We live in the greatest time in human history. Only 200 years ago, for most Europeans, life was a struggle rather than a pleasure. Without antibiotics and hospitals, every infection was fatal. There was only a small elite of citizens who lived in the cities in relative prosperity. Freedom of opinion, human and civil rights were far away. Voting rights and decision-making were reserved for a class consisting of nobility, clergy, the military and rich citizens. The interests of the general population were virtually ignored.
"When you are born, you know nothing." This is the kind of statement you expect to hear from a philosophy professor, not a Silicon Valley executive with a new company to pitch and money to make. A tall, rangy man who is almost implausibly cheerful, Hawkins created the Palm and Treo handhelds and cofounded Palm Computing and Handspring. His is the consummate high tech success story, the brilliant, driven engineer who beat the critics to make it big. Now he's about to unveil his entrepreneurial third act: a company called Numenta. But what Hawkins, 49, really wants to talk about -- in fact, what he has really wanted to talk about for the past 30 years -- isn't gadgets or source codes or market niches.
A survey of 50 Nobel laureates asked about the greatest threats to mankind has revealed that environmental issues such as over-population and climate change are the biggest threat. Meanwhile, the threat of nuclear warfare and infectious diseases and drug resistance follows in second and third place. Distortion or the truth and ignorant political leaders also ranked highly, with President Donald Trump called out by name in this category. The survey drew responses from almost a quarter of the living Nobel prize winners for chemistry, physics, physiology, medicine and economics. A survey of 50 Nobel laureates posed the question: 'What is the biggest threat to humankind, in your view?
One of the biggest sources of anxiety about AI is not that it will turn against us, but that we simply cannot understand how it works. The solution to rogue systems that discriminate against women in credit applications or that make racist recommendations in criminal sentencing, or that reduce the number of black patients identified as needing extra medical care, might seem to be "explainable AI." But sometimes, what's just as important as knowing "why" an algorithm made a decision, is being able to ask "what" it was being optimized for in the first place? Machine-learning algorithms are often called a black box because they resemble a closed system that takes an input and produces an output, without any explanation as to why. Knowing "why" is important for many industries, particularly those with fiduciary obligations like consumer finance, or in healthcare and education, where vulnerable lives are involved, or in military or government applications, where you need to be able to justify your decisions to the electorate.
In his 1990 book The Age of Intelligent Machines, the American computer scientist and futurist Ray Kurzweil made an astonishing prediction. Working at the Massachusetts Institute of Technology (MIT) throughout the 1970s and 1980s and having seen firsthand the remarkable advances in artificial intelligence pioneered there by Marvin Minsky and others, he forecast that a computer would pass the Turing test – the test of a machine's ability to match or be indistinguishable from human intelligence – between 2020 and 2050.