4 ms·

Sure, the ideas could be old (in ml research timeframes) but it works and they shipped it. It’s most people’s first exposure to a lot of innovative ideas that m

Sure, the ideas could be old (in ml research timeframes) but it works and they shipped it. It’s most people’s first exposure to a lot of innovative ideas that make up LLM at scale. I agree with his assertion that the only reason other people haven’t seen this out of Meta or Google is that they “have a lot to lose” from putting out bots “that make stuff up” - that’s the root of Google seemingly losing the narrative war I think we’re seeing here. Not sure how to get them to work through that fear.

password11 4y ago
>Google seemingly losing the narrative war I think we’re seeing here. Google doesn't care about the narrative war. They care about getting sued by the EU. They can put out a similar model whenever they want. The just don't want to, because the thing about AI research (vs., say, social media) is there's no first mover advantage. The best model wins and there's no network effect to bolster incumbents.
- mattnewton 4y ago
  > The just don't want to, because the thing about AI research (vs., say, social media) is there's no first mover advantage. That’s true and I am not worried for Google in the slightest. However, once a tool is good enough, both the branding and use of it becomes somewhat sticky. It’s why people still call any tablet an iPad, and it’s one of the reasons even if a large search engine launched tomorrow people would keep using google for a long time. The problem isn’t being first, it’s if they crack changing consumer habits while google is biding their time.
  - deleted 4y ago
    [deleted]
visarga 4y ago
> they “have a lot to lose” from putting out bots “that make stuff up” 2023 should be the year of AI validation. For starters, we could extract facts from all the training examples (trillions) and organise them in a knowledge base. Where there is inconsistency or variation, the model should learn the distribution, so later it can confidently say a fact is not in its training data or is controversial. Note that I didn't say the model should find the truth, just the distribution of "facts". We can add source reputation signals to this KB to improve its alignment with truth. And if it is controversies we want to know, we can look at news headlines, they usually reference facts being debated. So we can know a fact is contested and by who. Another interesting approach - probing the model for "truth" by identifying a direction in the latent space that aligns with it. The logic being, even when the model is deceptive, it has an interest to know it is not telling the truth to keep the narrative consistent. So we need to just identify this signal. This is also part of work for AI alignment. I hope this year we will see large investments in validation, because the unverified outputs of a generative model are worthless and they know it. At the very least hook the model up with a calculator and a way to query the web.

password11 4y ago

>Google seemingly losing the narrative war I think we’re seeing here. Google doesn't care about the narrative war. They care about getting sued by the EU. They can put out a similar model whenever they want. The just don't want to, because the thing about AI research (vs., say, social media) is there's no first mover advantage. The best model wins and there's no network effect to bolster incumbents.

mattnewton 4y ago
> The just don't want to, because the thing about AI research (vs., say, social media) is there's no first mover advantage. That’s true and I am not worried for Google in the slightest. However, once a tool is good enough, both the branding and use of it becomes somewhat sticky. It’s why people still call any tablet an iPad, and it’s one of the reasons even if a large search engine launched tomorrow people would keep using google for a long time. The problem isn’t being first, it’s if they crack changing consumer habits while google is biding their time.
- deleted 4y ago
  [deleted]

visarga 4y ago

> they “have a lot to lose” from putting out bots “that make stuff up” 2023 should be the year of AI validation. For starters, we could extract facts from all the training examples (trillions) and organise them in a knowledge base. Where there is inconsistency or variation, the model should learn the distribution, so later it can confidently say a fact is not in its training data or is controversial. Note that I didn't say the model should find the truth, just the distribution of "facts". We can add source reputation signals to this KB to improve its alignment with truth. And if it is controversies we want to know, we can look at news headlines, they usually reference facts being debated. So we can know a fact is contested and by who. Another interesting approach - probing the model for "truth" by identifying a direction in the latent space that aligns with it. The logic being, even when the model is deceptive, it has an interest to know it is not telling the truth to keep the narrative consistent. So we need to just identify this signal. This is also part of work for AI alignment. I hope this year we will see large investments in validation, because the unverified outputs of a generative model are worthless and they know it. At the very least hook the model up with a calculator and a way to query the web.