**Much of the recent progress in AI research has been courtesy of an approach known as deep learning.**
**Credit: PHOTOCREO Michal Bednarek | Shutterstock.com**

When most people think of artificial intelligence (AI) they think of HAL 9000 from "2001: A Space Odyssey," Data from "Star Trek," or more recently, the android Ava from "Ex Machina." But to a computerscientist that isn't what AI necessarily is, and the question "what is AI?" can be a complicated one.

One of the standard textbooks in the field, by University of California computer scientists Stuart Russell and Google's director of research, Peter Norvig, puts artificial intelligence in to four broad categories:

The differences between them can be subtle, notes Ernest Davis, a professor of computer science at New York University. AlphaGo, the computer program that beat a world champion at Go, acts rationally when it plays the game (it plays to win). But it doesn't necessarily think the way a human being does, though it engages in some of the same pattern-recognition tasks. Similarly, a machine that acts like a human doesn't necessarily bear much resemblance to people in the way it processes information.

machines that think like humans,
machines that act like humans,
machines that think rationally,
machines that act rationally.

**Decades of research and speculative fiction have led to today's computerized assistants such as Apple's Siri.**
**Credit: by Karl Tate, Infographics Artist**

Even IBM's Watson, which acted somewhat like a human when playing Jeopardy, wasn't using anything like the rational processes humans use.

Tough tasks

Davis says he uses another definition, centered on what one wants a computer to do. "There are a number of cognitive tasks that people do easily — often, indeed, with no conscious thought at all — but that are extremely hard to program oncomputers. Archetypal examples are vision and natural language understanding. Artificial intelligence, as I define it, is the study of getting computers to carry out these tasks," he said.

Computer vision has made a lot of strides in the past decade — cameras can now recognize faces in the frame and tell the user where they are. However, computers are still not that good at actually recognizing faces, and the way they do it is different from the way people do. A Google image search, for instance, just looks for images in which the pattern of pixels matches the reference image. More sophisticated face recognition systems look at the dimensions of the face to match them with images that might not be simple face-on photos. Humans process the information rather differently, and exactly how that process works is still something of an open question for neuroscientists and cognitive scientists.

Other tasks, though, are proving tougher. For example, Davis and NYU psychology professor Gary Marcus wrote in the Communications of the Association for ComputingMachinery of "common sense" tasks that computers find very difficult. A robot serving drinks, for example, can be programmed to recognize a request for one, and even to manipulate a glass and pour one. But if a fly lands in the glass the computer still has a tough time deciding whether to pour the drink in and serve it (or not).

Artificial Intelligence

https://youtu.be/9TRv0cXUVQw

Common sense

The issue is that much of "common sense" is very hard to model. Computer scientists have taken several approaches to get around that problem. IBM's Watson, for instance, was able to do so well on Jeopardy! because it had a huge database of knowledge to work with and a few rules to string words together to make questions and answers. Watson, though, would have a difficult time with a simple open-ended conversation.

Beyond tasks, though, is the issue of learning. Machines can learn, said Kathleen McKeown, a professor of computer science at Columbia University. "Machine learning is a kind of AI," she said.

Some machine learning works in a way similar to the way people do it, she noted. Google Translate, for example, uses a large corpus of text in a given language to translate to another language, a statistical process that doesn't involve looking for the "meaning" of words. Humans, she said, do something similar, in that we learn languages by seeing lots of examples.

That said, Google Translate doesn't always get it right, precisely because it doesn't seek meaning and can sometimes be fooled by synonyms or differing connotations.

One area that McKeown said is making rapid strides is summarizing texts; systems to do that are sometimes employed by law firms that have to go through a lot of it.

McKeown also thinks personal assistants is an area likely to move forward quickly. "I would look at the movie 'Her,'" she said. In that 2013 movie starring Joaquin Phoenix, a man falls in love with an operating system that has consciousness.

"I initially didn't want to go see it, I said that's totally ridiculous," McKeown said. "But I actually enjoyed it. People are building these conversational assistants, and trying to see how far can we get."

The upshot is AIs that can handle certain tasks well exist, as do AIs that look almost human because they have a large trove of data to work with. Computer scientists have been less successful coming up with an AI that can think the way we expect a human being to, or to act like a human in more than very limited situations.

"I don't think we're in a state that AI is so good that it will do things we hadn't imagined it was going to do," McKeown said.

Additional resources

Communications of the ACM: Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence
Stuart Russell, University of California, Berkeley: Q&A: The Future of Artificial Intelligence

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Centuries-Old Shipwreck Recreated with 3D Printing — The Drumbeg shipwreck lies almost 40 feet (12 meters) underwater in Eddrachillis Bay, off the northwest coast of Scotland. This 3D-printed model shows the three cannon, two anchors and partial hull that remain at the shipwreck site.
**Credit: John McCarthy/Copyright Wessex Archaeology 2016**

The seabed holds some fascinating historical secrets, but unlike monuments on land, they’re largely hidden from view. Now, archaeologists in the United Kingdom are using 3D printing to bring two historical shipwrecks to life for history enthusiasts and experts alike.

Using data from photogrammetry (measuring the distance between objects from photographs) and sonar imaging, the researchers have produced scale models of a 17th-century shipwreck near Drumbeg, in Scotland, and the remains of the HMHS Anglia, a steamship that was used as a floating hospital during World War I. The steamship was sunk by a mine off the south coast of England.

"It was a proof of concept for us, trying to establish what could be done using sound and light, but there are so many different applications you could use this for," said maritime archaeologist John McCarthy, a project manager at Wessex Archaeology who carried out dives at the Scottish site and was in charge of producing the 3D models. [Photos: Shipwrecks of the Deep Sea]

"People can engage much more easily with a physical object in front of them. You can bring it to schools and conferences, and we are hoping to donate both models to local museums, once we've finished with them," McCarthy told Live Science.

It was not particularly difficult to create 3D-printed representations of the shipwrecks, McCarthy said. The magic, he said, was in creating the virtual models that were fed into the 3D printer.

McCarthy carried out initial experimental surveys of the Drumbeg wreck in 2012 with his colleague Jonathan Benjamin, who is now a lecturer at Flinders University in Australia. McCarthy recently joined him there to begin Ph.D. studies under Benjamin's supervision.

At the Drumbeg wreck site, the pair found three heavily encrusted cannons with evidence of a preserved wooden hull underneath. The ship's identity is still unknown, but one theory holds that it is a Dutch trading vessel called the Crowned Raven, which is known to have been lost in the bay in the late 1600s.

After realizing the techniques they were using could provide enough data for a 3D model, the archaeologists went back to do a more detailed survey in 2014 and used the lessons they had learned from their first attempt.

The archaeologists used a technique called photogrammetry, which involves taking hundreds of overlapping photographs of a site and then feeding them into a computer program that can stitch them together. The application is able to establish the spatial relationships between photos, which allows it to create a so-called 3D point cloud that maps each image in 3D space.

"Once you have a point cloud, you can turn it into a solid surface," McCarthy said. "Then you have a 3D model of the site that's not subjective or an artist's impression, but entirely objective."

**The HMHS Anglia was sunk by a mine off the south coast of England. The steamship was used as a floating hospital during World War I.**
**Credit: John McCarthy/Copyright Wessex Archaeology 2016**

The benefits of photogrammetry are that it produces very high-resolution images and it can capture the true color of the site, McCarthy said. The method is easily thwarted, however, by excess marine growth or poor visibility, and it is not well-suited to covering large areas.

Sonar, on the other hand, can see through the murk and can cover much larger areas, McCarthy said. For the 329-foot-long (100 meters) HMHS Anglia, another team from Wessex Archaeology used multibeam sonar — which operates in a similar way to a laser scanner — to do a much larger survey of the shipwreck site.

While multibeam sonar can't match the subcentimeter resolution of photogrammetry, using higher-end equipment and doing many passes can boost accuracy, McCarthy said. The Anglia survey was a particularly high-resolution one, he added, which was part of the reason it was selected for the 3D printing project.

McCarthy pointed out that the Wessex Archaeology team is not the first to create 3D-printed models from underwater imaging data. He said that the field has been booming in recent years, with big advances in both sonar and photographic techniques, and even some novel laser-scanning approaches are beginning to come through.

"All maritime archaeologists are engaging heavily with these techniques now," McCarthy said. "Advances in hardware and software in the last five years has allowed us to do very rapid and cheap surveys, and it has added to the tools we use underwater."

Original article on Live Science.