1) doesn't make a whole lot of sense. A perfect understanding of something doesn't mean you can fix anything wrong with it.
3) has already happened. The AI chat bots aren't very smart, but they're clearly capable of some degree of basic reasoning.
3) has already happened. The AI chat bots aren't very smart, but they're clearly capable of some degree of basic reasoning.
https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...
>Could a Neuroscientist Understand a Microprocessor?
>We show that the [neuroscience experimental] approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data.
https://www.cell.com/cancer-cell/pdf/S1535-6108(02)00133-2.p...
Not sure if there is a difference, for sufficiently abstract interpretations of “satisfy the input”
LLMs don't think, they extrapolate. They are a filter, not capable of thought or reason. You can't reason with an LLM but plenty of people have tried and it fooled them well enough.
They would argue "but brains are just that, a big filter".LLMs don't get even close to what unicelulars can handle, and they don't have a brain.
A classic tell of this is people handling out of bounds errors in loops by trying to randomly add or subtract 1 from their for-loop parameters.
I realized that they didn't have a mental model for what a loop did, they had simply memorized the syntax for a loop and were doing advanced pattern matching. Code repeats = write the for-loop syntax I've memorized. And then after seeing that fail with out of bounds exceptions, they learned a new rule: modify the loop parameters and see if that fixes the problem.
When I think about how I write code, or I compare their approach to the other cohort of students I saw, it's a different process. I see in my mind's eye a type of 'machine' that performs the actions that I want to take place. I simulate running that machine in my mind and tweak its design until it works the way I want it to. Only then do I think about syntax and try to translate what's already happening in my mind into source code.
I've seen people get shockingly far into software engineering careers using the pattern matching / guess and check approach. I've wondered if a lot of the handwringing you see on programming forums about the 'leetcode grind' is coming from people who do this pattern matching approach. To them it must seems like the only way to solve these problems is to simply train their internal pattern matching neural networks on huge amounts of examples.
The code that I see GPT generate looks eerily similar to what I saw from those programmers. And that makes sense because I think that functionally they're doing the same thing. Only GPT does it at a superhuman level.
That seems to me to indicate that there's something that at least some humans do with a mental model that our current LLMs lack. If someone figures out how to simulate those mental processes in a computer program I think we'll see a huge inflection point and that's what the original comment (as I read it) is referring to.
There’s all kinds of other things it won’t do until it hears. And touches. Smell and taste might help too I guess!?
As a byproduct it can also be taught truth is what it can verify with sensors.
at the same time i do feel like pattern matching limits my growth, if i had a complete understanding of a majority of networking principals id be much higher up in my career
As long as it isn't making you feel like a complete fraud, this level of introspection is a good thing imo.
"I know that I know nothing"
In fairness and compassion to that crowd, a lot of it comes from the fact that a modern interview for a coveted FAANG job often requires 1-2 LC Medium (or Hard) problems cranked out in 45-60 minutes. Depending on the company and the org, the overall interview loop may well be multiple such one-hour sprints.
It's quite a pressure-cooker of an interview setting. Given that, it's understandable why many people converge on memorizing and brute-force pattern-matching as their interview strategy — if they can just memorize enough, the odds are actually pretty decent. (And the payoff is not bad, either.)
this is all assuming that someone is trying to be productive rather than stop and ponder the abstraction that is a loop and divine its nature in a rigorous way
if the students are having problems with loops, that's not surprising considering that computer science doesn't teach software development skills. like... at all.