What's in store for the future of Artificial Intelligence?
Updated: Jun 25, 2019
A few decades ago, it was only humans who could play chess or read handwriting. Having been the focus of research in artificial intelligence (AI) for several years, both are now routinely done by machines. Today, researchers are working on many more applications of AI which will revolutionize the ways in which we work, communicate, study and enjoy ourselves. Products and services incorporating such innovation will become part of people’s day-to-day lives within the next few years as we embark on what some AI experts describe as the age of implementation.
Yet AI remains a challenging subject for many people. Definitions vary, have changed over time and are in some cases contentious. The technology is complex and wide-ranging, potentially affecting many different areas of human activity. And AI raises complex questions about privacy, trust and autonomy that are difficult to grapple with, and this has led to fears about humans themselves being under threat.
One of the most striking characteristics of research in artificial intelligence (AI) is the rapid growth that has been seen over the past five years. The impressive numbers of patent filings in this period and the decrease in the ratio of number of scientific papers to inventions are indicative of a shift from theoretical research to the use of AI technologies in commercial products and services. This trend is also reflected in the types of patents being filed, with significant growth in specific AI applications and sector-specific fields.
Trends in AI techniques
Looking first at trends in AI techniques, machine learning predominates, representing a massive 89 percent of filings mentioning this AI technique and 40 percent of all AI-related patents. Machine learning grew by 28 percent from 2013 to 2016; in the same period, fuzzy logic has grown by 16 percent and logic programming by 19 percent. Within machine learning, every AI technique showed an increase in annual filing numbers for the same period, but some stand out. Deep learning is the fastest growing technique in AI, with an 175 percent increase over the period. Multi-task learning, the next fastest, grew by 49 percent. Other techniques with notable increases were neural networks, latent representation and unsupervised learning.
Trends in AI functional applications
Turning to trends in AI functional applications, computer vision, which includes image recognition, is the most popular. Computer vision was mentioned in 49 percent of all AI-related patents and grew by 24 percent during 2013 to 2016. The other two top areas in functional applications are natural language processing (14 percent of all AI-related patents) and speech processing (13 percent). While these three functional applications are the most important in terms of the total number of filings, others are emerging and growing fast. AI filings concerning both robotics and control methods have increased by 55 percent, for example, while those for planning/scheduling have grown by 37 percent. Within computer vision – the top functional application – biometrics has seen an average annual growth rate of 31 percent and scene understanding one of 28 percent. Within natural language processing, semantics has grown by 33 percent and sentiment analysis by 28 (though it still only accounts for 1 percent of natural language processing applications). Within speech processing, speech-to-speech has grown by 15 percent, and speech recognition and speaker recognition have both grown by 12 percent.
Trends in AI application fields
Lastly, in AI application fields, the top industries are transportation (15 percent of all AI-related patents), telecommunications (15 percent), and life and medical sciences (12 percent). Growing industries are transportation, agriculture, and computing in government, with annual growth rates of at least 30 percent between 2013 and 2016. Looking at trends over ten years, the boom in transportation technologies becomes more evident: representing just 20 percent of applications in 2006, by 2016 it accounted for one-third of applications (with more than 8,700 filings). Telecommunications, the second most important application field, has remained at around 24 percent during this period, but the proportion of filings mentioning business, document management and publishing or life and medical sciences has decreased.
For industry, there is a trend to combine hardware with software to make AI technologies more practically applicable. Deep learning framework with chips for AI could be another opportunity for players aiming to dominate the future AI industry. For application systems, the need for the combination of different AI techniques with functional applications is getting more serious. AI application systems also need to be integrated with business scenarios.
Recognising that AI potentially impacts the work of every UN agency in their efforts in contributing to the achievement of the SDGs, the UN is working hard to consider the implications of AI. Every year, ITU hosts the “AI for Good” Summit in partnership with sister UN agencies, XPRIZE Foundation and ACM to foster global multi-stakeholder dialogue to ensure trusted, safe and inclusive development of AI technologies and equitable access to their benefits. In January 2019, the ILO’s Global Commission on the Future of Work published its findings. The first countries are issuing frameworks for autonomous driving, including Germany, and UNECE is considering the implications of autonomous and self-driving cars on the Vienna Convention on Road Traffic. In these and other ways, the UN hopes to promote and advance the benefits of AI towards the three pillars of the UN: peace and security, human rights, and development.
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