…Duh. 🤓
Someone put 69 to research and then to article. Nice trolling.
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.
If the llm already knows the full sentence it’s going to output from the first word it “guesses” I wonder if you could short circuit it and say just give the full sentence instead of doing a cycle for each word of the sentence, could maybe cut down on llm energy costs.
interestingly, too, this is a technique when you’re improvising songs, it’s called Target Rhyming.
The most effective way is to do A / B^1 / C / B^2 rhymes. You pick the B^2 rhyme, let’s say, “ibruprofen” and you get all of A and B^1 to think of a rhyme
Oh its Christmas time
And I was up on my roof when
I heard a jolly old voice
Ask me for ibuprofen
And the audience thinks you’re fucking incredible for complex rhymes.
I don’t think it knows the full sentence, it just doesn’t search for the words in the order they will be in the sentence. It finds the end-words first to make the poem rhyme, than looks for the rest of the words. I do it this way as well just like many other people trying to create any kind of rhyming text.
How can i take an article that uses the word “anywho” seriously?
“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.
But you’re doing two calculations now, an approximate one and another one on the last digits, since you’re going to do the approximate calculation you might act as well just do the accurate calculation and be done in one step.
This solution, while it works, has the feeling of evolution. No intelligent design, which I suppose makes sense considering the AI did essentially evolve.
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.
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.
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
This is what the ARC-AGI test by Chollet has also revealed of current AI / LLMs. They have a tendency to approach problems with this trial and error method and can be extremely inefficient (in their current form) with anything involving abstract / deductive reasoning.
Most LLMs do terribly at the test with the most recent breakthrough being with reasoning models. But even the reasoning models struggle.
ARC-AGI is simple, but it demands a keen sense of perception and, in some sense, judgment. It consists of a series of incomplete grids that the test-taker must color in based on the rules they deduce from a few examples; one might, for instance, see a sequence of images and observe that a blue tile is always surrounded by orange tiles, then complete the next picture accordingly. It’s not so different from paint by numbers.
The test has long seemed intractable to major AI companies. GPT-4, which OpenAI boasted in 2023 had “advanced reasoning capabilities,” didn’t do much better than the zero percent earned by its predecessor. A year later, GPT-4o, which the start-up marketed as displaying “text, reasoning, and coding intelligence,” achieved only 5 percent. Gemini 1.5 and Claude 3.7, flagship models from Google and Anthropic, achieved 5 and 14 percent, respectively.
Its funny because i approach life with a trial and error method too, not efficient but i get the job done in the end. Always see others who dont and give up like all the people bad at computers who ask the tech support at the company to fix the problem instead of thinking about it for two secs and wonder where life went wrong.
