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The UK newspapers today contain some cooling fronts, with the leading warning from AI Guruk that smart machines can all kill. I blame myself – in 2014 I started the trend for scientists who predicted the end of humanity when I interviewed Professor Stephen Hawking.
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“When people develop artificial intelligence, it starts on their own and plans to plan it at increasing speed,” said the big man. “People limited by slow biological evolution could not compete and replace it.”
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My report on the night’s ten -hour news showed a more balanced view of a researcher who, unlike astrophenically Stephen Hawking, actually specializes in AI. At the same time, he also used a large cliché of dystopian TV news, which is the Terminator clip.
After nine years, the AI researchers sound after the alarm, and Arnie Schwarzenegger returned to TV news letters and dries humanity. But before we give up obsolescence, I have two questions.
First of all, how ??? No one has explained exactly how AI kills us after we decided to be unnecessary for the requirements. Of course, I think Chatgpt 9.0 or other major language models transform Booker -winning novels, code software without a single mistake, or even a legal system by making much wiser decisions than a human judge who stays awake after lunch. But how do you decide to free the murderous robots, and you all hunting or releasing a deadly virus? And when we are all dead, what do you do?
My second and urgent question is that AI finally destroys us, please hurry and see you first some promised benefits in health care? After all, it has been more than a decade since we have begun stories of robotic surgeons, algorithms, which can detect the difference between malignant and benign scanning tumors, such as any radiologist and AI techniques that can reduce the time and cost of developing new medicines.
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All kinds of innovations are created from technological communities. Microsoft Cambridge Lab worked with the brain tumor, IBM Watson Health Division Crunch data to help Cancer and Parkinson’s, while Britain’s Babylon in 2018 showed a chatbot claiming better than the diagnosis than the average general doctor.
And the excitement of AI was not limited to the private sector -indigenous doctors such as Dr. Pearse Keane worked with algorithms in Moorfields Eye Hospital that can help triage scanning of high street opticians and use the NHS AI Skunkworks as practical projects to predict the Traffic Department.
However, all investments and hype around AI are very difficult to label the significant impact on patient care or NHS productivity. When I do an MRI examination, it is still weeks to listen to the results that a person is not an algorithm (which I am very grateful anyway). IBM has disposed of most of Watson Health because it obviously decided not to have a large treasurer. When it comes to Babylon, famous by the then health secretary Matt Hancock as a HealthTech suction star, it largely gave up the UK, and in the United States, following a list of catastrophic shares, investors cleaned almost the total value of their share.
Part of the problem is summarized by Pearse Keane, who explained me that it can be done relatively quickly from the AI idea of the functional algorithm is created, but it takes a long time from the “code from the clinic”. With the Silicon Valley Tech founder, the “failure” loan does not work when it comes to health care, but some AI athletes appear to be very slow.
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When the calls for AI regulation are higher – although no one seems to be clear what the rules are – the prospects to quickly accept health care seem even less rosary. They tell us that the generative AI is moving so fast that even researchers on the front line cannot arrange exactly where they go. But maybe it’s time not to worry about long-term worries when Keynes reminds us all are dead and we continue to take advantage of the AI benefits.
