Microsoft has Copilot Plus PCs loaded with AI, and rumors are that Apple is all in on AI, too, but if you don't want AI in everything you do, there is another option: Linux.
The biggest joke is that the LLM in Windows is running locally, it uses your hardware and not some big external server farm. But you can bet your ass that they still use it to data harvest the shit out of you.
Exactly. And if I use or even pay for an external LLM service then that’s also my decision. But they force this scheme onto every user, whether they want it or not. It’s like the worst out of all possible scenarios.
That’s a pretty big joke, but I think the bigger joke is calling LLMs AI. We taught linear algebra to talk real pretty and now corps want to use it to completely subsume our lives.
Oh I agree. I typically put “AI” in quotation marks when using that term regarding LLMs, because to me they simply are not intelligent in anyway. In my mind an AI would need an actual level of consciousness of sorts, the ability to form actual thoughts and learn things freely based on whatever senses it has.
But AI is a term that’s good for marketing as well as fear mongering, which we see a lot of in current news cycles and on social media. The problem is that most people do not even understand the basic principles of how LLMs work, which lead to a lot of misconceptions about its uses & misuses and what we should do about it.
Weirdly enough this makes LLMs both completely overhyped as a product and completely stigmatized as some nefarious tool as well. But I guess it fits into our today’s societies that kinda seem to have lost all nuance and reason.
Frankly, LLMs (which are based on neural networks) seem a Hell of a lot closer to how actual brains work than “classical AI” (which basically boils down to a gigantic pile of if statements) does.
I guess I could agree that LLMs are undeserving of the term “AI”, but only in the sense that nothing we’ve made so far is deserving of it.
I’m not talking about interacting with it. I’m talking about how it’s implemented, from my perspective as a computer scientist.
Let me say it more concretely: if even shitty expert systems, which are literally just flowcharts implemented in procedural code, are considered “AI” – and historically speaking, they are – then the bar is really fucking low. LLMs, which at least make an effort to kinda resemble the structure of biological intelligence, are certainly way, way above it.
I’m actually sad that the state of AI deserves the hate it gets. Neural networks are so sick, just going through the example of detecting a diagonal on a 2x2 grid was like magic to me. And they made me second guess simulation theory for quite a while lmao
Tangentially, blockchain was a similar phenomenon for me. Or at least trust networks. One idea was to just throw away Certificate Authorities. Basically federate all the things, and this was before we knew about the fediverse. It gets all the hate because of crypto, but it’s cool tech. The CA thing would probably lead to a bad place too, though.
Let’s agree to disagree then. An LLM has no notion of semantics, it’s just outputting the most likely word to follow up to what it’s already written and the user’s input.
On the contrary, expert systems from back in the 90s for, say, predicting the atomic structure of an element, work like a human brain on steroids. It features an arbitrary large search tree that the software knows how to iterarively prune according to a well known set of chemical rules. We do the same when analyzing a set of options.
Debugging “current” AI models, on the other hand, is impossible because all we’re doing is prescripting a composition of functions and forcing it to minimize a loss function. That’s all we’re doing. How can you currently tell that a certain model is going to work? Unless the mathematical theory ever catches up with the technology, we’ll never know until we execute the code.
To ensure a seamless customer experience when their hardware isn’t capable of running the model locally or if there is a problem with the local instance.
The biggest joke is that the LLM in Windows is running locally, it uses your hardware and not some big external server farm. But you can bet your ass that they still use it to data harvest the shit out of you.
To me this is even worse though. They’re using your electricity and CPU cycles to grab the data they want which lowers their bandwidth bills.
It happening “locally” while still sending all the metadata home is just a slap in the face.
Also, CoPilot is going to be bundled with Office 365, a subscription service. You’re literally paying them to spy on you.
Capitalism almost perfected.
Exactly. And if I use or even pay for an external LLM service then that’s also my decision. But they force this scheme onto every user, whether they want it or not. It’s like the worst out of all possible scenarios.
That’s a pretty big joke, but I think the bigger joke is calling LLMs AI. We taught linear algebra to talk real pretty and now corps want to use it to completely subsume our lives.
Oh I agree. I typically put “AI” in quotation marks when using that term regarding LLMs, because to me they simply are not intelligent in anyway. In my mind an AI would need an actual level of consciousness of sorts, the ability to form actual thoughts and learn things freely based on whatever senses it has. But AI is a term that’s good for marketing as well as fear mongering, which we see a lot of in current news cycles and on social media. The problem is that most people do not even understand the basic principles of how LLMs work, which lead to a lot of misconceptions about its uses & misuses and what we should do about it. Weirdly enough this makes LLMs both completely overhyped as a product and completely stigmatized as some nefarious tool as well. But I guess it fits into our today’s societies that kinda seem to have lost all nuance and reason.
I have to disagree.
Frankly, LLMs (which are based on neural networks) seem a Hell of a lot closer to how actual brains work than “classical AI” (which basically boils down to a gigantic pile of
ifstatements) does.I guess I could agree that LLMs are undeserving of the term “AI”, but only in the sense that nothing we’ve made so far is deserving of it.
“seem” is the critical word there. Interacting with an LLM they do seem to be pretty clever.
I’m not talking about interacting with it. I’m talking about how it’s implemented, from my perspective as a computer scientist.
Let me say it more concretely: if even shitty expert systems, which are literally just flowcharts implemented in procedural code, are considered “AI” – and historically speaking, they are – then the bar is really fucking low. LLMs, which at least make an effort to kinda resemble the structure of biological intelligence, are certainly way, way above it.
I’m actually sad that the state of AI deserves the hate it gets. Neural networks are so sick, just going through the example of detecting a diagonal on a 2x2 grid was like magic to me. And they made me second guess simulation theory for quite a while lmao
Tangentially, blockchain was a similar phenomenon for me. Or at least trust networks. One idea was to just throw away Certificate Authorities. Basically federate all the things, and this was before we knew about the fediverse. It gets all the hate because of crypto, but it’s cool tech. The CA thing would probably lead to a bad place too, though.
Let’s agree to disagree then. An LLM has no notion of semantics, it’s just outputting the most likely word to follow up to what it’s already written and the user’s input.
On the contrary, expert systems from back in the 90s for, say, predicting the atomic structure of an element, work like a human brain on steroids. It features an arbitrary large search tree that the software knows how to iterarively prune according to a well known set of chemical rules. We do the same when analyzing a set of options.
Debugging “current” AI models, on the other hand, is impossible because all we’re doing is prescripting a composition of functions and forcing it to minimize a loss function. That’s all we’re doing. How can you currently tell that a certain model is going to work? Unless the mathematical theory ever catches up with the technology, we’ll never know until we execute the code.
Runs locally, mirrors remotely.
microsoft, probably.