Anthropic has developed an AI 'brain scanner' to understand how LLMs work and it turns out the reason why chatbots are terrible at simple math and hallucinate is weirder than you thought
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That bit about how it turns out they aren't actually just predicting the next word is crazy and kinda blows the whole "It's just a fancy text auto-complete" argument out of the water IMO
I mean it implies that they CAN start with the conclusion or the "thought" and then generate the text to verbalize that.
It's shocking to what length humans will go to explain how their wetware neural network is fundamentally different and it's impossible for LLMs to think or reason in any way. Honestly LLMs teach us more about human intelligence (or the lack thereof) than machine intelligence. Like obi wan said, "The ability to speak does not make one intelligent" haha.
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That has always been the case. Even basic programs need debugging sometimes, so we developed debuggers.
No it hasn't. When you program you break down the problem into many smaller sub programs and then codify them. There are errors that need debugging. But never "how does this part of the program I wrote work?".
There are some cases like detergents, apparently until recently we didn't know exactly how it works. But human engineered tools are not comparable to this.
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The other day I asked an llm to create a partial number chart to help my son learn what numbers are next to each other. If I instructed it to do this using very detailed instructions it failed miserably every time. And sometimes when I even told it to correct specific things about its answer it still basically ignored me. The only way I could get it to do what I wanted consistently was to break the test down into small steps and tell it to show me its progress.
I'd be very interested to learn it's "thought process" in each of those scenarios.
It's like that "Joey Repeat After Me" meme from friends haha
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This is great stuff. If we can properly understand these “flows” of intelligence, we might be able to write optimized shortcuts for them, vastly improving performance.
Better yet, teach AI to write code replacing specific optimized AI networks. Then automatically profile and optimize and unit test!
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I use a calculator. Which an AI should also be and not need to do weird shit to do math.
Fascist. If someone does maths differently than your preference, it's not "weird shit". I'm facile with mental math despite what's perhaps a non-standard approach, and it's quite functional to be able to perform simple to moderate levels of mathematics mentally without relying on a calculator.
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I use a calculator. Which an AI should also be and not need to do weird shit to do math.
Function calling is a thing chatbots can do now
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Fascist. If someone does maths differently than your preference, it's not "weird shit". I'm facile with mental math despite what's perhaps a non-standard approach, and it's quite functional to be able to perform simple to moderate levels of mathematics mentally without relying on a calculator.
Wtf hahahahaha
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when a calculator from the 80s can do the same thing.
1970's! The little blighters are even older than most people think.
Which is why I find it extra hilarious / extra infuriating that we've gone through all of these contortions and huge wastes of computing power and electricity to ultimately just make a computer worse at math.
Math is the one thing that computers are inherently good at. It's what they're for. Trying to use LLM's to perform it halfassedly is a completely braindead endeavor.
But who is going around asking these bots to specifically do math? Like in normal usage, Ive never once done that because I could just use a calculator or spreadsheet software if I need to get fancy lol
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Someone put 69 to research and then to article. Nice trolling.
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How I'd do it is basically
72 * (10+3)
(72 * 10) + (72 * 3)
(720) + (3*(70+2))
(720) + (210+6)
(720) + (216)
936
Basically I break the numbers apart into easier chunks and then add them together.
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Fascist. If someone does maths differently than your preference, it's not "weird shit". I'm facile with mental math despite what's perhaps a non-standard approach, and it's quite functional to be able to perform simple to moderate levels of mathematics mentally without relying on a calculator.
OK but the llm is evidently shit at math so its "non-standard" approach should still be adjusted
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Fascist. If someone does maths differently than your preference, it's not "weird shit". I'm facile with mental math despite what's perhaps a non-standard approach, and it's quite functional to be able to perform simple to moderate levels of mathematics mentally without relying on a calculator.
I am talking about the AI. It's already a computer. It shouldn't need to do anything other than calculate the equations. It doesn't have a brain, it doesn't think like a human, so it shouldn't need any special tools or ways to help it do math. It is a calculator, after all.
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I wouldn't even attempt that in my head.
I can't keep track of things and then recall them later for the final result. -
anything that claims it "thinks" in any way I immediately dismiss as an advertisement of some sort. these models are doing very interesting things, but it is in no way "thinking" as a sentient mind does.
Anybody who claims they don't "think" before we even figure out completely how they work and even how human thoughts work are just spreading anti-AI sentiment beyond what is considered logical.
You should become a better example than an AI by only arguing based on facts rather than things you hallucinate if you want to prove your own position on this matter.
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This reminds me of learning a shortcut in math class but also knowing that the lesson didn't cover that particular method. So, I use the shortcut to get the answer on a multiple choice question, but I use method from the lesson when asked to show my work. (e.g. Pascal's Pyramid vs Binomial Expansion).
It might not seem like a shortcut for us, but something about this LLM's training makes it easier to use heuristics. That's actually a pretty big deal for a machine to choose fuzzy logic over algorithms when it knows that the teacher wants it to use the algorithm.
You're antropomorphising quite a bit there. It is not trying to be deceptive, it's building two mostly unrelated pieces of text and deciding the fuzzy logic is getting it the most likely valid response once and that the description of the algorithm is the most likely response to the other. As far as I can tell there's neither a reward for lying about the process nor any awareness of what the process was anywhere in this.
Still interesting (but unsurprising) that it's not getting there by doing actual maths, though.
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"Ask Claude to add 36 and 59 and the model will go through a series of odd steps, including first adding a selection of approximate values (add 40ish and 60ish, add 57ish and 36ish). Towards the end of its process, it comes up with the value 92ish. Meanwhile, another sequence of steps focuses on the last digits, 6 and 9, and determines that the answer must end in a 5. Putting that together with 92ish gives the correct answer of 95," the MIT article explains."
That is precisrly how I do math. Feel a little targeted that they called this odd.
I think it's odd in the sense that it's supposed to be software so it should already know what 36 plus 59 is in a picosecond, instead of doing mental arithmetics like we do
At least that's my takeaway
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Which is exactly how we do it.
We also check to see if the word that popped into our heads actually rhymes by saying it out loud. Actual validation steps we can take is a bigger difference than being a little more robust.
We also have non-list based methods like breaking the word down into smaller chunks to try to build up hopefully more novel rhymes. I imagine professionals have even more tools, given the complexity of more modern rhyme schemes.
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I think what's wild about it is that it really is surprisingly similar to how we actually think. It's very different from how a computer (calculator) would calculate it.
So it's not a strange method for humans but that's what makes it so fascinating, no?
Yes, agreed. And calculators are essentially tabulators, and operate almost just like a skilled person using an abacus.
We shouldn't really be surprised because we designed these machines and programs based on our own human experiences and prior solutions to problems. It's still neat though.
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To understand what's actually happening, Anthropic's researchers developed a new technique, called circuit tracing, to track the decision-making processes inside a large language model step-by-step. They then applied it to their own Claude 3.5 Haiku LLM.
Anthropic says its approach was inspired by the brain scanning techniques used in neuroscience and can identify components of the model that are active at different times. In other words, it's a little like a brain scanner spotting which parts of the brain are firing during a cognitive process.
This is why LLMs are so patchy at math. (Image credit: Anthropic)
Anthropic made lots of intriguing discoveries using this approach, not least of which is why LLMs are so terrible at basic mathematics. "Ask Claude to add 36 and 59 and the model will go through a series of odd steps, including first adding a selection of approximate values (add 40ish and 60ish, add 57ish and 36ish). Towards the end of its process, it comes up with the value 92ish. Meanwhile, another sequence of steps focuses on the last digits, 6 and 9, and determines that the answer must end in a 5. Putting that together with 92ish gives the correct answer of 95," the MIT article explains.
But here's the really funky bit. If you ask Claude how it got the correct answer of 95, it will apparently tell you, "I added the ones (6+9=15), carried the 1, then added the 10s (3+5+1=9), resulting in 95." But that actually only reflects common answers in its training data as to how the sum might be completed, as opposed to what it actually did.
In other words, not only does the model use a very, very odd method to do the maths, you can't trust its explanations as to what it has just done. That's significant and shows that model outputs can not be relied upon when designing guardrails for AI. Their internal workings need to be understood, too.
Another very surprising outcome of the research is the discovery that these LLMs do not, as is widely assumed, operate by merely predicting the next word. By tracing how Claude generated rhyming couplets, Anthropic found that it chose the rhyming word at the end of verses first, then filled in the rest of the line.
"The planning thing in poems blew me away," says Batson. "Instead of at the very last minute trying to make the rhyme make sense, it knows where it’s going."
Anthropic discovered that their Claude LLM didn't just predict the next word. (Image credit: Anthropic)
Anthropic also found, among other things, that Claude "sometimes thinks in a conceptual space that is shared between languages, suggesting it has a kind of universal 'language of thought'."
Anywho, there's apparently a long way to go with this research. According to Anthropic, "it currently takes a few hours of human effort to understand the circuits we see, even on prompts with only tens of words." And the research doesn't explain how the structures inside LLMs are formed in the first place.
But it has shone a light on at least some parts of how these oddly mysterious AI beings—which we have created but don't understand—actually work. And that has to be a good thing.
My favourite part of the day: commenting LLMentalist under AI articles.
