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An interesting discussion of economic issues for AI in applications, ranging from customer service bots to finance.
Customer service bots: “I can tell you from personal experience that CVS Healthcare’s is miserable.” And expensive: “The thing is, both training the model and the specialized people who work with it are ongoing costs. A customer service bot, for example, may need to be fine-tuned every week or couple of weeks. What’s expensive is that you have to keep doing it, and you have to keep testing the model, and you have to make sure it’s doing what you expect it to do,” one practitioner says.
Finance is another widely discussed application. The optimist says: “It’s all about pattern recognition,” and pattern recognition is something AI is notoriously good at. So financial institutions would need fewer databases and data junkies to monitor trends — the AI could do all that, with a few higher-level professionals managing the #AI. “Probably lower-level quants would be more vulnerable.” Finance is also high-end, so humans would probably stick around to present to clients.
The pessimist refers to edge cases that are hard to solve: “That’s part of the reason we aren’t seeing whole-scale replacement and why we might not see it at all, says a machine learning practitioner. “If you have an automatic trading system, it’s going to make trades for you,” she says. “Like, there’s a lot of money on the line.”
For applications in general: “The way people think about AI now is to walk through a company’s workflow, identify tasks that a machine can do, and automate them. “But ultimately the upside is pretty limited because the best you can do is what you’re already doing but a little bit better,” a professor says. “Those typically don’t justify the huge expense — tens of millions, hundreds of millions, billions of dollars to build these kinds of things.” He continues: The real money, then, is in totally blowing up the workflow and replacing it with AI. “It’s riskier because once you talk about messing with the workflow, there’s lots of failures that can happen,” he says. “But that’s where the upside is in the billions, or the tens of billions, or more.”
Others talk about the general economics of #AI: “If you’re trying to create a startup that’s gonna build these large language models and do the compute yourself, that’s gonna cost a fortune,” says a professor. “So OpenAI is very expensive, billions and billions of dollars.” Even if the dataset is free, there are costs associated with cleaning and processing the data, and costs can range from the hundreds of thousands to millions of dollars, he says.
Some mention high labor costs: “People in #machinelearning are so highly paid because you’re competing with Google or other big tech companies, and literally, it’s like sometimes millions of dollars for researchers.”
#technology #innovation #startups #hype #ethics #tech #artificialintelligence
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