Working with The Associated Press to provide fresh results for the Gemini app
blog.google
blog.google
I'm much more excited about eventual emergence of underground homebrew models without any guardrails...
Not if AI gatekeepers and interest groups have anything to say about it. AI without guardrails could be classified as a "weapon" and made illegal such that we are only allowed to use models produced by regulated entities and meet certain "safety standards" (like how medical software has to be approved by FDA).
Edit: oh, I guess "underground" could be interpreted in a way that these models are still produced and distributed (but secretly, illegally, etc)
We still argued, but we did it from a place of passion, not commission.
Here's some simple example code in Go, for RAG with 5000 arXiv paper abstracts: https://github.com/philippgille/chromem-go/tree/v0.7.0/examp... (full disclosure it's using a simple vector DB I wrote)
I can only hope this data is being incorporated in some way that makes hallucinations less likely.
I would expect that number to go down from 1.3% to below 1% over the course of the year.
There's always a chance what you're reading is wrong - due to purposeful deception, negligence, or accident.
Realistically, hardly anything is 100% accurate besides math.
The whole isn’t about generating news articles, it’s about getting the model up to date on facts so it can synthesize a newspaper for you. I’d say it’s a way to get journalists to be journalists again instead of clickbait composers - as long as the model doesn’t inject clickbait there itself. I don’t trust Google to not do it sometime, but they aren’t doing it now and the infrastructure is being made for others to consume when Gemini suffers from inevitable enshittification.
This isn't what I meant. I pay directly for subscriptions/donations to news organizations that employee journalists that do this original reporting. I don't want a middle man that just messes it up. This goes for LLMs and for free news sites that don't do much more than summarize original reporting. I've seen more than a few times where they inject opinions, mess up facts or put focus on what was originally a small side point in the article.
I'm not replying to point that out, I think others have done a better job. It's mostly that this conversation made me think of this classic Babbage quote that I've always enjoyed.
"On two occasions I have been asked, – 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question"
I bet you think the news is accurate all other times. It’s called “Gell-Mann Amnesia”
I am quite certain my personal hallucinations level is more than 1.3%, obviously we want our machines to be better than us, but my doctor once said folic acid is not a vitamin.
I work with investigative reporters on stories that take many months to produce. Every time we receive a leak there is an extensive process of proving public interest before we can even start looking at the material. Once we can see it in we have to be extremely careful with everything we note down to make sure that our work isn't seen as prejudiced if legal discovery happens. We're constantly going back and forth with our editorial legal team to make sure what we're saying is fair and accurate. And in the end, the people we're reporting are given a chance to refute any of the facts we're about to present. Any mistakes can result in legal action that can ruin the lives of reporters and shut down companies.
Now, imagine I were to go to a reporter who has spent 6 months working on a story about, for example, a high profile celebrity sexually assaulted multiple women, how the royal family hides their wealth and are exempt from laws, or how multinational corporations use legal loopholes to avoid paying taxes, and said, "oh, 1% of people reading this will likely be given some totally made up details".
Given that stories often have more than a million impressions, this would lead tens of thousands of people with potentially libellous "hallucinations".
It simply should not be allowed.
LLMs have their place, for sure, but presenting the news is not it.
Of course that willm be shit, but there we are.
That is what you siphon up. And in output you can mad lib out an article just like those johnny on the spot AP reporters do anyhow, filling in the skeleton article about a death or an attack or a banquet or award show with the relevant input concerning the event. LLM isn't even used for finding this input but to just adjust the boilerplate, perhaps to tailor news specifically to the reader's own inclinations based on engagement with other articles collected via fingerprinting.
The key word is "real-time". LLMs can't be trained in realtime, so it's obviously going to call an API that pulls up and reads from AP news, just like their search engine.
Fine-tuning, which is cheaper and faster, has been proven to not be a good solution to "teach" models new facts.
I think what's most likely here is that Gemini will have access to a form of RAG based on a database of AP articles that gets updated in real-time as new articles are published.
The on device model that it uses is also literally 1% the size of the large models like Gemini
These models will interject information from their training whether or it is relevant or not. This is just due to the nature of how these models work.
Anyone trying to argue that it doesn't happen that often or anything is missing the key problem. Sure it may be right most of the time, but all that does is build a false sense of security and eventually you stop double checking or clicking through to a source. Whether it is a search result, manipulating data, or whatever.
This is made infinitely worse when these summaries are one and done, a single user is going to see the output and no one else will see it to fact check. It isn't like an article being wrong that everyone reading it is reading the same article, can then comment that something is wrong, it get updated, and so on and so forth. That feedback loop is non-existent with these models
Same problem existed before AI summaries.
"Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them.
In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know."
– Michael Crichton (1942-2008)