation, it falls into the "well, duh!"-category for me. The models are build with no mechanism for long term memory and thus suck at tasks that require long term memory. There is nothing surprising here. There was never any expectation that LLMs magically develop long term memory, as that's impossible given the architecture. They predict the next word and once the old text moves out of the context window, it's gone. The models neither learn as they work nor can they remember the past.

It's not even like humans are all that different here. Strip a human of their tools (pen&paper, keyboard, monitor, etc.) and have them try solving problems with nothing but the power of their brain and they'll struggle a hell of a lot too, since our memory ain't exactly perfect either. We don't have perfect recall, we look things up when we need to, a large part of our "memory" is out there in the world around us, not in our head.

The open question is how to move forward. But calling AI progress a dead end before we even started exploring long term memory, tool use and on-the-fly learning is a tad little premature. It's like calling quits on the development of the car before you put the wheels on.

_huayra_• 1 year ago

Ultimately, every AI thing I've tried in this era seems to want to make me happy, even if it's wrong, instead of helping me.

I describe it like "an eager intern who can summarize a 20-min web search session instantly, but ultimately has insufficient insight to actually help you". (Note to current interns: I'm mostly describing myself some years ago; you may be fantastic so don't take it personally!)

Most of my interactions with it via text prompt or builtin code suggestions go like this:

1. Me: I want to do X in C++. Show me how to do it only using stdlib components (no external libraries).

2. LLM: Gladly! Here is solution X

3. Me: Remove the undefined behavior from foo() and fix the methods that call it

4. LLM: Sure! Here it is (produces solution X again)

5. Me: No you need to remove the use of uninitialized variables as the out parameters.

6. LLM: Oh certainly! Here is the correct solution (produces a completely different solution that also has issues)

7. Me: No go back to the first one

etc

For the ones that suggest code, it can at least suggest some very simple boilerplate very easily (e.g. gtest and gmock stuff for C++), but asking it to do anything more significant is a real gamble. Often I end up spending more time scrutinizing the suggested code than writing a version of it myself.

rchaud5• 1 year ago

The difference is that interns can learn, and can benefit from reference items like a prior report, whose format and structure they can follow when working on the revisions.

AI is just AI. You can upload a reference file for it to summarize, but it's not going to be able to look at the structure of the file and use that as a template for future reports. You'll still have to spoon-feed it constantly.

red-iron-pine2• 1 year ago

interns can generally also tell me "tbh i have no damn idea", while AI just talks out it's virtual ass, and I can't read from it's voice or behavior that maybe it's not sure.

interns can also be clever and think outside the box. this is mostly not good, but sometimes they will surprise you in a good way. the AI by definition can only copy what someone else has done.

yifanl• 1 year ago

7 is the worst part about trying to review my coworker's code that I'm 99% confident is copilot output - and to be clear, I don't really care how someone chooses to write their code, I'll still review it as evenly as I can.

I'll very rarely ask someone to completely rewrite a patch, but so often a few minor comments get addressed with an entire new block of code that forces me to do a full re-review, and I can't get it across to him that that's not what I'm asking for.

kledru• 1 year ago

github copilot is a bit outdated technology to be fair...

edanm• 1 year ago

If you have a revolutionary intelligence product, why is it not working for me?

Is programming itself revolutionary? Yes. Does it work for most people? I don't even know how to parse that question, most people aren't programmers and need to spend a lot of effort to be able to harness a tool like programming. Especially in the early days of software dev, when programming was much harder.

Your position of "I'll only trust things I see with my own eyes" is not a very good one, IMO. I mean, for sure the internet is full of hype and tricksters, but your comment yesterday was on a Tweet by Steve Yegge, a famous and influential software developer and software blogger, who some of us have been reading for twenty years and has taught us tons.

He's not a trickster, not a fraud, and if he says "this technology is actually useful for me, in practice" then I believe he has definitely found an actual use of the technology. Whether I can find a similar use for that technology is a question - it's not always immediate. He might be working in a different field, with different constraints, etc. But most likely, he's just doing something he's learned how to do and I don't, meaning I want to learn it.

bdangubic• 1 year ago

If you have a revolutionary intelligence product, why is it not working for me?

This is kind of like if I said when the first dumbbell was invented “why don’t I look like arnold schwarzenegger…

colonCapitalDee• 1 year ago

Yeah, I'd buy it. I've been using Claude pretty intensively as a coding assistant for the last couple months, and the limitations are obvious. When the path of least resistance happens to be a good solution, Claude excels. When the best solution is off the beaten track, Claude struggles. When all the good solutions lay off the beaten track, Claude falls flat on its face.

Talking with Claude about design feels like talking with that one coworker who's familiar with every trendy library and framework. Claude knows the general sentiment around each library and has gone through the quickstart, but when you start asking detailed technical questions Claude just nods along. I wouldn't bet money on it, but my gut feeling is that LLMs aren't going to be a straight or even curved shot to AGI. We're going to see plenty more development in LLMs, but it'll be just be that. Better LLMs that remain LLMs. There will be areas where progress is fast and we'll be able to get very high intelligence in certain situations, but there will also be many areas where progress is slow, and the slow areas will cripple the ability of LLMs to reach AGI. I think there's something fundamentally missing, and finding what that "something" is is going to take us decades.

randomNumber75• 1 year ago

Yes, but on the other hand I don't understand why people think something that you can train something on pattern matching and it magically becomes intelligent.

danielbln4• 1 year ago

We don't know what exactly makes us humans as intelligent as we are. And while I don't think that LLMs will be general intelligent without some other advancements, I don't get the confident statements that "clearly pattern matching can't lead to intelligence" when we don't really know what leads to intelligence to begin with.

throw4847285• 1 year ago

This is the difference between the scientific approach and the engineering approach. Engineers just need results. If humans had to mathematically model gravity first, there would be no pyramids. Plus, look up how many psychiatric medications are demonstrated to be very effective, but the action mechanisms are poorly understood. The flip side is Newton doing alchemy or Tesla claiming to have built an earthquake machine.

Sometimes technology far predates science and other times you need a scientific revolution to develop new technology. In this case, I have serious doubts that we can develop "intelligent" machines without understanding the scientific and even philosophical underpinnings of human intelligence. But sometimes enough messing around yields results. I guess we'll see.

danielbln4• 1 year ago

A tip: ask Claude to put a critical hat on. I find the output afterwards to be improved.

mehphp• 1 year ago

Do you have an example?

Paradigma11• 1 year ago

I am not so sure about that. Using Claude yesterday it gave me a correct function that returned an array. But the algorithm it used did not return the items sorted in one pass so it had run a separate sort at the end. The fascinating thing is that it realized that, commented on it and went on and returned a single pass function.

That seems a pretty human thought process and shows that fundamental improvements might not depend as much on the quality of the LLM itself but on the cognitive structure it is embedded.

jemmyw2• 1 year ago

I've been writing code that implements tournament algorithms for games. You'd think an LLM would excel at this because it can explain the algorithms to me. I've been using cline on lots of other tasks to varying success. But it just totally failed with this one: it kept writing edge cases instead of a generic implementation. It couldn't write coherent enough tests across a whole tournament.

So I wrote tests thinking it could implement the code from the tests, and it couldn't do that either. At one point it went so far with the edge cases that it just imported the test runner into the code so it could check the test name to output the expected result. It's like working with a VW engineer.

Edit: I ended up writing the code and it wasn't that hard, I don't know why it struggled with this one task so badly. I wasted far more time trying to make the LLM work than just doing it myself.

gymbeaux• 1 year ago

Yeah agree 100%. LLMs are overrated. I describe them as the “Jack of all, master of none” of AI. LLMs are that jackass guy we all know who has to chime in to every topic like he knows everything, but in reality he’s a fraud with low self-esteem.

I’ve known a guy since college who now has a PhD in something niche, supposedly pulls a $200k/yr salary. One of our first conversations (in college, circa 2014) was how he had this clever and easy way to mint money- by selling Minecraft servers installed on Raspberry Pis. Some of you will recognize how asinine this idea was and is. For everyone else- back then, Minecraft only ran on x86 CPUs (and I doubt a Pi would make a good Minecraft server today, even if it were economical). He had no idea what he was talking about, he was just spewing shit like he was God’s gift. Actually, the problem wasn’t that he had no idea- it was that he knew a tiny bit- enough to sound smart to an idiot (remind you of anyone?).

That’s an LLM. A jackass with access to Google.

I’ve had great success with SLMs (small language models), and what’s more I don’t need a rack of NVIDIA L40 GPUs to train and use them.

arrakark2• 1 year ago

But original MC ran on JVM, which can run on ARM...

usaar3334• 1 year ago

Author also made a highly upvoted and controversial comment about o3 in the same vein that's worth reading: https://www.lesswrong.com/posts/Ao4enANjWNsYiSFqc/o3?comment...

Oh course lesswrong, being heavily AI doomers, may be slightly biased against near term AGI just from motivated reasoning.

Gotta love this part of the post no one has yet addressed:

At some unknown point – probably in 2030s, possibly tomorrow (but likely not tomorrow) – someone will figure out a different approach to AI. Maybe a slight tweak to the LLM architecture, maybe a completely novel neurosymbolic approach. Maybe it will happen in a major AGI lab, maybe in some new startup. By default, everyone will die in <1 year after that

demaga• 1 year ago

I would expect similar doom predictions in the era of nuclear weapon invention, but we've survived so far. Why do people assume AGI will be orders of magnitude more dangerous than what we already have?

amoss• 1 year ago

Nuclear weapons are not self-improving or self-replicating.

usaar3334• 1 year ago

More ability to kill everyone. That's harder to do with nukes.

That said, the actual forecast odds on metaculus are pretty similar for nuclear and AI catastrophies: https://possibleworldstree.com/

randomNumber75• 1 year ago

Most people are just ignorant and dumb, dont listen to it.

kragen2• 1 year ago

Because they've thought about the question to a deeper extent than just a strained simile.

HelloMcFly3• 1 year ago

Was that comment intended seriously? I thought it was a wry joke.

usaar3334• 1 year ago

I think so. Thane is aligned with the high p doom folks.

1 year may be slightly exaggerated, but it aligns with his view

gwern• 1 year ago

I never thought I'd see the day that LessWrong would be accused of being biased against near-term AGI forecasts (and for none of the 5 replies to question this description either). But here we are. Indeed do many things come to pass.

TeMPOraL7• 1 year ago

Yup. I was surprised to see this article on LW in the first place - it goes against what you'd expect there in the first place. But to see HN comments dissing an LW article for being biased against near-term AGI forecasts? That made me wonder if I'm not dreaming.

(Sadly, I'm not.)

achierius• 1 year ago

Oh course lesswrong, being heavily AI doomers, may be slightly biased against near term AGI just from motivated reasoning.

LessWrong was predicting AI doom within decades back when people thought it wouldn't happen in our lifetimes; even as recently as 2018~2020, people there were talking about 2030-2040 while the rest of the world laughed at the very idea. I struggle to accept an argument that they're somehow under-estimating the likelihood of doom given all the historical evidence to the contrary.

cglace2• 1 year ago

The thing I can't wrap my head around is that I work on extremely complex AI agents every day and I know how far they are from actually replacing anyone. But then I step away from my work and I'm constantly bombarded with “agents will replace us”.

I wasted a few days trying to incorporate aider and other tools into my workflow. I had a simple screen I was working on for configuring an AI Agent. I gave screenshots of the expected output. Gave a detailed description of how it should work. Hours later I was trying to tweak the code it came up with. I scrapped everything and did it all myself in an hour.

I just don't know what to believe.

hattmall10• 1 year ago

There are some fields though where they can replace humans in significant capacity. Software development is probably one of the least likely for anything more than entry level, but A LOT of engineering has a very very real existential threat. Think about designing buildings. You basically just need to know a lot of rules / tables and how things interact to know what's possible and the best practices. A purpose built AI could develop many systems and back test them to complete the design. A lot of this is already handled or aided by software, but a main role of the engineer is to interface with the non-technical persons or other engineers. This is something where an agent could truly interface with the non-engineer to figure out what they want, then develop it and interact with the design software quite autonomously.

I think though there is a lot of focus on AI agents in software development though because that's just an early adopter market, just like how it's always been possible to find a lot of information on web development on the web!

drysine• 1 year ago

a main role of the engineer is to interface with the non-technical persons or other engineers

The main role of the engineer is being responsible for the building not collapsing.

arkh• 1 year ago

just

In my experience this word means you don't know whatever you're speaking about. "Just" almost always hide a ton of unknown unknowns. After being burned enough times nowadays when I'm going to use it I try to stop and start asking more questions.

gerikson• 1 year ago

Most engineering fields are de jure professional, which means they can and probably will enforce limitations on the use of GenAI or its successor tech before giving up that kind of job security. Same goes for the legal profession.

Software development does not have that kind of protection.

ForHackernews• 1 year ago

Good freaking luck! The inconsistencies of the software world pale in comparison to trying to construct any real world building: http://johnsalvatier.org/blog/2017/reality-has-a-surprising-...

seanhunter• 1 year ago

   > "you basically just need to know a lot of rules..."

This comment commits one of the most common fallacies that I see really often in technical people, which is to assume that any subject you don't know anything about must be really simple.

I have no idea where this comment comes from, but my father was a chemical engineer and his father was mechanical engineer. A family friend is a structural engineer. I don't have a perspective about AI replacing people's jobs in general that is any more valuable than anyone elses, but I can say with a great deal of confidence that in those three engineering disciplines specifically literally none of any of their jobs are about knowing a bunch of rules and best practices.

Don't make the mistake of thinking that just because you don't know what someone does, that their job is easy and/or unnecessary or you could pick it up quickly. It may or may not be true but assuming it to be the case is unlikely to take you anywhere good.

spaceman_20202• 1 year ago

You’re biased because if you’re here, you’re likely an A-tier player used to working with other A-tier players.

But the vast majority of the world is not A players. They’re B and C players

I don’t think the people evaluating AI tools have ever worked in wholly mediocre organizations - or even know how many mediocre organizations exist

code_for_monkey• 1 year ago

wish this didnt resonate with me so much. Im far from a 10x developer, and im in an organization that feels like a giant, half dead whale. Sometimes people here seem like they work on a different planet.

cheevly3• 1 year ago

I promise the amount of time, experiments and novel approaches you’ve tested are .0001% of what others have running in stealth projects. Ive spent an average of 10 hours per day constantly since 2022 working on LLMs, and I know that even what I’ve built pales in comparison to other labs. (And im well beyond agents at this point). Agentic AI is what’s popular in the mainstream, but it’s going to be trounced by at least 2 new paradigms this year.

cglace2• 1 year ago

So what is your prediction?

handfuloflight• 1 year ago

Say more.

aaronbaugher• 1 year ago

It kind of reminds me of the Y2K scare. Leading up to that, there were a lot of people in groups like comp.software.year-2000 who claimed to be doing Y2K fixes at places like the IRS and big corporations. They said they were just doing triage on the most critical systems, and that most things wouldn't get fixed, so there would be all sorts of failures. The "experts" who were closest to the situation, working on it in person, turned out to be completely wrong.

I try to keep that in mind when I hear people who work with LLMs, who usually have an emotional investment in AI and often a financial one, speak about them in glowing terms that just don't match up with my own small experiments.

TeMPOraL7• 1 year ago

I used to believe that until, over a decade later, I read stories from those ""experts" who were closest to the situation", and it turns out Y2K was serious and it was a close call.

jmholla• 1 year ago

I just want to pile on here. Y2K was avoided due to a Herculean effort across the world to update systems. It was not an imaginary problem. You'll see it again in the lead up to 2038 [0].

[0] https://en.wikipedia.org/wiki/Year_2038_problem

naasking• 1 year ago

But then I step away from my work and I'm constantly bombarded with “agents will replace us”.

An assembly language programmer might have said the same about C programming at one point. I think the point is, that once you depend on a more abstract interface that permits you to ignore certain details, that permits decades of improvements to that backend without you having to do anything. People are still experimenting with what this abstract interface is and how it will work with AI, but they've already come leaps and bounds from where they were only a couple of years ago, and it's only going to get better.

tibbar• 1 year ago

LLMs make it very easy to cheat, both academically and professionally. What this looks like in the workplace is a junior engineer not understanding their task or how to do it but stuffing everything into the LLM until lint passes. This breaks the trust model: there are many requirements that are a little hard to verify than an LLM might miss, and the junior engineer can now represent to you that they "did what you ask" without really certifying the work output. I believe that this kind of professional cheating is just as widespread as academic cheating, which is an epidemic.

What we really need is people who can certify that a task was done correctly, who can use LLMs as an aid. LLMs simply cannot be responsible for complex requirements. There is no way to hold them accountable.

swazzy• 1 year ago

I see no reason to believe the extraordinary progress we've seen recently will stop or even slow down. Personally, I've benefited so much from AI that it feels almost alien to hear people downplaying it. Given the excitement in the field and the sheer number of talented individuals actively pushing it forward, I'm quite optimistic that progress will continue, if not accelerate.

danielbln4• 1 year ago

I hear you, I feel constantly bewildered by comments like "LLMs haven't changed really since GPT3.5.", I mean really? It went from an exciting novelty to a core pillar of my daily work, it's allowed me and my entire (granted , quote senior) org to be incredibly more productive and creative with our solutions.

And the I stumble across a comment where some LLM hallucinated a library that means clearly AI is useless.

Workaccount2• 1 year ago

If LLM's are bumpers on a bowling lane, HN is a forum of pro bowlers.

Bumpers are not gonna make you a pro bowler. You aren't going to be hitting tons of strikes. Most pro bowlers won't notice any help from bumpers, except in some edge cases.

If you are an average joe however, and you need to knock over pins with some level of consistency, then those bumpers are a total revolution.

esafak2• 1 year ago

That is not a good analogy. They are closer to assistants to me. If you know how and what to delegate, you can increase your productivity.

spaceman_20202• 1 year ago

The impression I get from using all cutting edge AI tools:

1. Sonnet 3.7 is a mid-level web developer at least

2. DeepResearch is about as good an analyst as an MBA from a school ranked 50+ nationally. Not lower than that. EY, not McKinsey

3. Grok 3/GPT-4.5 are good enough as $0.05/word article writers

Its not replacing the A-players but its good enough to replace B players and definitely better than C and D players

tcoff91• 1 year ago

A midlevel web developer should do a whole lot more than just respond to chat messages and do exactly what they are told to do and no more.

danielbln4• 1 year ago

When I use LLMs that what it does. Spawns commands, edits files, runs tests, evaluates outputs, iterates and solutions under my guidance.

id00• 1 year ago

I'd expect mid-level developer to show more understanding and better reasoning. So far it looks like a junior dev who read a lot of books and good at copy pasting from stackoverflow.

(Based on my everyday experience with Sonet and Cursor)

roenxi2• 1 year ago

This seems to be ignoring the major force driving AI right now - hardware improvements. We've barely seen a new hardware generation since ChatGPT was released to the market, we'd certainly expect it to plateau fairly quickly on fixed hardware. My personal experience of AI models is going to be a series of step changes every time the VRAM on my graphics card doubles. Big companies are probably going to see something similar each time a new more powerful product hits the data centre. The algorithms here aren't all that impressive compared to the creeping FLOPS/$ metric.

Bear cases always welcome. This wouldn't be the first time in computing history that progress just falls off the exponential curve suddenly. Although I would bet money on there being a few years left and AGI is achieved.

notTooFarGone• 1 year ago

hardware improvements don't strike me as the horse to bet on.

LLM Progression seems to be linear and compute needed exponential. And I don't see exponential hardware improvements besides some new technology (that we should not bet on coming ayntime soon).

redlock• 1 year ago

Moore's law is exponential

greazy• 1 year ago

Although I would bet money on there being a few years left and AGI is achieved.

Yeah? I'll take you up on that offer. $100AUD AGI won't happen this decade.

gmt20272• 1 year ago

The typical AI economic discussion always focuses on job loss, but that's only half the story. We won't just have corporations firing everyone while AI does all the work - who would buy their products then?

The disruption goes both ways. When AI slashes production costs by 10-100x, what's the value proposition of traditional capital? If you don't need to organize large teams or manage complex operations, the advantage of "being a capitalist" diminishes rapidly.

I'm betting on the rise of independents and small teams. The idea that your local doctor or carpenter needs VC funding or an IPO was always ridiculous. Large corps primarily exist to organize labor and reduce transaction costs.

The interesting question: when both executives and frontline workers have access to the same AI tools, who wins? The manager with an MBA or the person with practical skills and domain expertise? My money's on the latter.

randomNumber75• 1 year ago

Idk where you live, but in my world "being a capitalist" requires you to own capital. And you know what, AI makes it even better to own capital. Now you have these fancey machines doing stuff for you and you dont even need any annoying workers.

gmt20272• 1 year ago

By "capitalist," I'm referring to investors whose primary contribution is capital, not making a political statement about capitalism itself.

Capital is crucial when tools and infrastructure are expensive. Consider publishing: pre-internet, starting a newspaper required massive investment in printing presses, materials, staff, and distribution networks. The web reduced these costs dramatically, allowing established media to cut expenses and focus on content creation. However, this also opened the door for bloggers and digital news startups to compete effectively without the traditional capital requirements. Many legacy media companies are losing this battle.

Unless AI systems remain prohibitively expensive (which seems unlikely given current trends), large corporations will face a similar disruption. When the tools of production become accessible to individuals and small teams, the traditional advantage of having deep pockets diminishes significantly.

HenryBemis• 1 year ago

My predictions on the matter:

  LLMs are already super useful. 
  It does all my coding and scripting for me @home
  It does most of the coding and scripting at the workplace
  It creates 'fairly good' checklists for work (not perfect, but it takes a 4 hour effort and makes it 25mins - but the "Pro" is still needed to make this or that checklist usable - I call this a win)(need both the tech AND the human)
  If/when you train an 'in-house' LLM it can make some easy wins (on mega-big-companies with 100k staff they can get quick answers on "which Policy writes about XYZ, which department can I talk to about ABC, etc.)
  We won't have the "AGI"/Skynet anytime soon, and when one will exist the company (let's use OpenAI for example) will split in two. Half will give LLMs for the masses at $100 per month, the "Skynet" will go to the DOD and we will never hear about it again, except in the Joe Rogan podcast as a rumor.
  It is a great 'idea generator' (search engine and results aggregator): give me a list of 10 things I can do _that_ weekend in _city_I_will_be_traveling_to so if/when I go to (e.g. London): here are the cool concerts, theatrical performances, parks, blah blah blah

audessuscest• 1 year ago

It seems to me that "vibe checks" for how smart a model feels are easily gameable by making it have a better personality.

I don't buy that at all, most of my use cases don't involve model's personality, if anything I usually instruct to skip any commentary and give the result excepted only. I'm sure most people using AI models seriously would agree.

My guess is that it's most of the reason Sonnet 3.5.1 was so beloved. Its personality was made much more appealing, compared to e. g. OpenAI's corporate drones.

I would actually guess it's mostly because it was good at code, which doesn't involve much personnality

orangebread• 1 year ago

I think the author provides an interesting perspective to the AI hype, however, I think he is really downplaying the effectiveness of what you can do with the current models we have.

If you've been using LLMs effectively to build agents or AI-driven workflows you understand the true power of what these models can do. So in some ways the author is being a little selective with his confirmation bias.

I promise you that if you do your due diligence in exploring the horizon of what LLMs can do you will understand what I'm saying. If ya'll want a more detailed post I can get into the AI systems I have been building. Don't sleep on AI.

aetherson2• 1 year ago

I don't think he is downplaying the effectiveness of what you can do with the current models. Rather, he's in a milieu (LessWrong), which is laser-focused on "transformative" AI, AGI, and ASI.

Current AI is clearly economically valuable, but if we freeze everything at the capabilities it has today it is also clearly not going to result in mass transformation of the economy from "basically being about humans working" to "humans are irrelevant to the economy." Lots of LW people believe that in the next 2-5 years humans will become irrelevant to the economy. He's arguing against that belief.

mbil• 1 year ago

I agree with you. I recently wrote up my perspective here: https://news.ycombinator.com/item?id=43308912

lackoftactics3• 1 year ago

GPT-5 will be even less of an improvement on GPT-4.5 than GPT-4.5 was on GPT-4. The pattern will continue for GPT-5.5 and GPT-6, the ~1000x and 10000x models they may train by 2029 (if they still have the money by then). Subtle quality-of-life improvements and meaningless benchmark jumps, but nothing paradigm-shifting.

It's easy to spot people who secretly hate LLMs and feel threatened by them these days. GPT-5 will be a unified model, very different from 4o or 4.5. Throwing around numbers related to scaling laws shows a lack of proper research. Look at what DeepSeek accomplished with far fewer resources; their paper is impressive.

I agree that we need more breakthroughs to achieve AGI. However, these models increase productivity, allowing people to focus more on research. The number of highly intelligent people currently working on AI is astounding, considering the number of papers and new developments. In conclusion, we will reach AGI. It's a race with high stakes, and history shows that these types of races don't stop until there is a winner.

contagiousflow• 1 year ago

In conclusion, we will reach AGI

I'm a little confused by this confidence? Is there more evidence aside from the number of smart people working on it? We have a lot of smart people working on a lot of big problems, that doesn't guarantee a solution nor a timeline.

rscho• 1 year ago

It's also easy to spot irrational zealots. Your statement is no more plausible than OP's. No one knows whether we'll achieve AGI, especially since the definition is very blurry.

radioactivist• 1 year ago

Some hard problems have remain unsolved in basically every field of human interest for decades/centuries/millennia -- despite the number of intelligent people and/or resources that have been thrown at them.

I really don't understand the level optimism that seems to exist for LLMs. And speculating that people "secretly hate LLMs" and "feel threatened by them" isn't an answer (frankly, when I see arguments that start with attacks like that alarm bells start going off in my head).

ath3nd2• 1 year ago

I logged in to specifically downvote this comment, because it attacks the OP's position with unjustified and unsubstantiated confidence in the reverse.

It's easy to spot people who secretly hate LLMs and feel threatened by them these days.

I don't think OP is threatened or hates LLM, if anything, OP is on the position that LLM are so far away from intelligence that it's laughable to consider it threatening.

In conclusion, we will reach AGI

The same way we "cured" cancer and Alzheimer's, two arguably much more important inventions than a glorified text predictor/energy guzzler. But I like the confidence, it's almost as much as OP's confidence that nothing substantial will happen.

It's a race with high stakes, and history shows that these types of races don't stop until there is a winner.

So is the existential threat to humanity in the race to phase out fossil fuels/stop global warming, and so far I don't see anyone "winning".

However, these models increase productivity, allowing people to focus more on research

The same way the invention of the computer, the car, the vacuum cleaner and all the productivity increasing inventions in the last centuries allowed us to idle around, not have a job, and focus on creative things.

It's easy to spot people who secretly hate LLMs and feel threatened by them these days

It's easy to spot e/acc bros feeling threatened that all the money they sunk into crypto, AI, the metaverse, web3 are gonna go to waste and try to fan the hype around it so they can cash in big. How does that sound?

lackoftactics3• 1 year ago

I appreciate the pushback and acknowledge that my earlier comment might have conveyed too much certainty—skepticism here is justified and healthy.

However, I'd like to clarify why optimism regarding AGI isn't merely wishful thinking. Historical parallels such as heavier-than-air flight, Go, and protein folding illustrate how sustained incremental progress combined with competition can result in surprising breakthroughs, even where previous efforts had stalled or skepticism seemed warranted. AI isn't just a theoretical endeavor; we've seen consistent and measurable improvements year after year, as evidenced by Stanford's AI Index reports and emergent capabilities observed at larger scales.

It's true that smart people alone don't guarantee success. But the continuous feedback loop in AI research—where incremental progress feeds directly into further research—makes it fundamentally different from fields characterized by static or singular breakthroughs. While AGI remains ambitious and timelines uncertain, the unprecedented investment, diversity of research approaches, and absence of known theoretical barriers suggest the odds of achieving significant progress (even short of full AGI) remain strong.

To clarify, my confidence isn't about exact timelines or certainty of immediate success. Instead, it's based on historical lessons, current research dynamics, and the demonstrated trajectory of AI advancements. Skepticism is valuable and necessary, but history teaches us to stay open to possibilities that seem improbable until they become reality.

P.S. I apologize if my comment particularly triggered you and compelled you to log in and downvote. I am always open to debate, and I admit again that I started too strongly.

aetherson2• 1 year ago

What the author is referring to there as GPT-5, GPT-5.5, and GPT-6 are, respectively, "The models that have a pre-training size 10x greater than, 100x greater than, and 1,000x greater than GPT-4.5." He's aware that what OpenAI is going to actually brand as GPT-5 is the router model that will just choose between which other models to actually use, but regards that as a sign that OpenAI agrees that "the model that is 10x the pre-training size of GPT-4.5" won't be that impressive.

It's slightly confusing terminology, but in fairness there is no agreed upon name for the next three orders of magnitude size-ups of pretraining. In any case, it's not the case that the author is confused about what OpenAI intends to brand GPT-5.

mark_l_watson• 1 year ago

I have used neural networks for engineering problems since the 1980s. I say this as context for my opinion: I cringe at most applications of LLMs that attempt mostly autonomous behavior, but I love using LLMs as ‘side kicks’ as I work. If I have a bug in my code, I will add a few printout statements where I think my misunderstanding of my code is, show an LLM my code and output, explain the error: I very often get useful feedback.

I also like practical tools like NotebookLM where I can pose some questions, upload PDFs, and get a summary based in what my questions.

My point is: my brain and experience are often augmented in efficient ways by LLMs.

So far I have addressed practical aspects of LLMs. I am retired so I can spend time on non practical things: currently I am trying to learn how to effectively use code generated by gemini 2.0 flash at runtime; the gemini SDK supports this fairly well so I am just trying to understand what is possible (before this I spent two months experimenting with writing my own tools/functions in Common Lisp and Python.)

I “wasted” close to two decades of my professional life on old fashioned symbolic AI (but I was well paid for the work) but I am interested in probabilistic approaches, such as in a book I bought yesterday “Causal AI” that was just published.

Lastly, I think some of the recent open source implementations of new ideas from China are worth carefully studying.

hangonhn• 1 year ago

I'll add this in case it's helpful to anyone else: LLMs are really good at regex and undoing various encodings/escaping, especially nested ones. I would go so far to say that it's better than a human at the latter.

I once spend over an hour trying to unescape JSON containing UTF8 values that's been escaped prior to being written to AWS's Cloudwatch Logs for MySQL audit logs. It was a horrific level of pain until I just asked ChatGPT to do it and it figured out all the series of escapes and encoding immediately and gave me the step to reverse them all.

LLM as a sidekick has saved me so much time. I don't really use it to generate code but for some odd tasks or API look up, it's a huge time saver.

arrakark2• 1 year ago

LLMs are really good at regex

Maybe that's changed recently, but I have struggled to get all but the most basic regex working from GPT-4o-mini

carlosdp2• 1 year ago

At some point there might be massive layoffs due to ostensibly competent AI labor coming onto the scene, perhaps because OpenAI will start heavily propagandizing that these mass layoffs must happen. It will be an overreaction/mistake. The companies that act on that will crash and burn, and will be outcompeted by companies that didn't do the stupid.

Um... I don't think companies are going to perform mass layoffs because "OpenAI said they must happen". If that were to happen it'd be because they are genuinely able to automate a ton of jobs using LLMs, which would be a bull case (not for AGI necessarily, but for the increased usefulness of LLMs)

AlotOfReading• 1 year ago

I don't think LLMs need to be able to genuinely fulfill the duties of a job to replace the human. Think call center workers and insurance reviewers where the point is to meet metrics without regard for the quality of the work performed. The main thing separating those jobs from say, HR (or even programmers) is how much the company cares about the quality of the work. It's not hard to imagine a situation where misguided people try to replace large numbers of federal employees with LLMs, as an entirely hypothetical example.

Timber-6539• 1 year ago

AI has no meaningful input to real world productivity because it is a toy that is never going to become the real thing that every person who has naively bought the AI hype expects it to be. And the end result of all the hype looks almost too predictable similar to how the also once promising crypto & blockchain technology turned out.

mcintyre1994• 1 year ago

Scaling CoTs to e. g. millions of tokens or effective-indefinite-size context windows (if that even works) may or may not lead to math being solved. I expect it won't.
(If math is solved, though, I don't know how to estimate the consequences, and it might invalidate the rest of my predictions.)

What does it mean for math to be solved in this context? Is it the idea that an AI will be able to generate any mathematical proof? To take a silly example, would we get a proof of whether P=NP from an AI that had solved math?

daveguy• 1 year ago

I think "math is solved" refers more to AI performing math studies at the level of a mathematics graduate student. Obviously "math" won't ever be "solved" but the problem of AI getting to a certain math proficiency level could be. No matter how good an AI is, if P != NP it won't be able to prove P=NP.

Regardless I don't think our AI systems are close to a proficiency breakthrough.

Edit: it is odd that "math is solved" is never explained. But "proficient to do math research" makes the most sense to me.

Imnimo• 1 year ago

Test-time compute/RL on LLMs: It will not meaningfully generalize beyond domains with easy verification.

To me, this is the biggest question mark. If you could get good generalized "thinking" from just training on math/code problems with verifiers, that would be a huge deal. So far, generalization seems to be limited. Is this because of a fundamental limitation, or because the post-training sets are currently too small (or otherwise deficient in some way) to induce good thinking patterns? If the latter, is that fixable?

trashtester• 1 year ago

Is this because of a fundamental limitation, or because the post-training sets are currently too small (or otherwise deficient in some way) to induce good thinking patterns?

"Thinking" isn't a singular thing. Humans learn to think in layer upon layer of understandig the world, physical, social and abstract, all at many different levels.

Embodiment will allow them to use RL on the physical world, and this in combination with access to not only means of communication but also interacting in ways where there is skin in the game, will help them navigate social and digital spaces.

bilsbie• 1 year ago

I have times when I use an LLM and it’s completely brain dead and can’t handle the simplest questions.

Then other times it blows me away. Even figuring out things that can’t possibly have been in its training data.

I think there are groups of people that have either had all of the first experience or all of the latter. And that’s why we see over optimistic and over pessimistic takes (like this one)

I think the reality is current LLM’s are better than he realizes and even if we plateau I really don’t see how we don’t make more breakthroughs in the next few years.

klik99• 1 year ago

This almost exactly what I’ve been saying while everyone was saying we’re on the path to AGI in the next couple of years. We’re an innovation / tweak / or paradigm shift away from AGI. His estimate in the 2030s that could happen is possible but optimistic- you can’t time new techniques, you can only time progress on iterative progress.

This is all the standard timeline for new technology - we enter the diminishing returns period, investment slows down a year or so afterwards, layoffs, contraction of industry, but when the hype dies down the real utilitarian part of the cycle begins. We start seeing it get integrated into the use cases it actually fits well with and by five years time its standard practice.

This is a normal process for any useful technology (notably crypto never found sustainable use cases so it’s kind of the exception, it’s in superposition of lingering hype and complete dismissal), so none of this should be a surprise to anyone. It’s funny that I’ve been saying this for so long that I’ve been pegged an AI skeptic, but in a couple of years when everyone is burnt out on AI hype it’ll sound like a positive view. The truth is, hype serves a purpose for new technology, since it kicks off a wide search for every crazy use case, most of which won’t work. But the places where it does work will stick around

JTbane• 1 year ago

Anyone else feel like AI is a trap for developers? I feel like I'm alone in the opinion it decreases competence. I guess I'm a mid-level dev (5 YOE at one company) and I tend to avoid it.

namaria7• 1 year ago

I agree. I think the game plan is to foster dependency and than hike prices. Current pricing isn't sustainable, and a whole generation of new practitioners will never learn how to mentally model software.

A bear case: My predictions regarding AI progress

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