Seems within reach of a hobbyist maker, at least the slightly unhinged kind (in the best meaning). Since considerable energy is needed but also avoiding excessive heating, integrating temperature measurement into the bulb might be prudent. Or at the very least having regulation on current+voltage. There might be risk of thermal runaway, if conductivity goes up when voltage is applied to the epoxy mixture.
I would not call validation of PCBs easy conceptually. We had to develop a lot of automation in validation to enable today production. Optical verification has been standard for many years, and X-ray is now getting commonplace also. Flying probes commonly used. Functional automated testing is standard for any non-hobby product.
But you are right in that the automation overall was/is bottlenecks on the ability to do QA - which not only prevents defects from being sent out, but is also critical to systematic improvements in the production process (both tuning and new iteration).
And I believe you are right to call out LLM based systems to be weak in this area, and it is a limiting factor. I believe that automated QA will be more and more critical to positive LLM impact.
Are you sure you spend less time per week on clothes washing than people did before the clothes washer? I suspect that before, people would wash their clothes considerably more seldom - which might even out the efficiency gains of the machine.
Their first bet was than they were going to be the frontier model provider by a good margin, and that others would not be able to compete on the "intelligence". And that they could get distribution via big customers looking to buy model access.
The dominant-model-provider strategy has already failed, many actors have models that rival them - both established (Google) and newcomers (Anthropic). Open models are not to shabby either, enough to undermine the narrative "we are uniquely able to do powerful models". As you say, there is a commodification process started, and it might be a race to the bottom in terms of margin.
So, OpenAI has moved into a new/adapted strategy, where they want to own the customers to a much larger degree, and rely less on partners/customers for distribution. This is likely because their prospective partners have a bunch of viable models to select between (many end products for power users lets people select freely), and high competitive pressure on costs (as it defines the margin and competitiveness) of the end products. Codex, Sora, their new web browser announcement, adjustments in ChatGPT is all to ensure a lot of direct end users - more brand recognition, more influence, more monetization possibilities.
So I think it is a considerable pivot from their initial plan/hopes. But it is not an unraveling - it is a rather smart response to the fierce competition in the market.
These kinds of systems tends to also be used by abusive policemen monitoring their exes, and police tracking members of undesirable (but legal) activist groups, etc.
It seems here that Google provides the core IP, and that Synaptic packages this (and probably other related IP blocks) block that can be used to build a SoC. As of now there are no chips announced. So it will be some years before we as software/electronics engineers get to play with it.
The architecture seems to be RISC-V array with standard RVV vector instruction set. That is a quite familiar environment for software developers compared to custom systolic arrays.
If done at scale, they are kinda crowd sourcing the test set from the entire internet, personal and business world. It will be harder and harder at least to pinpoint weaknesses, at least for the general public. It probably has little to do with intelligence (at least fluid intelligence as defined by Chollet et al) - but I guess it is sound tactic if the strategy is "fake it till you make it". And we might be surprised as to how far along that can go...
What development have happened in robotics over the last 2 years, that changed your mind?
I am familiar with general industry trends in electronics over the last two decades, as well as the 1 decade in machine learning.
Personally I do not think that we have seen the full battlefield impact of the last 10-20 years developments in microcontrollers, battery, power electronics, electromotors, MEMS sensors, imaging sensors, control firmware, computer vision and object detection. Capability per dollar has increased by a few orders of magnitude in each of these domains. We are seeing some hints of the potential impact now after Russia invaded Ukraine - low-cost drones are now killing and damaging just as much or more as existing high tech weaponry. These systems are crude, with limited autonomy, but it is already enough to wreck havoc. I do not think development of these systems have stopped, unfortunately.
Early day for robotics and AI still. But for a superpower, the potential for (more) autonomous weapons in that intersection is something that (unfortunately) cannot be ignored.
Yeah and this was RPi first step into RISC-V. It seems to have been quite successful? So perhaps in a couple of generations from now we will see a pure RISC-V chip from them?
I would have loved more nitty gritty details also. But to conclude that they "have no idea what they are doing" seems to be an uncharitable take...
The described system seems like a useful test bed, and I did not see anything that claimed this covers all the tests such a system should have.
These days you could probably build a pretty performant numpy like using shared memory with Arrow format and IPC for control.
Though it would be considerably more complex and not at all easier than FFI...
There is topological naming available about USB ports from the Linux kernel. So one can find what device was connected to which hubs, and which root hub that hub was on, etc.
See lsusb -t, and the device paths exposed in udev. Old blogpost of mine about it:
https://www.jonnor.com/tag/usb/
Yep, the advertising packets can be used for things like this. And frequently are in IoT settings. Typically by using the AD type Manufacturer Specific (0xFF). BLE 5.x makes it possible to have many more bytes of advertisement data, including longer packets and chains of packets. Otherwise the mechanisms stayed the same as in 4.x. Which is good, as BLE 4.x only hardware/software is still around...
RISC-V is doing great in microcontrollers. From 10 cent Cortex-M0 competitors by WCH etc, to dual core WiFi/BLE enabled devices by Espressif etc, competing with Cortex M33. On Embedded Linux class devices the ARM Cortex Axx reign supreme, until one hits X86 industrial PC territory. I think these trends will continue, but RISC-V will likely get stronger in all of these as time goes on.
I think that the best way to address this potential ARC overfitting, would be to create more benchmarks - that are similar in concept, focusing on fluid intelligence, but from another angle than ARC.
Of course it is quite costly and also requires some "marketing" to actually get it established.
I believe Stack Overflow and Quora were screwed because chatbot interfaces became the new entrypoint. _If_ Instagram/TikTok/YouTube manage to stay the place people go habitually, then I think they will benefit, not be harmed. But there are probably many ways to step wrong here. There is a bit of an innovators dilemma for the platforms that currently relies on human produced content. If I were them, I would fund 2-3 new/separate efforts to be able to experiment while not immediately killing the current golden goose.
Why would it devalue the platform? The more time people spend watching, the more attention that can be monetized. More content, or higher quality content, or content produced more cheap does not detract deon that?
The algorithms will pick winners anyway, and can shape things into the familiar power law of popularity, if that is the desired mode. People parasocial needs can be exploited by human creators to differentiate from AI generated content.
Modern social media is like a casino, the house always wins.
Which companies would, if they alone become radically more effective, actually be able to radically change impact on their industry? The vast majority of companies are in value chains, where practical value creation depends also on the suppliers and customers of the company. Often there are multiple suppliers and customers, and sometimes there are multiple levels of relationships on each side... That places constrains/inefficiencies on how quickly and how much one can achieve.
This kind of dataset is useful also with a non-learned algorithm. Testing and benchmarking on realistic data is the best way to validate any solution to problems that are non-trivial and have a very diverse input space.
One could build a dataset out of Github data, by analyzing KiCAD files. They are likely to be "completed" projects, so one would have to rip out all the traces and start from that. And the parts are also placed (in a way which makes routing feasible for a human), which is a large part of routing. So one could have another task setting where the parts also have to be placed, and then routed.
Such a dataset would likely represent simple to medium cases, as open designs are usually on the lower side of the complexity compared to industry. And it would be hard to automatically infer important constraints such as differential pair matching. That would require manual annotation, most likely. But if there indeed are no open datasets, then I think this would be a worthwhile contribution to the field.
How is the stem support these days? In particular I would like to create some songs with vocals (my lyrics), then be able to remove the vocal track and replace it with my own.
I do not think the leveraging is going to end there. I suspect this will be used to justify/secure power generation investments, possibly even nuclear. Likely via one or more of the OpenAI/Altman adjacent power startups.
Doesn't a head chef in a restaurant context delegate a lot to other people for the cooking? And of course they use many tools also. And pre-prepared parts also, often from external suppliers.