Math Myth Conjecture: If one restricts one’s attention to the hardest cases, namely, graduates of top engineering schools such as MIT, RPI, Cal. Tech., Georgia Tech., etc., then the percent of such individuals holding engineering as opposed to management, financial or other positions, and using more than Excel and eighth grade level mathematics (arithmetic, a little bit of algebra, a little bit of statistics, and a little bit of programming) is less than 25% and possibly less than 10%.
One cybersecurity firm estimates that extortive attacks now cost small and medium companies at least $75 billion in expenses and lost productivity each year.
banks have started to keep tens of thousands of dollars in Bitcoin ready in case of an attack. “Buying bitcoin on any one of the U.S. exchanges is a three-to-five day wait time, so we’ve been forced into the position of having to stock bitcoin as if it were computer equipment and have it ready for our use,”
It’s not clear the organization can effectively confront—or even survive—today’s challenges.
“national political leaders are no longer, in substance, capable of delivering self-contained, national solutions to the problems faced by their people, as the policy levers available increasingly slip beyond their grasp”
— Kevin Rudd, 26th Prime Minister of Australia
Despite new biology-like tools, some insist interpretation is impossible.
Even if it were possible to impose this kind of interpretability, it may not always be desirable. The requirement for interpretability can be seen as another set of constraints, preventing a model from a “pure” solution that pays attention only to the input and output data it is given, and potentially reducing accuracy.
“What machines are picking up on are not facts about the world,” Batra says. “They’re facts about the dataset.” That the machines are so tightly tuned to the data they are fed makes it difficult to extract general rules about how they work. More importantly, he cautions, if you don’t know how it works, you don’t know how it will fail. And when they do they fail, in Batra’s experience, “they fail spectacularly disgracefully.”
They pick up on patterns invisible to their engineers; but can’t know which of those patterns exist nowhere else. Machine learning researchers go to great lengths to avoid this phenomenon, called “overfitting,” but as these algorithms are used in more and more dynamic situations, their brittleness will inevitably be exposed.
The One Hundred Year Study on Artificial Intelligence, launched in the fall of 2014, is a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society.
Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to humankind. No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future. Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers. At the same time, many of these developments will spur disruptions in how human labor is augmented or replaced by AI, creating new challenges for the economy and society more broadly.
Innovations relying on computer-based vision, speech recognition, and Natural Language Processing have driven these changes, as have concurrent scientific and technological advances in related fields.
In each domain, even as AI continues to deliver important benefits, it also raises important ethical and social issues, including privacy concerns. Robots and other AI technologies have already begun to displace jobs in some sectors. As a society, we are now at a crucial juncture in determining how to deploy AI-based technologies in ways that promote, not hinder, democratic values such as freedom, equality, and transparency. For individuals, the quality of the lives we lead and how our contributions are valued are likely to shift gradually, but markedly.
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