161–170 of 205 posts
I'm really surprised they don't charge a very high price with a generous free tier, and then have an army of salespeople who will offer you whatever discount is necessary to make your business model work. If you want to use GPT-3 to build an AI-lawyer, it'll be very expensive... but to build a tool for customer support for walmart will be cheap, etc.
Any chance of this making GitHub copilot cheaper?
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Because providing a template requires someone with knowledge of that particular problem space (in this case "writing letters to government agencies to inquire about disability benefits") to put in the work of designing the template, coming up with likely placeholders and predicting how people might need to use it. GPT-3 can do a good job of this all today, without any additional training. An expert could help out by…
A madlibs is more predictable, requires only one example, easy to understand for people with language issues or disabilities, more regulation-safe as it doesn't spit out unpredictable things. And the madlibs is free to use, for everyone. All things considered, GPT-3 is a more of a shibboleth of AI revolution than an actual one. Much better to give models like GPT-NeoX to talented development teams who can really opti…
If no-one has built the madlib template for "a letter to the council asking about my disability benefits" then madlibs being more predictable isn't going to help you.
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Why bother use a GPT-3 model when you can use the free BLOOM model? Large "foundation" models will become a commodity -- a race to the bottom.
I have used BLOOM a little, and I struggle to get it to produce the same kind of output as GPT-3. Anyone have tips there? It often seems to not keep the right context of the previously generated text.
In my small amount of testing of Bloom so far it seems capable of advanced behaviour but it can indeed be trickier to coax that out. Playing with temperature and sampling matters for sure.
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The problem is the word "cheaper". For the opposite word, "expensive", it all depends on the word before it: "as expensive" or "more expensive". "100 widgets cost $100. Unit price is $1." They are now twice _as_ expensive -> they now cost $200. Unit price is now $2. "100 widgets cost $100. Unit price is $1." They are now two times _more_ expensive -> they now cost $300. Unit price is now $3. But How do you do this wi…
> For the opposite word, "expensive", it all depends on the word before it: "as expensive" or "more expensive". Frustratingly, I find this isn't always true in practice. Lots of people use "x times as y" and "x times more y" interchangeably. To avoid ambiguity I try to only use the former in any context where precision is useful.
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Besides all the ideas of well meaning people in HN, I fear GPT-3 (and its competitors) will just bring almost impossible to spot fake Amazon reviews, SEO garbage sites, fake blogs, fake podcasts, etc. And even worse, now corporations and governments don't even need to have troll farms. Just rent some AI, give it basic directions, and let it loose. The Dead Internet Theory will become real.
i think this is a bit pessimistic. You can also use GPT-3 or similar technologies to much more easily train a fine-tuned "real vs fake" review classifier with fewer training examples than was previously necessary. GPT-3 like all tech is double-edged I think. Has good & bad things about it
I lack imagination. What is GPT-3 (and generative text models in general) useful for, other than toys? It can write essays, can we expect it to replace writers for newspapers and magazines? Can it write coherent technical documentation? Replace speechwriters? Edit: looks like the comment thread below is similar https://news.ycombinator.com/item?id=32554955
Besides all the ideas of well meaning people in HN, I fear GPT-3 (and its competitors) will just bring almost impossible to spot fake Amazon reviews, SEO garbage sites, fake blogs, fake podcasts, etc. And even worse, now corporations and governments don't even need to have troll farms. Just rent some AI, give it basic directions, and let it loose. The Dead Internet Theory will become real.
Maybe Google should require webmasters to mark the automated content with a special HTML tag or attribute. Failure to comply leading to deranking. This would be necessary for the future - to know what data was human generated when training the next model, and to have official reason for penalising spammers who try to pass artificial text as human written.
I lack imagination. What is GPT-3 (and generative text models in general) useful for, other than toys? It can write essays, can we expect it to replace writers for newspapers and magazines? Can it write coherent technical documentation? Replace speechwriters? Edit: looks like the comment thread below is similar https://news.ycombinator.com/item?id=32554955
It’s incredibly good at generating scholarly text. Put in a title and abstract and it will come up with a strong intro that is clear and compelling. It helps me write academic papers more clearly, as a result. Also, great for early stages of grant writing or proposals. It came up with a fantastic conference proposal for me that I spent 20 minutes editing and then just sent, unedited. It will aid speechwriters or help…
There is a bit of prompt engineering. If you start your question with things like "As a qualified therapist, what would you recommend to" .... There is also a bit of engineering with the question. I often ask about an analogous situation, rather than my own.
I'm not batting 100%, but I often get strategies and things to think about I wouldn't have come up with myself.
I was recently able to articulate to myself why I dislike language models like GPT-3 so much.
Language is fundamentally about communication. I have a mental state, and language lets me share that some of that mental state with another human being. It's quite remarkable, and the cornerstone for pretty much every aspect of our lives as social creatures.
GPT-3 can generate text that's convincingly human-like; it's an amazing technical achievement. But (so far, anyway!) it is just that; a generator of plausible text. To borrow a philosophical term, there's no "interiority" there.
You can see this clearly in GPT-3's output; it is at its weakest when writing about the relationships between a set of concepts, or when trying to carry a line of thought or argument through a longer text. This is because there is no underlying _thing_ that is being communicated; no mental model or grounding concept. Just text, generated solely based on plausibility in a given context.
So while GPT-3 is truly remarkable in its ability to generate text, it cannot, by definition, communicate (except on the meta level via prompt selection and output curation.)
Does the world really need more words but with less communication?
Honest question: What does GPT-3 add to my tookbox as a developer?