The concept of artificial intelligence (AI), or the ability of machines to perform tasks that typically require human-like understanding, has been around for more than 60 years. But the buzz around AI now is louder and shriller than ever. With the computing power of machines increasing exponentially and staggering amounts of data available, AI seems to be on the brink of revolutionizing various industries and, indeed, the way we lead our lives.

Vishal Sikka until last summer was the CEO of Infosys, an Indian information technology services firm, and before that a member of the executive board at SAP, a German software firm, where he led all products and drove innovation for the firm. India Today magazine named him among the top 50 most powerful Indians in 2017. Sikka is now working on his next venture exploring the breakthroughs that AI can bring and ways in which AI can help elevate humanity.

Sikka says he is passionate about building technology that amplifies human potential. He expects that the current wave of AI will “produce a tremendous number of applications and have a huge impact.” He also believes that this “hype cycle will die” and “make way for a more thoughtful, broader approach.”

In a conversation with Knowledge at Wharton, Sikka, who describes himself as a “lifelong student of AI,” discusses the current hype around AI, the bottlenecks it faces, and other nuances.

Knowledge at Wharton: Artificial intelligence (AI) has been around for more than 60 years. Why has interest in the field picked up in the last few years?

 Vishal Sikka: I have been a lifelong student of AI. I met [AI pioneer and cognitive scientist] Marvin Minsky when I was about 20 years old. I’ve been studying this field ever since. I did my Ph.D. in AI. John McCarthy, the father of AI, was the head of my qualifying exam committee.

The field of AI goes back to 1956 when John, Marvin, Allen Newell, Herbert Simon and a few others organized a summer workshop at Dartmouth. John came up with the name “AI” and Marvin gave its first definition. Over the first 50 years, there were hills and valleys in the AI journey. The progress was multifaceted. It was multidimensional. Marvin wrote a wonderful book in 1986 called The Society of Mind. What has happened in the last 10 years, especially since 2012, is that there has been a tremendous interest in one particular set of techniques. These are based on what are called “deep neural networks.”

Neural networks themselves have been around for a long time. In fact, Marvin’s thesis was on a part of neural networks in the early 1950s. But in the last 20 years or so, these neural network-based techniques have become extraordinarily popular and powerful for a couple of reasons.

First, if I can step back for a second, the idea of neural networks is that you create a network that resembles the human or the biological neural networks. This idea has been around for more than 70 years. However, in 1986 a breakthrough happened thanks to a professor in Canada, Geoff Hinton. His technique of backpropagation (a supervised learning method used to train neural networks by adjusting the weights and the biases of each neuron) created a lot of excitement, and a great book, Parallel Distributed Processing, by David Rumelhart and James McClelland, together with Hinton, moved the field of neural net-related “connectionist” AI forward. But still, back then, AI was quite multifaceted.

Second, in the last five years, one of Hinton’s groups invented a technique called “deep learning” or “deep neural networks.” There isn’t anything particularly deep about it other than the fact that the networks have many layers, and they are massive. This has happened because of two things. One, computers have become extraordinarily powerful. With Moore’s law, every two years, more or less, we have seen doubling of price performance in computing. Those effects are becoming dramatic and much more visible now. Computers today are tens of thousands of times more powerful than they were when I first worked on neural networks in the early 1990s.

“The hype we see around AI today will pass and make way for a more thoughtful and realistic approach.”

The second thing is that big cloud companies like Google, Facebook, Alibaba, Baidu and others have massive amounts of data, absolutely staggering amounts of data, that they can use to train neural networks. The combination of deep learning, together with these two phenomena, has created this new hype cycle, this new interest in AI.

But AI has seen many hype cycles over the last six decades. This time around, there is a lot of excitement, but the progress is still very narrow and asymmetric. It’s not multifaceted. My feeling is that this hype cycle will produce great applications and have a big impact and wonderful things will be done. But this hype cycle will die and a few years later another hype cycle will come along, and then we’ll have more breakthroughs around broader kinds of AI and more general approaches. The hype we see around AI today will pass and make way for a more thoughtful and realistic approach.

Knowledge at Wharton: What do you see as the most significant breakthroughs in AI? How far along are we in AI development?

Sikka: If you look at the success of deep neural networks or of reinforcement learning, we have produced some amazing applications. My friend [and computer science professor] Stuart Russell characterizes these as “one-second tasks.” These are tasks that people can perform in one second. For instance, identifying a cat in an image, checking if there’s an obstacle on the road, confirming if the information in a credit or loan application is correct, and so on.

With the advances in techniques — the neural network-based techniques, the reinforcement learning techniques — as well as the advances in computing and the availability of large amounts of data, computers can already do many one-second tasks better than people. We get alarmed by this because AI systems are superseding human behavior even in sophisticated jobs like radiology or legal — jobs that we typically associate with large amounts of human training. But I don’t see it as alarming at all. It will have an impact in different ways on the workforce, but I see that as a kind of great awakening.

But, to answer your question, we already have the ability to apply these techniques and build applications where a system can learn to conduct tasks in a well-defined domain. When you think about the enterprise in the business world, these applications will have tremendous impact and value.

Knowledge at Wharton: In one of your talks, you referred to new ways that fraud could be detected by using AI. Could you explain that?

Sikka: You find fraud by connecting the dots across many dimensions. Already we can build systems that can identify fraud far better than people by themselves can. Depending on the risk tolerance of the enterprise, these systems can either assist senior people whose judgment ultimately prevails, or, the systems just take over the task. Either way, fraud detection is a great example of the kinds of things that we can do with reinforcement learning, with deep neural networks, and so on.

Another example is anything that requires visual identification. For instance, looking at pictures and identifying damages, or identifying intrusions. In the medical domain, it could be looking at radiology, looking at skin cancer identifications, things like that. There are some amazing examples of systems that have done way better than people at many of these tasks. Other examples include security surveillance, or analyzing damage for insurance companies, or conducting specific tasks like processing loans, job applications or account openings. All these are areas where we can apply these techniques. Of course, these applications still have to be built. We are in the early stages of building these kinds of applications, but the technology is already there, in these narrow domains, to have a great impact.

Knowledge at Wharton: What do you expect will be the most significant trends in AI technology and fundamental research in the next 10 years? What will drive these developments?

Sikka: It is human nature to continue what has worked, so lots of money is flowing into ongoing aspects of AI. From chips, in addition to NVidia, Intel, Qualcomm etc., Google, Huawei and others are building their own AI processors and many startups are as well, and all this is becoming available in cloud platforms.  There is tons of work happening in incrementally advancing the core software technologies that sit on top of this infrastructure, like TensorFlow, Caffe, etc., which are still in the early stages of maturity. And this will of course continue.

But beyond this, my sense is that there are going to be three different fronts of development. One will be in building applications of these technologies. There is going to be a massive set of opportunities around bringing different applications in different domains to the businesses and to consumers, to help improve things. We are still woefully early on this front. That is going to be one big thing that will happen in the next five to 10 years. We will see applications in all kinds of areas, and there will be application-oriented breakthroughs.

“The development of AI is asymmetric.”

Two, from a technology perspective, there will be a realization that while the technology that we have currently is exciting, there is still a long way to go in building more sophisticated behavior, building more general behavior. We are nowhere close to building what Marvin [Minsky] called the “society of mind.” In 1991, he said in a paper that these symbolic techniques will come together with the connectionist techniques, and we would see the benefits of both. That has not happened yet.

John [McCarthy] used to say that machine learning systems should understand the reality behind the appearance, not just the appearance. I expect that more general kinds of techniques will be developed and we will see progress towards more ensemble approaches, broader, more resilient, more general-purpose approaches. My own Ph.D. thesis was along these lines, on integrating many specialists/narrow experts into a symbolic general-purpose reasoning system. I am thinking about and working on these ideas and am very excited about it.

The third area — and I wish that there is more progress on this front — is a broader awareness, broader education around AI. I see that as a tremendous challenge facing us. The development of AI is asymmetric. A few companies have disproportionate access to data and to the AI experts. There is just a massive amount of hype, myth and noise around AI. We need to broaden the base, to bring the awareness of AI and the awareness of technology to large numbers of people. This is a problem of scaling the educational infrastructure.

Knowledge at Wharton: Picking up on what you said about AI development being asymmetric, which industries do you think are best positioned for AI adoption over the next decade?

Sikka: Manufacturing is an obvious example because of the great advances in robotics, in advancing how robots perceive their environments, reason about these, and affect increasingly finer control over it. There is going to be a great amount of progress in anything that involves transportation, though I don’t think we are still close to autonomy in driving because there are some structural problems that have to be solved.

Health care is going to be transformed because of AI, both the practice of health care as well as the quality of health care, the way we build medicines, protein-binding is a great case for deep learning, personalize medicines, personalization of care, and so on. There will be tremendous improvement in financial services, where in addition to AI, decentralized/p2p technologies like blockchain will have a huge impact. Education, as an industry, will go through another round of significant change.

There are many industries that will go through a massive transformation because of AI. In any business there will be areas where AI will help to renew the existing business, improve efficiency, improve productivity, dramatically improve agility and the speed at which we can conduct our business, connect the dots, and so forth. But there will also be opportunities around completely new breakthrough technologies that are possible because of these applications — things that we currently can’t foresee.

The point about asymmetry is a broader issue; the fact that a relatively small number of companies have access to the relatively small talent of people and to massive amounts of data and computing, and therefore, development of AI is very disproportionate. I think that is something that needs to be addressed seriously.

Knowledge at Wharton: How do you address that? Education is one way, of course. Beyond that, is there anything else that can be done?

Sikka: I find it extraordinary that in the traditional industries, for example in construction, you can walk into any building and see the plans of that building, see how the building is constructed and what the structure is like. If there is a problem, if something goes wrong in a building, we know exactly how to diagnose it, how to identify what went wrong. It’s the same with airplanes, with cars, with most complex systems.

“The compartmentalization of data and broader access to it has to be fixed.”

But when it comes to AI, when it comes to software systems, we are woefully behind. I find it astounding that we have extremely critical and extremely important services in our lives where we seem to be okay with not being able to tell what happened when the service fails or betrays our trust in some way. This is something that has to be fixed. The compartmentalization of data and broader access to it has to be fixed. This is something that the government will have to step in and address. The European governments are further ahead on this than other countries. I was surprised to see that the EU’s decision on demanding explainability of AI systems has seen some resistance, including here in the valley.

I think it behooves us to improve the state of the art, develop better technologies, more articulate technologies, and even look back on history to see work that has already been done, to see how we can build explainable and articulate AI, make technology work together with people, to share contexts and information between machines and people, to enable a great synthesis, and not impenetrable black boxes.

But the point on accessibility goes beyond this. There simply aren’t enough people who know these techniques. China’s Tencent sponsored some research recently which showed that there are basically some 300,000 machine learning engineers worldwide, whereas millions are needed. And how are we addressing this? Of course there is good work going on in online education and classes on Udacity, Coursera, and others.  My friend [Udacity co-founder] Sebastian Thrun started a wonderful class on autonomous driving that has thousands of students. But it is not nearly enough.

And so the big tech companies are building “AutoML” tools, or machine learning for machine learning, to make the underlying techniques more accessible. But we have to see that in doing so, we don’t make them even more opaque to people. Simplifying the use of systems should lead to more tinkering, more making and experimentation. Marvin [Minsky] used to say that we don’t really learn something until we’ve learnt it in more than one way. I think we need to do much more on both making the technology easier to access, so more people have access to it, and we demystify it, but also in making the systems built with these technologies more articulate and more transparent.

Knowledge at Wharton: What do you believe are some of the biggest bottlenecks hampering the growth of AI, and in what fields do you expect there will be breakthroughs?

Sikka: As I mentioned earlier, research and availability of talent is still quite lopsided. But there is another way in which the current state of AI is lopsided or bottlenecked. If you look at the way our brains are constructed, they are highly resilient. We are not only fraud identification machines. We are not only obstacle detection and avoidance machines. We are much broader machines. I can have this conversation with you while also driving a car and thinking about what I have to do next and whether I’m feeling thirsty or not, and so forth.

This requires certain fundamental breakthroughs that still have not been happened. The state of AI today is such that there is a gold rush around a particular set of techniques. We need to develop some of the more broad-based, more general techniques as well, more ensemble techniques, which bring in reasoning, articulation, etc.

For example, if you go to Google or [Amazon’s virtual assistant] Alexa or any one of these services out there and ask them, “How tall was the President of the United States when Barack Obama was born?” None of these services can answer this, even though they all know the answers to the three underlying questions. But a 5-year-old can. The basic ability to explicitly reason about things is an area where tremendous work has been done for the last many decades, but it seems largely lost on the AI research today. There are some signs that this area is developing, but it is still very early. There is a lot more work that needs to be done. I, myself, am working on some of these fundamental problems.

Knowledge at Wharton: You talked about the disproportionate and lopsided nature of resource allocation. Which sectors of AI are getting the most investment today? How do you expect that to evolve over the next decade? What do traditional industries need to do to exploit these trends and adapt to transformation?

Sikka: There’s a lot of interest in autonomous driving. There is also a lot of interest in health care. Enterprise AI should start to pick up. So there are several areas of interest but they are quite lumpy and clustered in a few areas. It reminds me of the parable of the guy who lost his keys in the dark and looks for them underneath a lamp because that’s where the light was.

But I don’t want to make light of what is happening. There are a large number of very serious people also working in these areas, but generally it is quite lopsided. From an investment point of view, it is all around automating and simplifying and improving existing processes. There are a few developments around bringing AI to completely new things, or doing things in new ways, breakthrough ways, but there is a disproportionate usage of AI for efficiency improvements and automation of existing businesses and we need to do more on the human-AI experience, of AI amplifying people’s work.

“There simply aren’t enough people who know these techniques.”

If you look at companies like Uber or Didi [China’s ride-sharing service] or Apple and Google, they are aware of what is going on with their consumers more or less in real time. For instance, Didi knows every meter of every car ride done by every consumer in real time. It’s the same with Uber and in China, even in physical retail as I mentioned earlier, Alibaba is showing that real-time connection to customers and integration of physical and digital experiences can be done very well.

But in the traditional world, in the consumer packaged goods (CPG) industry or in banking, telecom or retail, where customer contact is necessary, businesses are quite disconnected from what the true end-user is doing. It is not real time. It is not large-scale. Typically, CPG companies still analyze data that is several months old. Some CPG companies still get DVDs from behavioral aggregators three months later.

I think an awareness of that [lag] is building in businesses. Many of my friends who are CEOs of large companies in the CPG world, in banking, pharmaceuticals and telecom, are trying to now embrace new technology platforms that bring these next generation technologies to life.  But beyond embracing technology, and deploying a few next-generation applications, my sense is, the traditional companies really need to think of themselves as technology companies.

My wife Vandana started and built up the Infosys Foundation in the U.S., and her main passion is computer science education. [She left the foundation in 2017.] She found this amazing statistic that in the dark ages some 6% of the world’s population could read and write, but if you think about computing as the new literacy, today some half a percent of the world’s population can program a computer. We are finally approaching 90% literacy in the world, and of course we are not all writers or poets or journalists, but we all know how to write and to read, and it has to be the same way with computing and digital technologies, and especially now with AI, which is as big a shift for us as computing itself.

So businesses need to reorient themselves from “I am an X company,” to “I am a technology company that happens to be in X.” Because if we don’t, we may be vulnerable to a tech company that better sees and executes and scales on that X, as we have already seen in many industries. The iPhone wasn’t so much as a phone, as it is a computer in the shape of a phone. The Apple Watch isn’t a watch, but a computer, a smart computing service, in the shape of a watch. The Tesla is not so much an electric car, but rather a computer, an intelligent, connected, computing service, in the shape of a car. So if you are simply making your car an electric one, this is not enough.

“The iPhone isn’t so much a phone as it is a computer in the shape of a phone.”

Too often companies don’t transform, and they become irrelevant. They may not die immediately. Indeed large, successful, complex structures often outlive us humans, and die long slow deaths, but they lose their relevance to the new very quickly. Transformations are difficult. One has to let go of the past, of what we have known, and embrace something completely new, alien to us. As my friend and teacher [renowned computer scientist] Alan Kay said, “We only make progress by going differently than we believe.” And of course we have to do this as individuals as well. We have to continually learn and renew our skills, our perspectives on the world.

Knowledge at Wharton: How should companies measure the return on investment (ROI) in AI? Should they think about these investments in the same way as other IT investments or is there a difference?

Sikka: First of all, it is good that we are applying AI to things where we already know the ROI. I was talking to a friend recently, and he said, “In this particular part of my business, I have 50,000 people. I could do this work with one-fourth the people, at even better efficiency.” In such a situation, the ROI is clear. In financial services, one area that has become exciting is active trading of asset management. People have started applying AI here. One hedge fund wrote about the remarkable results it got by applying AI. A start-up in China does the entire management of investments through AI. There are no people involved and the company delivers breakthrough results.

So, that’s one way. Applying AI to areas where the ROI is clear, where we know how much better the process can become, how much cheaper, how much faster, how much better throughput, how much more accurate, and so on. But again this is all based on the known, the past. We have to think beyond that, more broadly than that. We have to think about AI as becoming an augmentation for every one of our decisions, every one of the questions that we ask, and have that fed by data and analyzed in real time. Instead of doing generalizations or approximations, we must insist on AI amplifying all of our decisions. We must bring AI to areas where we don’t yet have ROIs clearly identified or clearly understood. We must build ROIs on the fly.

Knowledge at Wharton: How does investment in AI in the U.S. compare with China and other parts of the world? What are the relative strengths and weaknesses of the U.S. and Chinese approaches to AI development?

Sikka: I’m very impressed by how China is approaching this. It is a national priority for the country. The government is very serious about broad-based AI development, skill development and building AI applications. They have defined clear goals in terms of the size of the economy, the number of people, and the leadership position. They actively recruit [AI experts]. The big Chinese technology companies are [attracting] U.S.-based, Chinese-origin scientists, researchers and experts who are moving back there.

In many ways, they are the leaders already in building applications of AI technology, and are doing leading work in technology as well. When you think about AI technology or research, the U.S. and many European universities and countries are still ahead. But in terms of large-scale applications of AI, I would argue that China is already ahead of everybody else in the world. The sophistication of their applications, the scale, the complex conditions in which they apply these, is simply extraordinary. Another dimension of that is the adoption. The adoption of AI technology and modern technology in China, especially in rural areas, is staggering.

Knowledge at Wharton: Could you give a couple of examples of what impressed you most?

Sikka: Look at the payments space — at Alipay, WeChat Pay or other forms of payments from companies like Ping An Insurance, as well as Alibaba and Tencent. It’s amazing. Shops in rural China don’t take cash. They don’t take credit cards. They only do payments on WeChat Pay or on Alipay or others like that. You don’t see this anywhere else in the world at nearly the same scale.

Bike rentals are another example. In the past year, there has been an extraordinary development in China around bicycles. When you walk into a Chinese city, you see tens of thousands of bicycles across the landscape — yellow ones, orange ones, blue ones. When you look at these bicycles, you think, “This is a smart bicycle.” It is another example of an intelligent, connected computing service in the shape of a bicycle. You just have to wave your phone at it with your Baidu account or your Alibaba account or something like that and you can ride the bike. It has GPS. It is fully connected. It has all kinds of sensors inside it. When you get to your destination, you can leave the bike there and carry on with whatever you need to do. Already in the last nine months, this has had a huge impact on traffic.

“The adoption of AI technology and modern technology in China, especially in rural areas, is staggering.”

If you walk into any of Alibaba’s Hema supermarkets in Beijing and Shanghai, I think they have around 20 of these already, teeming with people, they are far ahead of any retail experiences we see today in the US, including at Whole Foods. The entire store is integrated into mobile experiences, so you can wave your phone at any product on the shelf and get a complete online experience. There is no checkout, the whole experience is on mobile and automated, although there are lots of folks there to help customers. The store is also a warehouse, in fact it serves some 70% of demand from local online customers, and fulfills that demand in less than an hour.

My friend ordered a live fish from the store for dinner and it, that particular fish that he had picked on his phone, was delivered 39 minutes later. Tencent has now invested in a supermarket company. And JD has its own stores. So this is rapidly evolving.  It would be wonderful to see convenience like this in every supermarket around the world in the next few years.

A more recent example is battery chargers. All across China, there are little kiosks with chargers inside. You can open the kiosk by waving your phone at it, pick up a charger, charge your phone for a couple of hours, and then drop it off at another kiosk wherever you are. What I find impressive is not that somebody came up with the idea of sharing based on connected phone chargers, but how rapidly the idea has been adopted in the country and how quickly the landscape has adapted itself to assimilate this new idea. The rate at which the generation [of ideas] happens, gets diffused into the society, matures and becomes a part of the fabric is astounding. I don’t think people outside of China appreciate the magnitude of what is going on.

When you walk around Shenzhen, you can see the incredible advances in manufacturing, electronic device manufacturing, drones and things like that. I was there a few weeks ago. I saw a drone that is smaller than the tip of your finger. At the same time, I saw a demo of a swarm of a thousand or so drones which can carry massive loads collectively. So it is quite impressive how broadly the advance of AI is being embraced in China.

“The act of innovating is the act of seeing something that is not there.”

At the other end of the spectrum, I would say that in Europe, especially in Germany, the government is much more rigorous and thoughtful about the implications of these technologies. From a broader, regulatory and governmental perspective, they seem to be doing a wonderful job. Henning Kagermann, who used to be my boss at SAP for many years, recently shared with me a report from the ethics commission on automated and connected driving. The thoughtfulness and the rigor with which they are thinking about this is worth emulating. Many countries, especially the U.S., will be well served to embrace those ideas.

Knowledge at Wharton: How does the approach of companies like Apple, Facebook, Google, Microsoft and Amazon towards AI differ from that of Chinese companies like Alibaba, Baidu, or Tencent?

Sikka: I think there is a lot of similarity, and the similarities outweigh the differences. And of course, they’re all connected with each other. Tencent and Baidu both have advanced labs in Silicon Valley. And so does Alibaba. JD, which is a large e-commerce company in China, recently announced a partnership around AI with Stanford. There’s a lot of sharing and also competitive aspects within these companies.

There are some differences. The U.S. companies are interested in certain U.S.-specific or more international aspects of things. The Chinese companies focus a lot on the domestic market within China. In many ways, the Chinese market offers challenges and circumstances that are even more sophisticated than the ones in the U.S. But I wouldn’t say that there is anything particularly different between these companies.

If you look at Amazon and Microsoft and Google, their advances, when it comes to bringing their platforms to the enterprise, are further ahead than the Chinese companies. Alibaba and Tencent have both announced ambitions to bring their platform to the enterprise. I would say that in this regard, the U.S. companies are further ahead. But otherwise, they are all doing extraordinary work. The bigger issue in my mind is the gap between all of them and the rest of the companies.

Knowledge at Wharton: Where does India stand in all of this? India has quite a lot of strengths in the IT area, and because of demonetization there has been a strong push towards digitization. Do you see India playing any significant role here?

Sikka: India is at a critical juncture, a unique juncture. If you look at it from the perspective of the big U.S. companies or the big Chinese companies, India is by far their largest market. We have a massive population and a relatively large amount of wealth. So, there is a lot of interest in all these companies, and consequently their countries, towards India and developing the market there. If that happens, then of course the companies will benefit. But it’s also a loss of opportunity for India to do its own development through educating its workforce on these areas.

One of the largest populations that could be affected by the impact of AI in the near-term is going to be in India. The impact of automation in the IT services world, or broadly in the services world, will be huge from an employment perspective. If you look at the growth that is happening everywhere, especially in India, some people call it “jobless growth.” It’s not jobless. It’s that companies grow their revenues disproportionately compared to the growth in the number of employees.

“Finding the problem, identifying the innovation — that will be the human frontier.”

There is a gap that is emerging in the employment world. Unless we fix the education problem it’s going to have a huge impact on the workforce. Some of this is already happening. One of the things I used to find astounding in Bangalore was that a lot of people with engineering degrees do freelance jobs like driving Uber and Ola cabs. And yet we have tremendous potential.  The value of education is central to us in India, and we have a large, young, generation of highly inspired youngsters ready to embrace and shape the future, who are increasingly entrepreneurial in their outlook. So we have to build on foundations like the “India stack,” we have to build our own technological strengths, from research and core technology to applications and services. And a redoubling of the focus on education, on training massive numbers of people on technologies of the future, is absolutely critical.

So, in India, we are at this critical juncture, where on one hand there is a massive opportunity to show a great way forward, and help AI be a great amplifier for our creativity, imagination, productivity, indeed for our humanity. On the other hand, if we don’t do these things, we could be victims of these disruptions.

Knowledge at Wharton: How should countries reform their education programs to prepare young people for a future shift by AI?

Sikka: India’s Prime Minister Narendra Modi has talked about this a lot. He is passionate about this idea of job creators, not just job seekers, and about a broad culture of entrepreneurship.

I’m an optimist. I’m an entrepreneur. I like to see the opportunity in what we have, even though there are some serious issues when it comes to the future of the workforce. My own sense is that in the time of AI, the right way forward for us is to become more evolved, more enlightened, more aware, more educated, and to unleash our imagination, to unleash our creativity.

John McCarthy was a great teacher in my life. He used to say that articulating a problem is half its solution. I believe that in our lifetime, certainly in our children’s lifetime, we will see AI technology advance to the point where any task, any activity, any job, any work that can be precisely formulated and precisely articulated, will be done automatically, far better than we can do with our senses and our muscles. However, articulating the problem, finding the problem, identifying the innovation — that will be the human frontier. It is the act of seeing something that is not there. The act of exercising our creativity. And then, using AI to become a great amplifier, to help us achieve our imagination, our vision. I think that is the great calling of our time. That is my great calling.

Five or six hundred million years ago, there was this unusual event that happened geologically. It was called the Cambrian explosion. It was the greatest creation of life in the history of our planet. Before that, the Earth was basically covered by water. Land had started to emerge, and oxygen had started to emerge. Life, as it existed at that point, was very primitive. People wondered, “How did the Cambrian explosion happen? How did all these different life forms show up in a relatively small period of time?” What happened was that the availability of oxygen, the availability of land, and the availability of light as a provider of life, as a provider of living, created a situation which formed all these species that had the ability to see. They all came out of the dark, out of the water, onto the land, into the air, where opportunities were much more plentiful, where they could all grow, they could all thrive. People wonder, “What were they looking for?” It turns out they were looking for light. The Cambrian explosion was about all these species looking for light.

When I think about the future, about the time in front of us, I see another Cambrian explosion. The act of innovating is the act of seeing something that is not there. Our eyes are programmed by nature to see what is there. We are not programmed to see what is not there. But when you think about innovation, when you think about making something new, everything that has ever been innovated was somebody seeing something that was not there.

I think the act of seeing something that is not there is in all of us. We can all be trained to see what is not there. It is not only a Steve Jobs or a Mark Zuckerberg or a Thomas Edison or an Albert Einstein who can see something that is not there. I think we can all see some things that are not there. To Vandana’s statistic, we should strive to see a billion entrepreneurs out there. A billion-plus computer literate people who can work with, even build, systems that use AI techniques, and who can switch their perspective from making a living to making a life.

When I was at Infosys, we trained 150,000 people on design thinking for this reason: To get people to become innovators. In our lifetime, all the mechanical, mechanizable, repeatable things are going to be done way better by machines. Therefore, the great frontier for us will be to innovate, to find things that are not there. I think that will be a new kind of Cambrian explosion. If we don’t do that, humanity will probably end.

Paul MacCready, one of my heroes and a pioneer in aerospace engineering, once said that if we don’t become creative, a silicon life form will likely succeed us. I believe that it is in us to refer back to our spirituality, to refer back to our creativity, our imagination, and to have AI amplify that. I think this is what Marvin [Minsky] and John [McCarthy] were after and it behooves us to transcend the technology. And we can do that. It is going to be tough. It is going to require a lot of work. But it can be done. As I look at the future, I am personally extremely excited about doing something in that area, something that fundamentally improves the world.