cuttothechase

Agree that chess is not the ideal example to use for the AI domain.. but used for building a quick mental image.. reason being that in chess, AI does have an objective truth built into its training.. and that is to checkmate.

The real world open ended research doesn't always have an evaluation function. A computer can calculate 10 million options to solve a problem, but it cannot tell you if you're solving the right problem from the human context. That's why centaur setups fail in closed games, but remain essential everywhere else.

Another loose example just to build a mental model .. AI driven cars do a great job in most driving situations but often fail to park in a good parking spot for most people! Good parking spot is partly human initiated and subjective.

Agree with the shock but there is no doom and gloom.

Looking at things after the temporary shock. Future is Human + computer assisted problem solving, working in tandem, just like what Garry Kasparov mentioned in his book [0].

[0] - "Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins"

Yes, 100% when goals are defined and rewards are a part of the feedback loop.

Isn't open ended research is truly open ended. Like there are a gazillion questions to ask and even more solutions that can be offered. Now which questions to pursue is human volition. Yes, humans will not be able to reach certain steps without computer assist.

However, which questions to ask is a human endeavor, out of infinite possible pool of questions.,

now computers can ask questions as well. But if it were to ask it will be more like a monkeys and typewriters type of a situation. Tons of questions and a few that are relevant for the interested human., who has to foot the bill for those questions.

The thing that is so confusing about this is the following fact -

Lets say the frontier of some specific problem or a field in math is at x. X was reached over millenia by incremental steps and sometimes to leaps and jumps.

Now if AI gives us a x + 1 or x + 10 state, aren't there still x + infinite steps to climb? Many of this the AI can never imagine and won't reach by itself without human assist.

Yes, there is a temporary shock and awe because of all the low hanging x + 1 through x+ 1000 fruits for a given sub-domain/problem have been carpet bombed or whatever.. but isn't it turtles all the way!? Won't people soon find something more to develop and solve that takes it further than where it was of this infinite realm of exploration?

Article is spot on!

The only issue is that the benefits would be more enmeshed with other gains in other industries.,

Heck, even in Software Development, the real benefits are not with mass labor elimination resulting in cost savings but with increasing available throughput. Technology if used to the fullest potential should be able to expand the proverbial pie., which is where the gains are!

That is not to say people won't try to save cost by eliminating labor due to AI., which is super shortsighted and misses the forest for the trees, but the overarching goal should be to increase the quality and quantity of deliverables, as demanded by the market.

Docker ended up as a primary way of delivering, packaging applications etc., for the web and beyond.

What was your imagined use case when you first came up with it?

Did you think Docker as it is used in the industry right now is where you imagined it would be used?

Any use cases for it that the industry missed but you think would have made things even better?

Could you explain what your infra set up looks like and what is your typical use case, if its more than a hobby?

Now that the unit economics are laid out in the open, What stops any of the companies or even startups from anywhere in the world with some investment $ and competence from spinning up and offering their own version of the inference engines, undercutting any moat that the current leaders have?

I could even see F1000 companies having their own backstop intelligence for most of the tasks and only use frontier where it is absolutely needed or as much as it is under their IT budget.

Won't it be race to the bottom when they compete on cost? Is regulatory capture the only moat left for the market leaders now?

15M is the average cost of 5 bay area houses! Not sure what they are even attempting to do with this effort rather than to get some free marketing!

LM Studio Qwen 2.5 7B Instruct (Q4_K_M GGUF) with metal offloading

On a base M1 air, running local inference (non tool calling).

Yes, it is AI-coded. But definitely not a one shot kind of a deal.

Much easier to work with a language you are most comfortable with right?

Wondering how well this does with tool calling. Any one has any numbers or videos or anything using this?

From the github repo it seems like you really don't need a big Mac with huge amounts of RAM but SSD is sufficient.

If this is anywhere near 50 TPS, that would be a game changer in the personal LLM space!