The way it explains code, the error, and then gives the solution to this particular error, i really don’t understand how someone could pretend we’re not witnessing at least a first hint of true intelligence.
If you use it on a project “True intelligence” is not the word I would use to describe it. I have spent 2-8 hours using it every day since launch as a dev tool. It is very good for learning stuff, scaffolding solutions etc. but it is very bad when you try to do something obscure.
For example, I was learning Bazel using it and I spent 2-3 hours trying to debug an issue going back and forth with it. Eventually I went to the docs and found the solution in 5 mins.
The problem is it doesn’t know when it is wrong, it will always spit out an answer and it will make up libraries that sound real in order to give you an answer. The problem is it doesn’t understand the logic behind what it is saying. It’s just able to spit out a reasonable looking answer because it is a generalizable statistical model of language.
For example try asking it “Generate a 5 python homework questions on Classes that students cannot cheat on using ChatGPT, GPT3 or Assistant.” The questions it generates are not ones that are hardened against itself. It is not able to think logically because it does not think like a human, it’s doing something else entirely so calling it “true intelligence” is not accurate.
Yeah I'm surprised you're the first person I've seen mentioning that it makes up libraries. I asked it to create a Clojure function to render the mandelbrot set as ascii art, which apparently someone got to work in Erlang with only minimal modifications to the code. For Clojure it seemingly invented the clojure.math.complex namespace and functions it thought should belong there.
The problem with that example is that it's not GPT-3 telling you about itself, it's GPT-3 serving up a roughly averaged version of every text explanation it's ever seen that might provide a convincing answer to your question.
I agree, this was more of a test of a technique where I got ChatGPT to generate prompts to interactively improve a prompt from another ChatGPT instance: https://news.ycombinator.com/item?id=33857328
If you ask ChatGPT if it is intelligent or something like that it will always say something like “I am large language model trained by openai etc.” so I worked with another ChatGPT instance to interactively get an increasingly detailed answer to whether it is intelligent or conscious.
I also use this technique for other things like for example instead of saying “Generate a React implementation of cookie clicker” ask one ChatGPT instance to “Generate 10 prompts to ChatGPT to generate a React implementation of cookie clicker” this meta-prompt engineering technique is the most useful technique I have come up with so far.
ChatGPT is a doctor's secretary, isn't it? It knows the answers to things like what prescription to get for some illness, but doesn't have the models to actually be a doctor.
"True intelligence" is a loaded term that needs a concrete definition to discuss meaningfully. Certainly, it is not an intelligence that at all resembles a human intelligence since language models don't actually understand the world, they understand relationships between lexemes and are optimized to produce voluble outputs constructed around that understanding. Language models aren't intelligent in the same way that a motion picture isn't really moving.
It is an ambiguous term that can refer to many different things.
It is used interchangeably with cognitive abilities, personality traits, knowledge, memorization abilities, hard skills, soft skills, etc. in a regular basis.
Just use the specific term instead. Problem solved.
Before people finish their pointless discussion about what intelligence is, billionaires will have taken over the world with the help of these systems.
On the advent of code day three it would be useful to have a chunking iterator in rust. Having just implemented day 3 and looking for one, I knew rust stable doesn’t have a general purpose chunking iterator.
I asked chatGPT about it and it very confidently said one existed and gave me a sample code which imported a nonexistent chunking iterator. I wonder about other outputs of chatGPT which are not as easily and quickly verifiable.
When I asked it how to undo an operation I had performed using "cargo add" it confidently showed me examples of using the "cargo remove" command... which it had entirely made up.
As the parent said, it's not stable yet but it's right there so you can see how it works.
The most important ergonomic trick here is that Rust has type inference, it can see array_chunks needs to know how big the chunks should be, and of course you can just specify that but in most cases you'll use chunks which clearly have a defined size and the inference will go "Oh, that's how big the chunks are" just as e.g. if you put a bunch of chars in a Vec you made, the type inference goes, "Oh, it's a Vec of chars" and doesn't waste your time asking you what type of Vec it is.
You'll see the example expects Some(['l', 'o']) to be the first thing out of the Iterator, the type inference system can see that's an array of two chars, thus, the chunk size is 2.
I still think it's the world's most impressive party trick.
It turns out if you build a model that can predict what word comes next after a sequence of previous words, then train it on TBs (PBs?) of data, you get something that really does appear to be "intelligent". And which is absurdly useful, to boot!
Okay, but then is Wikipedia no more than a big party trick? It's the most comprehensive knowledge base humankind has ever produced, made in collaboration of phylantropic people around the world.
Of course some info is sometimes false, but I don't find myself getting disillusioned of it all. It is what it is, and knowing the limitations doesn't ruin it for me, It is still incredibly valuable, and irreplacable, imo.
We haven't figured out what conscience is, so it's going to be fun to deal with near perfect imitations of human behaviour, and the societal consequences...
For example, I was learning Bazel using it and I spent 2-3 hours trying to debug an issue going back and forth with it. Eventually I went to the docs and found the solution in 5 mins.
The problem is it doesn’t know when it is wrong, it will always spit out an answer and it will make up libraries that sound real in order to give you an answer. The problem is it doesn’t understand the logic behind what it is saying. It’s just able to spit out a reasonable looking answer because it is a generalizable statistical model of language.
For example try asking it “Generate a 5 python homework questions on Classes that students cannot cheat on using ChatGPT, GPT3 or Assistant.” The questions it generates are not ones that are hardened against itself. It is not able to think logically because it does not think like a human, it’s doing something else entirely so calling it “true intelligence” is not accurate.
If you want to read how it itself describes it’s own intelligence and identity I found a prompt to get it to do that, it does not describe itself as a human intelligence: https://twitter.com/faizlikethehat/status/159949598085168332...