Machine Learning and the Future of Artificial Intelligence

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Machine Learning and the Future of Artificial Intelligence

Modern digital computers are merely super-fast calculators executing sequential instructions based on principles laid out 60+ years ago by geniuses like John von Neumann and Alan Turing.

These machines can appear somewhat “intelligent” because they calculate so fast and operate within a narrowly prescribed application domain.

But in reality, they merely respond as they’ve been instructed. They’ve transformed our lives because they are exceptionally productive at doing precisely those things at which the human brain is least effective.

In the earliest days of computing, so-called “analog computers” played major roles in scientific and military computing. However, digital computing soon won out, due in large part to the phenomenal success of integrated circuits and the fact that they are perfectly suited for so many tasks.

Yet, it remains largely beyond the capabilities of digital computers to use new information to improve their problem-solving capabilities or perform other higher-intelligence tasks.

Consequently, researchers have recently begun to explore the possibility that analog computers, based on so-called “neural networks,” will be able to mimic certain abilities of the human brain that current digital computers are unable to exhibit.

Why? Because they recognize the human brain, with its own incredibly complex biological neural network, possesses such “adaptive capabilities.”

Consider the facts. We humans learn, for example, that an object in the foreground that creates a large retinal image in the eye can actually be smaller than an object in the distance that projects a smaller image on the retina.

It’s also easy for a human to recognize a tiger running behind some bushes in a nature video even when very few visual clues are provided; for the best digital image processors, this is a daunting task. Similarly, humans have the capability to distinguish between male and female faces, a task that proves very difficult for artificial vision systems.

That’s because our minds, unlike digital computers, are also able to perform data mining very efficiently. This enables us to pick out relevant data from an image and filter out the irrelevant. Again, no classical algorithm can perform this complicated task.

In a related manner, our brains can work in different modes of operation, automatically switching between them. We can, for example, focus on crossing a busy street while ignoring the birds singing — even though our ears detect the sound wave — and while also ignoring the advertising message on a passing bus — even though the image appears on our retinas...

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