O1 isn't a chat model (and that's the point)

The skill that's worth learning is how to investigate, experiment and think about these kinds of tools.

A "Stable Diffusion" class might be a waste of time, but a "Generative art" class where students are challenged to explore what's available, share their own experiments and discuss under what circumstances these tools could be useful, harmful, productive, misleading etc feels like it would be very relevant to me, no matter where the technology goes next.

All knowledge degrades with time. Medical books from the 1800's wouldn't be a lot of use today.

There is just a different decay curve for different topics.

Part of 'knowing' a field is to learn it and then keep up with the field.

> whatever you learn will be obsolete next month

this is exactly the kind of attitude that turns university courses into dinosaurs with far less connection to the “real world” industry than ideal. frankly its an excuse for laziness and luddism at this point. much of what i learned about food groups and economics and politics and writing in school is obsolete at this point, should my teachers not have bothered at all? out of what? fear?

the way stable diffusion works hasn’t really changed, and in fact people have just built comfyui layers and workflows on top of it in the ensuing 3 years, and the more you stick your head in the sand because you already predetermined the outcome you are mostly piling up the debt that your students will have to learn on their own because you were too insecure to make a call without trusting that your students can adjust as needed

Integrating it into the curriculum is strange. They should do one time introductory lectures instead.

great advice, but difficult to apply given very small context window of o1 models

It’s churn because every new model may or may not break strategies that worked before.

Nobody is designing how to prompt models. It’s an emergent property of these models, so they could just change entirely from each generation of any model.

A constantly changing "API" coupled with a inherently unreliable output is not conducive to stable business.

An AGI will be able to do any task any humans can do. Or all tasks any human can do. An AGI will be able to get any college degree.

AGI currently is an intentionally vague and undefined goal. This allows businesses to operate towards a goal, define the parameters, and relish in the “rocket launches”-esque hype without leaving the vague umbrella of AI. It allows businesses to claim a double pursuit. Not only are they building AGI but all their work will surely benefit AI as well. How noble. Right?

It’s vagueness is intentional and allows you to ignore the blind truth and fill in the gaps yourself. You just have to believe it’s right around the corner.

In my opinion it's probably closer to real agi then it's not. I think the missing piece is learning after the pretraining phase.

it must be wonderful to live life with such supreme unfounded confidence. really, no sarcasm, i wonder what that is like. to be so sure of something when many smarter people are not, and when we dont know how our own intelligence fully works or evolved, and dont know if ANY lessons from our own intelligence even apply to artificial ones.

and yet, so confident. so secure. interesting.

I think it means a self-sufficient mind, which LLMs inherently are not.

Technically, the models can already learn on the fly. Just that the knowledge it can learn is limited to the context length. It cannot, to use the trendy word, "grok" it and internally adjust the weights in its neural network yet.

To change this you would either need to let the model retrain itself every time it receives new information, or to have such a great context length that there is no effective difference. I suspect even meat models like our brains is still struggling to do this effectively and need a long rest cycle (i.e. sleep) to handle it. So the problem is inherently more difficult to solve than just "thinking". We may even need an entire new architecture different from the neural network to achieve this.

I understand the hype. I think most humans understand why a machine responding to a query like never before in the history of mankind is amazing.

What you’re going through is hype overdose. You’re numb to it. Like I can get if someone disagrees but it’s a next level lack of understanding human behavior if you don’t get the hype at all.

There exists living human beings who are still children or with brain damage with comparable intelligence to an LLM and we classify those humans as conscious but we don’t with LLMs.

I’m not trying to say LLMs are conscious but just saying that the creation of LLMs marks a significant turning point. We crossed a barrier 2 years ago somewhat equivalent to landing on the moon and i am just dumb founded that someone doesn’t understand why there is hype around this.

Those are contradictory. Openai claim that you don't need a manual, since O1 performs best with simple prompts. The author claims it performs better with more complex prompts, but provides no evidence.

thanks for watching!

“prompts in source control” is kinda like “configs in source control” for me. recommended for small projects, but at scale eventually you wanna abstract it out into some kind of prompt manager software for others to use and even for yourself to track and manage over time. git isnt the right database for everything.