Something like old school Facebook in UI, but functions more like MSN Messenger. You connect to your contacts via P2P, and download/upload updates to your social media network.
Yep, if you were to watch what happens at a 996 shop, it's people literally living their at-home life with their fellow employees for most of the time.
This is great for skywatching but at 32GB it only takes brief photos, and rarely video.
A real setup needs multiple 4K cameras, some kind of LWIR, MWIR, etc. as well as SDR with proper antennae for each of their respective performance envelopes.
I think it's good, but it will not be good at picking up the "targets of opportunity".
It's not flying saucers, it's weird orbs / spheres. There is volume of footage, and sufficient quality. I think we're past the point of "is it real" and more at "okay so what is this really and what is it doing".
If we could take Tier 1 reports from NUFORC, have tons of metrics available with sensor data, we can make a better guess.
You don't need to load the entire world model in order to be effective at a task. LLM providers already do something similarly described with model routing.
There's a whole lot of undecidable (or effectively undecidable) edge cases that can be adequately covered. As a matter of fact, Decidability Logic is compatible with Prolog.
We would begin by having a Prolog server of some kind (I have no idea if Prolog is parallelized but it should very well be if we're dealing with Horn Clauses).
There would be MCP bindings to said server, which would be accessible upon request. The LLM would provide a message, it could even formulate Prolog statements per a structured prompt, and then await the result, and then continue.
A world model is a persistent representation of the world (however compressed) that is available to an AI for accessing and compute. For example, a weather world model would likely include things like wind speed, surface temperature, various atmospheric layers, total precipitable water, etc. Now suppose we provide a real time live feed to an AI like an LLM, allowing the LLM to have constant, up to date weather knowledge that it loads into context for every new query. This LLM should have a leg up in predictive power.
Some world models can also be updated by their respective AI agents, e.g. "I, Mr. Bot, have moved the ice cream into the freezer from the car" (thereby updating the state of freezer and car, by transferring ice cream from one to the other, and making that the context for future interactions).
The default philosophical position for human biology and psychology is known as Representational Realism. That is, reality as we know it is mediated by changes and transformations made to sensory (and other) input data in a complex process, and is changed sufficiently to be something "different enough" from what we know to be actually real.
Direct Realism is the idea that reality is directly available to us and any intermediate transformations made by our brains is not enough to change the dial.
Direct Realism has long been refuted. There are a number of examples, e.g. the hot and cold bucket; the straw in a glass; rainbows and other epiphenomena, etc.
Good. The world model is absolutely the right play in my opinion.
AI Agents like LLMs make great use of pre-computed information. Providing a comprehensive but efficient world model (one where more detail is available wherever one is paying more attention given a specific task) will definitely eke out new autonomous agents.
Swarms of these, acting in concert or with some hive mind, could be how we get to AGI.
I wish I could help, world models are something I am very passionate about.
Most of the time, I don't personally look at it as cheap labour because I am just ordering, e.g. 60,000 of something or 100,000 of something else.
It's cheap, yes. I can indeed buy 1,000 of something more locally or from other than China.
But when it comes to scale, needing vast shipments, then they are the ones who can actually ship it and do it reliably. It just also happens to be cheaper, too, which is more of a convenience or cherry on top, than the actual attractive part: vast scale.
Is it the noise cancellation making a feedback sound, or is it the pressure differential in the ear canal pulling the ear drum back to produce a white noise?
He said that it goes away when he yawns, so I'm thinking it might be the pressure differential.
You write the requirements, you write the spec, etc. before you write the code.
You then determine what are the inputs / outputs that you're taking for each function / method / class / etc.
You also determine what these functions / methods / classes / etc. compute within their blocks.
Now you have that on paper and have it planned out, so you write tests first for valid / invalid values, edge cases, etc.
There are workflows that work for this, but nowadays I automate a lot of test creation. It's a lot easier to hack a few iterations first, play with it, then when I have my desired behaviour I write some tests. Gradually you just write tests first, you may even keep a repo somewhere for tests you might use again for common patterns.
I've been thinking of this as well:
Something like old school Facebook in UI, but functions more like MSN Messenger. You connect to your contacts via P2P, and download/upload updates to your social media network.