41–50 of 162 posts
I like that very much. ChatGPT always states that it has no connection to reality, since it lacks the organ of perception (Internet) and offers a pure language game. Similar to a Carl Rogers machine
The «organ of perception»?! What about ontology, alethics and reasoning, critical thinking?
This is called "hallucination" and I find it to be the biggest flaw with GPT-3 interfaces like ChatGPT. Basically, the system will start convincingly BSing at any point and as a user, you're never sure if any particular detail it outputs is actually correct. The problem is, these large language models are inherently kind of a black box, so how do you fix this kind of behavior? I started looking and it turns out that…
I remember in school kids figured out you could make up a reference in your history essay, and nobody would ever check. (Humperdinck's History of Later Rome, vol 3, 1987).
This is called "hallucination" and I find it to be the biggest flaw with GPT-3 interfaces like ChatGPT. Basically, the system will start convincingly BSing at any point and as a user, you're never sure if any particular detail it outputs is actually correct. The problem is, these large language models are inherently kind of a black box, so how do you fix this kind of behavior? I started looking and it turns out that…
Guys, hear me out. What if we could replace the entire Management Consulting industry with ChatGPT?
> "List Processing and Programming" by John F. Sowa ( https://www.cs.cmu.edu/~fp/courses/lp/lectures.html ) The full link doesn't exist, but it's interesting that https://www.cs.cmu.edu/~fp does exist and leads to the homepage of a computer science prof at CMU (prof. Frank Pfenning, not "John F. Sowa" though). So at least it got the general vicinity right.
If you asked me to make up a book about world war 1, I could suggest "the great war" and I bet there's a dozen books with that name.
This is called "hallucination" and I find it to be the biggest flaw with GPT-3 interfaces like ChatGPT. Basically, the system will start convincingly BSing at any point and as a user, you're never sure if any particular detail it outputs is actually correct. The problem is, these large language models are inherently kind of a black box, so how do you fix this kind of behavior? I started looking and it turns out that…
Right, most uses of these model I've seen, you clearly want "hallucination" or something like it: when you ask it for a poem it wouldn't be so impressive if it just spat out The Raven .
One of the first big AI successes was the style transfer algorithm. Take an image and apply the higher level style elements from another image onto it. https://en.wikipedia.org/wiki/Neural_style_transfer - "Mona Lisa in the style of Starry Night", etc.
We saw the same thing emerge with DALL-E 2 earlier this year. Totally different architecture, but most of the things people like are the result of taking one known "style" and applying it to something novel, sometimes to the frustration of artists whose styles were gobbled up in the huge corpus of training data.
The same thing seems to be happening with ChatGPT. Spit out a script of some novel situation in the style of Seinfeld or Shakespeare or Edgar Allen Poe, etc
Unsurprising given how it works, but the higher-level implications are somewhat unexpected. These model are extremely good when they have creative freedom and are used to produce some kind of art. Poems, text in various styles, images without important details, and so on. But they fail miserably when you give them tasks that require world knowledge or precision. For example, when I asked a programming question I got…
Efforts to limit hallucination will just turn it into a powerful natural language query engine for the training data set. The real value in AI lies in its creative power, so although a strict mode will probably be a very useful tool, especially in recursion, I don’t think that it is the ultimate path.
In order to be relatable to humans, AI will have to have either a real or a physical embodiment so that there is distinction between the real and the imaginary; without this distinction, it will likely prove difficult to advance.
Another problem is, that they are mostly training from texts from the internet. And a lot of texts contain wrong information. They are not „smart“ enough to do fact checking on that.
Unsurprising given how it works, but the higher-level implications are somewhat unexpected. These model are extremely good when they have creative freedom and are used to produce some kind of art. Poems, text in various styles, images without important details, and so on. But they fail miserably when you give them tasks that require world knowledge or precision. For example, when I asked a programming question I got…