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Rodney Brooks on limitations of generative AI

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Re: Rodney Brooks on limitations of generative AI

#161

Post by Jensson »

lostmsu wrote: post #75
croes wrote: post #17

Earlier quoted context omitted.

People thought we have FSD now because of the early success, but the last 20% are the hardest. The same is true for LLMs. What they can do is impressive but to fix what they can't will be hard or even impossible.

There was never universally recognized early success in FSD.

When self driving cars started driving around public in 2010 that was a universally recognized success of self driving cars. That shocked many people, as before then self driving cars was entirely science fiction.

There was of course never a recognized success of FSD, since we don't have FSD, but when people started seeing cars drive themselves on public streets 2010 they assumed we would have FSD in 5-10 years, but we still barely have restricted self driving cars today 14 years later.

Re: Rodney Brooks on limitations of generative AI

#162

Post by Jensson »

mewpmewp2 wrote: post #137
enragedcacti wrote: post #98

Earlier quoted context omitted.

I don't think you and him are in disagreement. I read it as him saying "evaluating LLMs is extremely difficult and a big problem right now is that many people are treating them as basically human in capability". Its the opposite problem to the perception of computers in the 70s, early computers were seen by some as too alien to be as useful as a person across most tasks, llms are seen by some as too human to not be a…

WHO exactly is treating them as human? It is a strawman.

Have you never seen someone say "ChatGPT is smarter than most humans"?

Tons of people mistake knowledge and eloquence for intelligence, even here on HN, it isn't a strawman at all.

Re: Rodney Brooks on limitations of generative AI

#163

Post by KingOfCoders »

gjvc wrote: post #60
gjvc wrote: post #2

Amara's law -- "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run."

and bard/gemini and chatgpt consistently given look-good-but-broken examples when asked for help with code. the bubble on this is going to make the .com crash look like peanuts.

From my experience it depends on the length of the task.

For ~50 LOC examples ChatGPT can consistently modify the code to add some new parameter or change some behaviour etc.

For using new external APIs it hallucinates often - that said, all of my changes to my static Hugo websites: new shortcodes, modifying short codes like "change the list of random articles to only include articles that have the same 'type'" works excellent - are done by ChatGPT without problems.

> .com crash look like peanuts.

I think what people get wrong about the .com crash: It wasn't a technology crash but a crash of overvalued companies and the sudden fear of VCs. Internet usage and new applications just grew and grew. There was no internet technology crash - and many successful companies like Amazon and eBay just kept working - the pet.com's of the world died (and sadly my Wiki/Blog/Onthology startup died too)

Re: Rodney Brooks on limitations of generative AI

#164

Post by vidarh »

derefr wrote: post #124
pestaa wrote: post #109

Earlier quoted context omitted.

It's more than enough data for a specialized tool, yes. It's not even remotely enough data for a statistical language processor.

Why are young children able to quickly surpass state-of-the-art ML models at arithmetic tasks, from only a few hours of lecturing and a "training dataset" (worksheets) consisting of maybe a thousand total examples? What is happening in the human learning process from those few thousand examples, to deduce so much more about "the rules of math" per marginal datapoint?

Are they? Even before OpenAI made it hard to force GPT to do chain of thought for basic maths it usually took over a dozen digits per number before it messed up arithmetic when I tested it.

How many young children do you genuinely think would do problems like that without messing up a step before having drilled for quite some time?

I'm sure there are aspects to how we generalise that current LLM training processes does not yet capture, but so much of human learning processes involve repeating very basic stuff over and over again and still regularly making trivial mistakes because we keep tripping over stuff we learned how to do right as children but keep failing to apply it with sufficient precision.

Frankly, making average humans do these kind of things consistently right manually even for small numbers without putting a process of extensive checking and revision around it is an unsolved problem. And convincing an average human apply that kind of tedious process consistently is an unsolved problem.

Re: Rodney Brooks on limitations of generative AI

#165

Post by vidarh »

Propelloni wrote: post #89
aurareturn wrote: post #15

Earlier quoted context omitted.

I’m still waiting for SalesForce to integrate an LLM into Slack so I can ask it business logic and decisions long lost. Still waiting for Microsoft to integrate an LLM into outlook so I can get a summary of a 20 email long chain I just got CCed into. I don’t think the iPod comparison is a valid one. People only have so much time to listen to music. Past a certain point, no one has enough good music they like to put i…

Most data around is junk and the internet produces junk data faster then useful data and current GPT AIs basically regurgitate what someone already did somewhere on the internet. So I guess the more data we feed into GPTs the worse the results will get. My take to improve AI output is to heavily curate the data you feed your AI, much the like expert systems of old (which were lauded as "AI" also.) Maybe we can break…

There are multiple companies hiring people on contracts to curate and generate data for this. I do confidential contract work for two different ones at the moment, and while my NDAs limit how much I can say, it involves both identifying issues with captured prompt/response pairs that have been filtered, and writing synthetic ones from scratch aided by models (e.g. come up with a coding problem, and rewrite the response to be "perfect").

The first category has obviously been pre-filtered to put cheaper resources in simpler problems, as sometimes these projects pays reasonable tech contract rates for 1-2 hours of work to improve only 2-3 conversation turns of a single conversation, and it's clear they usually involve more than one person reviewing the same data.

A lot of money is pouring into that space, and the moats in the form of proprietary training data heavily curated by experts is going to be growing rapidly given how much cash the big players have.

Re: Rodney Brooks on limitations of generative AI

#166

Post by lukeschlather »

ImaCake wrote: post #154
lukeschlather wrote: post #138

I feel like "generative" might be the worst possible label, because while the generative capabilities are the most exciting features, they're not the most useful, and in most cases they aren't useful at all. But the sentiment analysis, summaries, and object detection seem incredibly capable and like the actual useful features of LLMs and similar tensor models.

"Generative" is a great technical term for LLMs because it is what they do. But it a bad marketing term because it doesn't describe what they can do well. >object detection This comes from a different class of machine learning models unless I am mistaken?

Multimodal ChatGPT and Gemini both seem pretty good at identifying objects in images.

Re: Rodney Brooks on limitations of generative AI

#167

Post by mvdtnz »

aurareturn wrote: post #15
fxtentacle wrote: post #13

To me, this reads like a very reasonable take. He suggests to limit the scope of the AI problem, add manual overrides in case there are unexpected situations, and he (rightly, in my opinion) predicts that the business case for exponentially scaling LLM models isn't there. With that context, I like his iPod example. Apple probably could have made a 3TB iPod to stick to Moore's law for another few years, but after they…

I’m still waiting for SalesForce to integrate an LLM into Slack so I can ask it business logic and decisions long lost. Still waiting for Microsoft to integrate an LLM into outlook so I can get a summary of a 20 email long chain I just got CCed into. I don’t think the iPod comparison is a valid one. People only have so much time to listen to music. Past a certain point, no one has enough good music they like to put i…

Slack literally launched that feature today! In fact I posted about it on hn this morning

https://news.ycombinator.com/item?id=40841057

Re: Rodney Brooks on limitations of generative AI

#168

Post by vidarh »

palad1n wrote: post #149
richrichie wrote: post #43

Earlier quoted context omitted.

Not quite. There are bounds on capacity of learning machines. https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_di...

Anyone got a TL;DR on this?

Think about an AI with a 1 bit model. If you feed that AI data that can't possibly be classified into less than 2 bits, it can't get it precisely right, no matter how much data you train it on, or what the 1 bit of the model represents.

For any given size of system, there will be a ceiling on what it can learn to classify or predict with precision.

I used "system" rather than "model" there for a reason:

Memory in any form, such as context and RAG or API access to anything that can store and retrieve data affects the maximum - a turing machine can be implemented with a very small model + a loop if there's access to an external memory to act like the tape. But if the "tape" is limited, there will be some limitation on what the total system can precisely classify.

Re: Rodney Brooks on limitations of generative AI

#169

Post by chime »

kjellsbells wrote: post #152
MattPalmer1086 wrote: post #72

Earlier quoted context omitted.

Haha, I'd be happy if outlook just integrated a search that actually works. Most of outlook search results aren't even relevant, and it regularly misses things I know are there. Literally the most useless search I've ever had to use.

Irony: they did. They bought LookOut, which was a simple and extremely good search plugin for desktop Outlook. And then, somehow, it was melted down into the rather weak beer search that 365 has today. There is an alternative, Lookeen, which positions itself as LookOut's successor, but I've yet to try it. https://lookeen.com/solutions/outlook-search/lookout-alterna...

I can vouch for Lookeen (circa 2012-2015). I set it up for 200+ users on Citrix and it worked great. I had the index for each user saved to their network share and yet the search was instant. It even indexed shared mailboxes. It barely used any CPU when doing background indexing.

It worked well with thin OSTs too but due to how Outlook and Exchange work, it would have to rebuild the index more often.

Definitely a blast from the past reading the word “Lookeen” but mostly good memories about it. I believe the ADMX integration was pretty decent too.

Re: Rodney Brooks on limitations of generative AI

#170

Post by unraveller »

mjburgess wrote: post #103

Were one to slice the corpus callosum, and burn away the body, and poke out the eyes.. and pickle the brain, and everything else besides... and attach the few remaining neurones to a few remaining keys.. then out would come a program like ChatGPT's -- "do not run it yet!" the little worker ants say dying on their hills, out here each day: it's only version four -- not five! Queen Altman has told us: he's been busy in…

Do run it now!

Don't worry about where it sleeps in your tool cabinet so much. Put that fastidiousness to better use by reaching in with your intellect to see what favorable positions you can tickle the LLM into. None to be found you say? In the tool world an LLM is closer to a mirror than a hammer.

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