Wednesday, August 30, 2017

The Weaponization of Artificial Intelligence

acq |  Recognizing that no machine—and no person—is truly autonomous in the strict sense of the word, we will sometimes speak of autonomous capabilities rather than autonomous systems.2
The primary intellectual foundation for autonomy stems from artificial intelligence (AI), the capability of computer systems to perform tasks that normally require human intelligence (e.g.,
perception, conversation, decisionmaking). 

Advances in AI are making it possible to cede to machines many tasks long regarded as impossible for machines to perform. Intelligent systems aim to apply AI to a particular problem or domain—the
implication being that the system is programmed or trained to operate within the bounds of a defined knowledge base. Autonomous function is at a system level rather than a component level. The study considered two categories of intelligent systems: those employing autonomy at rest and those employing autonomy in motion. In broad terms, systems incorporating autonomy at rest operate virtually, in software, and include planning and expert advisory systems, whereas systems incorporating autonomy in motion have a presence in the physical world and include robotics and autonomous vehicles. 

As illustrated in Figure 1, many DoD and commercial systems are already operating with varying kinds of autonomous capability. Robotics typically adds additional kinds of sensors, actuators, and mobility to intelligent systems. While early robots were largely automated, recent advances in AI are enabling increases in autonomous functionality.

One of the less well-known ways that autonomy is changing the world is in applications that include data compilation, data analysis, web search, recommendation engines, and forecasting. Given the limitations of human abilities to rapidly process the vast amounts of data available today, autonomous systems are now required to find trends and analyze patterns. There is no need to solve the long-term AI problem of general intelligence in order to build high-value applications that exploit limited-scope autonomous capabilities dedicated to specific purposes. DoD’s nascent Memex program is one of many examples in this category.3

Rapid global market expansion for robotics and other intelligent systems to address consumer and industrial applications is stimulating increasing commercial investment and delivering a diverse array of products. At the same time, autonomy is being embedded in a growing array of software systems to enhance speed and consistency of decision-making, among other benefits. Likewise, governmental entities, motivated by economic development opportunities in addition to security missions and other public sector applications, are investing in related basic and applied research.

Applications include commercial endeavors, such as IBM’s Watson, the use of robotics in ports and
mines worldwide, autonomous vehicles (from autopilot drones to self-driving cars), automated logistics and supply chain management, and many more. Japanese and U.S. companies invested more than $2 billion in autonomous systems in 2014, led by Apple, Facebook, Google, Hitachi, IBM, Intel, LinkedIn, NEC, Yahoo, and Twitter. 4

A vibrant startup ecosystem is spawning advances in response to commercial market opportunities; innovations are occurring globally, as illustrated in Figure 2 (top). Startups are targeting opportunities that drive advances in critical underlying technologies. As illustrated in Figure 2 (bottom), machine learning—both application-specific and general purpose—is of high interest. The market-pull for machine learning stems from a diverse array of applications across an equally diverse spectrum of industries, as illustrated in Figure 3.