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19 hours agoThe reason why you hear this so often is because academia is designed to teach students based on a logical, reasonable curriculum. The curriculum will be mostly well-thought-out and cover all the important topics.
Then you take someone who followed this perfectly reasonable path and you place them in front of the total shitshow that is most businesses. Everything they think they know won’t be applicable because most of the time, logic and reason were not what drove adoption of any given tool or practice.
Oh my. This is a huge can of worms—especially on Lemmy. There’s a lot of anti-AI hate on this platform. Almost to the point of it being a religion.
For reference, when people say, “AI” they’re usually talking about Large Language Models (LLMs) and other forms of generative AI (e.g. diffusion models that make images). Having said that, “AI” is an enormous topic of which LLMs are a small, but increasingly popular part.
Furthermore, when people here on Lemmy say, “AI” they’re normally talking about “Big AI” which consists of:
Is AI inherently bad or evil? No. It’s just the latest way of giving instructions to a computer. Considering that all computer programs are literally just instructions, an AI model is just a really fancy and often expensive way of performing the same function. Albeit with a lot more breadth and flexibility. Note that I didn’t say “depth”, haha.
The “bad” or “evil” part of AI is mostly due to the large players (aka “Big AI”) spending literally over $1 trillion so far on data centers and hardware. There’s so much demand for their services that they’re having to build their own—often dirty, fossil fuel—power plants just to power it all.
A lot of the talk around data centers is based on myths. For example, generating an image with AI doesn’t use a liter of water. A study came out that no one actually read (beyond the summary) that stated that a really long conversation with an LLM could in theory use up half a liter of water, assuming the data center was powered by a fossil fuel power plant that was using water for cooling (as in, the heat dissipation required 0.5 liters of water from the cooling pond next to the power plant, not potable/drinking water).
LLMs do use up a lot of power though! People often assume this is from training the AIs (which I’ll get to in a moment) because everyone “knows” it’s a long, involved process that can take months (even with a $50 billion data center specifically made for AI). However, it’s actually all the people and businesses using AI that uses up all that energy. The biggest, most power-hungry step is “inference” which is the point where the LLM tries to figure out what you just asked of it.
The important point here is that AI is actually being used.* There’s real demand for it! It’s not just fools asking ChatGPT for strange pizza recipes. It’s mostly businesses using it for things like writing and checking code or investigating server logs for malicious activity or any number of very businessy IT things.
The demand for AI services is so great that they can’t build data centers fast enough. Big AI, specifically is having trouble keeping responses within satisfactory time windows. The business models are still developing but they’re actually not charging enough to make up for their spending in a lot of cases. Specifically, OpenAI and Microsoft are losing money like crazy, trying to compete.
I ran out of time… I’ll reply again about the copyright situation, training costs, and open weight (aka open source) models in a bit…