31–40 of 83 posts
The Forer Effect can't make chatGPT generate syntactically correct and functional code snippets that I use every day at work.
However, what happened is that since the distance of your bullets is small, you would fly up towards them and they would 'trick' you by at first flying away then suddenly b-lining at you. and when you fired sometimes they would fire at the same time and get you.
The comments I got from people who played the game 'wow the AI is really good'. Not sure if this is a direct example of the Forer effect
The Forer Effect can't make chatGPT generate syntactically correct and functional code snippets that I use every day at work.
I've been using github copilot and I'm waving back and forth between impressed and unimpressed; it regularly generates syntactically invalid code for me. Eg calling lib functions that don't exist.
Thanks!
The Forer Effect can't make chatGPT generate syntactically correct and functional code snippets that I use every day at work.
Counter point that literally just bit me this morning. ChatGPT completely lied to me about the scaling properties of Kinesis. I was confused about the 1000 write / second but only 5 read / second throughput of a kinesis shard and was asking it a number of questions, including specifically if a batch read would count as only a single read operation. It explicitly told me that each record in the batch counted as an ind…
Technology is supposed to make us smarter. Blindly believing an AI that we know can hallucinate makes us dumb with confidence.
Earlier quoted context omitted.
That’s not really an issue when we’re talking about AI. I said in a separate comment on the topic: intelligence is such a complicated thing that we seem to only be able to define it by pointing to things and saying “that’s not it.” If we didn’t move the goalposts, we’d have declared Stockfish to be full AI, despite it only being a chess-playing program, long ago.
Except in a few years you may realize it was "it" all along.
The Forer Effect can't make chatGPT generate syntactically correct and functional code snippets that I use every day at work.
Let me make this very clear: Generating syntactically correct code in various languages that also looks plausible is no small feat. It is, in fact, extremely impressive and will certainly have an impact on SE. But. Every single test I ran lead to functionally wrong designs from smallish memory errors in C (that hilariously ChatGPT was able to correct ND explain when pointed to) over misplaced/hallucinated methods in…
The Forer Effect can't make chatGPT generate syntactically correct and functional code snippets that I use every day at work.
Let me make this very clear: Generating syntactically correct code in various languages that also looks plausible is no small feat. It is, in fact, extremely impressive and will certainly have an impact on SE. But. Every single test I ran lead to functionally wrong designs from smallish memory errors in C (that hilariously ChatGPT was able to correct ND explain when pointed to) over misplaced/hallucinated methods in…
I was legitimately afraid for my job when GPT-4 was released. After using it, however, I sleep easy now.
I agree with the other comments that this is a fairly useless article. Perhaps I’m looking too much into this specific example, but I fail to see how the entirety of AI’s success (per this title) is misrepresented due to its inability to provide a horoscope (good example by another comment) that’s specific to some person in question. We see a lot of articles that swing too far towards “AI will change everything!” jus…
The title does not claim that the "entirety" of AI's success is misrepresented. It is questioning "how much" is, and I think that this is a fair question. The author does admit that the first paragraph in the example does have valid information which shows the AI with knowledge. It's the second paragraph that is just fluff.
I think that it is valid to ask how much this "fluff" impresses us and leads us to see the AI as being knowledgeable. Perhaps the author does draw too strong of a conclusion, but he still makes a good point.
Earlier quoted context omitted.
Let me make this very clear: Generating syntactically correct code in various languages that also looks plausible is no small feat. It is, in fact, extremely impressive and will certainly have an impact on SE. But. Every single test I ran lead to functionally wrong designs from smallish memory errors in C (that hilariously ChatGPT was able to correct ND explain when pointed to) over misplaced/hallucinated methods in…
I have the same experience. I think the transformer-based models are impressive, but whenever I try to actually use them for something, e.g. having ChatGPT write something or using GitHub Copilot, the results I get are terrible. I was legitimately afraid for my job when GPT-4 was released. After using it, however, I sleep easy now.
The Forer Effect can't make chatGPT generate syntactically correct and functional code snippets that I use every day at work.
The conventional narrative is that chatGPT can code as well as a junior engineer, but I feel like it’s more like a senior engineer scribbling on a whiteboard—no, it’s not perfect code, but it’s the right idea.