What is artificial intelligence (AI)?
Definition and history of AI
Artificial intelligence (AI) is a set of computational technologies that are inspired by the ways human use their nervous systems and bodies to sense, learn, reason and take actions. Sensors, including microphones and cameras, collect data in the external environment from our day-today interactions. Algorithms are coded to condition machines to gradually learn and make inferences based on past data. Over the years, AI has grown tremendously in complexity, transforming from handling process-driven tasks to data-drive ones.
The term AI was officially born in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence. Between the late 1960s and early 1980s, theories such as heuristic search, computer vision, natural language processing, mobile robotics, machine learning and artificial neural networks emerged. However, by the mid-1980s, AI still saw no significant practical success. The gap was in part because of the lack of direct access to environmental signals and data, and in part because of over-emphasis on characterizing true or false logic and thereby overlooking uncertainty. Interest in AI began to drop and funding dried up. The “AI winter” loomed for the next decade and started to see a new boom in the 1990s.
Why is AI gaining serious momentum this time?
According to a Zignal Lab report from May 20171, 90% of the world’s data was created in the last two years thanks to media and social intelligence. 2.5 Exabytes of media data are now produced daily. Coupled with improved quality and wide availability of different hardware over the years, data can now be fully and accurately captured, processed and shared. Sophisticated software programs have also been developed to interpret the output of algorithms and present data in easily digestible visualizations, allowing humans to interface, analyze and synthesize insights without the necessity of prior trainings in AI. Moreover, computing and storage power have grown exponentially while costs are reduced dramatically, further accelerating the processing efficiency and the pace of innovation.
Changing demographics and economic needs
Another reason for this accelerating momentum hinges on the economic reality faced by the world’s major economies. The example of China is a telling one. The world’s second largest economy has relied heavily on its vast labor market and significant capital investment to sustain its economic growth over a long period. These two levers were the traditional drivers of production, yet they can no longer sustain the steady march of prosperity enjoyed in the past three decades. China’s demographics are turning from a tailwind to a headwind. With an aging population – nearly 50% of it is now middle-aged – China seems likely to fall well short of the workforce numbers needed to sustain economic growth at current productivity levels. The government acknowledges the increasing need for China to embrace a new growth model that relies less on a capital-intensive model (fixed investment and exporting), and more on private consumption, services and innovation to drive higher quality and more sustainable economic growth. China has to undergo structural reforms to address challenges arising from the past high-speed growth, such as excess capacity in numerous industries. The most realistic alternative for maintaining momentum would be to sharply accelerate productivity growth. The use of artificial intelligence is thus set to play an important role in boosting productivity. AI can augment labor by complementing human capabilities, offering employees new tools to enhance their natural intelligence. In our view, a significant part of China’s economic growth from AI will come, not from replacing existing labor and capital, but in enabling them to be used much more effectively. As AI technology continues to evolve and value slowly outweighs the cost of adoption, these emerging markets are more willing to adopt AI to solve some of their most pressing issues.Download to read more