When seemingly disparate fields, industries, and ways of thinking merge, a convergence happens, which, has the power to build more intuitive and advanced futures for both organizations and the everyday consumer, says Accenture communications, media and technology industry group chair, Kathleen O’Reilly and Daniela Rus, Director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology.
“Today, the kind of superpowers that seem to belong in storybooks can be achieved by mathematical models, computation, new materials, AI, robotics–this convergence of fields,” says Dr. Rus.
This episode is part of our “Building the future” podcast series. It’s a multi-episode series focusing on how organizations, researchers, and innovators are meeting our evolving global challenges. We understand the importance of inclusive conversations and have chosen to highlight the work of women on the cutting edge of technological innovation, and business excellence.
A combination of technology and human ingenuity will push boundaries as companies look to enter a new wave of innovation through data and AI to enable growth. Although O’Reilly estimates that we’re in the early stages of this transformation, she predicts that this convergence will be the biggest change since the industrial revolution.
“We are seeing with the exponential pace of technological innovation, which we believe is going to continue, that this is really creating an opportunity for one of the most exciting periods of positive change and progress for all of history,” says O’Reilly.
Much of this acceleration occurred over the course of these last pandemic years as many businesses and consumers alike take advantage of remote working operations including digital payments, telehealth appointments, and AR/VR experiences. But to anticipate and learn from the future, organizations and leaders always need to look to data and the insights derived from it.
“Intentional futurists,” says O’Reilly, “use AI-based analysis to find patterns, anticipate trends, detect new sources of growth opportunities, understand their consumers, their customers, other enterprises, the markets and their employees better.”
Practically, to bring this convergence from both leadership and academia, organizations need to be mindful of regulations and ethics to drive forward positive innovation and transformation.
“Whether you are a technologist, a national security leader, a policymaker or a human being,” says Dr. Rus. “We all have a moral obligation to use the AI tools to make our world safer, and better, and to make the lives of our citizens safer and better in a just and equitable way.”
This episode of Business Lab is sponsored.
Related reading
- Transforming the industry that transformed the world, 2022, Accenture
- Convergence unlocks adjacent growth, 2022, Accenture
- Equality = Innovation, 2019, Accenture
Full transcript
Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. This episode is part of our Building the Future series. We’re focusing on how organizations, researchers, and innovators are meeting our evolving global challenges. We understand the importance of inclusive conversations and have chosen to highlight the work of women on the cutting edge of technological innovation and business excellence.
Our topic today is convergence. Innovation thrives when ideas from various fields, industries, and ways of thinking merge. Building the future is a big task. Industries and fields of study need to be reimagined to make way for new opportunities. Enabling this will allow us as a society to learn from, act on, and build toward purposeful sustainability, insightful data and artificial intelligence, and a meaningful metaverse.
Two words for you: future forward.
My guests are Kathleen O’Reilly and Dr. Daniela Rus. Kathleen is the communications, media and technology industry group chair at Accenture and is a member of Accenture’s Global Management Committee. Daniela is a professor of electrical engineering and computer science, as well as the director of the Computer Science and Artificial Intelligence Lab, or CSAIL at the Massachusetts Institute of Technology.
Welcome, Kathleen and Daniela.
Daniela Rus: Thank you.
Kathleen O’Reilly: Thank you. Wonderful to be here.
Laurel: So Daniela, let’s start with you. What have you been working on that excites you, and what challenges are you preparing for?
Daniela: Thank you for this excellent question. So do you remember when Mickey summons the broomstick in the Sorcerer’s Apprentice? I’ve loved this piece ever since I can remember. The idea that you can animate and control everything around you. This is magic to Mickey, but today you don’t need magic to make that sort of thing happen. Today, the kind of superpowers that seem to belong in storybooks can be achieved by mathematical models, computation, new materials, AI, robotics, this convergence of fields. And I’m, for one, fascinated by all the superpowers we can achieve with these new technologies. I like to imagine a future with AI and robots supporting people with cognitive and physical tasks with the same pervasiveness with which smartphones support us with computing work. So how to get there? What do I do in order to aim in that direction?
Well, my current interests are to make more capable robots with softer bodies, better brains, whether the brains are for robots or other kinds of systems that are enabled by new models for machine learning, and to create more intuitive human/machine interactions with machines adapting to people, rather than the other way around. And so let me say a bit more about bodies and brains to be a little bit more concrete.
So the past 60 years have defined the field of industrial robots and have empowered hard bodied robots to execute complex assembly tasks in constrained industrial settings. Well, I believe the next 60 years we’ll be ushering in robots in human-centric environments, and our time with robots helping people with physical tasks.
Now, while the industrial robots of the past 60 years have mostly been inspired by the human form, they are humanoids, they’re robot arms, or they’re boxes on wheels. The next stage will be soft robots inspired by the animal kingdom, with its form diversity, and also by our built environments. Imagine your chair turning into a robot. And the application potential is huge.
The other thing I’d like to observe is that while the industrial robots of the past 60 years are made of hard plastics and metal, I believe the next 60 years will bring us machines made of all types of materials available to us naturally, or through engineered processes. Wood, plastic, paper, ice, even food. So in my lab, we are developing computational approaches for designing soft robots that are made out of a wide range of materials, and also their brains that enable new applications. And so among these applications are robots that swim like fish and move like turtles, robots that brush your hair, robots that pack your groceries, and can reason about how not to put milk on top of bok choy. Robots that recycle, robotic pills that enable incision free surgeries. And in each of these advances, the body of the robot and the brain of the robot needs to be designed and need to be worked with in a slightly different way than we’re currently used to.
And so I would just like to say a couple of words about these new ways, and in particular about the brains. Because this connects to the broader field of AI and machine learning. And so when it comes to brains, whether the brain controls a robot, or some other computational system, it is very important to know that today’s greatest advances are due to decades old ideas that are enhanced by vast amounts of data and computation. And so we need new ideas, because without new technical ideas, more and more people will be staying within the same current techniques and deep neural networks, and the results will be increasingly incremental. And so how to do this? How can we get to the point where we imagine machine learning that is different from today’s technologies, and what aspects of machine learning should we be thinking about?
Well, today’s machine learning solutions also have some challenges. The first one is in the data. Today’s AI methods require data availability. That means massive data sets that have to be manually labeled, and are not easily obtained in every field. The quality of the data has to be very high, and it needs to include critical corner cases for the application at hand. If the data is bad or biased then the performance of the model will be equally bad or biased. Furthermore, these systems are black boxes. There is no way for users to learn anything about how the system reasons by looking at the system’s workings. And as a result, it is difficult to anticipate failure modes tied to rare inputs that could lead to potentially catastrophic consequences. And also we have robustness challenges. And so we need to understand that these systems do not do deep reasoning. They mostly perform pattern matching.
And so in my work, I am trying to address these current shortcomings of machine learning. In other words, brittleness, the huge size of the models, the large computation requirements, the lack of explainability, the bias. And what I’m most excited about is our new machine learning model we call liquid networks. This is a continuous time model with a novel equation for the artificial neuron that has biological inspiration, and also wiring between neurons that is inspired by the wiring in the brains of small species. And it turns out that this model, liquid networks, yields to compact explainable and provably causal solutions that even have close form approximations. So we do not need the heavy computational machinery of ODE solvers to train or do inference in these systems.
And so let me just give you a quick example. If you want a machine learning model to learn how to steer a car, well, if you use a deep neural network, then you are going to use about 100,000 neurons and a half a million parameters. A liquid network, for the same task, only requires 19 neurons, and this network has extraordinarily sharp attention. In fact, the liquid network will make decisions by looking at the road horizon, and by looking at the sides of the road at the road horizon, whereas a deep neural network will be looking at all the bushes on the side of the road. So there are so many advantages with these new types of machine learning, and I’m very excited about the potential.
Laurel: No, that’s fascinating. But how do we specifically think about the evolution of technologies like machine learning in real world situations? You mentioned a robot pill, and I imagine soft robots can even reach places that others can’t. So there seems to be a lot of possible applications there.
Daniela: Well, the possibilities are endless, and I’m especially excited about empowering people with what seems like superpowers that belong to storybooks. But I’m also interested in how these technologies are broadly impacting industries. And I believe that in the future, these new technologies have the potential to reduce and even eliminate car accidents. They have the potential to better monitor, diagnose, and treat disease. They will keep your information safe and private. They will transport people and things faster and cheaper. They will make it easier to communicate globally. They will deliver education to everyone. In other words, these technologies will allow human workers to focus on bigger picture tasks like critical thinking and strategy.
And all the fields that have data can benefit. And so for example, in medicine, we have a lot of data, and machines today can look at more radiology scans in a day than a radiologist will see in an entire lifetime. So let me give you an example from an experiment where machine learning and doctors were given images of lymph node cells, and were asked to diagnose cancer or not cancer. And on its own, the machine learning system had an error rate of 7.5%, which is worse than the 3.5% rate of the human pathologist. But when both the machine learning system and the pathologist worked together, the error rate went down by 80% to only 0.5%, which is extraordinary. So it’s about how can we steer these tools to help empower us in our decision-making.
So now I would observe that today these systems may be deployed in the world’s most advanced cancer treatment centers. But imagine a future where every practitioner, even those working in small practices in rural settings, had access to these systems. Where a doctor may not have the time to review every new study or clinical trial, but working in tandem with these systems, the doctor will offer patients the most cutting-edge diagnosis and treatment options. And these possibilities are so broad. They go beyond medicine, they impact every industry that has data, and that can really use machine learning and AI as an enabler. So this includes using AI and data driven decision making to improve organization efficiency, it includes using computation to create optimized, dedicated AI hardware, and then use it for new products.
So what is exciting is this convergence in interests between universities, where many of the new ideas originate, and companies which take the ideas and turn them into products. And I just want to say that university/industry collaborations can be a really solid foundation for this kind of future progress, because these university industry collaborations drive innovation. The relationships are symbiotic, with universities pushing the boundaries of knowledge, leading the science, training the future workforce, and companies having the opportunity to see around the corner, to see the next big ideas early and consider their implications.
In fact, there is an NSF program, it’s called the NSF Industry University Cooperative Research Centers program. And as part of this program, it was calculated that every dollar put into a partnership by a company is leveraged 40 times. And so imagine all the possibilities when we think about the convergence between industry and the academy. There are so many opportunities.
However, I just want to end by saying to be successful, it is important to have the required AI infrastructure to have an educated AI workforce, and to have AI adoption and acceleration capabilities.
Laurel: Thank you, Daniela. You’ve certainly covered quite a bit. But Kathleen, I’m hooked on that idea of innovation and convergence and that idea of business and academia coming together. So what trends are you seeing within Accenture and with clients? How does that pairing of strategy and technology including bleeding edge technology that Daniela just worked us through, how can that help companies innovate?
Kathleen: Yeah, thanks Laurel. And I couldn’t agree more with Daniela’s point, and your question in terms of the power of the coming together of institutions that push the boundaries of science and technology knowledge and business. And that certainly underpins, I’ll take your second part of your question first, is how do we see strategy and technology coming together? I think at the end of the day, where we are right now is that underpinning really any successful strategy, what we’re seeing for clients that want to lead, for companies that want to lead, need to lead, and are pushing the boundaries, technology underpins those strategies. And we are seeing with the exponential pace of technological innovation, which we believe is going to continue, that this is really creating an opportunity for one of the most exciting periods of positive change and progress for all of history.
And it’s that combination of technology and human ingenuity, as we say, and as Danielle just alluded to in her medical example on cancer treatment, that is really where the greatest value and the greatest impact is going to come. We believe the companies which are going to be leaders in the next decade are going to need to harness five forces, and all of these forces are going to require technology and ingenuity to come together. They’re going to require organizations to work across all elements of their organization, to work with new partners, to expand into new areas and ecosystems, to learn and collaborate with innovators across industry, as well as across industry and academia and beyond to really push the boundaries of science and impact.
The five forces that we see right now, the trends that we’re seeing that are impacting our clients the most really start with what we believe underpins everything right now, and that is something we’re calling total enterprise reinvention. And we really started to see this come to the fore as we moved through covid. And what we’re seeing now is that as companies are looking to enter these new waves of change and opportunity, that they’re needing to execute strategies to change and transform all parts of their business through technology, data, and AI, as Daniela just talked about, to enable new ways of growth, new ways of engaging customers, new business models, new opportunities, but they’re doing it in a very different way. They’re doing it in a way where they’re looking at every part of their organization and the technology and digital core that underpins it at the same time, so we believe we’re in the early stages of this profound change, but we believe it’s going to be the biggest change since the industrial revolution.
And embracing total enterprise reinvention often requires something that we call compressed transformation, which are bold transformational programs that, as I said, span the entire organization with different groups working together in ways that they never did before in parallel, but in very accelerated timeframes. And underpinning all this is leading edge technology, data, and AI. At the same time, the second trend we’re seeing with our clients, and we certainly are all reading about it and of hearing about it for the past few years, is the power of talent and the importance of the human side of this equation. And we think that one of the forces that’s going to shape the next decade with talent at front and center is not just the ability to access talent, but really for organizations to learn to be creators of talent, not just consumers. To unlock the potential of the humans in their workforce. And that’s going to require technology to unlock that potential. And again, as Daniela just gave in some of her examples, to compliment the talent that they have in the organization.
The third is sustainability. That trend is … I would say personally, I’m very pleased to see this trend underpinning everything that we’re doing and everything that our clients are thinking about right now. We believe that every business needs to be a sustainable business. And every industry is looking at this in a way that is unique to their industries. But whether it’s consumers, employees, business partners, regulators, or investors, we know that we’re moving in a direction where companies are being required to act. To make a change, not just around climate and energy, but areas like food insecurity and equality. All of those issues are coming to the fore, and underpinning this, again, is the ability to leverage new bleeding technologies to accelerate the pace of change and find solutions to the issues that we’re facing as a planet and across society.
The fourth force that we’re seeing is the metaverse. Now, there’s been a lot of confusion, and a lot of talk about the metaverse, but our view is that the metaverse is a continuum, and we’re seeing this come to the fore in the marketplace right now. As we look at the metaverse and how that’s going to impact, just if you think all the way back to when the internet was in its early stages, we believe that the impact is going to be that great. And while it’s early stages and not everybody can see exactly how the impact is going to be there, we believe that this is going to impact not just consumers, and of course interesting areas like virtual reality and using AI to bring new experiences to life, but also to look at extended reality, to look at digital twins, smart objects. So how do cars and factories run? What’s happening with edge computing? Looking at blockchain and new ways of payment. All of those things are going to change the way businesses operate and really the way society operates, and we believe that this is going to underpin change as we move forward over the next five to 10 years.
And then lastly, the fifth force is what we’re calling ongoing tech revolution. And the ongoing tech revolution is a pretty broad expansive category, often pushed by our friends in the academia world around science, but we believe in the coming decade, the pace of technological innovation is not just going to continue but accelerate, which we believe is going to create positive change. New technology, whether it’s in quantum computing or it’s in areas, as I said, like blockchain or material science or biology, or even space, we believe this is going to open brand new areas of opportunity. And all of these things are allowing companies, our clients to find new ways to not just serve their customers, but to monetize their investments, to impact society, to impact their employees, and to drive positive change for their business as well as for the world around them.
Laurel: Yeah. Kathleen, I feel like some of that acceleration happened in these last few pandemic years so that businesses and consumers are operating differently from remote healthcare solutions to digital payments, greater expectations of those immersive virtual experiences. But how can organizations and technologists alike then continue to innovate to anticipate the future, or as Accenture likes to say, learn from the future? You have some good examples there, but the five different areas all kind of also lead to this acceptance of change.
Kathleen: Yeah, they do. And they also lead to embedding data in everything, in new ways into every change that organizations are putting forward. When we think of learning through the future, we think about organizations and leaders who are constantly seeking new data and insights, not just from inside their organization, but from outside their organizations’ four walls. So we like to use the phrase intentional futurists. These are people and leaders and organizations who use AI-based analysis to find patterns, anticipate trends, detect new sources of growth opportunities, understand their consumers, their customers, other enterprises, the markets and their employees better.
For example, we know AI is transforming agriculture at a time when climate change, as I just referenced with sustainability, makes feeding the world more challenging than ever. Not to mention some of the broader issues that we’re all seeing emerge around the world from a geopolitical standpoint. Advanced agricultural technologies employee sensors, cameras, connectivity to collect and process historical and real time data on planting conditions, weather patterns, and crop health. And AI enables the farmers to manage at the individual plant level and optimize their production around consistent high-quality crops. It’s technology and the use of technology combined with the human side that is going to drive that kind of change.
And we know that covid prompted an acceleration into these areas of businesses wanting to learn from the future, see around the corners, if you will, understand those patterns as well as invest more quickly into new technologies, particularly cloud platforms. And with those cloud platforms comes the privilege, and I would say the responsibility, of having access and use of significant amounts of data. And with responsibility, Daniela, I’ll reference what you just talked about for example, ensuring that responsible AI and how bias is handled is an example. These are new areas that we need to be thinking about, but we also know that in the next frontier of better data utilization, we have to think differently about how we use AI.
We believe that by 2025, we’re going to create an estimated 180 zetabytes of data. But right now, only 11% of the data created and captured is useful for analysis, and only 44% of that data is actually used in practice. So we are completely under-utilizing what we have access to, and we need to think about that. Accenture publishes a tech vision every year, and we call this computing the impossible. So how do you use high performance computers or parallel processing supercomputers to more quickly synthesize data and forecast outcomes, and figure out new areas of opportunity, new possibilities in solving big issues? we know that innovation’s all about creating those new ideas and that data’s going to underpin that, but again, when combined with the power of human ingenuity to design the strategies and how to use these things responsibly.
Daniela: So if I might just jump in, I just want to underscore what you have just said, Kathleen. The under-utilization of data is extraordinary, and we really need to be thoughtful about how to move forward. We need to find which data is important data and which data is not so important, and then we need to see how to harness the important data.
Kathleen: Absolutely.
Laurel: And I love that phrase, intentional futurist. Daniela, what you were talking to us before really sounds like that, doesn’t it? So if successful innovation is a convergence of those types of ideas, industries, and those lines of research, how are you seeing this actually play out in practice?
Daniela: Well, so I loved what Kathleen called compressed transformation, with different groups of people coming together. And I think this is exactly how we need to think about bringing the greatest ideas from the academy together with the greatest business minds to make practical impact on the world. But we need to be thoughtful and careful about creating private/public government partnerships that leverage the contributions of each entity. Because new products require the exciting ideas from the academy, they require the business minds of the people who understand what is marketable and useful and what is not, but then it also requires the policy side, it requires the regulation that talks about how all of this should be done in a way that is positive for the world.
And so this kind of convergence of people with different backgrounds and different lenses, about the ideas and the technology is important. And I’d like to give you an example. In 2019, MIT started a research partnership we call the AI accelerator, where the accelerator’s objective is to speed up the development of the science of AI, and also of the path from research to innovation and domain relevant products. Now, the current partnership is between MIT campus, MIT Lincoln Lab, and the U.S. Air Force, and together these three entities are defining a converging fruitful collaboration, with contributions to science and knowledge in general, but also with the aim of bringing the rapidly developed new tools and innovation to national security. And we have MIT researchers who are leading the development of the science, and they’re working shoulder to shoulder with Lincoln Lab and Air Force researchers.
So we have these integrated teams that bring all the stakeholders to the same level of knowledge and understanding. And then the idea is that Lincoln Lab and Air Force can partner on developing products beyond the research grade ideas that are being developed as part of this program. And applications in diverse areas such as disaster relief, weather modeling, which is so important for understanding climate, medical readiness, and really many other broad societal topics that are of great interest to the world. And so these interdisciplinary teams with experts from AI, from MIT, domain experts from the Air Force, and experts from MIT Lincoln Lab who understand both AI and the domain accelerate both the science advances, but also the adoption of AI in the DOD [U.S. Department of Defense]. So this is an example of how converging teams can really speed up the innovation, and also the adoption of that innovation.
So let me also say that broad adoption of AI also requires collaborations with policy makers who ensure that the deployments are positive and support the greater good. So we need conversations between technologists, business leaders, and policy-makers to get to positive and responsible adoption and deployments. But we don’t need our policy-makers to understand the intricate mathematical details of how AI works. However, we do need to educate everybody, our leaders and our citizens broadly about technology and about the impacts of our choices so that we can make the right ones. And I believe that it’s important to think about five vital questions in order to build a common understanding.
The first question is what can we do? In other words, what’s really possible with technology, and where can we improve? The second, what can’t we do? In other words, what is not yet possible? Then we have to think about: what should we do? What shouldn’t we do, because there are things about technology that we should rule out. For example, we shouldn’t be building better tools to enable this information. And also, finally, what must we do? Because I believe we have an obligation to consider how AI and machine learning can help, because ultimately this is what it’s all about. And whether you are a technologist, a national security leader, a policymaker or a human being, we all have a moral obligation to use the AI tools to make our world safer, and better, and to make the lives of our citizens safer and better in a just and equitable way.
Laurel: Yeah, I like that idea of really bringing it home, because it is for each person as well to have a safer and better life. So Kathleen, that same question to you. How is this convergence of ideas coming through in practice from leadership and research and industry innovation?
Kathleen: Yeah, we’re definitely seeing it from a business perspective also. First of all, we’re certainly seeing companies and leaders looking across industries to make sure that they’re learning from others, and how they’re using assets and tools and what new methodologies are making a change in their business. They’re applying what others are learning quickly. I actually think that what we saw happen in the pharmaceutical or life sciences industry during covid was the beginning of, my own observation, a new period of collaboration both within industry, certainly within organizations, across organizations as I’ve referenced earlier, but within industries and across industries. And we’re seeing leadership driving for, “Yes, I need to understand my market, my business, my customers, but I also need to understand how everybody else is using innovation and technology out there, and making sure that they’re learning versus reinventing a wheel, because there’s an imperative to move quickly.”
We’re also seeing that, of course, clients and their partners are diversifying, entering new and adjacent industries, anticipating trends, understanding what’s happening where there may be some new value pools. Those are probably more some of the more obvious areas. And certainly an example of this could be in e-commerce, something we’ve been talking about for years, I guess decades at this point. Advancements in consumer goods and new insights in that area, or in let’s say banking or security, are actually shaping, in my world, how some of the social platforms are thinking that they will advertise and monetize those investments and set up new marketplaces while also protecting their data. We’re seeing industries learning from each other.
If I take it a step further, I’ll go to the high-tech industry, in looking at how do you enable double digit growth or long-term growth? Trailblazers in this industry are really looking at other industries and new parts of the value chain. We recently did a survey of high-tech industry executives, and 87% of them agreed that convergence is a growth enabler, that multiple industries are ripe for tech led disruption, and that the high-tech company skills and capabilities are going to be able to change those industries and create new opportunities. Three examples of this, automotive. We often hear about smart mobility, whether it’s autonomous boats and cars and trucks or drones, military vehicles, all of those areas. How is that coming to the fore and what will that change? And again, I’ll harken back to my earlier statements where these forces kind of tie together. It also ties to ensuring that sustainability is built into everything that we’re doing, leveraging that new technology.
Another area is connected infrastructure. Certainly I spend a lot of time with my clients talking about edge and 5G enablement and the use cases for 5G is that comes into the fore. So think of things like smart buildings, smart grid. What are the energy and utilities companies doing to manage their businesses, and how can that be leveraged? Or another area that probably all of us are experiencing is digital health. AI powered smart hospitals, fitness wearables. Probably all of us have seen those, if not are wearing them ourselves, or even during covid if you think about contact tracing and some of the apps that came up there.
In all of these areas, we’re seeing industries looking across industry both to learn, as well as to expand, and to innovate together. It’s creating new solutions, and it’s a new approach to R&D and product development with a real customer-centric lens. It’s finding ways to leverage your installed base to find new markets and capitalize with new products. It’s enabling new strategic alliances that we’re seeing pop up across the board, and sometimes those cannibalize parts of businesses, but almost always lead to new innovative areas that drive greater value. And then certainly we’re seeing some inorganic change with mergers and acquisitions and new capabilities and organizations coming together in different ways.
And then lastly on leadership, I would say there has been, happily, a really big push on creating cultures of innovation. And not just creating a culture and a mindset for innovation but underpinning that with a culture of diversity and equality, which we know really puts the structure in place for innovation to take place wherever that may happen.
Laurel: I like the idea of having structure in place for innovation. Then you’re actually building that as part of the culture of a company, of a group of people, a group of ideas. You did mention though, smart grids and smart buildings and this idea of sustainability. Why is it so critical to address big challenges like this, like sustainability, with an inclusive approach to innovation?
Kathleen: Yeah, yeah. No, that’s a great question, Laurel. I would say sustainability is one, but I’ve mentioned a few times, at least our perspective, we look at impact in businesses, but also in society as a whole. Some of the biggest issues we’re facing are going to require us coming together in different ways. Hopefully covid and that pandemic are more in the rear-view mirror than not. But disruption is going to continue and the unexpected is going to happen, and we need to be prepared for that. And in order to be prepared for that, we need to be prepared to come together in an inclusive way, both within organizations, and again, across organizations. And certainly by intentionally engaging people, whether it’s a broader set of employees, a broader set of stakeholders or companies or markets, or even customers…under-tapped, underserved populations, the voices that we haven’t traditionally heard from. The data tells us that it drives a stronger, broader set of thinking and pushes us into new areas and expands ways of thinking that wouldn’t normally happen if you don’t have all the right voices in the room, if you will. Even if it’s a virtual room, which we certainly know that new technologies and new areas like the metaverse are going to allow us to bring people together in ways that never could have happened before, hopefully to solve problems in a much more inclusive and rapid way.
But bringing those voices together is maybe a statement of the obvious, but we also know that there’s some data behind this. Accenture’s research tells us that in organizations with an innovation mindset, but that also has an equal culture, and this is just within organizations, that the innovation mindset is six times higher in organizations or companies that have more equal cultures than least equal ones. We know that employees in equal cultures where they are included and brought together in ways that allow their voice to be heard, see much less in terms of barriers to innovation. As a matter of fact, in organizations that are more equal in their approach and have more of an equality and diverse viewpoint mindset, 40% of the employees see that nothing stops them from innovating, versus in organizations that don’t have that kind of a mindset, only 7% believe that they can innovate.
And first of all, there’s just something underpinning about bringing all those pieces together, but there’s also data that says that drives a very different set of outcomes. If you think about solving for sustainability, which is one of the big, big issues of our day, and in this case let’s just talk about climate because I’ve mentioned that before. That’s going to require all of those voices to be heard and all of the perspectives.
We also see that organizations are using inclusion to underpin their growth strategies. So many companies need to reach new customers, new markets, they need to achieve their growth ambitions, but they need to get beyond their current target audience, if you will, and to reach unreached populations or underserved populations. In order to reach them, you need to innovate with inclusion in mind. So in the tech world, my world, tech companies have a business imperative to close the digital divide. There are three and a half billion people in the world that are not using the internet because they don’t have access, or lack the digital literacy needed to benefit from that revolution that we’ve all benefited from.
Companies like Google, with their Next Billion Users initiative, are innovating inclusively to reach those consumers, and of course that will allow them to continue to innovate. Same thing in banking. Two billion adults don’t use formal financial services. Leaders like MasterCard, in order to grow, are designing inclusive ways to address the pain points of these people, whether it’s small farmers, factory workers, low-income consumers, and that financial inclusion is going to not just benefit society, but also benefit the businesses that are doing that. And the same thing happens with employees, as I mentioned. If you include employees in a different kind of way, you’re going to get a very different outcome, from a business perspective, in terms of their ability to see new solutions and help drive your business forward.
And so if you go back to sustainability, in order to solve the issues that we’re seeing around sustainability and particularly climate, we know that we need to think broadly and bring all the skills to the table on this. And whether that is technological innovation and the knowledge that’s around things like digital twins, or creating physical prototypes, or use of blockchain to enhance traceability, AI to understand customer experiences, all of these areas are critical for us to solve the crisis in front of us from a sustainability standpoint, particularly a climate standpoint. And we know that that’s going to take all those voices being at the table.
Laurel: And Daniela, Kathleen just really outlined some great examples of the challenges that enterprises have with not just sustainability, but also artificial intelligence and building the next future of work. How can artificial intelligence and other technologies help with these big challenges?
Daniela: So yes, I so agree with everything Kathleen explained about how diversity drives innovation and drives better solutions.
Now, sustainability, we can talk about sustainability at multiple levels. And I would like to start by underscoring that from a planetary point of view, AI can play an enormous role in sustainability, and it can do so by generating better insights, by helping us to collect and analyze data from vast sensor networks that monitor the oceans, the greenhouse, climate, other planet conditions. AI can also help businesses better monitor how they are expending and using their resources with a sustainability goal in mind. AI innovations can help optimize all our activities, and our carbon footprint and our energy footprint to slow the impacts of warming. And this is whether through optimizing the electricity utilization, the electricity cost of technology, making transportation more efficient, and also in other areas like monitoring and stopping deforestation, preserving biodiversity, ensuring that there is enough foods to go around, and food does not get wasted. But to do all of these things, whether at the planetary scale, at an individual scale, or for a business, AI systems consume enormous amounts of energy. And it’s important to talk about that.
Researchers at the University of Massachusetts at Amherst estimated that training a medium sized language model produces 626,000 pounds of carbon dioxide. This is equal to the lifetime emissions of five cars. That’s an enormous amount of energy, and a lot of these models are being trained right now. And so that’s just for one average model. I also know that it costs $4.6 million in energy to train the GPT3 language model, which is the foundation of the recently released ChatGPT you may have played with. So the more pervasive AI becomes, the more of these models will be needed. And these examples really highlight a place where policy action to combat emissions and to invest in renewable forms of energy can complement technological improvements. But technological improvements are critical.
The AI systems are so costly because each one contains hundreds of thousands of artificial neurons, and millions of interconnections. And so if we can develop simpler models, this can drastically reduce the carbon footprint of AI and make machine learning technology more sustainable. Now, some companies are placing their data centers next to renewable energy sources as a potential solution, but there is also the opportunity to tackle some of the questions around the size of the model. We are already making progress on creating simpler models. For example, our own work with closed form liquid networks aims to provide a more sustainable solution for machine learning.
Laurel: Thank you. And Kathleen, just thinking about this as a holistic kind of view, there’s so much in this one conversation that we’ve had. So much possibility and opportunity. How do you see the ideas of convergence really evolving in the next three to five years? Because there is that immediacy, there’s an urgency, and there’s sort of an excitement to, actually, let’s get on with it.
Kathleen: Yeah, well I think the first thing is I think it’s going to continue to accelerate as technological change pushes all of us, and the needs of businesses and the needs of our world push us. That urgency to move on and to push our thinking are going to force us into even new ways of bringing new ideas, converged ideas, collaboration to the table. And so I think over the next three to five years, besides just the acceleration, one of the things that we think is going to continue to accelerate is various organizations coming together in new ways. If we think about how one can leverage all those five forces, we believe that the continuation of that change in technology as that advances and critical talent and natural resources become more scarce, organizations may need to come together in different ways, and that may mean even formally.
We believe that the pace of M&A, as well as even divestitures, to streamline core competencies, to bring new capabilities together in new ways, different business models, different organizational models will continue to accelerate. We’re certainly seeing that. 36% of M&A deals, according to our research, have the main motivation right now to acquire new innovative technologies and capabilities. And this is up as much as four to five times in certain industries like the health industry, the life sciences industry, the chemicals industry, and beyond. And so bringing new capabilities together as well as streamlining for capabilities and understanding what you need and what you can borrow from others, what you can partner with others in your ecosystem is going to accelerate.
The second thing that I think we believe about some of these more structural changes is that if you look at the technology sector, where I spend the majority of my time, inquisitive companies in the technology sector over the past few years, where we’ve seen a lot of activity, have generated 95% more return for shareholders compared to the sector average. So again, I think that’s going to continue to push the thinking in that space.
We also think that organizations will continue to, as I mentioned, streamlining their core competencies, have an openness more of an openness to shared capabilities. Whether that’s front and back offices, services or consortiums among companies who may at one time have been competitors but recognize that if they come together in new and appropriate ways, they can bring some new capabilities and new solutions to market. So we believe we’re going to see more of that.
And I think if you look across the next three to five to 10 years, what we know is that the future that’s in front of us for all of these companies is going to be completely different than probably what they were originally designed for. So over the next decade, we believe most companies, as I mentioned, are going to completely need to transform their business. And that’s going to mean transforming the environment in which we do business. It also means that they’re going to need to accelerate their investment in technology, so we believe that that’s going to continue to move forward to really … Whether that’s on delayed cloud migrations, or whether it’s their use of AI and analytics, and those things that have been sidelined in the past, most clients we’re talking to are saying, “How do I go faster? I can see the power of this. I can see the power of technology, and if I don’t invest, I’m going to be left behind.” And we believe that that day for investing, accelerating in the digital, accelerating technology, accelerating in data and AI is only going to move more quickly as we move forward.
Laurel: And Daniela, same question for you. How are you seeing these next three to five years, and how convergence will evolve and really accelerate?
Daniela: So I don’t know exactly what will happen, but I would like to highlight three things that I would like to see happen. And the first one is about seeing more programs like the MIT AI Accelerator program. Because the convergence of expertise across disciplines and across public private government partnerships will truly enable great growth. University private partnerships are symbiotic, and they will enable innovation and progress.
I believe we will also see broader adoption of AI with tools like ChatGPT that are developed within a research context, but that will be adopted within a business context. And we will see new ideas and new applications of AI to enable discovery, and address some of the grand challenges that companies are facing and also that humanity is facing. And many of these challenges can only be addressed in a multidisciplinary way.
Second, I believe we will get serious about sustainability, and in particular about sustainable AI and sustainable technologies. This is important from a technological development point of view. This is also critical for the future of our planet and everything that lives on it. And third, I think we will get more serious about AI and privacy, because privacy is an example where the underlying technology needs to evolve. And it’s super important since machine learning is so rooted in data. For example, AI and computation holds so much potential to help in areas like healthcare. And I just want to highlight that MIT researchers were able to leverage AI to synthesize a new antibiotic, the first one created in 40 years. And this has created an opportunity for us to imagine if this work could be extended to synthesize customized medicines, allocated to individuals based on their environment and circumstances, and generated on the fly. So I don’t mean personalized healthcare, I mean individualized healthcare. An individualized cocktail of pills that is just right for the patient.
But of course to get there, we need data. And any time we use data, we need to consider risks to privacy, whether it’s in healthcare or in insurance, or in any other industry. So we can address the privacy challenge with regulation, but technological breakthroughs can also make this easier. And we are already seeing great advances in homomorphic encryption that allow us to use data without decrypting it. And so organizations that need information, for instance your insurance company can post queries against a vast pool of data without ever decrypting the data itself. And so if we can get this right, we can create and learn from the largest pools of knowledge ever created and never risk the types of exposures that we see today. So I’m a technologist optimist, and I believe that these positive advancements and positive outcomes can happen, and will happen, especially if we have conversations like this one today.
Laurel: Excellent. Daniela and Kathleen, thank you so much for joining me today on the Business Lab.
Kathleen: Thank you for having us.
Daniela: Thank you very much.
Laurel: That was Kathleen O’Reilly of Accenture and Daniela Rus of MIT, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review, overlooking the Charles River.
That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the global director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can also find us in print, on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
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