The Next Frontier for Artificial Intelligence

The Next Frontier for Artificial Intelligence?

Learning humans’ common sense

Spain’s artificial intelligence research institute is looking at teaching robots to know their limits, but think human-level AI is a way away just yet.

Nearly half a century has passed between the release of the films 2001: A Space Odyssey (1968) and Transcendence (2014), in which a quirky scientist’s consciousness is uploaded into a computer. Despite being 50 years apart, their plots, however, are broadly similar. Science fiction stories continue to imagine the arrival of human-like machines that rebel against their creators and gain the upper hand in battle.

In the field of artificial intelligence (AI) research, over the last 30 years, progress has been similarly slower than expected.

While AI is increasingly part of our everyday lives – in our phones or cars – and computers process large amounts of data, they still lack human-level capacity to make deductions from the information they’re given. People can read different sections of a newspaper and understand them, grasp the consequences and implications of a story. Just by interacting with their environment, humans acquire experience that gives them tacit knowledge. Today’s machines simply don’t have that kind of ability. Yet.

Gargoyle. Illustration: Elena

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As a result, common sense reasoning is still a challenge in AI research. “We have machines that are very good at playing chess, for example, but they cannot play domino too,” Ramon López de Mántaras, director of the of Spanish National Research Council’s Artificial Intelligence Research Institute said. “In the last 30 years, research has focused on weak artificial intelligence – that is to say, in making machines very good at a specific topic – but we have not progressed that much with common sense reasoning,” he said during a recent debate organized by the Catalan government’s ministry of telecommunications and information society.

Will this situation change with the development of smart city and cognitive computing systems, designed to be able to carry out human-like analysis of complex and diverse data sets? López de Mántaras, who has been exploring some of the most ambitious questions in AI since 1976, doesn’t think so. “Neither big data nor high performance computing are bringing us closer to robustness in AI,” he said.

Futurists who talk about ‘the singularity’, meaning the hypothetical advent of artificial general intelligence (also known as strong AI), predict it will occur between 2030 and 2045. López de Mántaras is skeptical, however: “If there is no big change in computer science, it won’t happen.”

The main difficulty in artificially reproducing the functioning of the human brain stems from the fact that the organ is analogue. Its ability to process information not only depends on the electrical activity of neurons, but also on many kinds of chemical activity, which can’t be modelled with current technologies. López de Mántaras speculates that non silicon-based technologies such as memristors, a type of passive circuit elements that maintain a relationship between the time integrals of current and voltage across a two terminal element, or DNA computing will be needed to move forward. However, he notes that we need something more than a technological change to solve the problem: we also need new mathematical models and algorithms to artificially reproduce the human brain – algorithms that are as yet unknown.

By 2030 though, humanoid robots that interact with the environment and that may have more general intelligence may have been developed, he said, and businesses will take advantage of the trend. Social robots as domestic assistants or to help elderly people or those with mobility problems are being worked on, as are self-driving vehicles, though their AI isn’t yet anywhere near human-level.