Last year, 18 percent of Stanford University seniors graduated with a degree in computer science, more than double the proportion of just a decade earlier. Over the same period at MIT, that rate went up from 23 percent to 42 percent. These increases are common everywhere: The average number of undergraduate CS majors at universities in the U.S. and Canada tripled in the decade after 2005, and it keeps growing. Students' interest in CS is intellectual--culture moves through computation these days--but it is also professional. Young people hope to access the wealth, power, and influence of the technology sector. That ambition has created both enormous administrative strain and a competition for prestige.

Last Monday, I began looking into why artificial intelligence is still so bad at creating hands. In recent weeks, lots of people have been sharing images that could be mistaken for photos of actual humans--until your eyes wander to the subjects' misshapen fingers. A.I.'s inability to create realistic hands is a long-standing issue, highlighting both that the technology needs refining and that fingers are extraordinary things. To compare various A.I. tools' hand skills, I entered this prompt into five different art generators: "A couple that has been together for 50 years holding hands after a fight." The hands were not stellar.

Meili Gupta is about to ask another question. A poised and eloquent rising senior at elite boarding school Phillips Exeter Academy, Gupta, 17, is anything but the introverted, soft-spoken techie stereotype. She does, however, know as much about computer science as any high school student you'd ever meet. She even grew up faithfully reading the MIT Technology Review, the university's flagship publication, which shows, because Meili is the most ubiquitous student attendee at EmTech Next, a conference the publication held on campus this past summer on AI, Machine Learning, and "the future of work." Ostensibly, the conference is an opportunity for executives and tech professionals to rub elbows while determining how next-generation technologies will shape our jobs and economy in the coming decades. For me, the gathering feels more like an opportunity to have an existential crisis; I could even say a religious crisis, though I'm not just a confirmed atheist but a professional one as well.

Seven members of the US Congress have sent letters to the Federal Trade Commission (pdf), Federal Bureau of Investigation (pdf), and Equal Employment Opportunity Commission (pdf) asking whether the agencies have vetted the potential biases of artificial intelligence algorithms being used for commerce, surveillance, and hiring. "We are concerned by the mounting evidence that these technologies can perpetuate gender, racial, age, and other biases," a letter to the FTC says. "As a result, their use may violate civil rights laws and could be unfair and deceptive." The letters request that the agencies respond by the end of September with complaints they've received of unfair use of facial recognition or artificial intelligence, as well as details on how these algorithms are tested for fairness before being implemented by the government. In the letter to the EEOC, senators Kamala Harris, Patty Murray, and Elizabeth Warren specifically ask the agency to determine whether this technology could violate the Civil Rights Act of 1964, the Equal Pay Act of 1963, or the Americans with Disabilities Act of 1990.

America is great at artificial intelligence--and it's going to get even greater. So the White House trumpeted Thursday, in the current administration's first substantial engagement with the technology widely predicted to upend every area of life and society. At a meeting that mingled industry, academia, and government, the Trump White House framed AI as a path to continued economic dominance over other nations. The exercise also seemed to expose an administration that lags other countries--and even its US predecessor--in developing its views and strategy on AI's implications and government's role in managing them. "America has been the global leader in AI, and the Trump administration will ensure our great nation remains the global leader in AI," the president's deputy assistant for technology policy, Michael Kratsios said at the meeting's opening.

While studying artificial intelligence during the 1990s for his Ph.D. at MIT, Charles Isbell broke the software some of his friends were working on. "I was breaking all of their facial recognition software because apparently all the pictures they were taking were of people with significantly less melanin than I have," Isbell, now executive associate dean at the Georgia Institute of Technology, told a hearing of Congressional Subcommittee of Information Technology today. "And so they had to come up with ways around the problem--of me." While the facial recognition algorithm worked for his lighter-skinned peers, it couldn't recognize his darker complexion. It's not a unique problem; in 2015, a Google algorithm classified faces of black people as gorillas.

Legislators are working to get a grip on the thorny issue of artificial intelligence by conducting a series of congressional hearings to guide government understanding and adoption of the technology. Senators explore government's role as both an end user and enabler of artificial intelligence. The use of artificial intelligence a mainstream business tool grew 60 percent over the last year. Successful integration of AI into government agencies will reduce costs and increase service management efficiencies. The hearings by the House Oversight and Government Reform's Subcommittee on Information Technology are "an opportunity to leverage technology to make us more efficient," Rep. Will Hurd (R-Texas) said in a video produced by the Committee.

Ian Bogost (previously) describes the "deflationary" use of "artificial intelligence" to describe the most trivial computer science innovations and software-enabled products, from Facebook's suicide detection "AI" (a trivial word-search program that alerts humans) to the chatbots that are billed as steps away from passing a Turing test, but which are little more than glorified phone trees, and on whom 40% of humans give up after a single conversational volley. Georgia Tech artificial intelligence researcher Charles Isbell says it's "Making computers act like they do in the movies." Isbell suggests two features necessary before a system deserves the name AI. First, it must learn over time in response to changes in its environment. Fictional robots and cyborgs do this invisibly, by the magic of narrative abstraction. But even a simple machine-learning system like Netflix's dynamic optimizer, which attempts to improve the quality of compressed video, takes data gathered initially from human viewers and uses it to train an algorithm to make future choices about video transmission.

In science fiction, the promise or threat of artificial intelligence is tied to humans' relationship to conscious machines. Whether it's Terminators or Cylons or servants like the "Star Trek" computer or the Star Wars droids, machines warrant the name AI when they become sentient--or at least self-aware enough to act with expertise, not to mention volition and surprise. What to make, then, of the explosion of supposed-AI in media, industry, and technology? In some cases, the AI designation might be warranted, even if with some aspiration. Autonomous vehicles, for example, don't quite measure up to R2D2 (or Hal), but they do deploy a combination of sensors, data, and computation to perform the complex work of driving.

Drama Managers, a specific type of the more general Experience Manager, have become a common subject of study in the interactive narrative literature. With a range of representational and computational approaches, authors have repeatedly developed techniques that enable computers to generate, reason about, and adapt narratives in an interactive virtual setting. In order to fully realize an experience manager, seven representational and computational problems need to be solved, generally on a case-by-case basis. In other words, the choice to use an Experience Manager is the choice to model the design as, and implement solutions to, seven inter-dependent design problems. We explicitly articulate those design problems and provide a number of examples of methods that both motivate the design problems as well as illustrate a range of approaches to solving them.