Honestly, outside of coding tasks, the AI Summary at the top of every Google search is adequate for 99% of what I need and I hardly even use ChatGPT any more.
To give a specific example of where I've seen this utterly fail... A few months ago one of my pets had an unexpected reaction to a new medication. It was late at night, well past our vet's hours. Google's AI response was basicaly "They are going to die, rush to an emergy clinic now".
But nothing else matched that. I was looking up all kinds of resources and that hadn't been the conclusion from any of them. It turned out Google AI was just wrong and put us in a bad spot for even looking at those results.
Realize that all the Google thing is doing is having an LLM summarize the search results in the page. If the page results are crap, the LLM content will also be crap. It's the LLM version of the "quick result" boxes that would shove wikipedia in your face.
If you then follow up with the thing using the little chat box, it actually kicks off a non-trivial model result, which can sometimes completely reverse the response. After all, your search results are usually based on a few words.
A large portion of the time I google something that I know a lot about the AI summary is misleading if not outright wrong. Frightening when you consider how many take their so-called AI as gospel.
It's just a google search but you can explain what you're searching for. AI summary may contain misleading details if you read it and take it literally, but 90% of the time it gives me pointer for the thing I need and I don't have to scroll through results that often could be just as misleading if not more.
It's demonstrably not. This summary, somehow, often fails to search, or fails to read what it searches and gives you the opposite answer. You shouldn't trust it.
In their world, it gives them the wrong answer that kills their dog, but it's fine because they never know because they don't look anywhere else. The dog dies, they're sad for a while, get a new dog, get on with their lives.
There's no reason not to trust it because since they trust it, they don't percieve anything wrong. They just take it and whatever happens happens, no annoying conflicting info and certainly none of that old grampa thinking and working and giving a shit about anything.
Just in the last few days I had an example where the Google Search version of Gemini had a much better and more comprehensive answer, by far, than ChatGPT. It had to do with Amazon’s use of Data Matrix barcodes on shopping bags. Gemini was able to fully describe how they’re used in Amazon’s logistics chain. ChatGPT essentially said it didn’t know. Your preconceptions may not be accurate.
They might be, but still, I’ll be difficult to convince that Google’s shitty-ass 10B params instant answer models are good at anything other than cookie recipes and producing farts real fast for real cheap.
For what it’s worth, any time I see that feature, it’s all farts.
It depends on what you’re searching. If you only ever need really basic information like keyboard shortcuts or “is it & or &&?” then it’s pretty good, and definitely faster. And if it gets it wrong, then the shortcut doesn’t work or the code doesn’t compile and I search again and try the links.
It strikes me as more talking up their valuation for their upcoming IPO. "Our AI could destroy human civilization. It just escaped it's confinement. We believe it can feel pain."
How are they defining "metadata"? 1TB of text in 10 days is........A LOT. Metadata about what exactly? What time you brew your coffee every morning, how many cups you drink per day, whether you like regular coffee or cappuccino? I'm having a hard time understanding how all that can add up to 1TB of data. If it's recording audio or packet sniffing, it becomes a lot easier to understand.
More than likely nmap type scanning across all devices on a segment. I had one customer that saturated our AAAA server with requests because they though sending in data on every even was just dandy. For one device in a test lap it was ok. For 10k+ of the things out in restuant across the country was a DDOS. That was all text. it was gigs of data per hour. We sat them down and showed them how to do bundling and better data management.
It is a question many devs miss. what happens when I put this in the wild and I now have hundreds of them.
In this case it is probably scanning aggressively. Just incase you plugged in a new devices in the past 100ms. Then some sort of filter with data compression (hopefully) and then sending its exfiltrated data back home. Honestly, not a cool thing to do and makes your customers NOT trust you. I had an old LG TV that was doing this 15 years ago where it would send every button press back to LG. I unplugged it. It agressivly scan its empty socket.
(As a human, it's more physically taxing to think, and less taxing to relax. Conserving internal resources improves our survival odds. So when an opportunity presents itself to use less effort and still gain out of that, we tend to take it.)
As human technologists, I believe we need to protect humanity more.
In everything that we do, we need to think about the ways that our proclivities as a species can be compromised by our development of technology.
This is the part that really surprised me: "Anthropic said nearly a quarter of its revenue came from two customers last year".
Who are these two customers? A quick Google search seems to indicate that Cognizant is their largest "enterprise" customer by number of seats, Microsoft is their largest API customer, and the $10B contract they recently signed with Meta is their largest contract ever. But those metrics don't necessarily translate directly into annual revenue.
Nvidia is hedging their bets against becoming too dependent on OpenAI, Anthropic, and the hyperscalers as their only customers. If on-prem deployments of open models starts taking off in corporate America, this acquisition positions them well.
Nvidia really is best positioned to benefit from the AI wave. They are the main provider for GPUs to the cloud-based LLM companies, and their GPUs are still preferred for local models as well.
There have been a few recent stories about businesses finding themselves spending more on tokens than they were spending on the workers these AI Agents were supposed to have replaced.
>compounds need to undergo a rigorous regiment of empiric testing before they are given to potentially millions of people
Particularly when the mechanism behind most of these peptides comes down to "promotes more rapid cell growth". The intent may be to repair the skin, muscles, or ligaments, but biology is rarely that specific.
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