For ARC v1 it was found that it was much less resistant to brute force than intended/designed. This was improved in v2, which LLMs are currently doing less good at. Note also that ARC tasks are explicitly designed to be slightly-out-of-reach, things that are quite simple for humans, but current models are pretty bad at - designed to measure and enable progress. But yeah there are many interesting approaches, and ARC is interesting to follow both because it attempts to measure ability to adapt to new takas ("fluid intelligence"), and because we have not saturated it yet.

Advertising is in many market like a tax or tariff - something all businesses needs to pay. Think of selling consumer goods online - you need ads on social media to bring in customers. Spending 10% on ads as COGS is a no brainer. 20% too. Maybe it could go as high as 50% - if the companies do not really have an alternative, and all the competitors ard doing it too? They are just going to pass the bill to the consumer anyway...

If you started with a deep neural network, one can't really use pruning to go all the way down to a parameter count that is directly intepretable (say under 100). One would at least have to try some techniques to get more disentangled representations. But local surrogate models are popular for explainability, see Shap and LIME. For interpretable time series I would encourage to construct features and transformations the old fashioned way, and then learn it all end to end as a differentiable program. Then you can get the best of both worlds.

ESP32 or RP2 based boards with for example MicroPython/CircuitPython, or platform.io + VSCode. Though the good old Arduino IDE seems to be unaffected by this change though.

Adafruit also created and maintains CircuitPython, which is targeted very much at the same audience as Arduino. Very beginner friendly, great for quick prototyping/one-offs, but serious enough that one can ship small scale projects with it. And Python is a much better language for that than C++ (which is what Arduino users do not really realize they are using).

Winning a battle and living to fight another day is useful. Does not mean the fight is over, of course.

For more on this perspective, see the paper On the measure of intelligence (F. Chollet, 2019). And more recently, the ARC challenge/benchmarks, which are early attempts at using this kind of definition in practice to improve current systems.

A RPi 4/5 has that? The OMAP 3/4 chips in Beaglebone is another alternative. Or Rockchip/Allwinner SoCs. Those are all Cortex A series though, but that can still be programmed bare metal if one wants. There are also Cortex M microcontrollers with support for SDRAM, like STM32H7. Or one can get an FPGA with DDR support like ECP5.

It i possible to have a digital twin which simulates the real physics of the robot and gives instant feedback based on that. It might help a bit to make the teleoperator work within the capabilities of the robot, be more in sync. But is peobsbly not realistic to simulate the real world physics faithfully, in such a way that operators can use it as actual feedback. Especially in sensitive scenarios like grabbing a glass, there are tipping points (sometimes literal), where a few millimeters and 100 milliseconds is the difference between close call and full smash.

Thinking about it now, if one would deliberately add much more latency (a few seconds), it might be possible to use real-world simulation as aid. At least for operations which can be decomposed into sequences of transitions between stable/safe states. Say moving dishes from dishwasher to cupboard. Picking up is critical, but holding in hand is (presumably) safe, placing in cupboard critical, once placed it is safe. Then one could let teleoperator do the entire critical move virtually, act it out in simulator only. See what the outcome is. If high risk of failure, deny operation. If good chance of success (per simulation) can allow to execute in the real world. More autonomous operation will need ability to simulate actions, project alternative approaches into the future, and a world model strong enough that can also plan and execute based on it. So there are potential synergies in a full-teleop, to hybrid teleop to autonomy transition. Note, this approach would also assume the relevant environment to be static. So it would not help handle the pet or toddler...

No longer randomized though, so it brings in confounding factors. Which one then has to try to control for.

Doesn't really change your point, but I should clarify that what I said was wrong, it was anyone in the study could choose not to take the blinded pill that could be placebo or active, they were removed from the study, and were provided with the active experimental drug + compensation as to not punish them for giving or not giving their consent after being sufficiently informed of the risks of either choice.