[svara]

I don't know, this sounds a lot like in the late 90s when we heard a lot about how anyone could put information on the internet and that you shouldn't trust what you read online.

Well it turns out you can manage just fine.

You shouldn't blindly trust anything. Not what you read, not what people say.

Using LLMs effectively is a skill too, and that does involve deciding when and how to verify information.

[andersa]

The difference is in scale. Back then, only humans were sometimes putting up false information, and other humans had a chance to correct it. Now, machines are writing infinitely more garbage than humans can ever read. Search engines like Google are already effectively unusable.

[vohk]

I think there will be solutions, although I don't think getting there will be pretty.

Google's case (and Meta and spam calls and others) is at least in part an incentives problem. Google hasn't been about delivering excellent search to users for a very long time. They're an ad company and their search engine is a tool to better deliver ads. Once they had an effective monopoly, they just had to stay good enough not to lose it.

I've been using Kagi for a few years now and while SEO spam and AI garbage is still an issue, it is far less of one than with Google or Bing. My conclusion is these problems are at least somewhat addressable if doing so is what gets the business paid.

But I think a real long term solution will have to involved a federated trust model. It won't be viable to index everything dumped on the web; there will need to be a component prioritizing trust in the author or publisher. If that follows the same patterns as email (ex: owned by Google and Microsoft), then we're really screwed.

[skeeter2020]

>> Well it turns out you can manage just fine.

You missed the full context: you would never be able to trust a bunch of amateur randos self-policing their content. Turns out it's not perfect but better than a very small set of professionals; usually there's enough expertise out there, it's just widely distributed. The challenge this time is 1. the scale, 2. the rate of growth, 3. the decline in expertise.

>> Using LLMs effectively is a skill too, and that does involve deciding when and how to verify information.

How do you verify when ALL the sources are share the same AI-generated root, and ALL of the independent (i.e. human) experts have aged-out and no longer exist?

[svara]

> How do you verify when ALL the sources are share the same AI-generated root,

Why would that happen? There's demand for high quality, trustworthy information and that's not going away.

When asking an LLM coding questions, for example, you can ask for sources and it'll point you to documentation. It won't always be the correct link, but you can prod it more and usually get it, or fall back to searching the docs the old fashioned way.

[closewith]

> Well it turns out you can manage just fine.

The internet has ravaged society with disinformation. It's a literal battlefield. How can you have come till this conclusion?

[svara]

This thread started from the question of where the experts with the ability to use LLMs effectively would still come from in the future.

I was making the point that it's still easy to find great information on the internet despite the fact that there's a lot of incorrect information as well, which was an often mentioned 'danger' on the internet since its early days.

I wasn't speaking to broader societal impact of LLMs, where I can easily agree it's going to make misinformation at scale much easier.

[closewith]

Fair point, well made.