The article doesn't mention the safety angle at all. It's reasonable to argue that even though the Ukraine conflict has popularized drones as weapons, given that we haven't had a major incident yet this temporary flight restriction was premature, but not mentioning it at all makes the bias pretty clear.
The TFR (which made it illegal to fly a drone within a bubble 1000 feet high and 3000 feet radius of a mobile asset) has been replaced by a NOTAM that basically says flying near them in a way that poses a credible safety or security threat means they can sieze or destroy your drone. I'd say this is the better resolution, and to me represents a "genuine change of heart" by the FAA, finding a way to serve the safety purpose of the TFR without overstepping.
> not mentioning it at all makes the bias pretty clear
Yes, the bias is assumed since they are the petitioners. It is not their responsibility to make the FAA's case, or even mention any aspect of it at all.
The FAA will be the ones responsible for making their case before the court.
To me it's more like the sign saying "No guns in the bank". It's not going to stop someone who intends on doing harm, but that's not it's real purpose. If it was normal to be able to openly carry a gun into the bank, that makes the security guards job much harder.
2x GPU performance[1] and RAM, along with it being your machine instead of a locked down Meta android device is worth 2x the price for many. Eye tracking is also huge, both as an additional input device, and lifting GPU performance even more with foveated rendering.
It'll be highly dependent on the game. Unlike foveated streaming the implementation of foveated rendering could mean many things. Actually rendering at a lower resolution in areas you aren't actively looking is probably the most complicated approach because it seems like you'd have to render two times (low res + high res) to get the desired effect.
> Actually rendering at a lower resolution in areas you aren't actively looking is probably the most complicated approach because it seems like you'd have to render two times (low res + high res) to get the desired effect
In the newer implementations, you can split the display into tiles, and render each tile at a different resolution, so it's not that bad. A bunch of the intermediate rendering targets are already rendered at reduced resolution, so you won't see a full 4x speed up in the low-res regions, but you should be able to achieve a ~30% overall speed up with modern foveated rendering.
The framebuffer that you are filling out is still at native resolution, but while computing that frame buffer you can do many of the steps at reduced resolution. For example, instead of evaluating the pixel shader code for every pixel, it could evaluate once for a block of four pixels in the reduced resolution region and use the result for all four.
Kimi / open models or jailbreaking frontier models. Your recollection of the Anthropic refusal isn't quite accurate, cyber hacking wasn't a sticking point, just domestic drag net surveillance and fully automated weaponry.
Note that the Model Intelligence score shown here is not the same as the Artificial Analysis Intelligence Index score they use for full size models, the top models on this Pocket-Scale chart have a score of 63, but that translates to just an 11 on the normal scale.
Difficulty scaling is done to such an extent that even common enemies scale right along with you. In the opening scene, there is a bear you come across and at level 1 or 2, you are able to one shot it. Then the story takes you on a grand quest where ostensibly you go from a nobody to the savior of the world. However, due to difficulty scaling, if you run into the same bear halfway through the game, it will be harder to kill than when you were level 1.
That’s just how all Diablo clones work; basically consider it asset reuse…
Power fantasy during leveling / campaigning always comes from being “ahead of the curve” gear/build wise — the subject of your combat is rarely meaningful
I'm doing something similar, using the directionality of the antenna to turn an ESP32 into a high RPM gyroscope for use in a combat robot. Standard gyro chips top out well under 1000 RPM, and are much more expensive than an ESP32 board, plus the ESP can serve as the radio control link and brain of the bot at the same time.
cool idea. care to share more? my thought would be continuously measuring antenna impedance over time, and looking for a peak in this signal's spectrum near the rotation frequency. could also try with a photodiode (+ optional light source, or use ambient light) (this would be similar to an optical computer mouse), which i guess could be more directional, and thus give a stronger signal
Combat robots are more like RC cars than autonomous robots, the driver stays in control of the bot over a radio link. I use ESP-NOW, and get about 6000 packets per second from the transmitter to the ESP32 in the box, and each comes with radio link stats, signal strength and Channel State Information. Using autocorrelation on the signal strength gives you the rotation period, and using that you can establish a Phase Locked Loop to remove the drift of integrating rotation speed to get absolute rotation angle.
An intuitive explanation for why reasoning tokens help is to remember that LLMs are just mathmatical functions f() that take in an input sequence x and produces the next token f(x). Without reasoning tokens, you require the function f() to immediately take you from x to the start of an output sequence that is a correct answer. With reasoning tokens, this is much relaxed, allowing for many repeated applications of f() to gradually steer you from the input sequence to the start of the correct output sequence.
It seems intuitive that continuing a correct output sequence is easier than the "discontinuity" of jumping from the input prompt to the output sequence.
Not only that! LLM can also learn how to repeatedly apply f() for certain arguments, and run the shortcut. Sometimes, the shortcut learned is not actually repeated application of f(), which breaks semantic soundness of LLM's reasoning chain.
These shortcuts can greatly help reasoning, but they are not guaranteed to be sound. So yeah, in that case, LLMs are reasoning right (using shortcuts) for the wrong reasons (learning them from data instead by running actual repeated application and memoizing the resulting rule).
The way I think about it is that it's unreasonable for a compute graph with a static number of operations to be able to answer both y=a*10 and something like y=((((x+x)*(x+1))/((2*x)+2))+((x*(x+3))/(x+3))-((x*x)/(x+1))+((x*x)/(x+1))-((x*(x+3))/(x+3))) in a single forward pass. Tokens are essentially a unit of work and can also be used for intermediate steps, not just final results.
CoT evidently helps but a bias towards both correctness and innovation has to come from somewhere. The article implies OpenAI's proofs may be supported by Lean but regardless, who knows how many people are trying to disprove the next conjecture each day throwing away nonanswers. Fundamentally these systems are more powerful with better training and sampling methods, or better prompting. Tokens matter but you can rewrite many prompts to get a much better, faster answer using fewer tokens vs allowing "thinking" to go on and on.
Indeed, and maybe that's all there is to it. Still, I'd hope we will eventually better understand what's exactly happening in the wake of many repeated applications of f().
Are you saying that cloud models are not verifying the draft model predictions? The way draft models are used in something like llama.cpp results in exactly zero degredation of output quality, with the larger model verifying each draft model token and discarding it if it does not match.
The TFR (which made it illegal to fly a drone within a bubble 1000 feet high and 3000 feet radius of a mobile asset) has been replaced by a NOTAM that basically says flying near them in a way that poses a credible safety or security threat means they can sieze or destroy your drone. I'd say this is the better resolution, and to me represents a "genuine change of heart" by the FAA, finding a way to serve the safety purpose of the TFR without overstepping.
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