301–310 of 1000 posts
Bard is such a funny name. I tried to, no pun intended, google it and it autocorrected to brad.
Earlier quoted context omitted.
I don't think most people realize how much infrastructure separates something like ChatGPT from Google-scale deployment. OpenAI isn't suddenly building transoceanic fiber and datacenters near most major population centers. They aren't signing production-ready contract vehicles with most major OEMs and governments. And in the gap it would take a new entrant to acquire 10% of those assets, Google has 100,000 engineers…
Based on the busy mock-up in TFA I don’t think it’ll take 100,000 engineers to beat them.
>AI can be helpful in these moments, synthesizing insights for questions where there’s no one right answer. Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether that’s seeking out additional perspectives, like blogs from people who play both piano and gui…
i wonder what kind of downstream effects on the ad click industry this will have. would kill for it all to die and be reinvented in a different, more personalizable and clever way
ChatGPT is awesome, but it is obviously hamstrung by the fact that it crawled the web at the end of 2021, so all of its data is essentially "frozen" and it doesn't "know" of any topics that occurred after 2021.
Seems to me like the next "holy grail" in large language models is building a model that can be continually updated. If Google can achieve that, I think they could leapfrog OpenAI. The example in the post about getting new information about the Webb space telescope seems to imply Google has this advantage.
So my question is, how hard of a problem is that with LLMs? I get the sense that LLMs are trained on a very large data set all at once, but that it is difficult to incrementally update them with new data. Is that a true assessment? Even incrementally updating an inverse text index can be a scalability challenge, and so it seems like, given the way that LLMs are trained, that it would be even harder to do with LLMs without spending an absolute fortune on training.
This is the ChatGPT reply to the exact same question asked in the preview in the article.
Here are a few things that the James Webb Space Telescope is expected to discover that you could share with your 9-year-old:
The formation of stars and planets: The James Webb Space Telescope will be able to observe the birth of stars and the formation of planetary systems, helping us understand how our own solar system was created.
The search for life on other planets: The telescope will be able to search for signs of life on other planets, such as the presence of water or certain gases in their atmospheres.
The history of the universe: The James Webb Space Telescope will be able to look back in time to observe some of the first galaxies that formed after the Big Bang, helping us learn about the early history of the universe.
Understanding black holes: The telescope will be able to study black holes and how they interact with the galaxies around them, helping us better understand these mysterious objects.
The study of exoplanets: The James Webb Space Telescope will be able to study exoplanets, or planets outside our solar system, in greater detail, helping us learn more about the diversity of planetary systems in the universe.
The Google reply is obviously better because it tells you actual discoveries, while ChatGPT changes the question to expected discoveries.
I agree this is bland corporate speak. But it reminded me of a question that's been floating around: A number of pundits, here on HN and elsewhere, keep referring to these large language models are "google killers." This just doesn't make sense to me. It feels like Google can easily pivot its ad engine to work with the AI-driven chat systems. It can augment answers with links to additional sources of information, be…
I don't think most people realize how much infrastructure separates something like ChatGPT from Google-scale deployment. OpenAI isn't suddenly building transoceanic fiber and datacenters near most major population centers. They aren't signing production-ready contract vehicles with most major OEMs and governments. And in the gap it would take a new entrant to acquire 10% of those assets, Google has 100,000 engineers…
Earlier quoted context omitted.
This "core inrastructure for the world" wıll also be maintained about as well as HVAC in the seminal film Brazil
Imagine a government controlled AI that partners with mega corporations to "assist" AI in getting you the "correct" results you desire. Call me a pessimist but we may already be staring down the gun of a dystopian future
I agree this is bland corporate speak. But it reminded me of a question that's been floating around: A number of pundits, here on HN and elsewhere, keep referring to these large language models are "google killers." This just doesn't make sense to me. It feels like Google can easily pivot its ad engine to work with the AI-driven chat systems. It can augment answers with links to additional sources of information, be…
I guess a couple of reasons: 1) Because it is disruptive. Things may get shook up, and Google may not end up in as exclusive position as they are currently in. There's risk. 2) Because it's not obvious how advertising would fit in with a conversational interface. Google may stay as #1 search/answer engine, but would revenue be adversely affected?
Full disclosure: I work at Google, but nowhere near the chatbot stuff. This is my humble opinion and nothing more.
(1) Google is behind and playing catchup, slow and bloated and weighed down by bureaucracy they are sinking
(2) Google has had this tech behind doors for years but not released it because of the risk of public perception and regulatory blowback. Now under the cover of OpenAI and Stability they can do it.
The second one seems plausible but I can't help being struck by how much their AI driven products seem to have stagnated over the last 5 years. Google Assistant (or whatever it is called now) seems worse at understanding my simple requests than it was 10 years ago. Things that seemed magical and worked very reliably like "remind me in 3 months that I left the oil can behind the paint tin" now fail so often I've stopped trying. Combined with the lack of detail in the blog post, I'm inclined to lean towards (1) - even if I do believe thew actual AI knownhow is buried somewhere inside Google, I think it is so many layers from anybody who could deliver it in the form of a product that it's effectively like it isn't there.