Yeah, if I’m making something “masked/obscured” I should export it so that it’s in a raw format. That way there is no Metadata or information that could be leaked by accident.

Think of the Trump Epstein files, in those they kept them as pdfs so you could just unhighlight the redacted sections. If they had export it as a jpeg/png you wouldn’t be able to extract any information.

There are ways to remove the content from a pdf, but as we’ve seen, that leaves rooms for errors.

If the image/video just has black pixels on the content, then there would be no information to extract and any attempt would just be filling things in.

When you talk about layers, you’re assuming that the creator left information behind in the Metadata, which wouldn’t require AI to extract.

I had someone swear to me that Github templating was better, but I’ve only worked with Gitlabs templates. Why do you like Gitlab over Github?

I was going to ask how this is different than a Reinforcement Learning algorithm but then they called out Deep Minds Alpha-Go

All of them. If you’re seeing sources cited, it means it’s a RAG (LLM with extra bits). The extra bits make a big difference as it means the response is limited to a select few points of reference and isn’t comparing all known knowledge on a subject matter.

Thanks for the details, seems like that may be why the older Chromecasts are still supported.

I thought for Chromecast the “casting” part is just telling the Chromecast what to play. Do you need your phone on while Chromecast shows content?

The qoute says the “authors”, so this law is not exclusively tied to actors, but generally works of art and the people involved in creating it. Thats why I called out things like remakes.

And while you are right that in many of my examples there would probably be contracts to avoid these issues, my point was to show how easy it is to break this law (and that copyright owners do it all the time themselves).

Also, fair use for parodies is not a thing in all countries - not sure if it is in Denmark.

Yeah, it also seems weird cause things like remakes, parodies, trailers, etc. all would technically violate that law.

ಠ_ಠ

I can’t tell is this is a joke playing off the Ai bubble being “17 times” larger or if you’re serious.

It could be good to layer in standard machine learning (ML), and it already does have some features (like line of best fit).

However, in today’s context AI means LLMs, and that is not a good fit due to its unpredictability.

I’m sure someone out there is running something on paper still, but that’s not how most things are run.

Additionally, unless people are legally required to do all exchanges on the public ledger (which seems highly unlikely), then you’d still end up with transactions not monitored on the public ledger.

I mean, we’re hundreds if not thousands of iterations into robotics. Hell, we’ve probably had tens if not hundreds of attempts to create humanoid robots.

This is just the current iteration of humanoid robots getting beaten up for not delivering on its promises.

How does a regular database not do that?

Either it’s tracked or its not, the medium for that tracking doesn’t really change much.

I think it’s a mixture of that and the fact that when OpenAI saw that throwing more data drastically improved the models, they thought they would continue to see jumps like that.

However, we now know that bad benchmarks were misleading how steep the improvements were, and much like with autonomous vehicles, solving 90% of the problem is still a farcry away from 100%.

For your first question, I think the average person would benefit from a simple digital currency that let’s them exchange “cash” without having to jump through a bunch of hoops. Venmo, Zelle, etc. are all proof that normal people want easy ways to pay each other.

As for your second point, I’m not sure I follow. But I assume you’re implying that crypto is better because it isn’t tied to the state?

So I’ve been reading into stable coins a lot, because I don’t understand why anyone would care about them. And what I’ve come to realize is they are a benefit to two groups:

• Financial institutions: Stablecoins have fewer regulations and basically allow for things you can’t do with actual dollars/currency
• People with limited access to financial institutions (think poorer people and/or countries): With fewer regulations it’s easier for people to transact Stablecoins than dollars/currency

At the end of the day, it feels like a “true” digital currency would be the better solution, but everyones jumping on Stablecoins because they’re here now and less regulated.

I think there is potential in a more cash-like digital currency, but Stablecoins seem ripe to break in some unforseen way, especially given the current administration.

Edit: Edited to fix formatting.

We definitely haven’t cracked AGI, that’s without a doubt.

But yeah, LLMs are big (I’d say really Transformers were the breakthrough). My point though was that Deep Learning is the underlying technology driving all of this and we certainly haven’t run out of ideas in that space even if LLMs may be hitting a dead end.

That’s the issue, AI right now means LLMs not deep learning/ML.

The Deep Learning/ML stuff will keep chugging along.

I think we may see an agentic AI winter, but there are so many other applications and systems that can benefit from deep learning still.