I seriously don't get this argument. Google can implement this themselves! It's not like they can't train a large language model akin to GPT-3 (they already have) or deploy it. And as others pointed out, language models are seriously not reliable right now in terms of producing true information.
Comparing Google and ChatGPT
131–140 of 280 posts
Re: Comparing Google and ChatGPT
#132Another person who doesn’t realise AI language models are just making shit up. Google results are quite often full of wrong information, but at least it has mechanism for surfacing better content: inbound links, domain authority, and other signals. It doesn’t guarantee correctness, but it’s better than the pseudo-authoritative fiction GPT-3 and friends come up with.
Most of the time, humans just make shit up too. I just made up the contents of this comment.
Thankfully, your comments aren't being used as a definitive source of truth by default :)
Re: Comparing Google and ChatGPT
#133No post body was provided.
Re: Comparing Google and ChatGPT
#134Earlier quoted context omitted.
Yep, as AI starts to get trained on AI-generated data the output may well become unstable, you can't build an infinite motion machine (or an infinite gain machine/infinite SNR amplifier) and the system may degrade to essentially white noise. Sort of a cyber-kessler syndrome basically. You really don't want AI-generated content in your AI training material, that's actually probably not generating signal for building f…
Am I alone in not being sure if the commenter here fed the parent into GPT as a prompt to generate output or actually wrote this?
Afraid not, I actually wrote all that shit...
Re: Comparing Google and ChatGPT
#135I work at Alphabet and I recently went to an internal tech talk about deploying large language models like this at Google. As a disclaimer I'll first note that this is not my area of expertise, I just attended the tech talk because it sounded interesting.
Large language models like GPT are one of the biggest areas of active ML research at Google, and there's a ton of pretty obvious applications for how they can be used to answer queries, index information, etc. There is a huge budget at Google related to staffing people to work on these kinds of models and do the actual training, which is very expensive because it takes a ton of compute capacity to train these super huge language models. However what I gathered from the talk is the economics of actually using these kinds of language models in the biggest Google products (e.g. search, gmail) isn't quite there yet. It's one thing to put up a demo that interested nerds can play with, but it's quite another thing to try to integrate it deeply in a system that serves billions of requests a day when you take into account serving costs, added latency, and the fact that the average revenue on something like a Google search is close to infinitesimal already. I think I remember the presenter saying something like they'd want to reduce the costs by at least 10x before it would be feasible to integrate models like this in products like search. A 10x or even 100x improvement is obviously an attainable target in the next few years, so I think technology like this is coming in the next few years.
Re: Comparing Google and ChatGPT
#136Earlier quoted context omitted.
Fair point, but Google is also exactly as confidently wrong as GTP. They are both based on Web scrapes of content from humans after all, who are frequently confidently wrong.
Sure, but Google at least presents itself as being a search engine, composed of potentially unreliable information scraped from the web. GPT looks/feels like an infallible oracle.
ChatGPT page every time you open it:
Limitations:
- May occasionally generate incorrect information
- May occasionally produce harmful instructions or biased content
- Limited knowledge of world and events after 2021
Hacker News, on reading this list of caveats:
This looks and feels like an infallible oracle.
Re: Comparing Google and ChatGPT
#137Quoted post unavailable.
Wow, how wrong can you be? And by you I mean you, the commenter.
Re: Comparing Google and ChatGPT
#138I work at Alphabet and I recently went to an internal tech talk about deploying large language models like this at Google. As a disclaimer I'll first note that this is not my area of expertise, I just attended the tech talk because it sounded interesting. Large language models like GPT are one of the biggest areas of active ML research at Google, and there's a ton of pretty obvious applications for how they can be us…
> and the fact that the average revenue on something like a Google search is close to infinitesimal already
Isn't Search ad revenue >$100Bn per year?
Isn't that >$0.07 per search?
Re: Comparing Google and ChatGPT
#139I work at Alphabet and I recently went to an internal tech talk about deploying large language models like this at Google. As a disclaimer I'll first note that this is not my area of expertise, I just attended the tech talk because it sounded interesting. Large language models like GPT are one of the biggest areas of active ML research at Google, and there's a ton of pretty obvious applications for how they can be us…
The problem for Google isn't just technical, it's organizational.
The entire organization and all its products are built around ads. If a new product comes along that drastically reduces the number of pages a user views, what happens to the ad revenue?
Right now, every click, every query is an impression. But if there's an all-knowing AI answering all my questions accurately, what incentive do I, as a user, have to search again, scroll through pages, and look around multiple pages?
Google will have to adopt a radically new business model and there's organizational inertia in doing that.
Re: Comparing Google and ChatGPT
#140Quoted post unavailable.
> Biology is not confused about this. Males produce gametes (sperm) and females produce large gametes (ova). There are no intermediate gametes, which is why there is no spectrum of sex. Biological sex in humans is a binary system.
This is not entirely true, due to the existence of various kinds of intersex genotypes, which may produce no gametes, or both gametes (functionality notwithstanding). Biological sex in humans is not a purely binary system.
That said, it absolutely is a bimodal distribution, so ChatGPT is still completely wrong.