In 1966, some Massachusetts Institute of Technology researchers reckoned that they could develop computer vision as a summer project, perhaps even get a few smart undergrads to complete the task.
The world has been working on the problem ever since.
Computer vision is where computers recognize objects like people do. That's a tree. He's Carlos. And so on. It's one of a number of tasks we consider essential for generalized artificial intelligence, in which machines can act and reason as humans do.
While we've been making some considerable headway in computer vision, especially in recent years, that it has taken 50 years longer than expected shows why AI (artificial intelligence) is such as difficult and elusive goal.
"How much progress is being made? It's really hard to get a handle on that," said Beau Cronin, a Salesforce.com product manager currently working on some AI-influenced technologies for the company. Cronin spoke Friday at the O'Reilly Strata + Hadoop World conference, in New York.
The main theme of the conference was big data. The need for big data analytics has given AI research a shot in the arm. Today the titans of the Internet industry -- Apple, Google, Facebook, Microsoft, IBM -- are putting AI research into the driver's seat, pushing forward the state of the art for seemingly routine tasks such as ad targeting and personalized assistance.
But in many ways, we are no closer to achieving an overall general artificial intelligence, in the sense that a computer can behave like a human, Cronin observed. Systems that use AI technologies, such as machine learning, are defined to execute very narrowly defined tasks.
The state of AI has always been hard to assess, Cronin said. AI systems are hard to evaluate: They may excel in one area but fall short in another, similar task. Many projects, even sometimes very well-funded ones, go nowhere.
Even basic definitions of AI are still not locked down. When two people talk about AI, one may be referring to a specific machine learning algorithm while the other may be talking about autonomous robots. AI still attracts oddballs, lone wolves working in their basements 10 hours a week hoping to solve the AI problem once and for all.
Moravec's Paradox asserts basically that things that are easy for people to do -- object recognition and perception -- are extremely difficult for computers to do, while simple tasks for computers -- proving complex theorems -- are extremely difficult if not impossible for people to do (some present readers excluded, no doubt).
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