Despite its name, the infrastructure used by the “cloud” accounts for more global greenhouse emissions than commercial flights. In 2018, for instance, the 5bn YouTube hits for the viral song Despacito used the same amount of energy it would take to heat 40,000 US homes annually.
Large language models such as ChatGPT are some of the most energy-guzzling technologies of all. Research suggests, for instance, that about 700,000 litres of water could have been used to cool the machines that trained ChatGPT-3 at Microsoft’s data facilities.
Additionally, as these companies aim to reduce their reliance on fossil fuels, they may opt to base their datacentres in regions with cheaper electricity, such as the southern US, potentially exacerbating water consumption issues in drier parts of the world.
Furthermore, while minerals such as lithium and cobalt are most commonly associated with batteries in the motor sector, they are also crucial for the batteries used in datacentres. The extraction process often involves significant water usage and can lead to pollution, undermining water security. The extraction of these minerals are also often linked to human rights violations and poor labour standards. Trying to achieve one climate goal of limiting our dependence on fossil fuels can compromise another goal, of ensuring everyone has a safe and accessible water supply.
Moreover, when significant energy resources are allocated to tech-related endeavours, it can lead to energy shortages for essential needs such as residential power supply. Recent data from the UK shows that the country’s outdated electricity network is holding back affordable housing projects.
In other words, policy needs to be designed not to pick sectors or technologies as “winners”, but to pick the willing by providing support that is conditional on companies moving in the right direction. Making disclosure of environmental practices and impacts a condition for government support could ensure greater transparency and accountability.
It isn’t resource efficient, simple as that. Machine learning isn’t something new and it indeed was used for decades in one form or another. But here is the thing: when you train a model to do one task good, you can approximate learning time and the quality of it’s data analyzis, say, automating the process of setting price you charge for your hotel appartments to maximize sales and profits. When you don’t even know what it can do, and you don’t even use a bit of it’s potential, when your learning material is whatever you was dare to scrap and resources aren’t a question, well, you dance and jump over the fire in the bank’s vault. LLM of ChatGPT variety doesn’t have a purpose or a problem to solve, we come with them after the fact, and although it’s thrilling to explore what else it can do, it’s a giant waste*. Remember blockchain and how everyone was trying to put it somewhere? LLMs are the same. There are niche uses that would evolve or stay as they are completely out of picture, while hyped up examples would grow old and die off unless they find their place to be. And, currently, there’s no application in which I can bet my life on LLM’s output. Cheers on you if you found where to put it to work as I haven’t and grown irritated over seeing this buzzword everywhere.
* What I find the most annoying with them, is that they are natural monopolies coming from the resources you need to train them to the Bard\Bing level. If they’d get inserted into every field in a decade, it means the LLM providers would have power over everything. Russian Kandinsky AI stopped to show Putin and war in the bad light, for example, OpenAI’s chatbot may soon stop to draw Sam Altman getting pegged by a shy time-traveler Mikuru Asahina, and what if there would be other inobvious cases where the provider of a service just decides to exclude X from the output, like flags or mentions of Palestine or Israel? If you aren’t big enough to train a model for your needs yourself, you come under their reign.
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?:
And back to the original topic:
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.
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.
The transformer technology did come built for a specific purpose, automated translation.
That is a good argument, they are natural monopolies due to the resources they need to be competitive.
Now do we apply this elsewhere in life? Is anyone calling for Boeing to be broken up or Microsoft to be broken up or Amazon to be broken up or Facebook?
We are missing big time on breaking them into pieces, yes. No argument. There’s something wrong if we didn’t start that process a long time ago.