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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Medicine is both art and science. While any doctor will quickly credit her rigorous medical training in the nuts and bolts of how the human body works, she will just as adamantly school you on how virtually all of the decisions she makes—about how to diagnose disease and how best to treat it—are equally the product of some less tangible measures: her experience from previous patients; her cumulative years of watching and learning from patients, colleagues and the human body.

Which is why the idea of introducing machines into medicine seems misguided at the very least, and also foolhardy. How can a robot, no matter how well-trained, take the place of a doctor?

Machine learning, the most basic form of artificial intelligence, is already infiltrating the medical field, and it turns out that machines can play an important role in improving our health—including making diagnoses more accurately and quickly and finding better treatments that save people time and money and prevent exposure to harmful side effects. In fact, with modern medicine increasingly dependent on large numbers of studies and drug options and reams of new information, machines may be better able to keep up with and interpret data than the human mind.

The idea behind artificial intelligence in medicine is not so much to replace the doctor (at least not any time in the near future) but to enhance the doctor’s medical expertise. A.I. programs take the amassed knowledge that every good physician has—which is the product of everything she learned in medical school and in training as well as her experience in treating patient after patient—and scale it to unprecedented levels.

Why should patients have access to just one particular doctor’s expertise when it’s now possible to provide them with the brainpower of hundreds of thousands? Why should patients in rural areas who live geographically far from the nation’s leading medical centers be deprived of all the up-to-date knowledge housed there? “The way artificial intelligence starts to really impact what’s going on in health care is to be able to start cloning all the expert knowledge, so now all of a sudden you get access to all types of care, anywhere,” says Steve Harvey, vice president of Watson Health at IBM.

And with the amount of data available to physicians today—from information about disease symptoms to new drugs, interactions between different drugs and how different people treated in the same way can have very different outcomes—the ability to access and digest information is fast becoming a required skill. And it’s one that machine learning is uniquely designed to master. “Doctors are realizing that if they want to make sense of massive amounts of data, machine learning is a way of allowing them to learn from that data,” says Francesca Dominici, a professor of biostatistics at the Harvard T.H. Chan School of Public Health and co-director of the Data Science Initiative at the university.

Harvard isn’t the only academic institution exploring how man and machine can better combine their skills to exploit unprecedented amounts of medical information. At the University of Texas MD Anderson Cancer Center, the APOLLO program is sifting through the genetic data generated by every patient’s cancer and directing doctors to the treatments that will give their patients the best chance of surviving longer. At the Boston company Neurala, researchers are busy replicating, in silicon, the neural network of the human brain in all its complexity and sophistication. “Today we can design the brain with the complexity of the mouse, which is incredibly smart,” says Massimiliano Versace, Neurala’s CEO. “Science and technology are now aligned for the perfect storm to make artificial intelligence possible.” And in the mental-health field, startups are jumping into machine-learning apps that can help detect when people with conditions such as depression or bipolar disorder are on the verge of a new episode of symptoms in a way that no psychiatrist, however dedicated, ever could.

The key to machine learning in medicine is, well, the machine. And machines from IBM and Google have recently flexed their cognitive muscle by besting the leading Jeopardy! champions, chess masters and Go experts—after learning from the knowledge of previous players, which became part of the machines’ programming.

Now IBM is bringing that idea to medicine, based on the concept that medical knowledge could be as programmable and amenable as the many possible iterations of chess moves and trivia answers. The company is working with experts at Memorial Sloan Kettering Cancer Center in New York to develop IBM Watson for Oncology, made up of three products that address different types of cancer patients. One level will focus on providing patients with the best available information for treating their cancer with existing therapies; Watson provides access to a database of the collected knowledge of Memorial Sloan Kettering’s cancer doctors, as well as the most important cancer studies in the medical literature that these doctors rely on when making their decisions about how to treat patients.

The system incorporates patients’ symptoms and other salient information, such as their family history of the disease and the stage of their cancer, before offering up three different levels of treatment options that the physician can consider. These range from current standard therapies that have already been approved for that type of cancer to treatments approved for other cancers that are currently being tested but are not yet approved for the patient’s specific cancer, and finally truly experimental treatments that some early studies hint might be effective at treating the disease. The different levels of options give both the doctor and patient a treatment plan—if the standard therapies don’t work, then they can move on to the less proven and more experimental ones.

Beyond available treatments, Watson is also helping people with more-advanced cancers who have exhausted the standard therapies. For them, machine learning can call up clinical trials of new therapies that might be effective, including genetic solutions, which are just emerging as a promising area of cancer treatment. The genetic options are based on a careful analysis of the patient’s specific tumor, the mutations driving the disease and drugs that might be targeted to address those mutations.

For human doctors to digest all this information would be nearly impossible, given the demands on physicians’ time to see patients and keep up to date on the latest advances in their field. The potential benefit of having a Watson “doctor” on call at every cancer hospital, no matter how small, can’t be overstated. People with rarer cancers that their local physicians haven’t treated before won’t have to travel great distances to a major hospital that has more experience with that disease, or have to miss the opportunity to get their cancer treated at all. Doctors with less experience with specific cancers can also care for their patients with more confidence, since they now have the institutional knowledge of leading experts in their field at their disposal.

As more information about different cancer patients and their tumors becomes part of Watson, doctors will be able to see patterns that will help them match specific patient profiles to survival rates and better outcomes. They will be able to recognize when people with similar genetic tumors, for example, who took different treatment paths have different health outcomes. That analysis would lead to more refined advice for people about which treatment route is best for them.

The system isn’t perfect yet. Some of IBM’s partners have found Watson cumbersome when it comes to entering all the relevant information from patients—mimicking, in other words, the way doctors incorporate everything they know about a patient into their treatment recommendations. But physicians support the idea that having a way to collect, collate and categorize the massive amounts of information being generated about each patient will be a big part of improving cancer care in coming years.

Such machine-learning approaches are proving remarkably helpful in another area of medicine that may not seem to be so appropriate for non-human interaction: mental health. For people suffering from depression and bipolar disorder, for example, one of psychiatrists and therapists’ most important roles is to help them avoid descending into emotional spirals from which it’s difficult to recover. Identifying when people are most vulnerable to depressive or manic episodes could keep them from the most harmful mental symptoms, and it turns out that machines—in this case a smartphone—might be able to do that better than any psychiatrist can.

That’s because, as is well-known, people verging on depressive episodes or succumbing to feelings of sadness and negativity have changes in their speech and behavior. They may speak less and, when they do, adopt a flat, monotonic tone. They may also disengage from friends and loved ones, calling them less or interacting less frequently on social media. Even the best psychiatrist can’t possibly keep up with all of his patients to monitor when they start to display such changes in behavior. But smartphones can.

Cogito, a mental-health app built on the idea of machine learning, is now being tested at facilities like Brigham and Women’s Hospital in Boston. The app, once installed on a smartphone, monitors activity on social media and phone calls to discern patterns of communication so that when depressive episodes strike, for example, and those patterns change, the app will detect it.

The app also contains a voice analyzer that can search vocal patterns for changes in affect and tone, which may be the first signs of a depressive episode. “The A.I. aspect may be in a better position to gather data over time and give us a better indicator of risk for someone having a mental-health problem and whether they warrant direct intervention with a clinician,” says David Ahern, the director of the Program in Behavioral Informatics and eHealth in the Department of Psychiatry at Brigham.

Machine learning could be especially helpful in alerting a physician or a patient’s family when things begin to spiral out of control. “Historically, we have been dismally poor at detecting dangerousness or self-harm,” says Ahern. “Potentially, with technology like Cogito, we may be able to develop an early-warning system that, for somebody who has a high risk profile because they have a history of depression or suicide attempts, could monitor and see changes in patterns to better determine when the risk gets to the level where intervention is needed to prevent episodes of self-harm or dangerous activity. That’s a place where we haven’t—with traditional models of care—been very good at. We’ve been very reactive, and we want to be more proactive.”

That’s where artificial intelligence can provide the most benefit to people’s health. Its ability to predict how aggressive or mild a person’s disease might be, and to know which treatments might work well and which might not, may make machine learning an integral, and eventually indispensable, part of medical care. It may be time to realize that it’s not man against machine but man andmachine together that can finally create the biggest improvements in human health.

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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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In June 2013, Sir Mark Walport and Professor Dame Nancy Rothwell, co chairs of the Council for Science and Technology, sent a letter to then Prime Minister David Cameron entitled ‘The Age of Algorithms’. The letter contained eight recommendations, the sixth of which was the establishment of a National Centre to promote advanced research and translational work in algorithms and the application of data science. “This could fittingly be named the ‘Alan Turing Centre’,” they noted.

‘Big Data’ also featured in the ‘Eight Great Technologies’ speech made in 2013 by ex Industry minister David Willetts in which he outlined areas where Government investment could support the development of innovative technologies and strengthen the UK’s competitive advantage.

Picking up on this theme, the Alan Turing Institute was founded in 2015, looking to research some of the big challenges in data science.

So what is ‘Big Data’? “It’s a buzz phrase with lots of layers,” said Dr Anthony Lee, one of the Institute’s strategic programme directors. “It refers to the huge amount of data available as a result of data collection and digitalisation in all sectors. And everyone is recognising the importance of data to industry and the economy, amongst other things.”

Why the focus on Big Data? “In some respects, Big Data has been around for a decade,” he said. “But lately, industry and scientists have realised how useful the data around us is. Before, we would try to learn more about the world around us using highly focused statistical experiments, for example,” Dr Lee explained. “Big Data has changed the questions we can ask; we can improve our understanding of society based on the analysis of data, while companies can build better products.

“The quality and quantity of data and information we are dealing with nowadays is very heterogeneous. Companies collect data about their customers and can extract valuable commercial benefit from it. Similarly, we may broaden our knowledge of the universe by analysing information collated by space telescopes that are either directed at carefully chosen regions of space or designed to cover a wide area.”

And Big Data is big; the term is a representation of the fact that, during the last 30 years, the amount of data generated per year has increased by a factor of 10 every two years.

Dealing with Big Data entails bringing together people with a range of skills. “There is a range of inter-related themes,” Dr Lee noted. “Machine learning is an important part of data science and one way of tackling the huge data sets. In the past, artificial intelligence was more about deterministic logic, while ML is more about probabilistic reasoning.”

But he admits it’s not always easy getting these people to work together. “We have created a strong interdisciplinary environment in which leading academics can collaborate to solve problems which they wouldn’t have been able to solve on their own.”

And the Institute is bringing together a diversity of people, with pure mathematicians, classical statisticians, computer scientists, social scientists and software engineers contributing to algorithms and hardware. “We really need these people to work together if they are to solve the challenges.”

Dr Anthony Lee

A different point of view
The Institute’s mission as the UK’s national institute for data science is to bring a different perspective on solving the challenges posed by Big Data. “It’s world class fundamental data science,” Dr Lee pointed out, “as well as applied research. It’s not only somewhere that people can start to understand the shape of problems surrounding data, but also a place where the next generation of leading data scientists can be trained.”

Alongside the Engineering and Physical Sciences Research Council, the Institute has five partner universities – Cambridge, Edinburgh, Oxford, University College London and Warwick – and four strategic partners – Intel, Lloyds Register Foundation, GCHQ and the Ministry of Defence, and HSBC.

Dr Lee, a computational statistician in the Department of Statistics at Warwick, is strategic programme director of the partnership between the Alan Turing Institute and Intel.

“Our university partners are complementary,” Dr Lee noted. “They all have mathematics and computer science departments with global reputations and each has specific strengths. It’s important that we can draw on this diverse talent base when we’re looking at problems.

“The UK enjoys a special position in terms of maths and there’s a huge strength in statistics. Alongside that, its engineering strength dates back to the industrial revolution.”

One of the issues with Big Data is that, as the name suggests, the problems are also big. So identifying which challenges to pursue can in itself be a problem

“We instigated a huge scoping process,” he explained. “More than 1000 people contributed to this process, which ended up with 100 research proposals being made.

“This allowed the Institute to create a matrix of areas of interest to researchers. The matrix blends horizontal themes with vertical industry sectors. And this is one of the interesting things; the cross cutting nature of the research being embarked on here provides challenges for everyone.”

A matrix of research interests blends horizontal themes with vertical industry sectors, highlighting the cross cutting nature of the institute’s work

Amongst the themes being explored are performance failures in large computer clusters, understanding why they happen and how to mitigate them. “There’s also data centric engineering and smart cities, as well as progress on secure cloud computing.”

With the focus on maths and computer science, observers might be convinced that the Institute is software oriented. But Dr Lee disagrees. “It’s about everything to do with Big Data and it’s why we have horizontal and vertical focuses. It’s not only about algorithms, it’s also about the systems on which they run; we’ll even be looking at chip design and network interconnects.

”Deep learning is a good example; you need to create a network, then train it. You need mathematical modelling, algorithms, hardware and software skills to solve the problems.”

Pointing out the close relationship between software and hardware, Dr Lee noted that Turing himself was a mathematician, but was involved with the early modelling of computers and had insights into data analysis. “Similarly,” he added, “von Neumann was a mathematician, but developed a computer architecture.”

Although established in 2015, the Institute’s full research programme began in October 2016. “We’ve made good progress on our initial focus areas,” he said. “Like all good scientists, we’re solving challenging problems, but ones that can be solved.

“But progress is not as difficult as some might think. While all academics have core research interests, some can see the value in collaboration and will recognise they can achieve things they couldn’t before.”

While the Institute is endeavouring to ‘hit the ground running’ and solve some immediate problems, Dr Lee said it was still important to keep an eye on the ‘long game’, where fundamental work might need to be done.

So is the UK the leading player in Big Data, as Willetts hoped for? “The UK is a world leader,” Dr Lee agreed. “That’s not to say other countries aren’t paying attention to the topic.

“Big data is almost a revolution,” he concluded, “and the important thing is that it underpins many aspects of the economy. Already, there has been a large scale uptake in the use of Big Data in decision making.”

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The Wall Street Journal recently reported that start-ups will have a prominent role at this year’s Paris Air Show.  Around 100 companies will have the opportunity to pitch to receive funding from the big boys of aerospace at an event where young companies were once shunted to the side.  It’s further evidence of a trend that is playing out across the science and technology sector and being picked up by the media: established industry players in science and technology are increasingly looking to start-ups and entrepreneurs for innovations, be they new designs, materials or just different ideas about operations.

The industry has definitely caught on to the fact that we are in the midst of a fourth industrial revolution, encompassing the advancement of 3D printing, artificial intelligence (AI) and energy storage, to name just a few elements.  Scientific and tech leaders also know that two heads are better than one when it comes to addressing the challenges this new revolution will throw our way.

This was especially evident at London’s recent CogXevent, where industry thought leaders from around the world gathered to discuss the irrefutable impact that AI is already having across a multitude of sectors, and also to try and begin to answer some of these pressing questions. How will this technology be regulated? Who will be accountable for making sure that this technology is used for good? (There’s no debate that there’s potential to be used for bad.) And, to state the questions that have been frequently splashed across the headlines of the consumer and trade press – how will this technology impact people’s jobs and lives?

There is no doubt that AI and other technological advancements will change the way we live and work, and it will undoubtedly make some roles redundant.  But this needn’t be a frightening or negative prospect. The leaders at the forefront of developing this technology need to remember that part of their role is to educate people on how they will benefit from this technology so that the inevitable realisation that the machines are coming is welcomed with anticipation for positive changes, not fear and uncertainty.

There are a multitude of positive examples demonstrating that AI is already impacting people’s lives in positive ways.  For example, by making employees more productive and freeing them up for more creative and meaningful work.

Many people do not realise how many ways they’ve already begun to embrace this technology.  The smartphones in their pockets and the virtual assistants they use to set alarms are continuously learning.

AI technology has huge potential to make people’s lives better.  Imagine how computers that have the capacity to keep track of patient’s care plans could help the healthcare industry – nurses and doctors would be relieved of mountains of administrative work and could focus on taking care of patients or pushing research forward that could help cure diseases. Or in the security industry, where vast amounts of data have to be analysed to spot patterns and identify potential threats, AI tools will help analysts make better, more informed decisions faster.

In retail, sales automation and analysis is helping companies present customers with products that fill genuine needs and desires in their lives, rather than just bombarding them with mass meaningless sales messages. Driverless cars will dramatically change the way we get from place to place – what could people do with an extra hour of time to explore their hobbies or interests on the way to or from work each day?

All of these innovations take on roles and tasks that used to be performed by people, but that doesn’t mean that humans are removed from the equation – rather that their talent, knowledge and skills and scope for creativity is supported by tools that learn as they do, continually enhancing their abilities, not competing with them.

The thinkers, designers and inventors driving these innovations forward need to remember to communicate the benefits of this new technology to everyone who will use it. Rather than focusing on what jobs will be lost, they should discuss the time that people will gain, the better care they’ll be able to provide and receive, how the world will become safer, and the value these new tools will add to human creative processes.

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