TinyML and Efficient Deep Learning Computing
efficientml.ai
efficientml.ai
Thanks for your intreset in helping others with your knowlegde , i would greatly recommend you to deeplearningindaba https://deeplearningindaba.com/mentorship/ , its one of the biggest ml communities in africa and has a host of students, companies and mentors
Also look into attending or supporting the next Deep Learning Indaba. This year has just finished I think. It's one of the largest African ML communities and they have a mentorship program.
[1] https://www.youtube.com/watch?v=-kFOXP026eE&list=PLS3_1JNX8d...
https://time.com/6247732/shein-climate-change-labor-fashion/
Bleeding edge research work should not be hindered by premature optimization concerns. Take quantization for example. Before people were able to train a model with the usual floating point precisions, nobody knew that INT8 or q4 quantization was feasible. (In fact, nobody would have a full precision model to compare performance with.)
Also, the idea that energy efficiency should be a top concern basically undermines the whole idea of developing new technology. It's obvious that if fancy things are to come out of the research, it's going to cost more energy to run than not running anything at all. That itself is an argument that if we don't want energy usage to keep ramping up, we should shut down ALL research that potentially give us new energy-depleting toys.
So, really, I'm personally not concerned with "one-off" resource usage if they advance human understanding of the state of the art. Since energy actually costs money, capitalist pressures will make people think of ways to save energy (and time). The moralistic arguments are just misguided in the big picture. IMHO it feels like luddites putting on the environmentalist hat here.
Instead of shaming machine learning researchers over their energy use, it's probably more effective from a energy use standpoint (for example) to ban "proof of work" schemes in cryptocurrency.
Banning a class of high energy consuming algorithms used towards a non-violent technological development you disagree with makes absolutely no sense to me.
isn't the ingenuinity in doing things efficiently rather than just turning off the "scarce resources" setting? really i wish someone would explain to me what is the substance of "bleeding edge research" wherein nothing has been optimized except the flow of grant dollars from DoE/NSF to NVIDIA. like what is the research in "we burned O(1,000,000) compute core hours to train a model". i was recently in a discussion with ANL people about rick stevens next big idea - "the trillion parameter consortium" - where he plans to just commandeer aurora for a month. lol.
A human may emit more CO2 than a computer doing the same work. As AI improves, this will happen more and more until they dominate humans by this measure. Then you'll have established a principle justifying a ban of biological humans.
If you believe in human rights, it's mostly bad to put prior restraint on people's use of computers, just as it's bad to restrict writing, talk, and privacy. The restriction of this kind that makes the most sense to me is on the development of superhuman general intelligence, until we understand better how to avoid that ending humanity.
I will say though that at this point in time it's ridiculous how little research has gone into reducing the energy use of networks.
I’m sorry but this is such a dumb argument. Of course new research will burn energy - there’s no question about that. To suggest that ML is like any other development, when it’s well documented how much power these algorithms (LLMs for example) use is kind of bad faith.
The argument is not that we should halt technological progress, but in doing so always be considering its impact on the planet and evaluating if it is really worth it or not.
Except nothing here is hindering the ability of scientists to develop whatever new technologies they want in their labs.
> shut down ALL research
> shaming machine learning researchers
> shut down ALL research
> shaming machine learning researchers
Even calling research work as “research” is not well received many times :)
This is one of the reasons I’d like this bubble to burst soon, so that we can focus on applying the resources we got efficiently, rather than pursing - many times pointless - “bleeding-edge” research.
This and similar facile arguments are getting tiresome. They seem predicated on nothing but the most basic Econ 101 understanding of value. Nothing exists in that idealized world -- in reality, complex mechanisms keep this kind of excess spend relevant (marketing and public image, "first to market" concerns, sunk cost spending, and a million more) regardless of more material considerations, so to lean on this trope is not really up to the standards of HN discussion in my book.
It's not about luddites putting on the environmental hat, if environment was the main concern your capitalist pressure couldn't care less (\o/ externalities). It's about not draining phones batteries and not racking up datacenter bills.
And capitalistic force are not fast enough and d not take environmental impact into account has carbon taxes are not there or not high enoigh or lobbied against or not measurable and gamed.
Moreover, the class also covers topics related to using and hacking with foundational models of all you have is the model versus a cluster.
Energy used to train foundation models is indeed extreme but the gross expenditure is more a function of corporate spending and competition than deep learning technology.
I would agree that efficient LMs should be a focus, but a framing like that is too pretentious and misses the point IMO. I'd expect Apple to heavily double down on this point though, because that's what they always do (see the latest Apple event for more).