Lua is a great language, including for embedded. And I am sure that their Lua-based product is good also. But this article was not very convincing as to why it "beats MicroPython". The criticisms are vague or not correct.
Extending MicroPython in C is quite easy, and one can implement third party modules in the same way all the official modules are made. There is build variable to set for including that into your custom firmware build - nothing particularly tricky about it.
The article goes on to lament that popular libraries from Python world are not available, including numpy. But actually there is a very good reimplementation of numpy (and parts of scipy), called ulab.
No, just saying that a claim of qualia would require some sort of evidence or methodical argument.
And that LLM outputs professing feelings or other state-of-mind like things should by default be assumed to be explained by that the training process (perhaps inadvertently) optimized for such output. Only if such an explanation fails, and another explanation is materially better, should it be considered seriously.
Do we have such candidates today?
Nothing an LLM says can in itself, right now, be used as evidence of what they 'feel'. It is not established that there is any linking of their output to anything else than the training process (data, loss function, optimizer, etc.). And definitely not to qualia.
On the other hand, it is well know that we can (and commonly do) make them come up with any output we choose. And that their general tendency is to regurgitate any kind of sequence that occurs sufficiently often in the training data.
Big party for Altman et al these days, who have been advocating (lobbying) a lot for this.
The US, and the world at large needs more electricity, and nuclear probably does have it's place in the mix. Just hope we do not spend all the new capacity on "AI". And most importantly, that nuclear safety continues to be highly prioritized every step of the way.
Did 6 years as bicycle mechanic/sales in highschool, was ok, could do again. Electronics engineering - presumably in this scenario the software aspects might be reduced, but probably longer before LLMs can do board design, debugging, type approvals etc.
How good are current LLMs at translating problems given as text into something SMT solvers can operate on? Be it MiniZinc, Z3, Smtlib, Python bindings, etc. Anyone tried it out?
There is no LLM suffering today. There is no evidence that there will be such suffering this year. I have seen no credible claims that it is probable to exist this decade, or ever, for that matter. This is not an issue we need to prioritize now.
Just a quick prompt for reflection. I do not suggest anything how people actually live their lives. But many have said that they got caught up in being too busy to actually enjoy life. Don't fall into that by accident!
They might (or might not). Extraterrestrial beings might also need moral standing. It is ok to spend a bit of thought on that possibility. But it is a bad argument for spending a non-trivial amount of resources that could be used to reduce human or animal suffering.
We are not even good at ensuring the rights of people in each country, and frankly downright horrible for denying other humans from across some "border" similar rights.
The current levels of exploitation of humans and animal are however very profitable (to some/many). It is very useful for those that profit from the status quo, that people are instead discussing, worrying and advocating for the rights of a hypothetical future being. Instead of doing something about the injustices that are here today.
I do not think that those researchers are fungible. We could however allocate a few hundred million less to AI research, and more to fighting human exploitation. We could pass stronger worker protection and have the big corporations pay for it - which then they have less money to spent on investments (in AI). Heck we could tax AI investments or usage directly, and spend it on worker rights or other cases of human abuse.
One month at 20 USD seems like it should be plenty to try it out on a small project or two to decide wether it is worth trying 100 bucks/month?
Or one can just wait a couple of months as people report their learnings.
The actual harms being done today are still more pressing than the hypothetical harms of future. And should be prioritized in terms of resources spent.
We are slave masters today. Billions of animals are livestock - they are born, sustained, and killed by our will - so that we can feed on their flesh, milk and other useful byproduct of their life. There is ample evidence that they have "a form of consciousness". They did not consent to this.
Are LLMs worthy of a higher standard? If so, why? Is it hypocritical to give them what we deny animals?
In case anyone cares: No, I am neither vegan nor vegetarian. I still think we do treat animals very badly. And it is a moral good to not use/abuse them.
Does you Google actually respect the keywords? For me, most of the times it replaces words with "synonyms" (mostly wrong context or not really replaceable). And results are pretty crap as a result - no what I was looking for, but just much more common/generic stuff.
pybind11 is your friend. Focus on small self-contained functions first. For numerical functions you can then take it mostly our of a book. See if you can speed up some simple and common operation within your problem domain of interest.
GPU memory bandwidth is the limiting factor, not PCIe bandwidth.
The memory bandwidth is critical because the models rely on getting all the parameters from memory to do computation, and there is a low amount of computation per parameter, so memory tends to be the bottleneck.
Agreed, 12 GB does not seem useful. For coding LLM, it seems 128 GB is needed to be even close to the frontier models.
For generative image processing (not video), it looks like one can get started with 16GB.
The school shooter type seems to want to be present locally, to go out with a bang, and to do it somewhere they are connected to.
They are not aiming for maximal scalability nor chance to get away.
Of course, other threat actors might, like a Unabomber type or groups designed to destabilize society (perhaps foreign sponsored) by doing repeated actions etc.
You are helping the companies train better LLMs... Both by just paying for their expenses, but also they will use the training data.
That may or may not be something one considers a worthwhile contribution.
Certainly it is less valuable than helping a person grow their intellectual capacity.
War is not amusing. In this war several hundred thousands have been killed, over a million are injured for life, and many millions are terrorized by frequent random bombings of civilian targets.
For knowledge workers in western companies, 20 USD/month for ChatGPT style tool, seems like it is likely worth it for employers to front.
I think over time we will see this included in "standard office tools" like email/messaging/videoconf/documents. Which would make Microsoft and Google very well positioned.
But probably everyone will adopt it - so it may not give a competitive advantage to any given company.
OpenAI is not at all profitable right now. Which is their plan. They have from the start been focusing on hypergrowth to become one of the very biggest players. The monitization phase comes later. Probably still a few years from now. Of course it remains to be seen if they are actually able to capture a significant amount of the value when the time comes. There are many other strong players - Google and Microsoft have a massive advantages wrt distribution and data collection. Meta as well. And they have existing profitable businesses, so they can afford to give away "AI" for a long time. Cost to serve users right now is quite high, that will likely go down by a factor 10x over the next 10 years (assuming the energy prices don't go haywire).
Duck typing is great, for example to support a range of numeric inputs - say fixed-precison integers, floats, dynamic precision integers, and numpy arrays, pandas series, tensorflow/pytorch tensor.
That duck typing can support functions with unbounded types, dos not mean that it is necessary, not generally sensible, for a particular function to support unbounded types.