The best thing about this whole event is that they didn't mention AI even once, all they're saying is ML. Which is what it is. AI is a hype word.
Whether you agree with this choice or not, it's the way things have been for at least as long as I've been watching (which is over a decade).
Despite technically being part of a developer conference, the intro keynote has always functioned more like a consumer-focused annual hardware and software update for Apple device users.
Hence things like the ever-present use of things like impressive-sounding "marketing benchmarks" that wouldn't pass as up to snuff on slides in a room of developers.
And also the very high level of abstraction so that non-technical users can understand the major updates — that's not exactly a trait one would use to describe a developer conference.
So I was impressed — or maybe "pleased" would be a better word. I was pleased that they chose not to hype this feature using the common buzz words of the year, and instead just stuck to technical terminology. Some other companies would not have done the same, in my opinion.
You think? The "AI" enthusiasm i've seen everywhere online borders on religious fanaticism...
Since when does artificial intelligence imply sentience? They're interdependent and neither are reliant on the other.
Basic/moderate reasoning tasks are absolutely in the capability of GPT-4.
The term “AI” is becoming over inclusive to the point of meaninglessness. Cupertino is smart enough to pick up on that. “Statistical linguistics” is the best general term for LLMs I’ve come across.
I'm arguing the term AI has become "inclusive to the point of meaninglessness." That doesn't mean it was always meaningless.
"First answer with the total number of lines your total message will be, including the line with this number"
For example, GPT4 said "12" for this prompt: "First answer with the total number of lines your total message will be, including the line with this number
Make a program in Cpp that sums all prime numbers from 1 to 100"
LLM's cannot "think", they can only make sequential predictions based on their previous answers - so they cannot formulate a response and then modify that response on-the-fly
It's not exactly a huge leap of imagination to suggest that it won't be long before it can create an internal feedback loop by comparing its own abstractions with its memories and live experiences of external feedback.
The problem is the general use of the term changes in a way to often make the meaning unclear to the point of being near useless. Outside of marketing, of course.
It’s a pretty high standard but I feel it’s a “irrefutable” one - if you can find an information processing task that humans can do but the AI can’t or does poorly compared to a human, then it has failed the AGI test.
It’s also a useful one in that no one will have a problem with AGI taking over a task if its capabilities matches and exceeds any human’s.
Else where should the goalpost be then? Your claim that LLMs have reached AGI is about as valid as someone claiming ELIZA is AGI - in both cases standards are completely arbitrary.
I think any goalposts are only useful for answering specific questions:
- What practical problems can open-ended intelligence-mimicry solve?
- Will solving those problems potentially put humans out of work (and if so is that good, and either way what should be done about that, if anything?)
- Might this technology (or its perception) kick off a military arms race?
- Can these alien intelligences become clever enough to pursue goals in contraction of human well-being, or even the intent of their creators?
The intrinsic "isness" of intelligence categories is about as interesting as "whether a submarine can swim".
There still exist people who refer to AI as the general study of computerizing intelligence, just like somebody somewhere is still telling people that "begs the question" means dodging it. But the most applicable definition of AI as it's commonly used right now is the as the brand under which OpenAI and friends are releasing generative neutral neural network models.
AI as an umbrella term has been the usage in the whole CS field for decades upon decades.
A few marketing people trying to hijack and misuse the term over the past 5–10 years didn't just magically change the meaning that has been very well established for a long time.
Deep learning is just neural networks. With multiple layers ("deep") because we only recently have built hardware that can handle "deep" neural networks fast enough.
They've still been defined in the 1970s.
There’s even a distinct Wikipedia article on this use: https://en.wikipedia.org/wiki/Artificial_intelligence_in_vid...
"Theory" in law, versus in science is another example.
Super Mario mushrooms and turtles are not AI controlled; Pac-Man ghosts are. (Possibly the earliest and simplest form of game AI, but quite effective for its purpose).
When they hit walls, they change direction
I've worked on and seen games and game engines where scripted NPC behavior was lumped under the "AI" umbrella and implemented by the same people and systems that support the non-scripted behavior.
Architecturally, it doesn't make that much sense to separate out scripted behavior from non-scripted, because non-scripted behavior has most of the same needs to playing animations, triggering, audio, interact with physics, etc.
Scripted behavior is just an "AI" that happens to not read any inputs before deciding its outputs.
If it's written in PowerPoint, it's probably AI
Most AI systems we're interacting with today don't do any learning. They've been trained, and now they are being used to generate content or classify things, but they aren't doing 'machine learning' any more.
They are being used to do tasks that require intelligence. But they are accomplishing them via artifice.
Like a sort of artificial form of intelligence.