The Stanford One Hundred Year Study on Artificial Intelligence, a project that launched in December 2014, is designed to be a century-long periodic assessment of the field of artificial intelligence (AI) and its influences on people, their communities, and society. Colloquially referred to as "AI100," the project issued its first report in September 2016. A standing committee of AI scientists and scholars in the humanities and social sciences working with the Stanford faculty director of AI100 oversees the project and the design of its activities. A little more than two years after the first report appeared, we reflect on the decisions made in shaping it, the process that produced it, its major conclusions, and reactions subsequent to its release. The inaugural AI100 report,6 called Artificial Intelligence and Life in 2030, examined eight domains of human activity in which AI technologies are already beginning to affect urban life.

Interacting with search systems, such as Web search engines, is the primary means of information access for most people. Search providers have invested billions of dollars developing search technologies, which power search engines and feature in many of today's virtual assistants (including Google Assistant, Amazon Alexa, Microsoft Cortana, and others). For decades, search has offered a plentiful selection of research challenges for computer scientists and the advertising models that fund industry investments are highly lucrative. Given the phenomenal success, search is often considered a "solved problem." There is some truth to this for fact-finding and navigational searches, but the interaction model and the underlying algorithms are still brittle in the face of complex tasks and other challenges, for example, presenting results in non-visual settings such as smart speakers.15

Government regulation is necessary to prevent harm. But regulation is also a blunt and slow-moving instrument that is easily subject to political interference and distortion. When applied to fast-moving fields like AI, misplaced regulations have the potential to stifle innovation and derail the enormous potential benefits that AI can bring in vehicle safety, improved productivity, and much more. We certainly do not want rules hastily cobbled as a knee-jerk response to a popular outcry against AI stoked by alarmists such as Elon Musk (who has urged U.S. governors to regulate AI "before it's too late"). To address this conundrum, I propose a middle way: that we avoid regulating AI research, but move to regulate AI applications in arenas such as transportation, medicine, politics, and entertainment.

Machine learning has evolved from an out-of-favor subdiscipline of computer science and artificial intelligence (AI) to a leading-edge frontier of research in both AI and computer systems architecture. Over the past decade investments in both hardware and software for machine learning have risen at an exponential rate matched only by similar investments in blockchain technology. This column is a technology check for professionals in a Q&A format on how this field has evolved and what big questions it faces. Q: The modern surge in AI is powered by neural networks. When did the neural network field start?

When the head of the U.S. Supreme Court says artificial intelligence (AI) is having a significant impact on how the legal system in this country works, you pay attention. That's exactly what happened when Chief Justice John Roberts was asked the following question: "Can you foresee a day when smart machines, driven with artificial intelligences, will assist with courtroom fact-finding or, more controversially even, judicial decision-making?" His answer startled the audience. "It's a day that's here and it's putting a significant strain on how the judiciary goes about doing things," he said, as reported by The New York Times. In the last decade, the field of AI has experienced a renaissance.

A nurse asks a patient to describe her symptoms. A fast-food worker greets a customer and asks for his order. A tourist asks a police officer for directions to a local point of interest. For those with all of their physical faculties intact, each of these scenarios can be viewed as a routine occurrence of everyday life, as they are able to easily and efficiently interact without any assistance. However, each of these interactions are significantly more difficult when a person is deaf, and must rely on the use of sign language to communicate.

His concern is warranted and will require us to strike a balance between protecting the democratic and egalitarian values that made the Internet great to begin with while ensuring those values are used for good. The fundamental issue, then, in creating a 21st-century Internet becomes what changes are warranted and who will be responsible for defining and administering them. On the technology dimension, computer scientists and engineers must develop smarter systems for detecting, addressing, and preventing malicious content on the Web. Cerf's argument on behalf of user training is helpful but will not ultimately solve the problem of an untrustworthy, ungovernable, potentially malicious network. I myself recently fell for a phishing attack, which only proves that today's attacks can fool even savvy, experienced users.

Today, people increasingly rely on computer agents in their lives, from searching for information, to chatting with a bot, to performing everyday tasks. These agent-based systems are our first forays into a world in which machines will assist, teach, counsel, care for, and entertain us. While one could imagine purely rational agents in these roles, this prospect is not attractive for several reasons, which we will outline in this article. The field of affective computing concerns the design and development of computer systems that sense, interpret, adapt, and potentially respond appropriately to human emotions. Here, we specifically focus on the design of affective agents and assistants.

Amazon, Apple, Google, IBM and their peers could be subject to new restrictions on how they export the technology behind voice-activated smartphones, self-driving cars and speedy supercomputers to China under a proposal floated Monday by the Trump administration. For the U.S. government, its pursuit of new regulations marks a heightened effort to ensure that emerging technologies, including artificial intelligence, don't fall into the hands of countries or actors that might pose a national security threat. The official request for public comment, published in the Federal Register, asks whether a long list of AI tools should be subject to stricter export-control rules. The Trump administration's potential targets include image-recognition software, ultrafast quantum computers, advanced computer chips, self-driving cars and robots. Companies that make those products and services might, for instance, have to obtain licenses before selling them to foreign governments or partnering with some researchers in certain countries.

NASA's spacecraft OSIRIS-REx is just 75 miles from its destination and, just like you would near the end of a light, it's starting to stretch out. The craft successfully tested its Touch-and-Go Sample Acquisition Mechanism (TAGSAM), a robotic arm that will allow it to grab samples from the surface of the asteroid Bennu. According to NASA, the test run went as planned. OSIRIS-REx, with the help of engineers from Lockheed Martin, showed off the full range of motion of its arm. The test's success was confirmed by telemetry data and photos captured by an onboard camera.