White House Office of Science and Technology Policy Request for Information on the Future of Artificial Intelligence Public Responses September 1, 2016 Respondent 1 Chris Nicholson, Skymind Inc. This submission will address topics 1, 2, 4 and 10 in the OSTP’s RFI: • the legal and governance implications of AI • the use of AI for public good • the social and economic implications of AI • the role of “market-shaping” approaches Governance, anomaly detection and urban systems The fundamental task in the governance of urban systems is to keep them running; that is, to maintain the fluid movement of people, goods, vehicles and information throughout the system, without which it ceases to function. Breakdowns in the functioning of these systems and their constituent parts are therefore of great interest, whether it be their energy, transport, security or information infrastructures. Those breakdowns may result from deteriorations in the physical plant, sudden and unanticipated overloads, natural disasters or adversarial behavior. In many cases, municipal governments possess historical data about those breakdowns and the events that precede them, in the form of activity and sensor logs, video, and internal or public communications. Where they don’t possess such data already, it can be gathered. Such datasets are a tremendous help when applying learning algorithms to predict breakdowns and system failures. With enough lead time, those predictions make pre- emptive action possible, action that would cost cities much less than recovery efforts in the wake of a disaster. Our choice is between an ounce of prevention or a pound of cure. Even in cases where we don’t have data covering past breakdowns, algorithms exist to identify anomalies in the data we begin gathering now. But we are faced with a challenge. There is too much data in the world. Mountains of data are being generated every second. There is too much data for experts to wade through, and that data reflects complex and evolving patterns in reality. That is, neither the public nor the private sectors have the analysts necessary to process all the data generated by our cities, and we cannot rely on hard-coded rules to automate the analyses and tell us when things are going wrong (send a notification when more than X number of white vans cross Y bridge), because the nature of events often changes faster than new hard-coded rules can be written. One of the great applications of deep artificial neural networks, the algorithms responsible for many recent advances in artificial intelligence, is anomaly detection. Exposed to large datasets, those neural networks are capable of understanding and modeling normal behavior – reconstructing what should happen – and therefore of identifying outliers and anomalies. They do so without hard-coded rules, and the anomalies they detect can occur across multiple dimensions, changing from day to day as the neural nets are exposed to more data. That is, deep neural networks can perform anomaly detection that keeps pace with rapidly changing patterns in the real world. This capacity to detect new anomalies is causing a shift in fraud detection practices in financial services, and cybersecurity in data centers; it is equally relevant to the governance of urban systems. The role of these neural networks is to surface patterns that deserve more attention. That is, they are best used to narrow a search space too large for human analysts, and the flag for them a limited number of unusual patterns that may precede a crisis, failure or breakdown. Artificial intelligence, public health and the public good At the center of medical practice is the act of inference, or reaching a conclusion on the basis of evidence and reasoning. Doctors and nurses learn to map patients’ symptoms, lifestyles and metadata to a diagnosis of their condition. Any mathematical function is simply a way of mapping input variables to an output; that is, inference is also at the heart of AI. The promise of AI in public health is to serve as a automated second opinion for healthcare professionals; it has the ability to check them when they slip. Because an algorithm can be trained on many more instances of data – say, X-rays of cancer patients – than a healthcare professional can be exposed to in a single lifetime, an algorithm may perceive signals, subtle signs of a tumor, that a human would overlook. This is important, because healthcare professionals working long days under stressful conditions are bound to vary in their performance over the course of a given day. Introducing an algorithmic check, which is not subject to fatigue, could keep them from making errors fatal to their patients. In the longer-term, reinforcement learning algorithms (which are goal oriented and learn from rewards they win from an environment) will be used to go beyond diagnoses and act as tactical advisors in more strategic situations where a person must choose one action or another. For now, various deep-learning algorithms are good at classifying, clustering and making predictions about data. Given symptoms, they may predict the name of the underlying disease. Given an individual’s metadata, activity and exercise logs, they may predict the likelihood that that person will face the risk of heart disease. And by making those inferences sooner, more efficiently and more accurately than previous methods, such algorithms put us in a position to alleviate, cure or altogether avoid the disease. To broaden the discussion beyond healthcare, AI is leading us toward a world of (slightly) fewer surprises. It is putting us in a position to navigate the future that we are able to perceive, in germ, in the present. That trend should be kept in mind whenever and wherever we are faced with outcomes that matter (for example, disasters, disease or crime), and data that may correlate to them. Visibility will increase. Indeed, while criminal risk assessment has undergone negative publicity recently (https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal- sentencing), newer algorithms and bigger datasets will make pre-crime units possible. We may see a shift from punitive enforcement to preventative interventions. The legal implications are important, and those in governance should require transparency for all algorithms that filter and interpret data for the judicial system and law enforcement agencies. The social and economic implications of AI As AI advances and its breakthroughs are implemented by large organizations more widely, its impact on society will grow. The scale of that impact may well rival the steam engine or electricity. On the one hand, we will more efficiently and accurately process information in ways that help individuals and society; on the other, the labor market will be affected, skill sets will be made obsolete, and power and wealth will further shift to those best able to collect, interpret and act on massive amounts of data quickly. Deep technological changes will throw people out of work, reshape communities, and alter the way society behaves, connects and communicates collectively. The automation of trucking through driverless vehicles, for example, will affect America’s 3.5 million truckers and the more than 5 million auxiliary positions related to trucking. The same can be said for taxis, delivery and ride-haling services. If history is any indication, our governmental response to disruptions in the labor market will be insufficient. In the hardest-hit sectors, workers, families and communities will suffer and break down. Unemployment, drug use and suicides will go up, along with political instability. Policies such as “basic income” or the Danish “flexicurity” should be explored as ways to soften the blow of job loss and fund transitional retraining periods. The role of “market-shaping” approaches Just as DARPA helped finance the explosion in data science in the Python community through repeated grants to such key players as Continuum, government agencies are in a position to support the researchers, technologies, tools and communities pushing AI in promising directions. • Initiatives focused on Java and the JVM will pave the way for AI to be implemented by large organizations in need of more accurate analytics. This includes government agencies, financial services, telecommunications and transport, among others. • Grants related to embeddable technologies will help AI spread to edge devices such as cell phones, cars and smart appliances. On personal devices such as phones or cars, various forms of inference might allow people to make better decisions about lifestyle, nutrition or even how run their errands. • Initiatives that focus on gathering high-quality datasets and making them public could vastly improve the performance of algorithms trained on that data, much as Li Fei Fei’s work on ImageNet helped usher in a new era of computer vision. Respondent 2 Joyce Hoffman, Writer I am most interested in safety and control issues, and yes, I agree that AI should be developed to the utmost. Respondent 3 kris kitchen, Machine Halo Artificial Intelligence Immune System should be thought about. Billions of friendly AI agents working to identify and act on nefarious agents. Respondent 4 Daniel Bryant, Kaufman Rossin This response is relevant to #8 directly and #6 tangentially. I am a software engineer that primary uses web development tools to build business solutions and have implemented machine learning in relevant algorithms. One of the core items holding back some of the most interesting applications of AI in practice is the lack of available data. Machine learning is the literal embodiment of garbage in, garbage out. The PDF format, while great for it’s time, has significantly impaired the ability of AI to process information. AI must regularly rely on the often poor results of OCR in order to attempt to extract the information that is contained in the PDF.