Artificial intelligence does not exist

Artificial intelligence does not exist

Artificial intelligence does not exist

No one sells the future more successfully than the tech industry. According to its proponents, we will all live in the “metaverse”, build our financial infrastructure on “web3” and power our lives with “artificial intelligence”. These three terms are mirages that have fetched billions of dollars, despite the bite of reality. Artificial intelligence evokes in particular the notion of thinking machines. But no machine can think, and no software is truly intelligent. The phrase alone may be one of the most successful marketing terms of all time.

Last week, OpenAI announced GPT-4, a major upgrade to the technology that underpins ChatGPT. The system feels even more human than its predecessor, naturally reinforcing notions of its intelligence. But GPT-4 and other large language models like it only mirror databases of text – nearly a trillion words for the previous model – whose scale is hard to fathom. Aided by an army of humans reprogramming it with corrections, the models gloss words together based on probability. It’s not intelligence.

These systems are trained to generate text that looks plausible, but they are marketed as new oracles of knowledge that can be hooked up to search engines. It’s reckless as GPT-4 continues to make mistakes, and only a few weeks ago Microsoft and Alphabet’s Google both suffered embarrassing demos in which their new search engines failed on facts.

Not helping matters: Terms like “neural networks” and “deep learning” only reinforce the idea that these programs look like humans. Neural networks are in no way copies of the human brain; they are only vaguely inspired by its operation. Long-running efforts to try to replicate the human brain with its roughly 85 billion neurons have all failed. The closest scientists are to mimicking a worm’s brain, with 302 neurons.

We need a different lexicon that doesn’t spread wishful thinking about computer systems and absolve the people who design those systems of their responsibilities. What is the best alternative? Reasonable technologists have tried for years to replace ‘AI’ with ‘machine learning systems’, but it doesn’t come out of the language the same way.

Stefano Quintarelli, a former politician and technologist from Italy, offered another alternative, “Systems Approaches to Learning Algorithms and Automatic Inferences” or SALAMI, to highlight the ridiculousness of the questions people have about AI: Is SALAMI sensitive? Will SALAMI one day have supremacy over humans?

The most desperate attempt at a semantic alternative is probably the most precise: “software”.

“But,” I hear you ask, “what’s wrong with using a little metaphorical shorthand to describe technology that seems so magical?”

The answer is that ascribing intelligence to machines gives them undeserved independence from humans, and it abdicates their creators of responsibility for their impact. If we view ChatGPT as “smart,” we’re less likely to try to hold San Francisco startup OpenAI, its creator, responsible for its inaccuracies and biases. It also creates a fatalistic complacency in humans who experience the adverse effects of technology; although “the AI” won’t take your work or plagiarize your artistic creations – other humans will.

The problem is increasingly pressing now that companies from Meta Platforms to Snap to Morgan Stanley are racing to plug chatbots and text and image generators into their systems. Spurred by its new arms race with Google, Microsoft is integrating OpenAI’s still largely untested language model technology into its most popular business applications, including Word, Outlook and Excel. “Copilot will fundamentally change how people work with AI and how AI works with people,” Microsoft said of its new feature.

But for customers, the promise of working with smart machines is almost misleading. “(AI is) one of those labels that expresses a sort of utopian hope rather than a present reality, much like the rise of the phrase ‘smart weapons’ during the first Gulf War implied a vision bloodless from totally precise targeting that is still not possible,” says Steven Poole, author of the book Unspeak, of the dangerous power of words and labels.

Margaret Mitchell, a computer scientist fired by Google after she published an article criticizing the biases of large language models, reluctantly described her work as “AI”-based in recent years. “Before…people like me would say we were working on ‘machine learning.’ It’s a great way to get people’s attention,” she admitted during a conference panel on Friday.

Her former Google colleague and founder of the Distributed Artificial Intelligence Research Institute, Timnit Gebru, said she also only started saying ‘AI’ in 2013: “It’s become the thing to say”.

“It’s terrible but I do that too,” Mitchell added. “I call everything I touch ‘AI’ because then people will listen to what I say.”

Unfortunately, “AI” is so ingrained in our vocabulary that it will be almost impossible to get rid of it, the obligatory air quotes difficult to remember. At the very least, we should remember how dependent these systems are on human handlers who should be held accountable for their side effects.

Author Poole says he prefers to call chatbots like ChatGPT and image generators like Midjourney “giant plagiarism machines” because they mostly recombine prose and images that were originally created by humans. “I’m not convinced it will catch on,” he said.

In more ways than one, we’re really stuck with “AI”.

© 2023 Bloomberg LP

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