The Current State of AI
Remarkable progress has been made on what is known as Narrow AI, which addresses specific application areas such as playing strategic games, language translation, self-driving vehicles, and image recognition. Narrow AI underpins many commercial services such as trip planning, shopper recommendation systems, and ad targeting, and is finding important applications in medical diagnosis, education, and scientific research. These have all had significant societal benefits and have contributed to the economic vitality of the Nation.
General AI (sometimes called Artificial General Intelligence, or AGI) refers to a notional future AI system that exhibits apparently intelligent behaviour at least as advanced as a person across the full range of cognitive tasks. A broad chasm seems to separate today’s Narrow AI from the much more difficult challenge of General AI. Attempts to reach General AI by expanding Narrow AI solutions have made little headway over many decades of research. The current consensus of the private-sector expert community, with which the NSTC Committee on Technology concurs, is that General AI will not be achieved for at least decades.
People have long speculated on the implications of computers becoming more intelligent than humans. Although prudence dictates some attention to the possibility that harmful superintelligence might someday become possible, these concerns should not be the main driver of public policy for AI.
Machine Learning
Machine learning is one of the most important technical approaches to AI and the basis of many recent advances and commercial applications of AI. Modern machine learning is a statistical process that starts with a body of data and tries to derive a rule or procedure that explains the data or can predict future data. This approach—learning from data—contrasts with the older “expert system” approach to AI, in which programmers sit down with human domain experts to learn the rules and criteria used to make decisions, and translate those rules into software code. An expert system aims to emulate the principles used by human experts, whereas machine learning relies on statistical methods to find a decision procedure that works well in practice.
Deep Learning
In recent years, some of the most impressive advancements in machine learning have been in the subfield of deep learning, also known as deep network learning. Deep learning uses structures loosely inspired by the human brain, consisting of a set of units (or “neurons”). Each unit combines a set of input values to produce an output value, which in turn is passed on to other neurons downstream. For example, in an image recognition application, a first layer of units might combine the raw data of the image to recognize simple patterns in the image; a second layer of units might combine the results of the first layer to recognize patterns-of-patterns; a third layer might combine the results of the second layer; and so on.
Deep learning networks typically use many layers—sometimes more than 100— and often uses a large number of units at each layer, to enable the recognition of extremely complex, precise patterns in data.
In recent years, new theories of how to construct and train deep networks have emerged, as have larger, faster computer systems, enabling the use of much larger deep learning networks. The dramatic success of these very large networks at many machine learning tasks has come as a surprise to some experts, and is the main cause of the current wave of enthusiasm for machine learning among AI researchers and practitioners.
Applications of AI for Public Good
One area of great optimism about AI and machine learning is their potential to improve people’s lives by helping to solve some of the world’s greatest challenges and inefficiencies. The promise of AI has been compared to the transformative impacts of advances in mobile computing. Public- and private-sector investments in basic and applied R&D on AI have already begun reaping major benefits for the public in fields as diverse as health care, transportation, the environment, criminal justice, and economic inclusion.
At Walter Reed Medical Center, the Department of Veteran Affairs is using AI to better predict medical complications and improve treatment of severe combat wounds, leading to better patient outcomes, faster healing, and lower costs. The same general approach—predicting complications to enable preventive treatment—has also reduced hospital-acquired infections at Johns Hopkins University. Given the current transition to electronic health records, predictive analysis of health data may play a key role across many health domains like precision medicine and cancer research.
In transportation, AI-enabled smarter traffic management applications are reducing wait times, energy use, and emissions by as much as 25 percent in some places. Cities are now beginning to leverage the type of responsive dispatching and routing used by ride-hailing services, and linking it with scheduling and tracking software for public transportation to provide just-in-time access to public transportation that can often be faster, cheaper and, in many cases, more accessible to the public.
Autonomous sailboats and watercraft are already patrolling the oceans carrying sophisticated sensor instruments, collecting data on changes in Arctic ice and sensitive ocean ecosystems in operations that would be too expensive or dangerous for crewed vessels. Autonomous watercraft may be much cheaper to operate than manned ships, and may someday be used for enhanced weather prediction, climate monitoring, or policing illegal fishing.
AI also has the potential to improve aspects of the criminal justice system, including crime reporting, policing, bail, sentencing, and parole decisions. The Administration is exploring how AI can responsibly benefit current initiatives such as Data Driven Justice and the Police Data Initiative that seek to provide law enforcement and the public with data that can better inform decision-making in the criminal justice system, while also taking care to minimize the possibility that AI might introduce bias or inaccuracies due to deficiencies in the available data.
Several U.S. academic institutions have launched initiatives to use AI to tackle economic and social challenges. For example, the University of Chicago created an academic program that uses data science and AI to address public challenges such as unemployment and school dropouts. The University of Southern California launched the Center for Artificial Intelligence in Society, an institute dedicated to studying how computational game theory, machine learning, automated planning and multi-agent reasoning techniques can help to solve socially relevant problems like homelessness. Meanwhile, researchers at Stanford University are using machine learning in efforts to address global poverty by using AI to analyse satellite images of likely poverty zones to identify where help is needed most.
Many uses of AI for public good rely on the availability of data that can be used to train machine learning models and test the performance of AI systems. Agencies and organizations with data that can be released without implicating personal privacy or trade secrets can help to enable the development of AI by making those data available to researchers.
Developing and studying machine intelligence can help us better understand and appreciate our human intelligence. Used thoughtfully, AI can augment our intelligence, helping us chart a better and wiser path forward.
Recommendations
In all the Committee made 23 recommendations. A lot of the 23 were concerned with instilling the correct ethos with Federal Agencies so that open data sets, the prioritisation of AI research, the employment of AI specialists, reporting back and other administration issues were correctly dealt with. Recommendations 8 and 9 are worth quoting in full as they recognise the importance of autonomous aircraft and motor vehicles.
Recommendation 8: The U.S. Government should invest in developing and implementing an advanced and automated air traffic management system that is highly scalable, and can fully accommodate autonomous and piloted aircraft alike.
Recommendation 9: The Department of Transportation should continue to develop an evolving framework for regulation to enable the safe integration of fully automated vehicles and UAS, including novel vehicle designs, into the transportation system.
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