Police said … Checklick “intentionally accelerated his vehicle into the dispersing crowd.” … faces a charge of reckless driving…

Dude intentionally accelerated his SUV into a crowd and gets charged with reckless driving. I sincerely hope it’s a felony at least.

Clearly the author doesn’t understand how capitalism works. If Apple can pick you up by the neck, turn you upside down, and shake whatever extra money it can from you then it absolutely will do so.

The problem is that one indie developer doesn’t have any power over Apple… so they can go fuck themselves. The developer is granted the opportunity to grovel at the feet of their betters (richers) and pray that they are allowed to keep enough of their own crop to survive the winter. If they don’t survive… then some other dev will probably jump at the chance to take part in the “free market” and demonstrate their worth.

More than enough people who claim to know how it works think it might be “evolving” into a sentient being inside it’s little black box. Example from a conversation I gave up on… https://sh.itjust.works/comment/18759960

I think the word “learning”, and even “training”, is an approximation from a human perspective. MLs “learn” by adjusting parameters when processing data. At least as far as I know, the base algorithm and hyperparameters for the model are set in stone.

The base algorithm for “living” things is basically only limited by chemistry/physics and evolution. I doubt anyone could create an algorithm that advanced any time soon. We don’t even understand the brain or physics at the quantum level that well. Hell, we are using ML to create new molecules because we don’t understand it well.

I think you’re either being a little dismissive of the potential complexity of the “thinking” capability of LLMs or at least a little generous if not mystical in your imagination of what the purely physical electrical signals in our heads are actually doing to learn how to interpret all these little shapes we see on screens.

I don’t think I’m doing either of those things. I respect the scale and speed of the models and I am well aware that I’m little more than a machine made of meat.

Babies start out mimicking. The thing is, they learn.

Humans learn so much more before they start communicating. They start learning reason, logic, etc as they develop their vocabulary.

The difference is that, as I understand it, these models are often “trained” on very, very large sets of data. They have built a massive network of the way words are used in communication - likely built from more texts than a human could process in several lifetimes. They come out the gate with an enormous vocabulary and understanding of how to mimic, replicate it’s use. If they had been trained on just as much data, but data unrelated to communication, would you still think it capable of reasoning without the ability to “sound” human? They have the “vocabulary” and references to mimic a deep understanding but because we lack the ability to understand the final algorithm it seems like an enormous leap to presume actual reasoning is taking place.

Frankly, I see no reason for models like LLMs at this stage. I’m fine putting the breaks on this shit - even if we disagree on the reasons why. ML can and has been employed to achieve far more practical goals. Use it alongside humans for a while until it is verifiably more reliable at some task - recognizing cancer in imaging or generating molecules likely of achieving a desired goal. LLMs are just a lazy shortcut to look impressive and sell investors on the technology.

Maybe I am failing to see reality - maybe I don’t understand the latest “AI” well enough to give my two cents. That’s fine. I just think it’s being hyped because these companies desperately need VC money to stay afloat.

It works because humans have an insatiable desire to see agency everywhere they look. Spirits, monsters, ghosts, gods, and now “AI.”

Yes, both systems - the human brain and an LLM - assimilate and organize human written languages in order to use it for communication. An LLM is very little else beyond this. It is then given rules (using those written languages) and then designed to create more related words when given input. I just don’t find it convincing that an ML algorithm designed explicitly to mimic human written communication in response to given input “understands” anything. No matter *how convincingly" an algorithm might reproduce a human voice - perfectly matching intonation and inflexion when given text to read - if I knew it was an algorithm designed to do it as convincingly as possible I wouldn’t say it was capable of the feeling it is able to express.

The only thing in favor of sentience is that the ML algorithms modify themselves and end up being a black box - so complex with no way to represent them that they are impossible for humans to comprehend. Could it somehow have achieved sentience? Technically, yes, because we don’t understand how they work. We are just meat machines, after all.

LLMs (Large Language Modles, like Claude) are not AGIs (Artificial General Intelligence). LLMs generate convincing text by mapping the relationships between words scraped from their training data. Even if they are given “tools” that give them interfaces to reference new data or output data into other systems, they still don’t really learn, understand, comprehend, gain actual awareness, or feel… they just mimic their training data.