531–540 of 622 posts
not long ago these people would have you believe that a next word predictor trained on reddit posts would somehow lead to artificial general superintelligence
Expecting AGI from Reddit training data is peak "pray Mr Babbage".
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The visual input and sensory input to the self driving function are of the 3D world but the car is still constrained to move along a 2D topological surface, it’s not moving up and down other than by following the curvature of that
So based on your argument they actually operate in 1D since roads go in one direction and lanes and intersections are constrained to a predetermined curly line.
If it becomes obvious that LLM's have a more narrow set of use cases, rather than the all encompassing story we hear today, then I would bet that the LLM platforms (OpenAI, Anthropic, Google, etc) will start developing products to compete directly with applications that supposed to be building on top of them like Cursor, in an attempt to increase their revenue. I wonder what this would mean for companies raising toda…
Maybe in like 5yrs+. For now they will rake in billions just from API usage alone just with GPT4 and whatever 5 is. Amazon and Google didn't mess with their core business by competing with the players using it until they REALLY ran out of ways to make money.
https://www.cnbc.com/2024/10/30/microsoft-cfo-says-openai-in...
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If the last 50 commits were reviewed by an AI and it took that long for an issue to happen I’d immediately mandate all PR’s are reviewed by an AI.
There's a difference between an issue being introduced and being noticed.
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It all just sounds to me like we're back at expert systems. Doesn't bode well...
Honest question, how would you expect systems to get external knowledge etc without tools like the OP is suggesting? Action oriented through self exploration? What is your thought for how these systems integrate with the existing world? Why does the OP's suggested mode of integration make you think of those older systems?
If we're back to curating it by hand and imparting it by writing code manually, how exactly are these systems an improvement on the 80's idea of building expert systems?
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Also it took hundreds of millions of years to get here. We're basically living in an atomic sliver on the fabric of history. Expecting AGI with 5 of years of scraping at most 30 years of online data and the minuscule fraction of what has been written over the past couple of thousand years was always a pie-in-the-sky dream to raise obscene amounts of money.
We built planes, which works quite differently from birds, in the span of what, 100 years? I think we've long left evolution behind when building machines, thinking or otherwise, so I'm not sure why the powerful but inefficient evolutionary process is held to some gold standard here.
Flying machines rest on the excess power of internal combustion. They have nothing to do with bird evolution.
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> Except our users experience the failed attempts (LLM replies that are wrong, even when backed by RAG) and it's incredibly hard to hide those from them. This has been my team's experience (and frustration) as well, and has led us to look at using LLMs for classifying / structuring, but not entrusting an LLM with making a decision based on things like a database schema or business logic. I think the technology and to…
> "let's only allow the LLM to do things we know it is rock-solid at." Even this is insanely hard in my opinion. The one thing that you would assume LLM to excel at is spelling and grammar checking for the English language, but even the top model (GPT-4o) can be insanely stupid/unpredictable at times. Take the following example from my tool: https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples... 5 models are…
I think that, too, is a UX problem.
If you present the output as you do, as simple text on a screen, the average user will read it with the voice of an infallible Star Trek computer and be irritated by every mistake.
But if you present the same thing as a bunch of cartoon characters talking to each other, users might not only be fine with "egg in your face moments", as you put it, they will laugh about them.
The key is to move the user away from the idealistic mental model of what a computer is and does.
"While the model was initially expected to significantly surpass previous versions of the technology behind ChatGPT, it fell short in key areas, particularly in answering coding questions outside its training data." Right. If you generate some code with ChatGPT, and then try to find similar code on the web, you usually will. Search for unusual phrases in comments and for variable names. Often, something from Stack Ov…
> Right. If you generate some code with ChatGPT, and then try to find similar code on the web, you usually will. People who "follow" AI, as the latest fad they want to comment on and appear intelligent about, repeat things like this constantly, even though they're not actually true for anything but the most trivial hello-world types of problems. I write code all day every day. I use Copilot and the like all day every…
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I thought maybe they were on the right track until I read Attention Is All You Need. Nah, at best we found a way to make one part of a collection of systems that will, together, do something like thinking. Thinking isn’t part of what this current approach does. What’s most surprising about modern LLMs is that it turns out there is so much information statistically encoded in the structure of our writing that we can u…
I see takes like this all the time and its so confusing. Why does knowing how things work under the hood make you think its not on the path towards AGI? What was lacking in the Attention paper that tells you AGI won't be built on LLMs? If its the supposed statistical nature of LLMs (itself a questionable claim), why does statistics seem so deflating to you?
Now, neural nets that have a copy of themselves, can look back at what nodes were hit, and change through time... then maybe we are getting somewhere
Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…
But the knowledge system here is doing the grunt of the work, and progressing past it's own limitations goes right hack to the pitfalls of the rules based AI winter. That's not a engineering problem, it's a foundational mathematics problems that only a few people are seriously working on.