"The 2 May 2023, 6 months later, the regulation started applying and the potential gatekeepers had 2 months to report to the commission to be identified as gatekeepers. This process would take up to 45 days and after being identified as gatekeepers, they would have 6 months to come into compliance, at the latest the 6 March 2024.[8][32] From 7 March 2024, gatekeepers must comply with the DMA. [33]"
Legitimate scrapers, maybe. Everyone else does it to circumvent the API limitations, by posing as real traffic. APIs imply API keys which can be traced and banned.
Well, that's what happens when you make the API more limited than what people can do with scraping (or with using the public "our own web client" API key).
I don't think it's easy at all without access to the training data.
I could ask about some information I find obscure in my native language but I can't be confident someone didn't write about it in English on e.g. reddit.
How exactly are you proposing he tests this without access to hundreds of thousands of dollars worth of compute? Toy models don't work for this kind of thing, small language models behave qualitatively differently from large ones.
this also implies that you can train for more sustained intensity, which I think is absolutely true. You can become a brilliant engineer by finding the right balance of intensity and R&R, assuming this is what you want
As usual, journalists trying to explain what scientists do and misrepresenting the facts.
The paper mentions accuracy i.e. (true positives + true negatives) / total examples. And it's actually 100% accurate i.e. there are no false positives _or_ false negatives.
But the big caveats are:
1. this was tested only on 180 examples, which is a very very small dataset to draw conclusions on, and
2. this is obviously an adversarial space so any classifier will be obsolete with the next training run
I'm bearish on any attempt to distinguish real content vs. AI generated content (on any medium, text, image or anything else). This is an adversarial game and the AIs can incorporate your fancy algorithm to fool you better. In the end these projects only end up improving the AI models in terms of realism.
> 1. this was tested only on 180 examples, which is a very very small dataset to draw conclusions on, and
If you have 180 samples, and a >99% accuracy (meaning a single misprediction), that is a statistically significant conclusion with a p-value of 99.994%.
well, to be fair, the app store has ads in search. When you search for app X, you're likely to see its competitor Y as the first result. (Though this is obviously not Jobs' doing.)
Nice. I'd be super interested in deeper AI integrations for note taking apps, to help you reach certain goals, e.g.:
- calorie counting or diets: just write down what you ate, and it should be good enough to compute calories, macros etc.
- gym logging: write down when you went to the gym and what you did, and it should give you tips, help you maintain your routine and other helpful things
I'd be more interested in the ability to implement such in a way that respects privacy and customizing per folder/note/line. A difficult task given how many are just relying on OpenAI as-is.
There are apps that already do this, and don't need any AI. MyFitnessPal for example does calorie counts by scanning a product's bar code and looking at the user-specified intake amount.
You're pretty wrong on all of those points. Take bodybuilders for example. Most bodybuilders track number of reps daily because if they're not progressively overloading, they're not making progress. It also tells them when to take a deload week.
Track your meals for a week or two and you'll see the actual calories and macros are pretty far from what you might think in your mind. When you're doing a lean bulk and shooting for a +250 calorie surplus, how in the world are you supposed to actually know you're around a +250 calorie surplus without tracking? It would be almost impossible to know your actually TDEE without counting calories for a few weeks or months.
Nothing is "fattening", because you can lose fat while eating any one thing. You can lose weight while only eating lasagna, thus, it is not necessarily fattening. But in general I agree, saying "I ate lasagna" could be 400 calories, or it could be 2400 calories.
However, a calorie estimating AI could be useful if it indicates a level of uncertainty. You list "lasagna", and the AI estimates it's 1200 calories, plus or minus 1000, or something like that. Maybe this level of accuracy is good enough for some people, and it makes tracking and losing weight easy. Or maybe you're not losing weight, and so you realize you have to be more specific, listing the food weights and individual ingredients, etc.
in my opinion that is adding AI for the sake of AI. If you're making homemade lasagna it takes a minute to add the ingredients you used to get a very accurate calorie count. There's really no way AI can do it better and more accurate.
Truth is though, I often skip logging calories because it's a PITA to do it. I have to scan the package, but I already threw the package away or it's dirty or covered in blood. I have to weigh things, but the food scale is in the other room, and I already mixed the ingredients. It's just a lot of extra steps to log calories using the currently available apps; I don't think I've ever logged a meal with less than 20 clicks. Removing small annoyances like this is what good apps are made of.
I agree with you, logging calories manually sucks, especially if you’re preparing whole foods. But how does adding AI into the mix solve the issue? E.g. it seems unlikely to me that we’ll ever get to the point of snapping a picture of a home cooked meal and getting accurate calories, maybe that’s where I’m being shortsighted?
"The 2 May 2023, 6 months later, the regulation started applying and the potential gatekeepers had 2 months to report to the commission to be identified as gatekeepers. This process would take up to 45 days and after being identified as gatekeepers, they would have 6 months to come into compliance, at the latest the 6 March 2024.[8][32] From 7 March 2024, gatekeepers must comply with the DMA. [33]"
https://en.wikipedia.org/wiki/Digital_Markets_Act