The Mavericks Who Brought AI to Google, Facebook, and the World
July 7, 1958. Men huddle around a massive refrigerator-sized computer deep within the offices of the United States Weather Bureau in Washington, DC. They watch intently as Frank Rosenblatt, a young professor from Cornell University, shows the computer a series of cards.
Each card has a black square printed on one side. The machine is supposed to identify which have the mark on their left side and which have it on the right. At first, it can’t tell the difference. But as Rosenblatt continues the flash cards, accuracy improves. After 50 tries, it identifies the cards’ orientation nearly perfectly.
Rosenblatt calls the machine the Perceptron. While these days it seems rudimentary, it’s actually an early precursor to what we now call artificial intelligence, or AI. Though, at the time, it was dismissed as a novelty.
The key message here is: Early research into artificial intelligence was met with skepticism.
Today, we recognize Rosenblatt’s Perceptron, and its successor, the Mark I, as very early versions of a neural network. Neural networks, computers that use a process sometimes called machine learning, underlie much of what we currently call artificial intelligence. At the most basic level, they work by analyzing massive amounts of data and searching for patterns. As a network identifies more patterns, it refines its analytical algorithms to produce ever more accurate information.
Back in 1960, this process was slow and involved a lot of trial and error. To train the Mark I, scientists fed the computer pieces of paper with a letter, such as an A, B, or C, printed on each. Using a series of calculations, the computer would guess which letter it saw. Then a human would mark the guess as correct or incorrect. The Mark I would then update its calculations so it could guess more accurately the next time.
Scientists like Rosenblatt compared this process to those of the human brain, arguing that each calculation was like a neuron. By connecting many calculations that update and adapt over time, a computer could learn as humans do. Rosenblatt called this connectionism. Yet there were detractors, like MIT computer scientist Marvin Minsky. In a 1969 book, Minsky criticized the concept of connectionism. He argued that machine learning could never scale up to solve more complex problems.
Minsky’s book proved very influential. Throughout the 1970s and early 80s, interest in researching neural networks declined. During this “AI winter” few institutions funded neural network research and progress on machine learning stalled. But it didn’t stop completely. A few scientists continued toying with connectionism, as we’ll see in the next blink.
Genius Makers (2021) tells the story of the current race to develop artificial intelligence. This expansive report covers the sprawling history of AI, from its early development to today’s current controversies.
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Blink 3 von 12 - Eine kurze Geschichte der Menschheit
von Yuval Noah Harari
