There are so many people on-board with this idea, hypemen collaborators, that I think they might be safe for a year or two more. The hypers will shout about how miraculous it is, and tell everyone that does not get the promised value that "you are just holding it wrong".
This buys them a fair amount of time to improve things.
The modern Microsoft with Azure, Office360, etc is not much threatened by open source software. Especially with Azure, open source is a fantastic compliment which they would like the world to produce as much of as possible. The same with AI models. They would look at charge for AI hosting and services, at premium due to already being integrated in businesses. They are going to bundle it with all their existing moneymakers, and then just jack up the price. No sale needed, just a bump in the invoices that are flowing anyway.
You made me think of the role of mental effort/exercise. In parts of the western world, we are already experiencing a large increase in dementia/alzheimer and related. Most of it is because we are doing better with other killers like heart etc, and many cancers also. But is said that mental activity is important to stave off degenerative diseases of the brain. Could widespread AI trigger a dementia epidemic? It would be 30 years out, but still...
The "numpy" Sobel code is not that good, unfortunately - all the iteration is done in Python, so there is not much benefit from involving numpy.
If one would use say scipy.convolve2d on a numpy.array, it would be much faster.
There are several datastructures for numeric data that do not need marshalling, and are suitable for very efficient interoperetion between Python and C/C++/Rust etc. Examples include array.array (in standard library), numpy.array, and PyArrow.
Why is that concerning? The VCs job is to sell to the bigger fool. They are going to pump that narrative for years, so that they can make some exits. Regardless of underlying realism.
We know that learning and building mental capabilities require effort over time. We know that when people have not been applying/practicing programming for years, their skills have atrophied. I think a good default expectation is that unused skills will go away over time. Of course the questions are, is the engagement we have with LLMs enough to sustain the majority of the skills? Or is there new skills one builds that can compensate foe those lost (even when the LLM is no longer used)? How quickly do the changes happen? Are there wider effects, positive and/or negative?
They have started. Their strategy is just for others to build it on top of their platform. And not build one, but hundreds of businesses. Much faster way to grow. Why retain 100+ internal product/integration teams when you can have 100+ external teams that will pay you and do the building, the trying and failing, in each niche.
The things that actually work out you can just buy or outcompete later. But that is another phase, perhaps 1 decade from now. Right now it is about starting alll the snowballs.
Every actor making codecs is trying to pull off an MP3, so that they extract rents from everyone else via licensing. They carpet bomb the field with patents to prevent free codecs from succeeding.
aptX is an example of an non-free codec made in this manner :)
If anyone is interested in fast dynamic native C modules for MicroPython, you can check out emlearn-micropython - a machine learning and digital signal processing library.
https://github.com/emlearn/emlearn-micropython
Disclaimer: I am the maintainer
Ok, you guys have successfully nerd sniped me this morning... Here some experiments showing the use of code emitter to speed this code up massively. Link to code: https://github.com/jonnor/embeddedml/tree/master/handson/mic...
The results on ESP32S3 (M5Stick AtomS3U), running MicroPython 1.24.1. All times in seconds, for the 2000 number sort.
bubble.py 19.119
bubble_native.py 9.482
bubble_viper.py 0.904
heapsort_viper.py 0.02
So one can do 100x better without changing the algorithm, and 1000x by changing it :)
Microcontrollers are a constrained platform. They can be plenty fast - but it is much more important to use the tools available, compared to on PC. MicroPython has excellent tools available for this.
This should not be used to conclude on the viability of using MicroPython for numeric type tasks. For that one should at least take into account the following:
Integers are much faster than floats (floats involve a pointer and a heap allocation, integers are stored in a single word/object).
array.array is preferred over list for something like sort. Continuous memory representation of numbers versus general purpose list of objects.
MicroPython has on-board "JIT" (native/viper emitters), have to explicitly annotate the function. Should give 4-10x improvements for this case.
MicroPython has an on-board assembler, so one can write ARM assembly and get that to a function.
MicroPython also has support for C modules, which expose a Python API. Including dynamic native modules which can be installed at runtime with the package manager.
Bubblesort is O(n*2), which hurts for even a few thousand numbers. Actual sorting on a microcontroller should be done with an O(n log n) algorithm.
Above average people can also use it to get average results. Which can actually be useful. For many tasks and usecases, the good enough threshold can actually be quite low.
There is a hack for that: write the comments before and/or as you write the code. When things are still unclear, weird.
Of course do a final pass on them to ensure that they are correct and useful in the end.
This is one example of document as-you-go, instead of doing it after "the work" is done. I find it generally leads to better outcomes. Doing documenting only at the end is in many ways the worst way to do it.
Norway is currently building very little new power generation at all, unfortunately. NIMBYism is blocking onshore wind, ecological concerns blocking hydro. This has already started hurting, and is projected to get worse. Before taking any AI datacenters into account.
The wealth tax in itself probably does not explain the issues you see. They are more likely related to the global increases in interests.
Now the recently introduced exit tax has some serious problems, in that is has triggered and exodus both of genuinely rich people, and of prospective founders who can easily get rich on paper and have massively disproportionate tax effects. That law is very stupid - especially that unrealized gains are taxable 100% at shares market rate.
Education is free. Whether one considers the social support to be UBI is a matter of debate, I guess, but right now it would leave you decidedly poor/low-class.
Print in TPU? There is a limit to how soft one can get, and the finish is not so great (stringing). But fantastic for very tough semi-soft functional objects. Ninjaflex Cheetah and Armadillo are my favorites, but generic TPU also works. Direct drive extruder recommended, though I manage on my cheapo Bowden fed Ender 3.
If enough people believe it is true and act on this belief, it will become true (to a larger degree, at least). There is a massive shift of the direction of money happening right now, away from workers into "AI" (LLM based systems). The more of that shift can be produced now, the stronger the AI companies will be in the future. Because they need massive capital investments in compute, engineering investments in efficiency, and to reach economies of scale, to be able to really compete with human-based workforce.
Yeah it's partly a "this is the inevitable winning team" - join before it is too late. The earlier the better! There are some parallels to how MLM and ponzi schemes are sold.
Another aspect is for distraction. OpenAI for example have had periods where they talked a lot on existential risk (humanity is doomed due to AI overloads, yadda yadda). This can serve as a very useful distraction to avoid talk about more plausible risks in the foreseeable future - such as massive concentrations of wealth, increased inequality, large amounts of unemployment, large scale personalized psychological manipulation, etc.
One of the ways it can work is that people may reject the most extreme case (say end of humans beings in control), but accept some milder version (most jobs will be done by AI, or most jobs will be AI assisted). The fear of an extreme can cause people to rationalize the "milder" outcome - independently of whether there are good arguments for that outcome, or even whether the outcome is desirable, or better than status quo.
The investor class are not dependent on wages, so their livelihoods are not at stake. Same with big corporate partners, they are hoping to improve competitiveness by having fewer employees and CEO take a bonus for that.
Regular users in fear of their jobs may act on that, in hope that they can reskill and transition by being AI savvy.
I do not agree with the argument they make, but I understand what they are are playing at, and that unfortunately it can be effective.