An AI that can predict cell structures 05-12

Fluorescent-labeled cells used to train neural networks. Image: Allen Institute. 


New 3D models of living human cells generated by machine-learning algorithms are allowing scientists to understand the structure and organization of a cell's components from simple microscope images.

Why it matters: The tool developed by the Allen Institute for Cell Science could be used to better understand how cancer and other diseases affect cells or how a cell develops and its structure changes — important information for regenerative medicine.

"Each cells has billions of molecules that, fortunately for us, are organized into dozens of structures and compartments that serve specialized functions that help cells operate," says Allen Institute's Graham Johnson, who helped develop the new model.

What they did: The researchers used gene editing to label the nucleus, mitochondria and other structures inside live human induced pluripotent stem cells (iPSC) with fluorescent tags and took tens of thousands of images of the cells.

They then used those images to train a type of neural network known as Generative Adversarial Networks (GANs). That yielded a model that can predict the most likely shape of the structures and where they are in cells based on just the cell's plasma membrane and nucleus.

Using a different algorithm, they created a model that can take an image of a cell that hasn't been fluorescent-labeled — in which it's difficult to distinguish the cell's components ("it looks like static on an old TV set," Graham Johnson says) — and find the structures.

What they found: When they compare the predicted image to actual labeled ones, the Allen Institute researchers said they are nearly indistinguishable.

The advance: Gene editing and fluorescent dyes often used to study cells only allow a few components to be visualized at once and can be toxic, limiting how long researchers can observe a cell.

Plus, "knowledge gained from more expensive techniques or ones that take a while to do and do well can be inexpensively applied to everyone’s data," says the Allen Institute's Greg Johnson, who also worked on the tool. "This provides an opportunity to democratize science."

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Vishal Sikka: Why AI Needs a Broader, More Realistic Approach 02-16

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@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@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@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@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@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@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@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@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@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@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@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@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@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@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@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.

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Turning workers into 'super workers' with robotic suits 02-14

If you've watched the Iron Man film franchise, you'll know that a powered suit gives inventor Tony Stark superhuman strength to fight the bad guys.

But away from the the fictional world of blockbusting movies, robotic exoskeletons offer more prosaic and useful help for humans.

The military has been in on the act for years, using them to help soldiers carry more weight for longer periods of time. Meanwhile manufacturers have been busy creating robotic suits to give mobility to people with disabilities.

But now exoskeletons are becoming an important part of the scene in more conventional workplaces, mainly because of their unique offering.

"Exoskeletons act as a bridge between fully-manual labour and robotic systems. You get the brains of people in the body of a robot," says Dan Kara, research director at ABI Research.

"But there's more to it than that. You can tie the use of exoskeletons to business benefits that are very easy to quantify. The main one is a reduction in work-related injuries, and we know that outside the common cold, back injury is the main reason people are off work."

The motor industry has used robots for many years. But robots can't do everything, points out technical expert Marty Smets, of Ford's human systems and virtual manufacturing unit.

"In our plants, we see a need for both people and robots," he says.

Some Ford assembly line workers lift their arms up to 4,600 times a day - that's about a million times a year. That sort of repetition leaves many suffering from back-ache and neck pain.

Now, though, the company has equipped staff at two US assembly plants with a device called the EksoVest, from California-based Ekso Bionics. It helps take the strain by giving workers an extra 5-15lb (2.2-6.8kg) of lift per arm.

"Incredible is the only word to describe the vest," said Paul Collins, an assembly line worker at Ford Michigan assembly plant. "It has made my job significantly easier and has given me more energy throughout the day."

The company says it's already seeing a dramatic decline in work-related injuries and is now planning to introduce the exoskeletons at facilities in Europe and South America.

Currently, the industrial use of exoskeletons is relatively small - this year only a few thousand have been sold, says ABI's Kara. But, he says, the potential market could be in the millions.

The types of exoskeleton used for rehabilitation can cost more than $100,000 (£75,000), needing, as they usually do, to replace a user's muscles altogether. However, industrial versions can be far cheaper, at around $5,000.

They generally augment human strength rather than replace it and tend to enhance one part of the body only. They also often don't need any external power. Instead, they can deliver a 10-20% boost to the user's lifting power by transferring weight to the ground.

In Japan, exoskeletons are being used for heavy lifting in the shipbuilding industry as well as in large commercial construction projects.

Meanwhile, US retailer Home Depot is testing exoskeletons to help workers unload trucks and bring materials onto the floor.

Another early adopter is Lockheed Martin, which is using its own Fortis exoskeleton to allow workers to operate tools for much longer periods. It has a support structure that transfers the weight of heavy loads from the operator's body directly to the ground through a series of joints at the hips, knees and ankles.

It can also be used with an arm that supports the weight of a tool helps isolate vibration and torque kick - rotational force - from the user. Workers using the devices, says Lockheed Martin, report two-thirds less fatigue, with higher quality work, greater productivity and fewer musculoskeletal injuries.

Other companies are producing powered industrial exoskeletons that are rather more like the suits from the movies. Sarcos, for example, offers three models, with the biggest - the Guardian GT (pictured) - handling more than 450kg with its 2m (7ft) arms.

"I think powered exoskeletons will become ubiquitous for industrial applications around the world. These devices will materially reduce occupational injuries while also dramatically improving productivity," says chief executive officer Ben Wolff.

"Additionally, these devices can extend the useful life of an aging work force, and can make jobs open for more people that previously could have only been handled by people of larger physical stature." 

Other augmentation technologies are even stranger. Researchers at Cornell's Sibley School of Mechanical and Aerospace Engineering, for example, have developed a robotic "third arm" that attaches to the user's elbow. The group says it sees applications in package handling, warehouses, and even restaurants.

"A third arm device would enhance a worker's reach, and allow them to access objects without having to reach or bend. This would be useful in pick-and-place tasks where the worker is moving, such as retrieving packages from warehouse shelves," says researcher Vighnesh Vatsal.

"It would also provide support in assembly tasks in challenging environments such as construction sites, for instance by holding a work piece steady while a worker operates on it with power tools using their own hands."

In the longer term, industry experts say the price of exoskeletons will fall further, meaning they could move into many more areas of work. They could even find a place in private life, with applications in DIY, gardening and sports such as hiking.

So while we'll never be likely to be able to emulate the exploits of comic book heroes, exoskeletons could help with mundane household chores such as ironing. So not so much Iron Man - more "ironing man", perhaps? 

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Robocop Ford is developing a robot police car 01-31

robocop

Ford is developing a robot police car to catch speeding drivers and slap them with fines. 

The car giant has been issued with a patent for the artificial intelligence tech that will catch motorists breaking road laws.

The sketches filed with the US Patent & Trademark Office even show the sneaky Robocop hiding behind a tree.

Cameras and wireless sensors will allow it to track cars and connect over wifi to let them know they've broken the law.

The regular car would then message back revealing if it was in driverless mode - or with the driving licence photo of the person in control.

Robocop could then issue a warning or fine remotely.

The robotic cop car will be fitted with cameras and wifi allowing it to be used as a remote surveillance device - or to call in back up.

And the patent reveals it can even drive itself to hunt down rogue motorists - or be used with an officer inside.

The Ford patent said: "While autonomous vehicles can and will be programmed to obey traffic laws, a human driver can override that programming to control and operate the vehicle at any time.

"When a vehicle is under control of a human driver there is a possibility of violation of traffic laws. Thus, there will still be a need to police traffic.

"An autonomous police vehicle may enforce traffic laws by identifying violators, pulling over the offending vehicle, capturing an image of the licence plate of the offending vehicle, and determining whether to issue a warning or a ticket.

“Autonomous police vehicle may be trained or otherwise programmed using machine learning tools – deep neural networks – to find good hiding spots to catch violators of traffic laws.”

The futuristic tech is still some way off but the very fact Ford has filed a patent means it's on their radar.

It mimics the sci-fi cop cars seen in Sylvester Stallone's 1993 cult classic Demolition Man - plus the iconic 80s action star Robocop.

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An AI Bot Named Einstein Critiques Salesforce Execs Every Week 01-29

Imagine going to a staff meeting and being second-guessed by Einstein. Well, that’s what happens at Salesforce—sort of.

Chief executive Marc Benioff told Davos attendees on Thursday that, at a senior staff meeting every Monday for the last year, one seat has been occupied by the company’s “artificial intelligence” software, which is called Einstein.

“I ask Einstein, ‘I heard what everybody said but what do you actually think?'” Benioff said, according to a CNBC report.

Benioff explained that Einstein recently upset a European employee by saying “Well, I don’t think this executive is going to make their number, I’m so sorry,” before identifying the problems at hand.
While this early AI deployment is unusual, to say the least, it’s probably unsurprising that it’s happening at Salesforce.

The cloud software company has been very bullish on AI, having last year launched a $50 million fund to invest in startups that are deploying such technology.

Salesforce revealed Einstein’s existence a year and a half back, bringing together technologies it had recently acquired.

The company has been pitching the Einstein AI—which it originally wanted to call “Optimus Prime“—as a tool for things like identifying sales leads more quickly and efficiently. It is trying to encourage the functionality’s incorporation into a variety of business applications. 

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AI in the Boardroom: The Next Realm of Corporate Governance 10-25

Just as artificial intelligence is helping doctors make better diagnoses and deliver better care, it is also poised to bring valuable insights to corporate leaders — if they’ll let it. 

Image Credit : Shyam's Imagination Library

At first blush, the idea of artificial intelligence (AI) in the boardroom may seem far-fetched. After all, board decisions are exactly the opposite of what conventional wisdom says can be automated. Judgment, shrewdness, and acumen acquired over decades of hard-won experience are required for the kinds of complicated matters boards wrestle with. But AI is already filtering into use in some extremely nuanced, complicated, and important decision processes.

Consider health care. Physicians, like executives and board members, spend years developing their expertise. They evaluate existing conditions and deploy treatments in response, while monitoring the well-being of those under their care.

Today’s medical professionals are wisely allowing AI to augment their decision-making. Intelligent systems are enabling doctors to make better diagnoses and deliver more individualized treatments. These systems combine mapping of the human genome and vast amounts of clinical data with machine learning and data science. They assess individual profiles, analyze research, find patterns across patient populations, and prioritize courses of action. The early results of intelligent systems in health care are impressive, and they will grow even more so over time. In a recent study, physicians who incorporated machine-learning algorithms in their diagnoses of metastatic breast cancer reduced their error rates by 85%. Indeed, by understanding how AI is transforming health care, we can also imagine the future of how corporate directors and CEOs will use AI to inform their decisions.

Complex Decisions Demand Intelligent Systems

Part of what’s driving the use of AI in health care is the fact that the cost of bad decisions is high. That’s the same in business, too: Consider that 50% of the Fortune 500 companies are forecasted to fall off the list within a decade, and that failure rates are high for new product launches, mergers and acquisitions, and even attempts at digital transformation. Responsibility for these failures falls on the shoulders of executives and board members, who concede that they’re struggling: A 2015 McKinsey study found that only 16% of board directors said they fully understood how the dynamics of their industries were changing and how technological advancement would alter the trajectories of their company and industry. The truth is that business has become too complex and is moving too rapidly for boards and CEOs to make good decisions without intelligent systems.

We believe that the solution to this complexity will be to incorporate AI in the practice of corporate governance and strategy. This is not about automating leadership and governance, but rather augmenting board intelligence using AI. Artificial intelligence for both strategic decision-making (capital allocation) and operating decision-making will come to be an essential competitive advantage, just like electricity was in the industrial revolution or enterprise resource planning software (ERP) was in the information age.

For example, AI could be used to improve strategic decision-making by tracking capital allocation patterns and highlighting concerns — such as when the company is decreasing spending on research and development while most competitors are increasing investment — and reviewing and processing press releases to identify potential new competitors moving into key product markets and then suggesting investments to protect market share. AI could be used to improve operational decision-making by analyzing internal communication to assess employee morale and predicting churn, and by identifying subtle changes in customer preference or demographics that may have product or strategy implications.

The Medical Model: Advances That Have Enabled AI in Health Care

What will it take for boards to get on board with AI supplements? If we go back to the health care analogy, there have been three technological advances that have been essential for the application of AI in the medical field:

• The first advance is an enormous body of data. From the mapping of the human genome to the accumulation and organization of databases of clinical research and diagnoses, the medical world is now awash in vast, valuable new sources of information. 

• The second advance is the ability to quantify an individual. Improvements in mobile technology, sensors, and connectivity now generate extraordinarily detailed insights into an individual’s health.

• The third advance is the technology itself. Today’s AI techniques can assimilate massive amounts of data and discern relevant patterns and insights — allowing the application of the world of health care data to an individual’s particular health care situation. These techniques include advanced analytics, machine learning, and natural language processing.

As a result of the deployment of intelligent systems in health care, doctors can now map a patient’s data, including what they eat, how much they exercise, and what’s in their genetics; cross-reference that material against a large body of research to make a diagnosis; access the latest research on pharmaceuticals and other treatments; consult machine-learning algorithms that assess alternative courses of action; and create treatment recommendations personalized to the patient.

Three Steps Companies Can Take to Bring AI Into the Boardroom

A similar course will be required to achieve the same results in business. Although not a direct parallel to health care, companies have their own components — people, assets, history — which could be called the corporate genome. In order to effectively build an AI system to improve corporate decision-making, organizations will need to develop a usable genome model by taking three steps:

Create a body of data by mapping the corporate genome of many companies and combine this data with their economic outcomes

Develop a method for quantifying an individual company in order to assess its competitiveness and trajectory through comparison with the larger database; and

Use AI to recommend a course of action to improve the organization’s performance — such as changes to capital allocation.

Just as physicians use patient data to create individualized medical solutions, emerging intelligent systems will help boards and CEOs know more precisely what strategy and investments will provide exponential growth and value in an increasingly competitive marketplace. Boards and executives with the right competencies and mental models will have a real leg up in figuring out how to best utilize this new information. While technology is growing exponentially, leaders and boards are only changing incrementally, leaving many legacy organizations further and further behind.

It’s time for leaders to courageously admit that, despite all their years of experience, AI belongs in the boardroom.



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