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Rodney Brooks on limitations of generative AI
81–90 of 202 posts
Re: Rodney Brooks on limitations of generative AI
#82Amara's law -- "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run."
Also, the author focuses on the fact that LLMs are not much better at things that robots already do, such as moving stuff in a store. Yeah. But they are surprisingly good at many things that robots already didn't do, such as writing texts and composing songs.
It's like getting a flying car and saying "meh, on a highway it's not really much faster than the classical car". Or getting a computer and saying that the calculator app is not faster than actual calculator.
A robot powered by some future LLM may not be much better at moving stuff, but it will be able to follow commands such as "I am going on a vacation, pack my suitcase with all I need" without giving a detailed list.
Re: Rodney Brooks on limitations of generative AI
#83After using Copilot that is pretty bad at guessing what I exactly want to do, but still occasionally right on the money and often pretty close: AI is not really AI and it won't kill us all, but the realization is that a lot of work is just repetitive and really not that clever at all. If I think about all the work I did in my life it follows the same pattern: a new way of doing things comes along, then you start figu…
> a new way of doing things comes along, then you start figuring out how to do it and how to use it, and once you're there you rinse and repeat
This happens a billion times a month in chatGPT rooms. User comes with a task, maybe gives some references and guidance. The model responds. User gives more guidance. And this iterates for a while. The LLM gets tons of interactive sessions, it can learn how to rank the useful answers higher. This creates a data flywheel where people generate experience and LLMs learn and iteratively improve. LLMs have the tendency to make people bring the world to them, they interact with the real world through us.
Re: Rodney Brooks on limitations of generative AI
#84i dont know much about machine learning but what i think i know is that its getting an outcome based on averages of witnessed data/events. so how's it going to come up with anything novel? or outside of normal?
It can't. Without the ability to propose a hypothesis and then experimentally test it in the physical real world, no ML technique or app can add new information to the world. The only form of creativity possible using AI (as it exists today) is to recombine existing information in a new way -- as a musical composer or jazz artist creates a variation on an theme that already exists. But that can't be compared to devising something new that we would call truly creative and original, especially novel work that advances the frontier of our understanding of the world, like scientific discovery.
Re: Rodney Brooks on limitations of generative AI
#85[deleted]
Re: Rodney Brooks on limitations of generative AI
#86The iPod didn't stop growing. It turned into an iPhone - a much more complex system which happened to include iPod features, almost as a trivial add-on.
If you consider LLMs as the iPod of ML, what would the iPhone equivalent be?
Re: Rodney Brooks on limitations of generative AI
#87Earlier quoted context omitted.
It’s in theory. The more information you have, the better the decision in theory.
It's quality not quantity. You need to have accurate, properly reasoned information for better decisions.
It’s a good thing that SFDC and Slack are both well known for being a repository of high quality data.
/sarc
Re: Rodney Brooks on limitations of generative AI
#88"He says the trouble with generative AI is that, while it’s perfectly capable of performing a certain set of tasks, it can’t do everything a human can"
This kind of strawman "limitations of LLMs" is a bit silly. EVERYONE knows it can't do everything a human can, but the boundaries are very unclear. We definitely don't know what the limitations are. Many people looked at computers in the 70s and saw that they could only do math, suitable to be fancy mechanical accountants. But it turns out you can do a lot with math.
If we never got a model better than the current batch then we still would have a tremendous amount of work to do to really understand its full capabilities.
If you come with a defined problem in hand, a problem selected based on the (very reasonable!) premise that computers cannot understand or operate meaningfully on language or general knowledge, then LLMs might not help that much. Robot warehouse pickers don't have a lot of need for LLMs, but that's the kind of industrial use case where the environment is readily modified to make the task feasible, just like warehouses are designed for forklifts.
Re: Rodney Brooks on limitations of generative AI
#89To 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…
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 the vicious circle of "I trained my GPT on billions of Twitter posts and let it write Twitter posts to great sucess", "Hey, me too!"
Re: Rodney Brooks on limitations of generative AI
#90Earlier quoted context omitted.
> However, the more data you feed into an LLM, the smarter it should be in the response. Is it that way? For example if it lacks a certain reasoning capability, then more data may not change that. So far LLMs lack useful ideas of truth, it will easily generate untrue statements. We see lots of hacks how to control that, with unconvincing results.
That has not been my experience with GPT4 and GPt4o. Maybe you’re using worse models? The point is that the more context an LLM or human has, the better decision it can make in theory. I don’t think you can debate this. Hallucinations and LLM context scale are more engineering problems.
I think the argument was, GPT4 can't learn to do Math from more data. I'd be surprised if that's not true.