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Other practical application

Spotting Newly formed craters on Mars
To identify newly formed craters on Mars, scientists will spend about 40 minutes analyzing a single photo of the Martian surface taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO), looking for a dark patch that wasn't in earlier photos of the same location.

If a scientist spots the signs of a crater in one of those images, it then has to be confirmed using a higher-resolution photograph taken by another MRO instrument: the High-Resolution Imaging Science Experiment (HiRISE).

This method of spotting new craters on Mars makes it easy to determine an approximate date for when each formed — if a crater wasn't in a photo from April 2016 but is in one from June 2018, for example, the scientists know it must have formed sometime between those two dates. By studying the characteristics of the craters whose ages they do know, the scientists can then estimate the ages of older ones. This information can improve their understanding of Mars' history and help with the planning of new missions to the Red Planet.

The problem: this is incredibly time-consuming
The MRO has been taking photos of the Red Planet's surface for 15 years now, and in that time, it has snapped 112,000 lower-resolution images, with each covering hundreds of miles of the Martian surface.

To free scientists from the burden of manually analyzing all those photos, researchers trained an algorithm to scan the same images for signs of new craters on Mars — and it only needs about five seconds per picture. To speed it up, the researchers ran the AI on a supercomputer cluster at NASA's Jet Propulsion Laboratory (JPL).

With the power of all those computers combined, the AI could scan an image in an average of just five seconds. If it flagged something that looked like a fresh crater, NASA scientists could then check it out themselves using HiRISE. Now, no one can disparage the advantages AI plays in this scenario.

All Pervasive

The prospect of pervasive new base-layer intelligence like the ones mentioned earlier raises some obvious questions. Who should build and control them? And, given that they might become common building blocks for many of the world’s more specialized AI systems, what happens when something goes wrong?

The second interesting feature of exceptionally large AI models, meanwhile, has been their apparent ability to spontaneously learn new tricks. This is where things get weird. GPT-3 produced a technique known as in-context learning to master unfamiliar problems — even though its developers didn’t teach it to do this and a smaller predecessor, built to the same design, didn’t think of the idea.

So-called emergent capabilities such as this are the big unknown of large AI models. What happens when the people who build a system can no longer anticipate the full powers it will develop? The results might be hugely beneficial if the computers come up with new ways to solve intractable problems that their makers haven’t thought of. But there is also an obvious downside to machines working things out for themselves.

For now, exactly where this may lead is still confined to the pages of science fiction. But with the race to build ever-larger AI models gathering pace, we may not have to wait long to find out.

The Corollary

Companies of all sorts use machine learning to analyze all sorts of information - people's desires, dislikes or faces. Some researchers are now asking a different question - How can we make colossal computer network machines forget?

A nascent area of computer science dubbed machine unlearning seeks ways to induce selective amnesia in artificial intelligence software. The goal is to remove all traces of a particular person or data point from a machine-learning system, without affecting its performance.

If this comes to fruition, the concept could give people more control over their data and the value derived from it. Although users can in some situations ask companies to delete personal data, they are always in the dark about what algorithms their information helped tune or train. Machine unlearning could make it possible for a person to withdraw both their data and a company's ability to profit from it.

Although intuitive to anyone who has rued what they shared online, that notion of artificial amnesia requires some new ideas in computer science. Companies spend millions of dollars training machine-learning algorithms to recognize faces or rank social posts because the algorithms often solve a problem more quickly than human coders alone. And here is the kicker - once trained, a machine-learning system is not easily altered or even understood. A more conventional way to remove the influence of a particular data point is to rebuild the system from the ground up, a potentially costly exercise. The conundrum thickens are you still with us?

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