MOUNTAIN VIEW, Calif. — In a May memo to President Trump, Defense Secretary Jim Mattis implored him to create a national strategy for artificial intelligence.

Mr. Mattis argued that the United States was not keeping pace with the ambitious plans of China and other countries. With a final flourish, he quoted a recent magazine article by Henry A. Kissinger, the former secretary of state, and called for a presidential commission capable of “inspiring a whole of country effort that will ensure the U.S. is a leader not just in matters of defense but in the broader ‘transformation of the human condition.’” Mr. Mattis included a copy of Mr. Kissinger’s article with his four-paragraph note.

Mr. Mattis’s memo, which has not been reported before and was viewed by The New York Times, reflected a growing sense of urgency among defense officials about artificial intelligence. The consultants and planners who try to forecast threats think A.I. could be the next technological game changer in warfare.

The Chinese government has raised the stakes with its own national strategy. Academic and commercial organizations in China have been open about working closely with the military on A.I. projects. They call it “military-civil fusion.”

It is not clear what impact, if any, Mr. Mattis’s memo had. Though the White House announced in May — about three weeks before he sent his note — that it would establish a panel of government officials to study A.I. issues, critics say the administration still has not done enough to set federal policy. Officials with the Office of Science and Technology Policy, which would most likely take a leadership role in setting an agenda for A.I., said that A.I. is a national research and development priority and that it is part of the president’s national security and defense strategies.

Nonetheless, the Pentagon appears to be pushing ahead on its own, looking for ways to strengthen its ties with A.I. researchers, particularly in Silicon Valley, where there is considerable wariness about working with the military and intelligence agencies.

In late June, the Pentagon announced the creation of the Joint Artificial Intelligence Center, or JAIC. Defense officials have not said how many people will be dedicated to the new program or where it will be based when it starts next month. It could have several offices around the country.

The Defense Department wants to shift $75 million of its annual budget into the new office and a total of $1.7 billion over five years, according to a person familiar with the matter who was not allowed to speak about it publicly.

Known as “the Jake,” the center is billed as a way of facilitating dozens of A.I. projects across the Defense Department. This includes Project Maven, an effort to build technology to identify people and things in video captured by drones that has come to symbolize the ideological gap between the government and Silicon Valley.

Around the time Mr. Mattis wrote his memo to Mr. Trump, thousands of Google employees were protesting their company’s involvement in Project Maven. After the protests became public, Google withdrew from the project.

The protests might have been a surprise to Pentagon officials, since big tech companies have been defense contractors for as long as there has been a Silicon Valley. And there is some irony in any industry reluctance to work with the military on A.I., given that research competitions sponsored by an arm of the Defense Department, called Darpa, jump-started work on the technology that goes into the autonomous vehicles many tech companies are now trying to commercialize.

But in the eyes of some researchers, creating robotic vehicles and developing robotic weapons are very different. And they fear that autonomous weapons pose an unusual threat to humans.

“This is a unique moment, with so much activism coming out of Silicon Valley,” said Elsa Kania, an adjunct fellow at the Center for a New American Security, a think tank that explores policy related to national security and defense. “Some of it is informed by the political situation, but it also reflects deep concern over the militarization of these technologies as well as their application to surveillance.”

The Joint Artificial Intelligence Center, officials hope, will help close that gap.

“One of our greatest national strengths is the innovation and talent found in our private sector and academic institutions, enabled by free and open society,” Brendan McCord, a former Navy submarine officer and an A.I. start-up veteran who will lead the center, said during a public meeting in Silicon Valley last month. “The JAIC will help evolve our partnerships with industry, academia, allies.”

The center, he added, will work with “traditional and nontraditional innovators alike,” meaning longtime government contractors like Lockheed Martin as well as newer Silicon Valley companies. The Pentagon has worked with more than 20 companies on Project Maven so far, but it hopes to expand this work and overcome the reluctance among workers.

This summer, a Pentagon researcher worked alongside a small but influential Silicon Valley artificial intelligence lab, Fast.ai, on a public effort to build technology capable of accelerating the development of A.I. systems.

Autonomous systems are based on algorithms that can learn to do things like recognize objects by analyzing vast amounts of data. The Fast.ai project would improve the speed of that A.I. “training.”

The Pentagon is also offering an olive branch to its Silicon Valley critics. While unveiling the JAIC, Mr. McCord said its focus would include “ethics, humanitarian considerations, and both short-term and long-term A.I. safety.”

It was an important step toward reaching détente with A.I. researchers, said Sophie-Charlotte Fischer, a researcher at Center of Security Studies at ETH Zurich University in Switzerland who specializes in the relationship between the tech industry and government. “There needs to be a clear understanding of what it means to develop and deploy these A.I. technologies,” she said.

Will it be enough? Skeptics want to see the details. “So far, the plans remain very abstract,” Ms. Fischer said. “What kind of systems do they want to allow? Do they want to attach weapons systems to A.I.?”

Robert Work, the former deputy secretary of defense who founded Project Maven, worries that the Google protest has skewed the perception of the project, which does not yet involve lethal weapons, and stunted public discussion of how military technology should evolve.

“We need to have an open debate about A.I. and its consequences and hear arguments from all sides,” he said in a recent interview.

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EARLIER THIS SUMMER Google engineer James Damoreposted a treatise about gender differences on an internal company message board and was subsequently fired. The memo ignited a firestorm of debate about sex discrimination in Silicon Valley; this followed months of reporting on accusations of harassment at Uber and elsewhere. Sex discrimination and harassment in tech, and in science more broadly, is a major reason why women leave the field. Nationally, there has long been handwringing about why women are underrepresented in STEM (science, technology, engineering, and math), which has led to calls for increased mentoring, better family leave policies, and workshops designed to teach women how to negotiate like men.

Last month three senior researchers at the Salk Institute for Biological Studies in La Jolla filed lawsuitscomplaining of long-term gender discrimination; the complaints allegethat women don’t have equal access to internal funding and promotions. These lawsuits highlight the real reason for the lack of women in science: Leaders in the field—men and sometimes women—simply don’t believe that women are as good at doing science.

A vast literature of sociology research shows time after time, women in science are deemed to be inferior to men and are evaluated as less capable when performing similar or even identical work. This systemic devaluation of women results in an array of real consequences: shorter, less praise-worthy letters of recommendation; fewer research grants, awards, and invitations to speak at conferences; and lower citation rates for their research. Such wide-ranging devaluation of women’s work makes it harder for them to progress in the field.

One early study evaluated postdoctoral fellowship applications in the biomedical sciences and found that the women had to be 2.5 times more productive than the men in order to be rated equally scientifically competent by the senior scientists evaluating their applications. The authors concluded, “Our study strongly suggests that peer reviewers cannot judge scientific merit independent of gender. The peer reviewers over-estimated male achievements and/or underestimated female performance.” The study finds that “gender discrimination of the magnitude we have observed… could entirely account for the lower success rate of female as compared with male researchers in attaining high academic rank.”

A more recent study showed that science faculty at research-intensive universities were more likely to hire a male lab manager, mentor him, pay him more, and rate him as more competent than a female candidate with the exact same résumé. Another paper found that faculty respond to emails from male prospective PhD students more than from female prospective students, showing that men have greater access to professors. This are just a few of the hundreds of peer-reviewed studies that clearly show, on average, the bar is set higher for women in science than for their male counterparts.

Given the enormous amount of data to support these findings, and given the field in question, one might think male scientists would use these outcomes to create a more level playing field. But a recent paper showed that in fact, male STEM faculty assessed the quality of real research that demonstrated bias against women in STEM as being low; instead the male faculty favored fake research, designed for the purposes of the study in question, which purported to demonstrate that no such bias exists.

Why do men in science devalue such research and the data it produces? If anyone should be willing to accept what the peer-reviewed research consistently shows and use it to correct the underlying assumptions, it should be scientists.

But it is in large part because they are scientists that they do not want to believe these studies. Scientists are supposed to be objective, able to evaluate data and results without being swayed by emotions or biases. This is a fundamental tenet of science. What this extensive literature shows is, in fact, scientists are people, subject to the same cultural norms and beliefs as the rest of society. The systemic sexism and racism on display every day in this country also exist within the confines of science. Scientists are not as objective as they think they are. It is an extremely destabilizing realization for someone whose entire career has been rooted in the belief in human objectivity.

Even more pernicious, however, is the understanding that results from reading these studies, the realization that those who have succeeded in science (and in many fields—the implications reach far beyond science) have not done so entirely due to their own innate brilliance. Statistically speaking, just being male will automatically give you a leg up. And no one wants to believe that they did not achieve their success, even in some small part, based on their gender or ethnicity. We all want to feel that we deserve the success and accolades that we have received based on our own merit.

And this is a major reason why there aren’t more women in science. While sexual harassment is certainly an issue, we need to look deeper at gender bias. Those women who do make it to the upper ranks have often been told that they were only given that job or that award because they are women, implying that the field is admitting less-deserving women simply to increase their numbers. But in fact, these studies show that many of the women in science must be more capable than the men, to even have advanced in the field. And who wants to admit that?

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Musk is wrong to worry about artificial intelligence (AI) being a threat to humanity, so I agree with Zuckerberg. And Zuckerberg is wrong to dismiss all concerns about AI, so I agree with Musk. But neither of them are worrying about the right things.

AI is transforming almost every aspect of our lives, from the workspace to the political arena. You can’t open a newspaper today without reading a story about some impressive advance in AI.

Are machines taking over people’s jobs? Are algorithms having an impact on political debate? Will robots transform warfare? Are we sleepwalking into some dystopian future?

We’re working on ‘AI safety’

First, let’s put to rest Elon Musk’s worry. The machines aren’t about to take over the world anytime soon. Those of us working on building intelligent machines appreciate how much of a challenge remains. We’re not going to wake up anytime soon and discover the machines are in charge.

Most of my colleagues working in AI estimate it is at least 50 years before we can build machines as smart as humans. And when we do, it’s not inevitable they’ll be able to make themselves even smarter still.

So, there is plenty of time to ensure the machines are working in our best interests. And there’s a healthy community of researchers working on the topic of “AI safety” to ensure that outcome.

But that doesn’t mean we can simply put our feet up and wait for the bright future. There’s a lot to worry about. Some AI is smart, some is stupid. We’re starting to give responsibility to algorithms that aren’t actually very intelligent.

Joshua Brown discovered this to his cost in May last year. He was immortalised as the first person killed by their autonomous car. His Tesla was driving down the highway in “autopilot mode” when it hit a truck turning across the road. Mr Brown had too much faith in the technology.

Another worry is the impact AI is having on political discourse. When millions of Donald Trump’s Twitter followers are robots, you have to worry if human voices are being drowned out by computers. If the news you see on Facebook is decided by algorithms, who decides on the biases in these algorithms?

A third worry is the impact AI will have on the workforce. There’s no fundamental law of economics that requires new technologies to create more jobs than they destroy, which has been the case so far. There are more people working today than ever, and unemployment is at historically low levels.

There were 50 years of pain after the Industrial Revolution

But this time could be different. In the Industrial Revolution, machines took over much manual labour but left us with many cognitive tasks. In the AI revolution, machines will take over many of these cognitive tasks. What is left for us?

The Industrial Revolution offers us a good historical precedent for dealing with change like this. Before the industrial revolution, many people worked out in the fields. After the Industrial Revolution, machines took over many of these jobs. And new jobs were created in offices and factories.

We invented universal education so people were educated for these new jobs. We invented labour laws and unions so the owners of the production didn’t exploit their workers. We invented a welfare state and pensions so all of us shared the increased wealth. We made some deep, structural changes to society so everyone shared the benefits of increasing productivity.

These changes didn’t happen overnight. Indeed, there were 50 years or so of pain before many workers saw their quality of life lift above what is was before the Industrial Revolution.

This then is the challenge we face today — except the AI revolution will likely happen even faster than the Industrial Revolution. For this reason, we need more regulation.

Many tech companies like Facebook and Google are driven by opaque algorithms and are increasingly impacting on our lives in undesirable ways.

Facebook is now the largest news organisation on the planet, yet it doesn’t have the same responsibilities as the traditional press.

Google is starting to know too much about our lives, and will need to be broken into parts to prevent it becoming a monopoly. Actually, by creating the holding company Alphabet, Larry Page and Sergey Brin have made the regulators’ job much easier.

And it’s hard to know where to begin with Uber, one of the most badly behaved of them all.

If Google or other companies won’t pay taxes, then more countries besides Australia and the UK need to make a Google tax to force them to do so.

Silicon Valley can’t wash its hands of the responsibility that comes with immense reach.

For too long, we (and our governments) have been seduced by the promises spun by technologists.

AI is one of the few hopes for tackling many of the problems that challenge us today like climate change and the ongoing global financial crisis.

But with immense power comes responsibility.

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