grumbelbart

> The straws are longer, which is why they cannot be fertilized with nitrogen.

Why is that? I thought nitrogen is taken in through the roots, then distributed as other ground-based nutrition, regardless of the height.

The point is probably to encode the emoji (image) in the codepoint itself.

Get one with an inverter compressor, they are very quiet. They have a compressor that can be run at varying power levels, so instead of having one that goes of noisy at 100%, then sits off for a while, you have one that runs for example at 10% most of the time.

It's bad. I believe them not to use it for training, but t means relevant data can and will be exfiltrated by US agencies or through court orders (see NY Times vs. OpenAI, where only traffic without any rentention was safe).

Of course, should it be as cost efficient as claimed and if you don't use it but everybody else does, you might be pushed out of the market.

Agreed. The solution will likely be some vision foundation model that directly sends controls to the robot ("move here, grab, move there"), trained by Amazon with RL to integrate collision avoidance, object detection, grasping point detection, grasp verification etc.

> But electricity is under half (30%?40%?) and the rest of that energy isn't fossil-free.

The trick of course is that if you electrify heating and transportation they'll need much less energy. Your average car with an ICE has an efficiency of 20-40%, electric cars have 60-80%. Heating your house with gas has an efficiency of around 100%, heat pumps have 300%-500%.

> If you were to design an entire ATC system from scratch (pilot interfaces, sensors everywhere in the airport and planes etc) it can be automated.

Even then you'll probably run into the long-tail distribution issues, similar to self-driving cars. 99.9% of all situations are pretty standard, but once in a while something so abstruse happens that it's not pre-programmed and requires some creativity to solve.

> What you can probably do is create software which observes traffic and simulates it into the future and notifies the human ATCs about risks.

Fully agree. Some of the recent close calls really were "obvious" much earlier, meaning they were not caused by late course changes.

Is this some kind of calibration then? I'd expect that the probabilities automatically adjust during training, such that in "lock" mode, for example, syntax-breaking tokens have a very low probability and would not be picked even wich higher temperature.

I'm reasonably sure Russia would take you.

Would using an actors face and voice as training data be fair use?

What it the model then creates a virtual actor that is very close to the real actor?

The data behind the app is pretty solid, but lightningmaps.org has a much better visualization (based on the same data).

I use them as cheap-man's VPN. A ssh server on a public IP but a non-obvious port brings you into the network, and port forwarding allows you to connect to relevant endpoints in your remote network via localhost:12345.

> My Weird Hill is that we should be building things with GPT-4.

Absolutely. I always advocate that our developers have to test on older / slower machines. That gives them direct (painful) feedback when things run slow. Optimizing whatever you build for an older "something" (LLM model, hardware) will make it excel on more modern somethings.