In tech, everything is labeled “AI” now

Tech companies are slapping an “artificial intelligence” label on anything they can, Axios managing editor Scott Rosenberg writes.

• Why it matters:The more our understanding of a new technology is distorted by hype, the less thoughtfully we can apply it — and the more likely it is we will cause harm with it.

What’s happening: Real advances in machine-learning-based pattern recognition have sparked a new bubble in tech-industry investment, encouraging companies to apply the “AI” label wildly.

• The catalyst for the current craze was the introduction of ChatGPTlate last year, which showcased the impressive conversational abilities of today’s large language models.

 Between the lines: Today’s AI promoters are trying to have it both ways. They insist that AI is crossing a profound boundary into untrodden territory with unfathomable risks. But they also define AI so broadly as to include almost any large-scale, statistically-driven computer program.

• Under this definition,everything from the Google search engine to the iPhone’s face-recognition unlocking tool to the Facebook newsfeed algorithm is already “AI-driven” — and has been for years.

Backstory: The term “artificial intelligence” emerged in the 1950s to name the goal of duplicating human capabilities of reasoning in code and circuitry, which experts at the time predicted might take 15 or 20 years to achieve.

• For decades, scientists sought to do so by painstakingly modeling the real world in data so that computers could understand it.
• When that routeproved slow and unrewarding, AI experienced a cycle of “winters” when funding dried up and progress dwindled.

 How it works: A different and long-neglected road involving the creation of neural networks emerged as a promising alternative, beginning to take form in the ’90s and accelerating in the aughts.

• Instead of painstakingly organizing the world’s information for the computer to ingest, this approach had the machine consume vast quantities of disorganized data to identify patterns.

Exponential growth in processing power and storage capacity turned this machine learning technique into an increasingly effective student — and the internet offered a plunderable trove of digital-ready course material.

• AI effectivelybecame synonymous with “efficient pattern-matching on a large scale.” Under this definition, almost any kind of automation or probability-based system qualifies as “artificially intelligent.”

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