{"id":12825,"date":"2022-05-06T19:00:09","date_gmt":"2022-05-06T17:00:09","guid":{"rendered":"https:\/\/www.sciencefocus.com\/?p=118979"},"modified":"2022-05-06T19:17:37","modified_gmt":"2022-05-06T17:17:37","slug":"its-time-to-accept-ai-will-never-think-like-a-human-and-thats-okay","status":"publish","type":"rss_feed","link":"https:\/\/c01.purpledshub.com\/bbcsciencefocus\/rss_feed\/its-time-to-accept-ai-will-never-think-like-a-human-and-thats-okay\/","title":{"rendered":"It\u2019s time to accept AI will never think like a human \u2013 and that\u2019s okay"},"content":{"rendered":"

<\/p>

By Dr Kate Darling\n \t\t<\/p>

Published: Friday, 06 May 2022 at 12:00 am<\/p>

\n\n

Since the start of the pandemic, artificial intelligence<\/a> (AI) developers have deployed hundreds of machine learning tools to help diagnose COVID-19. The promise: to find patterns in the medical data like an algorithmic version of the television character Dr House.<\/p>\n

Recently, researchers have discovered that these AI tools were overhyped. Instead of discovering relevant connections between cases, the algorithms were making a litany of false assumptions, including predicting COVID cases based on the text font that hospitals happened to use in their documents.<\/p>\n

This does not mean that machine learning is useless. It means that we need to better understand the strengths and limitations of AI, and, like we\u2019ve done with animals, embrace that people think differently.<\/p>\n

To a human, it\u2019s obvious that a text font is not a good predictor of infectious diseases. But to a machine, that\u2019s not obvious at all. AI may be able to use informational input to make predictions, but it\u2019s not aware of what it\u2019s doing. It doesn\u2019t understand concepts or context and is easily thrown off by biased or mislabelled data that wouldn\u2019t fool a four-year-old.<\/p>\n

As machine learning expert Janelle Shane explains in her AI weirdness book You Look Like A Thing And I Love You<\/em>, the mistakes machines make feel absurd to us because they don\u2019t perceive the world like we do.<\/p>\n

Unlike AI, human intelligence is extremely generalisable and adaptive. We\u2019re flexible thinkers, understand broad concepts, and we can contextualise unexpected results or situations. And yet, a Google image search for \u2018artificial intelligence\u2019 in 2022 returns mostly pictures of human brains.<\/p>\n

It\u2019s not just our stock photo images: we use our own intelligence as a model when talking about AI, whether in casual conversation, science fiction thrillers, or in our news headlines. In part, this is because the AI pioneers originally did set out to understand and recreate human intelligence. So far, they haven\u2019t succeeded.<\/p>\n

It\u2019s not that technology isn\u2019t smart or getting smarter. Given the right data, training and circumstances, machines are great at computation, predictions and recognising patterns. My phone can do calculus and parse voice commands (at least most of the time).<\/p>\n

Newer deep-learning methods can leave human ability in the dust. In 2016, when an AI system named AlphaGo beat the best Go player in the world, it made a move that astonished the experts: a move no human player would ever have thought to try. So rather than viewing AI as a less-developed version of ourselves, maybe it\u2019s time to embrace our differences.<\/p>\n

Roboticist Rodney Brooks<\/a> once wrote, \u201cIt is unfair to claim that an elephant has no intelligence worth studying just because it does not play chess.\u201d Animals are a more useful comparison to AI, because they, too, perceive and engage with the world differently from humans. They sense things we can\u2019t, and are totally oblivious to things that are obvious to us.<\/p>\n

That\u2019s why, throughout history, we\u2019ve relied on animals to help us do things we couldn\u2019t do alone. We domesticated beasts of burden to help plough our fields, and carry people and economic goods to new places. We\u2019ve used canaries in coal mines, created pigeon postal services, trained ferrets to run electrical wire through pipes, and taught dolphins to recover lost underwater equipment.<\/p>\n

You wouldn\u2019t trust a dog to give you a medical diagnosis or relationship advice, but you might trust it to sniff out explosives, assist the blind, or provide therapeutic comfort. Similarly, AI may be lousy at appreciating your jokes or responding in an unexpected situation, but it can navigate traffic, detect safety hazards in nuclear plants, and collect data on Mars.<\/p>\n

Robots like the PARO<\/a>, a snuggly medical device that looks and moves like a baby harp seal, are even surprisingly effective in therapy when using real dogs isn\u2019t feasible. The point isn\u2019t that AI should replace dogs. The point is that the animal thought exercise lets us set aside the human comparison and imagine what AI can help us with that we can\u2019t do alone.<\/p>\n

Understanding the strengths and limitations of AI is key to avoiding the types of harmful mistakes we\u2019re seeing today. The idea that we\u2019re dealing with a different kind of intelligence inspires us to leverage this technology to support people \u2013 rather than replacing them. It encourages us to invent new practices and find new solutions \u2013 rather than recreating what we already have. And it prompts us to think more creatively and inclusively about how to situate AI in our infrastructure, workplaces and personal lives.<\/p>\n

The best possible future isn\u2019t one in which our technology thinks or acts like a human. It\u2019s one in which we\u2019ve envisioned a better world, and partnered with technology to create it.<\/p>\n