Q: How many N's are there in Normation?

A: There is one N in the word "Normation"

Note that the answer is the same when asked n's instead of N's.

And this is but one example of many simple cases demonstrating that these model are indeed not reasoning in a similar manner to humans. However, the outputs are useful enough that I myself use Claude and GPT-4o for some work, but with full awareness that I must review the outputs in cases where factual accuracy is required.

they need to find a different derogatory slur to refer to tech workers

ideally one that isn't sexist and doesn't erase the contributions of women to industry

I have mixed feelings. On the one hand, I have a ton of schadenfreude for the AI maximalists (see: Leopold Aschenbrenner and the $1 trillion cluster that will never be), hype men (LinkedIn gurus and Twitter “technologists” that post threads with the thread emoji regurgitating listicles) or grifters (see: Rabbit R1 and the LAM vaporware).

On the other hand, I’m worried about another AI winter. We don’t need more people figuring out how to make bigger models, we need more fundamental research on low-resource contexts. Transformers are really just a trick to be able to ingest the whole internet. But there are many times where we don’t have a whole internet worth of data. The failure of LLMs on ARC is a pretty clear indication we’re not there yet (although I wouldn’t consider ARC sufficient either).

AI is following more a seasonal pattern with a AI Winters, can we expect a new winter soon?

> “An alarming number of technology trends are flashes in the pan.”

this has been a trend that seems to keep on recurring but does not stop from the tech bros from pushing the marketing beyond the realities.

raising money in the name of the future will give you similar results as self-driving cars or vr. the potential is crazy, but it is not going to make you double your money in a couple financial years. this should help serious initiatives find better-aligned investors.

The Economist, seriously?

The first was started with simple non-ML image manipulation and video analysis (like finding baggage left unmoved for a certain amount of time in a hall, trespassing alerts for gates and so on) and reach the level of live video analysis for autonomous drive. The second date back a very big amount of time, maybe with the Conrad Gessner's libraries of Babel/Biblioteca Universalis ~1545 with a simple consideration: a book is good to develop and share a specific topic, a newspaper to know "at a glance" most relevant facts of yesterday and so on but we still need something to elicit specific bit of information out of "the library" without the human need to read anything manually. Search engines does works but have limits. LLMs are the failed promise to being able to juice information (in a model) than extract it on user prompt distilled well. That's the promise, the reality is that pattern matching/prediction can't work much for the same problem we have with image, there is no intelligence.

For an LLM if a known scientist (as per tags in some parts of the model ingested information) say (joking in a forum) that eating a small rock a day it's good for health, the LLM will suggest such practice simply because it have no knowledge of joke. Similarly having no knowledge of humans a hand with ten fingers it's perfectly sound.

That's the essential bubble, PRs and people without knowledge have seen Stable Diffusion producing an astronaut riding a horse, have ask some questions to ChatGPT and have said "WOW! Ok, not perfect but it will be just a matter of time" and the answer is no, it will NOT be at least with the current tech. There are some use, like automatic translation, imperfect but good enough to be arranged so 1 human translator can do the same job of 10 before, some low importance ID checks could be done with electronic IDs + face recognition so a single human guards can operate 10 gates alone in an airport just intervening where face recognition fails. Essentially FEW low skill jobs might be automated, the rest is just classic automation, like banks who close offices simply because people use internet banking and pay with digital means so there is almost no need to pick and deposit cash anymore, no reasons to go to the bank anymore. The potential so far can't grow much more, so the bubble burst.

Meanwhile big tech want to keep the bubble up because LLM training is a thing not doable at home as single humans alone, like we can instead run a homeserver for our email, VoIP phone system, file sharing, ... Yes, it's doable in a community, like search with YaCy, maps with Open Street Maps etc but the need of data an patient manual tagging is simply to cumbersome to have a real community born and based model that match or surpass one done by Big Tech. Since IT knowledge VERY lately and very limited start to spread a bit enough to endanger big tech model... They need something users can't do at home on a desktop. And that's a part of the fight.

Another is the push toward no-ownership for 99% to better lock-in/enslave. So far the cloud+mobile model have created lock-in but still users might get data and host things themselves, if they do not operate computers anymore, just using "smart devices" well, the option to download and self host is next to none. So here the push for autonomous taxis instead of personal cars, connected dishwashers who send 7+Gb/day home and so on. This does not technically work so despite the immense amount of money and the struggle of the biggest people start to smell rodent and their mood drop.

- AI is currently hyped to the gills - Companies may find it hard to improve profits using AI in the short term - A crash may come - We may be close to AGI - Current models are flawed in many ways - Current level generative AI is good enough to serve many use cases

Reality is nobody truly knows - there's disagreement on these questions among the leaders in the field.

An observation to add to the mix:

I've had to deliberately work full time with LLM's in all kinds of contexts since they were released. That means forcing myself to use them for tasks whether they are "good at them" yet or not. I found that a major inhibitor to my adoption was my own set of habits around how I think and do things. We aren't used to offloading certain cognitive / creative tasks to machines. We still have the muscle memory of wanting to grab the map when we've got GPS in front of us. I found that once I pushed through this barrier and formed new habits it became second nature to create custom agents for all kinds of purposes to help me in my life. One learns what tasks to offload to the AI and how to offload them - and when and how one needs to step in to pair the different capabilities of the human mind.

I personally feel that pushing oneself to be an early adopter holds real benefit.

We have to realize that there is a ton of money right now behind pushing AI everywhere. We have entire conventions for leadership pushing that a year later "is the time to move AI to Prod" or "Moving past the skeptics".

We have investors seemingly asking every company they invest in "how are you using generative AI" before investing. We have Microsoft, Google, and Apple (to a lesser degree) forcing AI down our throats whether we like it or not and ignoring any reliability (inaccurate) issues.

FFS Microsoft is pushing AI as a serious branding part of Windows going forward.

We have too much money committed to pushing the idea that we already have general AI, too much marketing, etc.

Consumer hype and money in this situation are going to be very different things. I do think a bust is going to happen, but I don't think in any meaningful way the "hype" has died down. I think and I hope it will die down, we keep seeing how the technology just simply can't do what they are claiming. But I honestly don't think it is going to happen until something catastrophic happens, and it is going to be ugly when it does. Hopefully your company won't be so reliant on it to not recover.

AI ain’t going nowhere. And certainly isn’t overhyped. LLMs however, certainly are overhyped.

Then again I find it a good interface for assistants and actual AI and APIs that it can call on your behalf

NVDA's high closes were $135.58 June 18, down to $134.91 July 10th and $130 close today. It's highest sale is $140.76. So it's close today is 8% off its highest sale ever, and 4% off its highest close ever, not a big thing for a volatile stock. It's earnings are next week and we'll see how it does.

Nvidia and SMCI are the ones who have been earning money selling equipment for "AI". For Microsoft, Google, Facebook, Amazon, OpenAI etc., it is all big initial capital expenditure which they (and the scolding investment bank analysts) hope to regain in the future.

among which audience? is the hype necessary for further development? we attained much, if not all, of the recent achievements without hype. if anything, i'm strongly in favor of ai losing all the hype so that our researchers can focus on what's necessary, not what will win the loudest applause from so fickle a crowd. i'd be worried if ai was attracting less researchers than, say, two or three years ago. that doesn't seem to be the case.

The future is most definitely exciting though, and sadly quite scary, too.

Those who do not know history are doomed to repeat it.

But then, the current hype wasn't there to produce something useful, but for "serial entrepreneurs" to get investor money. They'll just move to the next hyped thing.

Yann LeCunn had a great tweet on this: Sometimes, the obvious must be studied so it can be asserted with full confidence: - LLMs can not answer questions whose answers are not in their training set in some form, - they can not solve problems they haven't been trained on, - they can not acquire new skills our knowledge without lots of human help, - they can not invent new things. Now, LLMs are merely a subset of AI techniques. Merely scaling up LLMs will not lead systems with these capabilities.

link https://x.com/ylecun/status/1823313599252533594?ref_src=twsr...

To focus on this: - LLMs can not answer questions whose answers are not in their training set in some form, - they can not solve problems they haven't been trained on

Given that we are close to maximum in the size of the training set, this means they are not going to improve without some completely unknown at the moment technical breakthrough. Going from "not intelligent" to "intelligent" is a massive shift.

- Startups whose entire business model is to just provide a wrapper around OpenAI's API.

- Social Media "AI Influencers" and their mindless "7 Ways To Become A Millionaire With ChatGPT" videos.

- Non-technical pundits claiming we are 1-2 years from AGI (and AGI talk in general).

- The stock market assigning insane valuations to any company that claims to somehow be "doing AI".

Things that are NOT coming to an end:

- Ongoing R&D in AI (and not just LLMs).

- Companies at the frontier of AI (OpenAI, Anthropic, Mistral, Google, Meta) releasing ever more capable models and tooling around those models.

- Forward looking companies in all industries using AI both to add capabilities to their products and to drive efficiencies in internal processes.

For the record, before spelling the recipes out, it made sure I understood that collecting elk eggs may be unlawful in some jurisdictions.

I think part of it is due to the politically and internet-induced death of nuance. But part of it I can't fully understand.

Personally I think it's rather useful. I don't consider myself a heavy user and still use it almost every day to help code, I ask it a lot of questions about specific and general stuff. It's partially or totally substituted for me: Stack Overflow, Google Search, Google Translate, most tech references. In the office I see people using it all the time, there's almost always a chatgpt window open in some of the displays.

I think it's very difficult to say this is 100% hype and/or a "phase". It's almost a proven fact it's useful and people will want it in their lives. Even if it never improves again, ever. It's a new tool in the toolbox and there will be businesses providing it as a service, or perhaps we will get to open source general availability.

On the other extreme, all the AI doomerism and AGI stuff to me seems almost as unfounded as before generative AI. Sure, it's likely we'll get to AGI one day. But if you thought we were 100 years away, I don't think chatgpt put us any closer to it and I just don't get people who now say 5. I'd rather they worried about the impact of image gen AI in deepfakes and misinformation. That's _already_ happening.

This sounds super useful. Can you please elaborate on the setup?

Love Kagi but they’re definitely not replacing Google anytime soon.

The 'bug' presented above is a simple case of not understanding correctly. Larger models, models with MOE, models with truth guages, better selection functions, etc will make this better in the future.

> ...they don't. They are prediction machines. They don't "understand" anything.

Implementation detail.

Scary stuff. And it's not science fiction- it's based on real, observed trends and scaling laws. Seems impossible? Well, they said the four-minute mile was impossible, too.

It’s great at helping me automate things I normally do not have time to attempt.

That is very believable, the people who say it made them super productive is a bit less believable.

I would rather say that the innovation happens in spikes. I see no slowing down whatsoever.

I am probably through some ETF an investor in MS, so I do hope the openai API usage is showing a more stable and upward trend.

[1]: https://explodingtopics.com/blog/chatgpt-users

You seem to think these models haven't already been trained on pirated versions of this content, for some reason.

OpenAI still hasnt released Sora video prompting for the general public and have already been leapfrogged by half a dozen competitors. I would say its still niche, but only as niche as using professional video editing tools are for creatives

That's one result. Another result is, due to "checking how often suggestions are accepted" is to objectively record how much help it is providing.

I assume the sitewide license is costly - this could simply be the company's way of determining if the cost is worth it.

That's why there are a lot of tools that help to setup a proper workflow around these LLMs.

For terminal-based workflow, you can checkout aider or plandex.

For GUI-based workflow, you can try 16x Prompt (I built it).

I don’t remember the last time I thought one of its suggestions was useful. For me LSP has been the real game changer.

This is a good thing. We need more tests on such critical places like regexes because they can be finicky and non-obvious. Tedious or not, we are not artists; the job must be done. Kudos for sticking to the good practices.

The sweet spot is when you need something and you’re sure it’s possible but just don’t know (or it’s too time consuming) how. E.G. change the css to to X, rewrite this python code in typescript, use the pattern of this code to do Y, etc.

Reminds me of the early days of Google where you had to learn how write a good search query. You learn you need more than a word or two, but don’t write a whole essay, etc.

Even where microsoft includes it for free, like their automation tools, its always been jank. I found myself going to GPT4 for better answers which is bad when you spend any time thinking about it.

If that is the case, then the AI isn't really adding enough value.

I mean, if it was adding enough value, those companies refusing to adopt it will be out-competed before the next round of retirements, and so won't even be around.

We'll see how the landscape looks in 10 years: if there are still major companies who have not invested some money or time into sprinkling the AI over their operations, then that's a signal that the positive impact of AI was overblown.

If, OTOH, there exists no large company in 10 years who have not incorporated AI into their operations in any way, then that's a signal too - the extra value of AI is above the cost of adding it to the operations.

Yes. Quite a lot. I walk the talk.

Echo chamber/bias I guess. I know many, many seniors with big pay checks working for big companies who are vastly worse than sonnet at the moment. Juniors stand literally no chance unless very talented.

I think that none of the initials are boolean; things can be degrees of artificial, degrees of general, and degrees of intelligent.

I think most would assert that humans count as a "general" intelligence, even if they disagree about most of the other things I've put in this comment.

It definitely has that Tesla Autopolit feel to it, where the marketing is ... enthusiastic, while the onus to "use the tool responsibly" is left up to the end user.

I'm building a persentation/teaching tool for teachers and trying to not sweep the halicination angle under the rug. My marketing angle is that the AI is a huge time saver, but the classroom teacher is the subject expert and they need to review the output. At the end of the day, it's still much more time efficient for an expert to review the content than it is to produce it from scratch which is a huge win.

I don't really take those taxis as a form of solved automation. It's a nice step though.

This has to do with ML and numerical computing, how?

edit: oh sorry I didn't get that it was sarcasm

Ants have a brain, albeit a small one - some 250,000 nerve cells vs 100 billion neurons in the human brain [1]

It is somewhat like saying an Intel 4004 is not really a microprocessor as it only has 2,300 transistors vs an Apple M4 with 28 billion transistors.

[1] https://www.smorescience.com/do-ants-have-brains/

My personal belief is that AIS is not a real thing (in the sense I wrote above) because narrow domain competence is tightly coupled to general domain competence . Even very autistic people that are functional in some domain actually have a staggering range of competences that we tend to ignore because we expect them in humans. I think machines will be similar.

Anyway, AIG or AIS is not round the corner at all. But that doesn't mean that there isn't a lot of value to be had from generative AI in the near future or now. Will this be a small fraction of the value from Web1.0 and Web2.0? Will it be approximately the same? Will it be a multiple? I think that's the question. I think it's clear that assistants for software engineers are somewhat valuable now (evidence: I get value out of them) how valuable? Well, more than stackexchange, less than a good editor. That's still alot, for me. I won't pay for it though...

And this points to the killer issue: there isn't a good way to monetize this. There isn't a good way to monetize the web, so we got adverts (a bad way). What will be the equivalent for LLM's? We just don't know right now. Interestingly there seems to be very little focus on this! Instead folks are studying the second order value. Using this "free thing" we can drive productivity... or quality... increase opportunities... create a new business?

To be clear, I think it is. It's just not going to be a hologram of a wizard in a room you can ask a question to for a quarter, which is what these chat bots and copilots you see today are modeled around.

Something to keep in mind is that gambling rules apply: if enough people flip coins, there is always someone experiencing a winning streak and someone experiencing a losing streak and majority that gets a mixed bag of roughly breaking even (mediocre usefulness and a waste of time)

my first week of using GPT4 every day I experienced great answer after great answer, and I was convinced the world would change in a matter of months, that natural language translation was now a solved problem etc etc

But my luck changed, and now I get some good answers and some idiotic answers, so it's mostly not worth my time. Some people never get a good answer in their few dice rolls before writing off the technology

I have accounts for all three and will generally try to branch out to test them with each new update. Admittedly, I haven’t gotten to Grok yet, but Claude is far and away the best model at the moment. It’s not even close really.

// reverse list

And it writes code in the proper language.

The manual is my first tool.

However, my main job is trying to come up with "elegant" architectures for complex business logic that interacts with an existing large code base. AI is just completely out of its depth in such cases due to lack of context but also lack of source material to draw from. Even unit tests only work with the most basic of cases, most of the time the setup is so complex it just produces garbage.

I've also had very little luck getting it to write performant code. I almost have to feed it the techniques or algorithms before it attempts to write such code, and even then it's usually wrong or not as efficient as it could be.

I just tried the latest Cursor + Sonnet and it failed in every task. The problem is that there is no way to understand the code without either complete understanding of the domain and the intents or running it in some context.

Telecom and media domains in particular are well documented in specs and studied in forum discussions. I am sure they are part of the training data, because I can get most answers if asked directly. So far the LLMs fail to reason about anything useful for me.

The Jetbrains thing is rather rudely incompetent, it consistently insists on suggestions that use variables and fragments that are supposed to be replaced by what I'm writing and also much more complex than what I actually need. I suffered through at least a hundred mistaken tabbed out shitty suggestions before I disconnected it.

However, I use copilot autocomplete consistently. That makes me much more productive. It’s just really good autocomplete.

As an experienced developer I’d put my productivity improvement at 2-3x. It’s huge but not 10x. I’m limited by my decision speed, I need to decide what I want the code to do, AI can’t help with that - it can only do the “how”.

Far from introducing more bugs, using Copilot frees some mental cycles for me to be more aware of the code I’m writing.

So it will be with inexperienced people coding with LLMs.

On stuff that's a bit obscure, but not really (like your Vulkan example), ChatGPT tends to write 60-95% correct code, that's flawed in the exact ways a noob wouldn't be able to fix.

In this case, nicking code from Github seems to fix the issue, even if I need to adapt it a bit.

Then comes the licensing issue. Often, when searching for an obscure topic, the code ChatGPT generates is very close to what's found on Github, but said code often comes with a non-permissive license, unlike what the AI generates.

Re: prompt engineering, we have a prompt guide if that helps, was that what you are getting at?

https://docs.greptile.com/prompt-guide

I am an experienced programmer, but I find myself (for the first time) doing a deep-dive in SQL and specifically building code that will run against multiple SQL engines.

I fed my list of engines into AI. As I'm discovering the really weird corners of SQL I asking the AI to compare one db against the other. My prompts are usually no more than 3 or 4 words.

It gives me quick helpful answers highlighting where things are the same and where they are different. I can then follow up in the specific docs if necessary (now that I know the function name.)

Personally I'm somewhat anti-hype, I'll let others rave about "changing the world". But I have found it a useful tool - not so much for "writing my code" but for acting as my tutor as I learn new things. (I'm using it for more than just SQL or computers now.)

I'm not sure it "changes thr economy" - but it can certainly change individual lives. Some jobs will go away. Others might be easier to do. It might make it easier to learn new skills.

If you don't know the language at all it's dangerous because you may not understand the proposed program (and of course if you're an expert you don't need it at all).

But llms won't help to find solutions to a general, still unspecified problem.

After that it goes completely off the rails by trying to issue draw commands before binding a graphics pipeline, which is both illogical and illegal. After a lot of prodding, I did manage to get it to bind a graphics pipeline, but it forgot about the texture.

So Claude Sonnet is definitely better than GPT-4o, but it still feels raw, like a game of whack-a-mole where I can get it to fix a mistake, but it reintroduces an old one. I also have to be the one offering the expertise. I can prompt it to fix the issues because I know exactly what the issues are. If I was using this to try to fill in for a gap in my knowledge, I would be stuck when I ran the code and it crashed - I would have no idea where to go next.

Update: Took about 50 min of experimenting, but I did get Claude to generate code that doesn't have any obvious defects on first inspection, although it cut off about halfway through because of the generation limit. That's the best result that I've seen from an LLM yet. But that's after about a dozen very broken broken programs, and again, I think the domain expertise here is key in order to be able to reprompt and correct.

- Simple interpolation of the progress is exactly the problem here. Look at the historical graphs of AI funding and tell me with a straight face that we absolutely must use simple interpolation.

- Nope, human-level intelligence is not even close. It remains as nebulous and out of reach as ever. ChatGPT's imitation of intelligent speech falls apart very quickly when you chat with it for more than a few questions.

Have you considered that you getting almost angry at somebody "not seeing the light" means you might hold preconceived notions that might not hold to reality? You would not be practicing critical thinking if you are not willing to question your assumptions.

It seems your assumption is very standard: "revolution is just around the corner, how can you not see it?".

OK, let the revolution come and I'll apologize to you personally. Ping me when it happens. For real. But make sure it's an actual revolution and not "OMFG next Midjourney can produce moon-scapes!", okay?

---

RE: 1, cool, I heard such success stories and I like them. But I also heard about executives flunking contracts because they over-relied on ChatGPT to summarize / synthesize contract items. I am glad it's making progress but people are being people and they will rely on a 100% fault-free AI. If that's not in place yet then the usefulness drops sharply because double-checking is even more time-consuming than doing the thing by yourself in the first place.

RE: 2, your side projects are not representative of anything at all. And I for one recognize AI images from a mile away and steer clear of projects that make use of them. Smells like low-effort to me and makes me wonder if the author didn't take other, much more fatal, shortcuts (like losing my information or selling my PII). And yes I am not the only one -- before you attempt that low-effort ad hominem technique.

I was not convinced by your comment, very little facts and it mostly appeals to the future that's forever just around the corner. Surely as an engineering-minded person you see how that's not convincing?

If we’ve just copied our feeble abilities, is that supposed to be exciting?

Is god like intelligent just a prediction machine too ?

I've also assembled a Kubernetes cluster overnight, despite not knowing much about Kubernetes before, and I ran the kubectl files ChatGPT made for me past some devops folks, and it passed the smell test.

I consider much of coding to be a magic spellcasting tutorial - we do conceptually simple things, and the difficult lies in figuring out how to use novel libraries and get them to do what you want.

Edit: After checking out the Arduino sketch, I'd take issue with all the floating point calculations in there - most microcontrollers don't have FPUs, and the performance is awful on 8-bit AVRs. It's not great on Cortex M3s either as all this stuff is done in software, and each FP operation is like a hundred cycles.

I'd definitely try to rephrase the issue with integer math. It might work, but no self-respecting embedded dev would write like this.

Its using the old format where the Program.cs file has an actual class, whereas as of .NET 6 thats not required.

You said barebones, but for any real server you would want to use the generic host https://learn.microsoft.com/en-us/dotnet/core/extensions/gen... which gets you a lot of the boilerplate and enables you program to be wrapped in a windows or systemd service.

Finally, parsing can be simplified since ASCII is a proper subset of UTF-8, you can just parse the entire string as UTF-8. IMHO I am disappointed that the AI didn't point that out.

I just don’t understand where the hell is this magical LLM capable of generating flawless files or even entire projects that many people are talking about. I rarely accept a large block of LLM-generated code without close inspection, and I’ve ended up with a subtle bug that I wouldn’t have written myself at least ~5 times now. Unless you don’t give a shit about code quality, error handling, proper logging, and subtle bugs, you shouldn’t run LLM-generated stuff without painstakingly reading and fixing everything. Or maybe there really is a magical LLM somewhere.

The way the OP here was talking about sonnet being above way above chatgtp in this case, it could be true. Google probably has the largest Go codebases on search to train the AI on higher quiality inputs. Go is a simpler language with less variation over time compared to something like .net also adding to its corner.

ive always been the type of person to use the right language for the each use case. For the last 10+ years ive primarly been building cross platform apps that target every common OS. So these "ai" tools like phind.com give me a universal API interface and generate code which is equiv to a SO answer. They are the ability of an outsourced junior dev who you would never let push code to prod, that doesnt have the language barrier but retains the fake dergee overheads ;)

Don't feel too bad; until I read the next response I was in two minds about whether sarcasm was intended or not.

It's bloody hard to tell, sometimes :-/

Don’t put chips on top of each other and you’re fine.

My experience is in no way unique, and yes, I think it's just laziness or ignorance to think otherwise. Or in your case, a kind of zealous hostility as a reaction against hype.

I remind you that my initial comment said that yes, there are some aspects of AI that are definitely over-hyped, but that I have used the tools in ways that obviously seem to have huge economic impacts.

P.S. - if you were more familiar with AI image makers, you'd know that it's not difficult to make images that are indistinguishable from non-AI ones. But that's really not relevant here, because my point was that this new tool enabled me to create something that didn't exist before – not what your personal qualms were about AI images.

What an absurd response. Yes, you'd probably cause the human brain to start malfunctioning terribly at the form of consciousness it's well -accustomed to managing within the context of its normal physical substrate and environment. You'd be doing that (and thus degenerating it badly) because you removed it from that ancient context whose workings we still don't well understand.

Your LLM on the other hand, has no context in which it shows such a level of cognitive capacity, higher-order reasoning, self direction and self awareness that we daily see humans to be capable of.

>By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do.

really? An appeal to authority? Many smart, educated people can still fall for utter nonsense and emotional attachment to bad ideas.

The point of my colored shape example is that it is an illusion that there is anything resembling a mind inside an LLM. I thought that was obvious enough I didn't need to explain it further than I did.

As far as the original commenter's credentials; there's lots of people who should know better but buy into hype and nonsense.

MMmm.

I mean, don't get me wrong; this is impressive stuff; but it needs to be an order of magnitude less 'screwing around trying to fix the random crap' for this to be 'wow, amazing!' rather than a technical demonstration.

You could have done this more quickly without using AI.

I have no doubt this is transformative technology, but people using it are choosing to use it; it's not actually better than not using it at this point, as far as I can tell.

It's slower and more error prone.

Music generation is one of the easiest ways to "spook the normies" since most people are completely unaware of the current SOTA. Anyone with a good ear and access to these tools can create a listenable song that sounds like it's been professionally produced. Anyone with a good ear and competence with a DAW and these tools can produce a high quality song. Someone who is already a professional can create incredible results in a fraction of the time it would normally take with zero budget.

One of the main limitations of generative AI at the moment is the interface, Udio's could certainly be improved but I think they have something good here with the extend feature allowing you to steer the creation. Developing the key UI features that allow you to control the inputs to generative models is an area where huge advancements can be made that can dramatically improve the quality of the generated output. We've only just scratched the surface here and even if the technology has reached its current limits, which I strongly believe it hasn't since there are a lot of things that have been shown to work but haven't been productized yet, we could still see steady month over month improvements based on better tooling built around them alone.

Text generation has gone from markov chain babblers to indistinguishable from human written.

Image generation has gone from acid trip uncanny valley to photorealistic.

Audio generation has gone from 1930's AM radio quality to crystal clear.

Video generation is currently in fugue dream state but is rapidly improving.

3D is early stages.

???? is next but I'm guessing it'll be things like CAD STL models, electronic circuits, and other physics based modelling outputs.

The ride's not over yet.

> has no context in which it shows such a level of cognitive capacity

You claim LLMs have no context at all in which it shows a similar level of cognitive capacity.

Yet clearly this claim is in contention with the fact that an LLM will indeed be able to evince this, much like a human brain would: by attesting to its own confusion. That is ostensibly empirical and evidential to a nonzero degree.

Thus your claim is too strong and therefore quite simply wrong. Claim mimicry? Then prove human brain consciousness does not derive from the process of mimicry in any form. You can't. In fact, the free energy principle, neuroscience's leading theory of human consciousness, argues the opposite: that prediction and mimicry encompass the entirety of what brains actually do. https://en.wikipedia.org/wiki/Predictive_coding

> There is no empirical evidence of any awareness whatsoever in any LLM, at all.

And no such claim was made--"awareness" was quoted for a reason.

> It's literally designed to mimic in such a way. And you speak of discussions of consciousness being about the philosophical and unanswerable?

Yes, as this was parent's claim: "They are not cognizant let alone conscious'.

And talking about sloppy argument--it may well turn out that something can be "designed to mimic" yet still be conscious. I'll leave that for you to puzzle out how on earth that might be possible. The exercise might help you form less sloppy arguments in the future.

> You're applying the same notion to something that shows no evidence of awareness while then criticizing assumptions of consciousness in a human brain?

No. But I suppose you've lost the plot a few inferential steps prior this so your confusion is not surprising.

Protip, instead of claiming everything that goes against your sensibilities as nonsense, perhaps entertain the possibility that you might just not be as well informed as you thought.

Oh and I expect it to be free, I ain't paying for this just like I wasn't paying for stackoverflow.

Finally I hope than in few years I will be able to just "sudo apt-get install llm llm-javascript llm-cooking llm-trivia llm-jokes" and it will all run locally on my low end computer and when I report bug, six months later it will be fixed when I update OS.

It's like having an unusually fast-but-clumsy intern, except interns learn the ropes fast and understand context.

I have some stand tests for LLMs: write a web app version of tetris, write a fluid dynamics simulation, etc., and these regularly fail (I must try them again on 4o).

But also, I have examples of them succeeding wildly, writing a web based painting app just from prompting — sure, even with that success it's bad code, but it's still done the thing.

As there are plenty of examples to confirm what we already believe, it's very easy to get stuck, with nay-sayers and enthusiasts equally unaware of the opposite examples.

I welcome people picking apart chats that I link to. it's not that I believe that LLMs are magic and refuse to adjust my model of how good these things are and aren't, but when people don't give specific evidence is hard to actually move the conversation forwards.

because yeah, these things are plenty stupid and have to be tricked into doing things sometimes (which is stupid, but here we are). they're also pretty amazing but like any hammer, not everything is a nail.

Anyway, considering all these things can be done on device, where is the long term business prospect of which you speak?

This is quite literally the entire history of technology: what was once expensive becomes cheap and then unlocks new developments. Bizarre that I have to point this out when you’re likely reading this comment on a device made of commoditized components that cost a fraction of what they did a couple decades ago.

This has follow-on consequences for a shattering phase transition between “persuasive demo” and “useful product”.

We can now make arbitrarily convincing demos that will crash airplanes (“with no survivors!”) on the first try in production.

This is institutionalized by the market capitalizations of 7 companies being so inflated that if they were priced accurately the US economy would collapse.

Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples.

> in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.

This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases.

> today's stuff is useful now

Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage?

I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.

> We also know how they work, they are token predicters. That is all they are and all they can do

Ah there it is, you've betrayed a deep lack of understanding in all relevant disciplines (neuroscience, cognition, information theory) required to even appreciate the many errors you've made here.

You sure understand the subject matter and have nothing possibly to learn. Enjoy.

https://en.wikipedia.org/wiki/Predictive_coding

Basically it's kinda useful to put time tags, but I need to manually fix each and every sentence. Sometimes I need to fix the time tags as well.

I just spoke about youtube because it's more popular and easy to test.

I disagree that it's a threat to the recording industry. They aren't going to be able to sell AI music, but nobody else is either, because anyone who wants AI music can just create it themselves. Record labels will continue to sell and promote real artists, because that's how they can make money. That's what people will pay for.

Is the output of average C students not commercially valuable in the listed fields? If AI is competing reliably with students then we've already hit AGI.

Also these all tend to have an option where you simply ask it and it will redirect you to a person.

Those agents deal with the same queries all day, despite what you think your problem likely isn't special, in most cases may as well start calling the agents "stochastic parrots" too while you're at it.

How about DDG AI Chat? https://duckduckgo.com/?q=DuckDuckGo+AI+Chat&ia=chat&duckai=...

TBH I hope im wrong, and that there is magic in HJ that makes him special in the universe in a way that GPT26 can never be. But increasingly, I doubt this premise. Not because of the "amazing capabilities of LLMs" which i think are frequently overstated and largely misunderstood, but more because of the dumbfounding shortcomings of intelligent creatures. We keep moving the bar for AGI, and now AGI is assumed to be what any rational accounting would classfy as ASI.

Where we are really going to see the bloom of AI is in goal directed systems, and I think those will come naturally with robotics. I predict we are in for a very abrupt 2nd industrial revolution, and you and I will be able to have this discussion either over a 55 gallon barrel of burning trash, or in our robot manicured botanical gardens sometime in the near future lol.

good times, maybe. Interesting times , for sure.

What is intelligence? We must have very different definitions!

As someone who uses ChatGPT and Claude daily, but cancelled my Copilot subscription after a year of use because it intimately just wasn’t that helpful to me and didn’t provide enough benefit over doing it by hand, I kind of sort of agree. Maybe not entirely, but I can’t shake the feeling that there might be some truth in it.

The code that AI generates for me is rarely good. It’s possible to get good code out of it, but it requires many iterations of careful review and prompting, but for most cases, I can write it quicker by hand. Where it really shines for me in programming and what I still use ChatGPT and Claude for is rubber ducking and as an alternative to documentation (eg “how do I do x in css”).

Besides the code quality being mediocre at best and outright rubbish at worst, it’s too much of a “yes man”, it’s lazy (choose between A and B: why not a hybrid approach? That’s… not what I asked for), and it doesn’t know how to say “I don’t know”.

I also feel it makes you, the human programmer, lazy. We need to exercise our brains, not delegate too much to a dumb computer.

I know c++ dart golang java html css javascript typescript lua react vue angular angularjs c# swift sql in various dialects including mysql and postgres, and have worked professionally in all these regards. I love to challenge myself. In fact, if I done something before, I find it boring.

So copilot helps me because I always find something new to do, something I don't understand, something I'm not good at.

So yes, I'm confident I'm competent. But I always do things I'm not good at for fun. So it helps me become well rounded.

So your assertion it only helps me because I'm incompetent is true and false. I'm competent, I just like to do new stuff.

I think the unfortunate reality is that this makes up a shockingly large amount of software engineering. Take this object and put it into this other object, map this data to that data, create these records and move them into this object when then goes into that other object.

A competent engineer architects their systems to make their tools as effective as possible, so maybe your idea of competent is "first order" and you need higher order conception of a good software engineer.

That’s the point of my flying car comparison. We HAVE flying cars: they’re called helicopters. Because as it turns out there is just no physical way to make a vehicle in the form factor of a car fly, except by rotary wing. But people will still say “where’s my flying car?” because they are hung up on reality resembling science fantasy, as you are.

We have AI. We even already have AGI. It just doesn’t resemble the Terminator, because The Terminator is a made up story disconnected from reality.

Inventing a fricken time machine wasn't creative?

I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day.

I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions.

Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question.

There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.

> Back in the days of lisp machines and expert systems, we started playing with early neural networks in an attempt to advance towards 'general intelligence' rather than the highly constrained talents of expert systems.

Interesting, you must be the first person I "meet" who was "back then and there". Still, if you allow me to point out for the second time, your "we" is really throwing me off because it sounds like "we the council of elders" and "we who self-appointed to determine what an actual true AI is". Positions of implied or directly claimed authority murder my motivation to take people doing them seriously. Hopefully that's a useful piece of feedback for you.

I would think that a random guy like myself who watched Terminator and that was part of his inspiration to become a programmer has just as much "authority" (if we can even call it that but I can't find the right word at this moment) to claim what a general AI should be. Since we don't have it, why not try and dream the ideal AI for us, and then pursue that? It's what the people who wanted humanity on the Moon did after all.

I feel too many people try to define general AI through the lenses of what we have right now -- or we'll have very soon -- and that to me seems very short-sighted and narrow-minded and seems like bronze-age people trying to predict what technology would be. To them it would likely be better carts that shake less while sitting in them. And faster cart-pulling animals.

That's how current AI practitioners trying to enforce their view on what we should expect sound to me.

> Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.

Sure, but this gets dangerously close to the disingenuous argument of "people who want AI constantly move the goalposts every time we make progress!" which is a stance I can't disagree with more even if I tried. I in fact hate this trope and fight it at every opportunity.

Why? Because to me that looks like AI practitioners are weaseling out of responsibility. It's in fact not that difficult to understand what the common people would want. Take a look at the "I, Robot" movie -- robot butlers that can do many tasks around the house or even assist you when you are outside.

What does that take? Let the practitioners figure it out. I, like yourself, believe LLMs are definitely not that -- but you are also right that it's likely a key ingredient. Being able to semi-informedly and quickly digest and process text is indeed crucial.

The part I hate is the constant chest-pounding: "We practically have general AI now, you plebs don't know our specialized terms and you just don't get it. Stop it with your claims that we don't have it! Nevermind that we don't have robot butlers, that's always in the future, I am telling you!".

And yes that happens even here in this thread, not in that super direct form of course, but it still happens.

> My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".

Here we disagree. It's true that people want useful and don't care how it's achieved; as a fairly disgruntled-by-tech guy I want the same. Put rat brains in my half-intelligent but problem-solving butler for all I care; if it works well people will buy it en masse and ask zero questions.

...But I'd still put strong correlation between a machine being actually intelligent and being able to solve very different problems with the same "brain", and being useful. For the simple reason that a lot of our problems that don't require much intelligence at all have been mostly solved by now.

So it naturally follows that we need intelligent tools for the problems we have not yet solved. Would you not agree with that conclusion?

> Small animals also have limited utility, but with given names, language, tool use, and problem solving skills I think arguing that they do not exhibit "intelligence" would be a tough sell for me.

I agree. Some can actually adapt to conditions that their brain has not had to tackle in generations. But take koalas for example... I actually could easily call these animals not possessing a general intelligence and just being pretty complex bots reacting to stimuli and nothing else (though there's also the possibility that since they ingest such low nutrition food their brains constantly stay in a super low-energy mode where they barely work as problem-solving machines -- topic for another time).

> By my observation, we have made giant leaps in the past 15 years, and we now have perhaps the vast majority of the components required to make artificial general intelligence...but it won't necessarily be all that useful at first, except maybe as a "pet robot" or something like that. Even if we scale it, it might not magically get smarter, just faster. a million hyper-speed squirrels still has a very limited level of utility.

Agree with that as well, just not sure that the path to a better general AI is in scaling higher what we [might] have now. IMO the plateau that the LLMs hit quite quickly partially supports my hypothesis.

As you are alluding to, the path to general AI is to keep adding more and more components to the same amalgam and try and connect them in creative ways. Eventually the spark of artificial life will ignite.

---

To summarize, my problem with the current breed of AI practitioners is that they argue from a position of authority that to me is imaginary; they are working in one of the least clear areas in science and yet they have the audacity to claim superiority to anyone whereas to me it's obvious that a random truck driver might have more interesting ideas than them for their area (exaggerated example but my point is that they lack perspective and become too entrenched in their narrow views, I guess like all scientists).

Yes, LLMs are likely integral part of a future artificial and working brain that can solve general tasks. And no we will not get any further there. Throwing another trillion parameters will achieve nothing but even more elaborate hallucinations. To me it became blindingly obvious that without throwing some symbol logic in there the LLMs will forever have zero concept of what they're talking about so they'll never improve -- because they also rely on truthful sources of info. That's not problem solving; that's regurgitating words with some extra steps.

Time to move on to other stuff -- maybe the transformers? Speaking of which, do you have any material on them that you would recommend for a layman? Just a TL;DR what they do and roughly how? Obviously I can Google it in literal seconds but that sadly does not mean much these days -- so maybe you have a source that's more interesting to read.

So it's not exactly an infinite loop IMO, it's more like that the first iteration was more crude and the machines were difficult to kill but then people learned and passed the information along back in time, eventually forming the infinite loop -- it still had a first step though, it didn't come out of nothing.

>Content Generation

I'm working on AI tools for teachers and I can confidently say that GPT is just unbelievably good at generating explanations, exercises, quizes etc. The onus to review the output is on the teacher obviously, but given they're the subject matter experts, a review is quick and takes a fraction of the time that it would take to otherwise create this content from scratch.

I appreciate it.

It is true it is faster than humans at some tasks, but the remaining tasks were most of the time, you can't gain more than 11% speedup by speeding up 10% of the work.

I just can't make peace with the fact that I inhabit the same planet as people who can't make elementary distinctions.

Fortunately, as pdimitar pointed out, so far it is an ineffective scam that mostly produces LoC.

But many jobs are not like that. Imagine an AI nurse giving bad health advice on phone. Somebody might die. Or AI salesman making promises that are against company policy? Company is likely to be held legally liable, and may lose significant money.

Due to legal reasons, my company couldn't enable full LLM generative capabilities on chatbot we use, because we would be legally responsible for anything it generates. Instead, LLM is simply used to determine which of the pre-determined answers may fit the query the best, which it indeed does well when more traditional technologies fail. But that's not revolutionary, just an improvement. I suspect there are many barriers like that, which hinder its usage in many fields, even if it could work most of the time.

So, nearly all use cases I can think of now will still require a human in the loop, simply because of the unreliability. That way it can be a productivity booster, but not a replacement.

IME LLMs are great at giving you the experience of learning, in the same way sugar gives you the experience of nourishment

How much is "enough"? Neither myself nor my coworkers have found LLMs to be all that useful in our work. Almost everybody has stopped bothering with them these days.

LLM outputs aren't always perfect, but that doesn't stop them from being extremely helpful and massively increasing my productivity.

They help me to get things done with the tech I'm familiar with much faster, get things done with tech I'm unfamiliar with that I wouldn't be able to do before, and they are extremely helpful for learning as well.

Also, I've noticed that using them has made me much more curious. I'm asking so many new questions now, I've had no idea how many things I was casually curious about, but not curious enough to google.

I don't care how it's called. We don't have it. I am not "confused over terminology", I want to see results and yet again they don't exist. Let's focus on results.

> In reality the whole notion of AGI->ASI auto-FOOM has been experimentally discredited

Sure. Because we actually have this super-intelligence already and we can compare with it, right? Oh wait, no we don't. So what's your point? Some people gave up and proclaimed that it can't be done? Like we haven't seen historical examples of this meaning exactly nothing, hundreds of times already.

Look, we'll never be able to talk about it before you stop confusing industry gate-keepers who learned how to talk to get VC money and obfuscate reality with, you know, the actual reality in front of us. You got duped by the investor talk and by the scientists never wanting to admit their funding might have been misplaced by being given to them, I am afraid.

Finally, nope, again and again, we don't have AGI even if I accept your definition. Show me a bot that can play chess, play StarCraft 2, organize an Amazon warehouse item movements and shipping, and coordinate a flight's touch-down with the same algorithms / core / whatever-you-want-to-call it. Same one, not different ones. One and the same.

No? No AGI then either.

> Furthermore, the very idea of ASI can’t be taken for granted. A machine that trivially solves humanity’s pressing problems makes nice sci-fi, but there is absolutely no evidence to presume such a machine could actually exist.

The people in the bronze age could have easily said "there is no evidence we would be able to haul goods while only pressing pedals and rotating a wheel". That's not an argument for anything at all, it's a short-sighted assertion that we might never progress that's only taking the present and the very near future into account. Well, cool, you don't believe it will happen. And? That's not an interesting thing to say.

Other people didn't believe we could go to the Moon. We still did. I wonder how quickly the naysayers hid under the bed after that so nobody could confront them about it. :D

But anyway. I got nothing more to say to people who believe VC talk and are hell-bent on inventing many acronyms to make sure they are never held accountable.

I for one want machines that solve humanity's problems. I know they can exist. I know nearly nobody wants to work on them because everybody is focused on the next quarter's results. All this is visible and well-understood yet people like you seem to think that this super narrow view is the best humanity can achieve.

Well, maybe it's the best you can achieve. I know people who can do more.

Which means that after a brief honeymoon period, the effect of AI will be to heavily reduce labor costs, rather than push for turbocharged "productivity" with greatly diminishing returns.

The documentary he's referring to is likely "Farewell, Etaoin Shrdlu," released in 1980. It chronicles the last day of hot metal typesetting at The New York Times before they transitioned to newer technology. The title comes from the nonsense phrase "etaoin shrdlu," which appeared frequently in Linotype machine errors due to the way the keys were arranged. The documentary provides a fascinating look at the end of an era in newspaper production.

No, it isn't. It just increases noise. I don't need any more info, I need just to make decisions "how?".

> Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.

I'm not amazed. At best it sounds like some 60+ year old (like me) trying to be in the "age group" 14 while after only hearing from someone how young people talk. Especially in small cultures like ours here (~1M people).

Are you suggesting a chatbot is a suitable replacement for a teacher?

I'm using both Kagi & LLM; depending on my need, I'll prefer one or the other.

Maybe I can access the same result with a LLM, but all the conversation/guidance required is time-consuming than just refining a search query and browsing through the first three results.

After all the answer is rarely exactly available somewhere. Reading people's questions/replies will provide a clues to find the actual answer I was looking for.

I have yet been able to achieve this result through a LLM.

Sure, you have your John Carmaks' or Antirezs' of the industry who are 10x programmers and also successful founders but those guys are 1 in a million.

But your usual 10x engineer you'll meet is the guy who knows the ins and outs of all running systems at work giving him the ability to debug and solve issues 10x quicker than the rest, knowledge which is highly specific to the products of that company and is often non-transferrable and also not useful at entrepreneurship.

Becoming the 10x engineer at a company usually means pigeonholing yourself in the deep inner workings of the products, which may or may not be useful later. If that stack is highly custom or proprietary it might work in your favor making you the 10x guy virtually unfireable being able to set your own demands since only you can solve the issues, or might backfire against you at a round of layoffs as your knowledge of that specific niche has little demand elsewhere.

So sorry, we're back to spam generator. Even if it's "good spam".