Effectiveness of AI powered search - Agreed, it is a very subjective topic. I don’t use LLMs for the majority of my searches (who needs hallucinated dates and times for the movies playing at a cinema near me?) and it sounds like Google is trying to use their LLM with every search now… In my opinion we should have a button to activate the LLM on a search rather than have it respond every time (but I don’t really use Google search anyway).
Translation/Transcription tech - It’s incredibly useful for anyone who’s deaf. Your average person doesn’t need this, although I’m sure they benefit from the auto-generated subtitles if they’re trying to watch a video in a noisy environment (or with the volume off).
In my own personal use I’ve found it useful for cutting through the nonsense posted by both sides of either the Ukraine/Russia conflict or the Israel/Gaza conflict (in the case of misinformation targeting those who don’t speak the language).
Generative AI - Yeah, this will be interesting to see how it plays out in courts. I definitely see good points raised by both sides, although I’m personally leaning towards a ruling that would allow smaller startups/research groups to be able to compete with larger corporations (when they will be able to buy their way into training data). It’ll be interesting to see how these cases proceed on the text vs audio vs image/art fronts.
Wasteful AI - Agreed… too many companies are jumping in on the “AI” bandwagon without properly evaluating whether there’s a better way to do something.
Anyway, thanks for taking the time to read through everything.
TL/DR: We end up comparing LLM executions with Google searches (a single prompt to ChatGPT uses about 10x as much electricity as a single Google search execution). How many Google searches and links do you need to click on vs requesting information from ChatGPT? I also touch on different use cases beyond just the use of LLMs.
The true argument comes down to this: Is the increase in productivity worth the boost in electricity? Is there a better tool out there that makes more sense than using an AI Model?
For the first article:
The only somewhat useful number in here just says that Microsoft had 30% higher emissions than what it’s goals were from 2020… that doesn’t breakdown how much more energy AI is using despite how much the article wants to blame the training of AI models.
The second article was mostly worthless, again pointing at numbers from all datacenters, but conveniently putting 100% of the blame on AI throughout most of the article. But, at the very end of the article it finally included something a bit more specific as well as an actual source:
AI could burn through 10 times as much electricity in 2026 as it did in 2023, according to the International Energy Agency.
A 170 page document by the International Energy Agency.
Much better.
Page 8:
Electricity consumption from data centres, artificial intelligence (AI) and the cryptocurrency sector could double by 2026.
Not a very useful number since it’s lumping in cryptocurrency with all Data centers and “AI”.
Moreover, we forecast that electricity consumption from data centres in the European Union in 2026 will be 30% higher than 2023 levels, as new data facilities are commissioned amid increased digitalisation and AI computations.
Again, mixing AI numbers with all datacenters.
Page 35:
By 2026, the AI industry is expected to have grown exponentially to consume at least ten times its demand in 2023.
OK, I’m assuming this is where they got their 10x figure, but this does not necessarily mean the same thing as using 10x more electricity especially if you’re trying to compare traditional energy use for specific tasks to the energy use required by executing a trained AI Model.
Page 34:
When comparing the average electricity demand of a typical Google search (0.3 Wh of electricity) to OpenAI’s ChatGPT (2.9 Wh per request)
It’s behind a paywall, but if you’re on a college campus or at certain libraries you might be able to access it for free.
Finally we have some real numbers we can work with. Let’s break this down. A single Google search uses a little more than 1/10th of a request made to ChatGPT.
So here’s the thing, how many times do you have to execute a Google search to get the right answer? And how many links do you need to click on to be satisfied? It’s going to depend based on what you’re looking for. For example, if I’m working on doing some research or solving a problem, I’ll probably end up with about 10-20 browser tabs open at the same time by the time I get all of the information I need. And don’t forget that I have to click on a website and load it up to get more info. However, when I’m finally done, I get the sweet satisfaction of closing all the tabs down.
Compare that to using an LLM, I get a direct answer to what I need, I then do a little double checking to verify that the answer is legitimate (maybe 1-2 Google equivalent searches), and I’m good to go. Not only have I spent less time overall on the problem, but in some cases I might have even used less electricity after factoring everything in.
Let’s try a different use case: Images. I could spend hours working in Photoshop to create some image that I can use as my Avatar on a website. Or I can take a few minutes generating a bunch of images through Stable Diffusion and then pick out one I like. Not only have I saved time in this task, but I have used less electricity.
In another example I could spend time/electricity to watch a Video over and over again trying to translate what someone said from one language to another, or I could use Whisper to quickly translate and transcribe what was said in a matter of seconds.
On the other hand, there are absolutely use cases where using some ML model is incredibly wasteful. Take, for example, a rain sensor on your car. Now, you could setup some AI model with a camera and computer vision to detect when to turn on your windshield wipers. But why do that when you could use this little sensor that shoots out a small laser against the window and when it detects a difference in the energy that’s normally reflected back it can activate the windshield wipers. The dedicated sensor with a low power laser will use far less energy and be way more efficient for this use case.
Of course we still need to factor in the amount of electricity that’s required to train and later fine-tune a model. Small models only need a few seconds-minutes to train. Other models may need about a month or more to train. Once the training is complete, no more electricity is required, the model can be packaged up and spread out over the internet like any other file (of course electricity is used for that, but then you might as well complain about people streaming 8k video to their homes for entertainment purposes).
So everything being said, it really comes down to this:
Does the increase in productivity warrant the bump in electricity usage?
Is there a better tool out there that makes more sense than using an AI Model?
You know what’s ironic? We’re all communicating on a decentralized network which is inefficient when compared to a centralized network.
I’m sure we could nitpick and argue over what’s the most efficient solution for every little thing, but at the end of the day we need to see if the pros outweigh the cons.
But a local model that’s been fine-tuned for coding? Perfection.
It’s not that you use the LLM to do everything, but it’s excellent for pseudo code. You can quickly get a useful response back about most of the same questions you would search for on stack overflow (but tailored to your own code). It’s also useful for issues when you’re delving into a newer programming language and trying to port over some code, or trying to look at different ways of achieving the same result.
It’s just another tool in your belt, nothing that we should rely on to do everything.
Ok, first off, I’m a big fan of learning new expressions where they come from and what they mean (how they came about, etc). Could you please explain this one?:
well, you dance and jump over the fire in the bank’s vault.
And back to the original topic:
It isn’t resource efficient, simple as that.
It’s not that simple at all and it all depends on your use case for whatever model you’re talking about:
For example I could spend hours working in Photoshop to create some image that I can use as my Avatar on a website. Or I can take a few minutes generating a bunch of images through Stable Diffusion and then pick out one I like. Not only have I saved time in this task, but I have used less electricity.
In another example I could spend time/electricity to watch a Video over and over again trying to translate what someone said from one language to another, or I could use Whisper to quickly translate and transcribe what was said in a matter of seconds.
On the other hand, there are absolutely use cases where using some ML model is incredibly wasteful. Take, for example, a rain sensor on your car. Now, you could setup some AI model with a camera and computer vision to detect when to turn on your windshield wipers. But why do that when you could use this little sensor that shoots out a small laser against the window and when it detects a difference in the energy that’s normally reflected back it can activate the windshield wipers. The dedicated sensor with a low power laser will use far less energy and be way more efficient for this use case.
Cheers on you if you found where to put it to work as I haven’t and grown irritated over seeing this buzzword everywhere.
Makes sense, so many companies are jumping on this as a buzzword when they really need to stop and think if it’s necessary to implement in the first place. Personally, I have found them great as an assistant for programming code as well as brainstorming ideas or at least for helping to point me in a good direction when I am looking into something new. I treat them as if someone was trying to remember something off the top of their head. Anything coming from an LLM should be double checked and verified before committing to it.
And I absolutely agree with your final paragraph, that’s why I typically use my own local models running on my own hardware for coding/image generation/translation/transcription/etc. There are a lot of open source models out there that anyone can retrain for more specific tasks. And we need to be careful because these larger corporations are trying to stifle that kind of competition with their lobbying efforts.
Edit: Ok it really doesn’t help when you edit your comment to provide clarification on something based on my reply as well as including additional remarks.
I mean, that’s kind of the whole point of why I was trying to nail down what the other user meant when they said “AI doesn’t provide much benefit yet”.
The definition of “AI” today is way too broad for anyone to make statements like that now.
And to make sure I understand your question, are you asking me to provide you with the definition of “AI”? Or are you asking for the definition of “AGI”?
Do bosses from video games count?
Count under the broad definition of “AI”? Yes, when we talk about bosses from video games we talk about “AI” for NPCs. And no, this should not be lumped in with any machine learning models unless the game devs created a model for controlling that NPCs behaviour.
In either case our current NPC AI logic should not be classified as AGI by any means (which should be implied since this does not exist as far as we know).
“AI” doesn’t mean what it used to and if you use it today it encompasses a very wide range of tech including machine learning models:
Speech to text (STT), text to speech (TTS), Generative AI for text (LLMs), images (Midjourney/Stable Diffusion), audio (Suno). Upscaling, Computer Vision (object detection, etc).
But since you’re looking for AGI there’s nothing specific to really point at since this doesn’t exist.
I’m going to assume that when you say “AI” you’re referring to LLMs like chatGPT. Otherwise I can easily point to tons of benefits that AI models provide to a wide variety of industries (and that are already in use today).
Even then, if we restrict your statement to LLMs, who are you to say that I can’t use an LLM as a dungeon master for a quick round of DnD? That has about as much purpose as gaming does, therefore it’s providing a real benefit for people in that aspect.
Beyond gaming, LLMs can also be used for brainstorming ideas, summarizing documents, and even for help with generating code in every programming language. There are very real benefits here and they are already being used in this way.
And as far as resources are concerned, there are newer models being released all the time that are better and more efficient than the last. Most recently we had Llama 3 released (just last month), so I’m not sure how you’re jumping to conclusions that we’ve hit some sort of limit in terms of efficiency with resources required to run these models (and that’s also ignoring the advances being made at a hardware level).
The first thing I said was, “the more you compress something, the more processing power you’re going to need [to decompress it]”
I’m not removing the most computationally expensive part by any means and you are misunderstanding the process if you think that.
That’s why I specified:
The drawback is that you need a powerful computer and a lot of energy to regenerate those images, which brings us back to the problem of making this data conveyed in real-time while using low-power.
And again
But of course, that’s still going to take time to decompress as well as a decent spike in power consumption for about 30-60+ seconds (depending on hardware)
Those 30-60+ second estimates are based on someone using an RTX 4090, the top end Consumer grade GPU of today. They could speed up the process by having multiple GPUs or even enterprise grade equipment, but that’s why I mentioned that this depends on hardware.
So, yes, this very specific example is not practical for Neuralink (I even said as much in my original example), but this example still works very well for explaining a method that can allow you a compression rate of over 20,000x.
Yes you need power, energy, and time to generate the original image, and yes you need power, energy, and time to regenerate it on a different computer. But to transmit the information needed to regenerate that image you only need to convey a tiny message.
Sure, but this is just a more visual example of how compression using an ML model can work.
The time you spend reworking the prompt, or tweaking the steps/cfg/etc. is outside of the scope of this example.
And if we’re really talking about creating a good pic it helps to use tools like control net/inpainting/etc… which could still be communicated to the receiving machine, but then you’re starting to lose out on some of the compression by a factor of about 1KB for every additional additional time you need to run the model to get the correct picture.
The reward for developing this miraculous leap forward in technology? A job interview, according to Neuralink employee Bliss Chapman. There is no mention of monetary compensation on the web page.