Anytime algorithms are great for robotics planning, for example. A plan does not have to be perfect to be useful, especially when it can be refined further in the next timestep. And the robot cannot act out the plan instantaneously, so by the time one is close to the point where a non-ideal segment would be, one has had many timesteps to refine/optimize it. But robot could start moving right away.
If we could only complement that battery with a small nuclear reactor, then we'd be in business.
Come to think of it, shipping would be quite interesting for a SpaceX style disruption - there is a market for many many thousands of units - enough to actually get good at building them in a repeatable fashion.
Of course there are considerable engineering and political challenges, to put it mildly.
I saw was because I do not think anyone has used or maintained it since the company behind went belly up. But the code is still out there as open source, probably can be learned from.
Disclaimer: I used to contract for them, but in other areas.
Yeah the USB stick enables the participants to replicate it more easily at home or with friends etc.
Encouraging that the participants are in the driver seat also helps with this.
High performing types in those areas network a lot, and just as much off the clock (or more) as on. From those I know, many of them seem to pick hobbies, friends, holidays, activities, gym, etc that is compatible with connecting with the right people. And that is considered very normal, more ore less expected if aiming for partner type roles.
Yeah that really should have been default. Very big footgun, especially when preserving ordering is default in pandas, numpy, etc. And especially when there is no ingrained index concept in polars, people might very well forget that one needs to have some natural keys and not rely on ordering. One needs to bring more of an SQL mindset.
The pandas workflows have also been stable for the last decade. That there is a new kid on the block (polars) does not make the existing stuff any less stable. And one can just continue writing pandas for the next decade too.
Code using pandas is testable and reusable in much the same way as any other code, make functions that take and return data.
That said, the polars/narwals style API is better than pandas API for sure. More readable and composable, simpler (no index) and a bit less weird overall.
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).
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...
They will brag about each unit sold to investors to demonstrate product/market fit, and then raise probably 10-100x the sales price per unit from investors.
Transformers are very powerful also for non-language data. For example time series, sequences like DNA or audio (also outside of speech and music).
Of course the vast amount of human text is key to training a typical LLM, but it is not the only use.
All language is referential. Even in everyday speech the meaning is not in the words themselves, in so much as they are pointers to concepts that (hopefully) exists already in the brains of the people we are conversing with. So when someone is very well aligned, one can convey ideas that go much further than conventionally expressed in the "general" language which is mutually intellible with most speakers of the same language. It is a rare experience though, at least for deep or personal topics.
I suspect OpenAI wants the user data from Sky/Atlas, and Apple does not want to give it. Also, Apple are probably wanting to keep their options open wrt other AI providers - after all, it is increasingly clear that OpenAI is not the only game in town for thd core LLM tech.
Lack of Accountability as-a-Service! Very attractive proposition to negligent and self-serving organizations. The people in charge don't even have to pay for it themselves, can just funnel the organization money to the vendor. Encouraging widespread adoption helps normalizes the practice. If anyone objects, shut them down as not thinking-of-the-children and something-must-be-done (and every other option is surely too complicated/expensive).
Nice. Looks like it could be a good tool for image labeling at least. But no support for audio or time-series data? :( Maybe it will get it down the line?
Makes business sense. The two companies have similar customer profiles in the embedded space, so this should make for cross selling opportunities both ways. Just being a pre-approved vendor is often a big win, as many times that involves long and costly processes.