Artificial Intelligence: Comeback Chance for Japanese Manufacturing

Matsuo Yutaka [Profile]

[2015.09.30] Read in: 日本語 | 简体字 | 繁體字 | FRANÇAIS | ESPAÑOL | العربية | Русский |

The cutting-edge field of artificial intelligence is attracting great attention and massive funding in Western countries and China. Japan now lags behind these leaders, but it is in a good position to use AI as a tool for revival of the manufacturing sector.

The Third AI Boom

AI is booming. Artificial intelligence has a history of about 60 years as a research field, dating back to 1956, when this name was adopted. The field experienced a boom first in the 1960s and again in the 1980s; the current AI boom, which started around 2010, is the third.

Some easy-to-see examples of AI at work have attracted popular notice, such as the development of a computer program that can beat professional human players at shōgi (Japanese chess), IBM’s Watson, which beat champions on a popular quiz program, and the Siri voice-interaction software on iPhones. But in technological terms the focus is now on what is called “deep learning,” a field in which research is progressing at a breakneck pace.

For example, it is hard for computers to recognize the objects in images—to identify a flower, for example, or a sailboat, or a coffee cup. And until recently it was thought that they would be unable to match humans at image-recognition tasks like these for decades to come. As Marvin Minsky, a leader in AI research, observed, the easier something is for a child, the tougher it is for a computer. Even something like playing with blocks was a challenge for computers. And image recognition was said to be a typical example of this sort of task—easy for humans but hard for computers.

Computers Surpass Humans in Image Recognition

But then, in 2012, a major advance was achieved in deep learning, and less than three years later computers had already surpassed humans in image recognition accuracy. This sort of ability was seen in programs developed by Microsoft and Google, announced in February and March this year, respectively. So computers are now better than humans at identifying objects and people in photographs. This is a huge step forward.

Deep learning, a form of representation learning, involves having computers learn what parts of the real world to focus on. In earlier AI approaches—and, one might well say, in every sort of engineering-based model—the unimportant elements were discarded and the key elements were turned into a model to allow efficient computation. And humans decided what parts of the real world computers would direct their vision toward. This was a big problem. Though there were various approaches designed to allow automatic computation, human involvement was indispensable at the initial stage. Deep learning has been making it possible to leave humans out of the loop. This is a highly significant development.

Rapid Progress in Corporations and Universities Overseas

The lead actors in this technological innovation are researchers mainly in the United States and Canada and corporations largely based in Silicon Valley. France is also catching up fast, tapping its strength in theoretical mathematics. And major Chinese firms are striving to get a piece of the action.

Google has for some time been devoting great efforts to AI research, and in 2013, the year after bursting on to the deep learning scene, it hired Geoffrey Hinton, a leading light in the field. Early in 2014 it followed up with the acquisition of DeepMind Technologies, a small British start-up, for about ¥40 billion. At the time many were surprised at the purchase, but now it looks like an excellent investment to have made.

Meanwhile, Facebook has set up AI research labs in New York, Menlo Park (Silicon Valley), and Paris, reportedly with tremendous budgets. The AI research effort is being lead by Professor Yann LeCun of New York University, who is French by birth. France has traditionally been strong in the field of theoretical mathematics, and it is emerging as a major player in AI now that this field of math has come to be a key component of deep learning. Facebook’s strategy seems to involve looking from the East Coast of the United States across the Atlantic to Europe.

Japan Lags Behind in the Second Pack

Baidu, China’s major search-engine company, has set up its own deep-learning research institute, headed by Andrew Ng, a star researcher from Stanford University. Ng is a Chinese American, educated in Hong Kong, Singapore, and the United States. China’s approach involves combining its big business capital with the talents of ethnic Chinese researchers working all around the United States.

Aside from the above three Internet giants, we see a proliferation of start-ups that are seeking to tap the potential of advanced AI and deep learning. The United States, which won decisively in the Internet race, is maintaining its overwhelming lead in the current competition to develop AI, the key technology for the years to come. Its closest rivals at this point are Asian: China’s Baidu and Tsinghua University, Hong Kong University, and the National University of Singapore. Japan is part of the second pack, which lags far behind the leaders.

  • [2015.09.30]

Associate Professor, Department of Technology Management for Innovation, Graduate School of Engineering, University of Tokyo. Born in 1975. Received his Ph.D. from the University of Tokyo in 2002. Served as researcher at the National Institute of Advanced Industrial Science and Technology, and visiting scholar at Stanford University. Specializes in AI, web mining, and big data analysis. Currently chairs the ethics committee of the Japanese Society for Artificial Intelligence.

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