Head on down to our on-demand library to check out sessions from VB Transform 2023. You won’t wanna miss it, trust me. It’s all about generative AI, baby. We’re talking about machines pumping out content, images, music, and code just as fast and accurate as humans, maybe even faster. This shit is next level. And let me tell you, as AI becomes more popular, people are starting to ask some important questions. Like, what does it actually take to make this stuff happen? And what’s the cost, both financially and environmentally?
Well, here’s the deal. The most energy-intensive and expensive part of building AI models is the process called inference. That’s when the models use their artificial neurons to analyze new data based on what they’ve learned. It’s a crucial step, but man, it’s a power-hungry one. We gotta find a balance. We need more sustainable solutions without sacrificing quality and speed.
Now, if you’re not familiar with the whole AI thing, let me break it down for you. There are two stages. First, there’s training the model. This is where it learns and categorizes information. For example, an e-commerce business might train the model on images of its products and customer habits. Then comes the second stage, inference. This is when the model uses what it’s learned to understand new data. So, that e-commerce site can categorize products, personalize recommendations, and all that jazz.
But here’s the kicker. Inference is expensive. It’s attached to the cost of doing business, while training is usually separate. You gotta invest in specialized hardware to make it work. And that shit ain’t cheap. It’s a big ol’ drain on your resources. Only the big players with deep pockets can afford to go all in on this stuff, leaving others out in the cold.
But wait, there’s more. This AI stuff is also bad for the environment. It’s a power-hungry beast that spews out carbon emissions like there’s no tomorrow. Seriously, the carbon footprint of a single AI query can be four to five times higher than a search engine query. That’s not good, my friends. We’re talking about a serious carbon problem here.
Now, I won’t bore you with all the technical details, but I’ll just say this. We need to find better ways to handle the processing power and energy consumption of AI. Right now, CPUs are struggling to keep up with the demand. We need system-wide solutions that can support all that processing power without breaking the bank or destroying the planet. It’s gonna be a whole new way of computing, from chips to systems.
So, what’s the solution? Well, there are a few ideas floating around. We could use renewable energy to power AI. We could time our processes to take advantage of renewable energy availability. We could even come up with AI-driven energy management systems for data centers. But the real game-changer will be in the hardware. We need a hardware platform that can handle all this processing without draining our resources. That’ll democratize AI and make it accessible to smaller companies.
So, there you have it. AI is a powerful tool, but it’s got its problems. We need to find sustainable solutions and rethink our hardware game. It’s the only way we can keep pushing forward without destroying ourselves in the process.
