Is there anyway to make it use less at it gets more advanced or will there be huge power plants just dedicated to AI all over the world soon?
It’s mostly the training/machine learning that is power hungry.
AI is essentially a giant equation that is generated via machine learning. You give it a prompt with an expected answer, it gets run through the equation, and you get an output. That output gets an error score based on how far it is from the expected answer. The variables of the equation are then modified so that the prompt will lead to a better output (one with a lower error).
The issue is that current AI models have billions of variables and will be trained on billions of prompts. Each variable will be tuned based on each prompt. That’s billions to the power of billions of calculations. It takes a while. AI researchers are of course looking for ways to speed up this process, but so far it’s mostly come down to dividing up these billions of calculations over millions of computers. Powering millions of computers is where the energy costs come from.
Unless AI models can be trained in a way that doesn’t require running a billion squared calculations, they’re only going to get more power hungry.
This is a pretty great explanation/simplification.
I’ll add that because the calculations rely on floating point math in many cases, graphics chips do most of the heavy processing since they were already designed for this pipeline in mind with video games.
That means there’s a lot of power hungry graphics chips running in these data centers. It’s also why NVidia stock is so insane.
Would AI inferencing, or training be better suited to a quantum computer? I recall thouse not being great at conventional math, but massively accelerates computations that sounded similar to machine learning.
My understanding of quantum computers is that they’re great a brute forcing stuff, but machine learning is just a lot of calculations, not brute forcing.
If you want to know the square root of 25, you don’t need to brute force it. There’s a direct way to calculate the answer and traditional computers can do it just fine. It’s still going to take a long time if you need to calculate the square root of a billion numbers.
That’s basically machine learning. The individual calculations aren’t difficult, there’s just a lot to calculate. However, if you have 2 computers doing the calculations, it’ll take half the time. It’ll take even less time if you fill a data center with a cluster of 100,000 GPUs.
My understanding is that traditional AI essentially takes a bruteforce approach to learning and because it is hardwired, its ability to learn and make logical connections is impaired.
Newer technologies like organic computers using neurons can change and adapt as it learns, forming new pathways for information to travel along, which reduces processing requirements and in turn, reduces power requirements.
Lower voltage chip advancement along with better cooling options may come along some day.
They should consider building their super centers underwater in places like Iceland.
Thats only a short term solution, global warming will negate those benefits.
Southern ocean currents just reversed and will likely cause rapid warming of water temps.
Southern Ocean circulation reversed
Southern Ocean current reverses for first time, signalling risk of climate system collapse
France and Switzerland just had to shutdown their nuclear reactors due to the water sources they use for cooling being too warm.
France and Switzerland shut down nuclear power plants amid scorching heatwave
will there be huge power plants just dedicated to AI all over the world soon?
Construction has started(or will soon) to convert a retired coal power plant in Pennsylvania to gas power, specifically for data-centers. Upon completion in 2027 it will likely be the third most powerful plant in the US.
The largest coal plant in North Dakota was considering shutting down in 2022 over financial issues, but is now approved to power a new data-center park.
Location has been laid out for a new power plant in Texas, from a single AI company you’ve probably never heard of.
And on it goes.
Data is the new oil. Collecting it, refining it, and distributing it.
OpenAI noticed that Generative Pre-trained Transformers get better when you make them bigger. GPT-1 had 120 million parameters. GPT-2 bumped it up to 1.5 billion. GPT-3 grew to 175 billion. Now we have models with over 300 billion.
To run, every generated word requires doing math with every parameter, which nowadays is a massive amount of work, running on the most power hungry top of the line chips.
There are efforts to make smaller models that are still effective, but we are still in the range of 7-30 billion to get anything useful out of them.
The current algorithmic approach to AI hit a wall in 2022.
Since then they have had to pump exponentially more electricity into these systems that result in exponentially diminishing returns.
We should have stopped in 2022, but marketing teams had other plans.
There’s not a way to do AI and use less electricity than the current models, and there most likely won’t be any more advances in AI until someone invents a fundamentally different approach.
Supercomputers once required large power plants to operate, and now we carry around computing devices in out pockets that are more powerful than those supercomputers.
There’s plenty of room to further shrink the computers, simplify the training sets, formalize and optimize the training algorithms, and add optimized layers to the AI compute systems and the I/O systems.
But at the end of the day, you can either simplify or throw lots of energy at a system when training.
Just look at how much time and energy goes into training a child… and it’s using a training system that’s been optimized over hundreds of thousands of years (and is still being tweaked).
AI as we see it today (as far as generative AI goes) is much simpler, just setting up and executing probability sieves with a fancy instruction parser to feed it its inputs. But it is using hardware that’s barely optimized at all for the task, and the task is far from the least optimal way to process data to determine an output.
It is also a very large data set it has to go through the average English speaker knows 40kish words and it has to pull from a large data set and attempt to predict what’s the most likely word to come next and do that a hundred or so times per response. Then most people want the result in a very short period of time and with very high accuracy (smaller tolerances on the convergence and divergence criteria) so sure there is some hardware optimization that can be done but it will always be at least somewhat taxing.
Your answer is intuitively correct, but unfortunately has a couple of flaws
Supercomputers once required large power plants to operate
They didn’t, not that much anyways, a Cray-1 used 115kW to produce 160 MFLOPS of calculations. And while 150kW is a LOT, it’s not in the “needs its own power plant to operate” category, since even a small coal power plant (the least efficient electricity generation method) would produce a couple of orders of magnitude more than that.
and now we carry around computing devices in out pockets that are more powerful than those supercomputers.
Indeed, our phones are in the Teraflops range for just a couple of watts.
There’s plenty of room to further shrink the computers,
Unfortunately there isn’t, we’ve reached the end of Moore’s law, processors can’t get any smaller because they require to block electrons from passing on given conditions, and if we built transistors smaller than the current ones electrons would be able to quantum leap across them making them useless.
There might be a revolution in computing by using light instead of electricity (which would completely and utterly revolutionize computers as we know them), but until that happens computers are as small as they’re going to get, or more specifically they’re as space efficient as they’re going to get, i.e. to have more processing power you will need more space.
If people continue investing in AI and computing power keeps growing we would need more than dedicated power plants.
It takes a lot of energy to do something you are not meant to do, whether that’s a computer acting like a person or an introvert acting like an extrovert
