A human-centric approach to adopting AI

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A human-centric approach to adopting AI

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July 6, 2023

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From MIT Technology Review, I’m Laurel Ruma, and that is Business Lab, the show that helps business leaders make sense of latest technologies coming out of the lab and into the marketplace. This episode is a component of our “Constructing the longer term” series. We’re specializing in how organizations, researchers, and innovators are meeting our evolving global challenges. We understand the importance of inclusive conversations and have chosen to spotlight the work of ladies on the leading edge of technological innovation and business excellence.

Our topic today is artificial intelligence. Advances in AI and robotics help not only explore the unknown but surface latest possibilities and innovations. And with more and higher data, AI, and robotics, researchers and organizations have a chance to learn from the longer term. But for the near term, enterprises are making the most of current capabilities and technologies to be smarter from manufacturing to beyond.

Two words for you: smart robots.

My guests are Lan Guan, who’s the worldwide lead for data and AI at Accenture. Julie Shah is a professor within the Department of Aeronautics and Astronautics on the Massachusetts Institute of Technology. She also co-leads the Work of the Future Initiative at MIT.

This episode of Business Lab is sponsored.

Welcome Lan and Julie.

Thanks a lot.

Thanks for having us.

Lan, let’s start off with you. How would you describe the present state of AI? How are Accenture’s customers using it?

Sure. Let me start by providing you with lay of the land of what is happening in artificial intelligence the more within the enterprise and industry type of context. I believe from my perspective, AI has been making significant headlines throughout the last two, three years, right? And the recent advances in machine learning and deep learning have made it possible to construct models, I mean gigantic models, which can be able to automating quite a lot of human tasks including what we call using AI for perceptual data, right? Perceptual data meaning speech and vision recognition, natural language processing, and even doing reasoning. So, it’s just incredible to see a few of these models perform at levels higher than what humans are able to. So, I believe that is pretty amazing.

Also, one other thing that I’m seeing: AI isn’t any longer just in assistant mode, but is now playing autonomous roles in robotics, driving, knowledge generation, simulating our hands, feet, and brains. I believe that AI has turn into very pervasive. After which playing these autonomous roles, that is something that I’ve seen across different industries. But we also recognize AI remains to be young and data hungry. There’s still so way more to be done to make it more robust, explainable and fair, responsible in lots of, many various ways. Moreover, there’s still so many challenges, right? Limitations for business leaders to beat before we are able to achieve true artificially generated intelligence where a machine can actually perform any mental task that a human can.

So, I’ve seen many purchasers from mainly all industries with different maturities approach us to implement or advance their AI journey, some in experimentation and others who are literally already high achievers in AI. Let me offer you one example. In traditional industries like manufacturing, firms are adopting AI in robots to construct smart factories. One digital native client in e-commerce has asked us to assist them provide ultra-personalized experiences to fulfill growing customer taste and demands. We’ve even delved into animal precision health with the client where we created models to watch cows’ lactation cycles, predict their milk production based on their genetics, and even project how briskly they will reproduce through natural and artificial insemination.

So in a short time, I gave you examples of how AI has turn into pervasive and really autonomous across multiple industries. This can be a type of trend that I’m super enthusiastic about because I imagine this brings enormous opportunities for us to assist businesses across different industries to get more value out of this amazing technology.

Julie, your research focuses on that robotic side of AI, specifically constructing robots that work alongside humans in various fields like manufacturing, healthcare, and space exploration. How do you see robots helping with those dangerous and dirty jobs?

Yeah, that is right. So, I’m an AI researcher at MIT within the Computer Science & Artificial Intelligence Laboratory (CSAIL), and I run a robotics lab. The vision for my lab’s work is to make machines, these include robots. So computers turn into smarter, more able to collaborating with people where the intention is to find a way to reinforce relatively than replace human capability. And so we deal with developing and deploying AI-enabled robots which can be able to collaborating with people in physical environments, working alongside people in factories to assist construct planes and construct cars. We also work in intelligent decision support to support expert decision makers doing very, very difficult tasks, tasks that lots of us would never be good at irrespective of how long we spent attempting to train up within the role. So, for instance, supporting nurses and doctors and running hospital units, supporting fighter pilots to do mission planning.

The vision here is to find a way to maneuver out of this kind of prior paradigm. In robotics, you would consider it as… I believe of it as kind of “era one” of robotics where we deployed robots, say in factories, but they were largely behind cages and we needed to very precisely structure the work for the robot. Then we have been in a position to move into this next era where we are able to remove the cages around these robots and so they can maneuver in the identical environment more safely, do work in the identical environment outside of the cages in proximity to people. But ultimately, these systems are essentially staying out of the best way of individuals and are thus limited in the worth that they will provide.

You see similar trends with AI, so with machine learning particularly. The ways that you just structure the environment for the machine usually are not necessarily physical ways the best way you’ll with a cage or with establishing fixtures for a robot. However the technique of collecting large amounts of information on a task or a process and developing, say a predictor from that or a decision-making system from that, really does require that if you deploy that system, the environments you are deploying it in look substantially similar, but usually are not out of distribution from the info that you’ve got collected. And by and enormous, machine learning and AI has previously been developed to unravel very specific tasks, to not do kind of the entire jobs of individuals, and to do those tasks in ways in which make it very difficult for these systems to work interdependently with people.

So the technologies my lab develops each on the robot side and on the AI side are aimed toward enabling high performance and tasks with robotics and AI, say increasing productivity, increasing quality of labor, while also enabling greater flexibility and greater engagement from human experts and human decision makers. That requires rethinking about how we draw inputs and leverage, how people structure the world for machines from these kind of prior paradigms involving collecting large amounts of information, involving fixturing and structuring the environment to essentially developing systems which can be way more interactive and collaborative, enable individuals with domain expertise to find a way to speak and translate their knowledge and data more on to and from machines. And that may be a very exciting direction.

It’s different than developing AI robotics to exchange work that is being done by people. It’s really enthusiastic about the redesign of that work. That is something my colleague and collaborator at MIT, Ben Armstrong and I, we call positive-sum automation. So the way you shape technologies to find a way to attain high productivity, quality, other traditional metrics while also realizing high flexibility and centering the human’s role as an element of that work process.

Yeah, Lan, that is really specific and in addition interesting and plays on what you were just talking about earlier, which is how clients are enthusiastic about manufacturing and AI with a fantastic example about factories and in addition this concept that perhaps robots aren’t here for only one purpose. They may be multi-functional, but at the identical time they can not do a human’s job. So how do you have a look at manufacturing and AI as these possibilities come toward us?

Sure, sure. I really like what Julie was describing as a positive sum gain of this is strictly how we view the holistic impact of AI, robotics form of technology in asset-heavy industries like manufacturing. So, although I’m not a deep robotic specialist like Julie, but I have been delving into this area more from an industry applications perspective because I personally was intrigued by the quantity of information that’s sitting around in what I call asset-heavy industries, the quantity of information in IoT devices, right? Sensors, machines, and in addition take into consideration every kind of information. Obviously, they usually are not the everyday sorts of IT data. Here we’re talking about a tremendous amount of operational technology, OT data, or in some cases also engineering technology, ET data, things like diagrams, piping diagrams and things like that. So initially, I believe from an information standpoint, I believe there’s just an infinite amount of value in these traditional industries, which is, I imagine, truly underutilized.

And I believe on the robotics and AI front, I definitely see the same patterns that Julie was describing. I believe using robots in multiple other ways on the factory shop floor, I believe that is how the various industries are leveraging technology in this type of underutilized space. For instance, using robots in dangerous settings to assist humans do these sorts of jobs more effectively. I all the time discuss one among the clients that we work with in Asia, they’re actually within the business of producing sanitary water. So in that case, glazing is definitely the technique of applying a glazed slurry on the surface of shaped ceramics. It is a century-old type of thing, a technical thing that humans have been doing. But since precedent days, a brush was used and dangerous glazing processes may cause disease in staff.

Now, glazing application robots have taken over. These robots can spray the glaze with 3 times the efficiency of humans with 100% uniformity rate. It’s just one among the various, many examples on the shop floor in heavy manufacturing. Now robots are taking on what humans used to do. And robots and humans work together to make this safer for humans and at the identical time produce higher products for consumers. So, that is the type of exciting thing that I’m seeing how AI brings advantages, tangible advantages to the society, to human beings.

That is a extremely interesting type of shift into this next topic, which is how can we then discuss, as you mentioned, being responsible and having ethical AI, especially once we’re discussing making people’s jobs higher, safer, more consistent? After which how does this also play into responsible technology basically and the way we’re taking a look at your complete field?

Yeah, that is an excellent hot topic. Okay, I might say as an AI practitioner, responsible AI has all the time been at the highest of the mind for us. But think concerning the recent advancement in generative AI. I believe this topic is becoming much more urgent. So, while technical advancements in AI are very impressive like many examples I have been talking about, I believe responsible AI is just not purely a technical pursuit. It is also about how we use it, how each of us uses it as a consumer, as a business leader.

So at Accenture, our teams strive to design, construct, and deploy AI in a fashion that empowers employees and business and fairly impacts customers and society. I believe that responsible AI not only applies to us but can be on the core of how we help clients innovate. As they appear to scale their use of AI, they wish to be confident that their systems are going to perform reliably and as expected. A part of constructing that confidence, I imagine, is ensuring they’ve taken steps to avoid unintended consequences. Meaning ensuring that there isn’t any bias of their data and models and that the info science team has the appropriate skills and processes in place to supply more responsible outputs. Plus, we also be sure that that there are governance structures for where and the way AI is applied, especially when AI systems are using decision-making that affects people’s life. So, there are numerous, many examples of that.

And I believe given the recent excitement around generative AI, this topic becomes much more necessary, right? What we’re seeing within the industry is that this is becoming one among the primary questions that our clients ask us to assist them get generative AI ready. And just because there are newer risks, newer limitations being introduced due to generative AI along with a few of the known or existing limitations previously once we discuss predictive or prescriptive AI. For instance, misinformation. Your AI could, on this case, be producing very accurate results, but when the knowledge generated or content generated by AI is just not aligned to human values, is just not aligned to your organization core values, then I do not think it’s working, right? It might be a really accurate model, but we also have to listen to potential misinformation, misalignment. That is one example.

Second example is language toxicity. Again, in the normal or existing AI’s case, when AI is just not producing content, language of toxicity is less of a problem. But now that is becoming something that’s top of mind for a lot of business leaders, which suggests responsible AI also must cover this latest set of a risk, potential limitations to deal with language toxicity. So those are the couple thoughts I even have on the responsible AI.

And Julie, you discussed how robots and humans can work together. So how do you consider changing the perception of the fields? How can ethical AI and even governance help researchers and never hinder them with all this great latest technology?

Yeah. I fully agree with Lan’s comments here and have spent quite a good amount of effort over the past few years on this topic. I recently spent three years as an associate dean at MIT, constructing out our latest cross-disciplinary program and social and ethical responsibilities of computing. This can be a program that has involved very deeply, nearly 10% of the college researchers at MIT, not only technologists, but social scientists, humanists, those from the business school. And what I’ve taken away is, initially, there isn’t any codified process or rule book or design guidance on methods to anticipate the entire currently unknown unknowns. There is no world by which a technologist or an engineer sits on their very own or discusses or goals to check possible futures with those throughout the same disciplinary background or other kind of homogeneity in background and is in a position to foresee the implications for other groups and the broader implications of those technologies.

The primary query is, what are the appropriate inquiries to ask? After which the second query is, who has methods and insights to find a way to bring to bear on this across disciplines? And that is what we have aimed to pioneer at MIT, is to essentially bring this kind of embedded approach to drawing within the scholarship and insight from those in other fields in academia and people from outside of academia and produce that into our practice in engineering latest technologies.

And just to provide you a concrete example of how hard it’s to even just determine whether you are asking the appropriate query, for the technologies that we develop in my lab, we believed for a few years that the appropriate query was, how can we develop and shape technologies in order that it augments relatively than replaces? And that is been the general public discourse about robots and AI taking people’s jobs. “What is going on to occur 10 years from now? What’s happening today?” with well-respected studies put out just a few years ago that for each one robot you introduced right into a community, that community loses as much as six jobs.

So, what I learned through deep engagement with scholars from other disciplines here at MIT as an element of the Work of the Future task force is that that is actually not the appropriate query. So because it seems, you only take manufacturing for instance because there’s superb data there. In manufacturing broadly, just one in 10 firms have a single robot, and that is including the very large firms that make high use of robots like automotive and other fields. After which if you have a look at small and medium firms, those are 500 or fewer employees, there’s essentially no robots anywhere. And there is significant challenges in upgrading technology, bringing the newest technologies into these firms. These firms represent 98% of all manufacturers within the US and are coming up on 40% to 50% of the manufacturing workforce within the U.S. There’s good data that the lagging, technological upgrading of those firms is a really serious competitiveness issue for these firms.

And so what I learned through this deep collaboration with colleagues from other disciplines at MIT and elsewhere is that the query is not “How can we address the issue we’re creating about robots or AI taking people’s jobs?” but “Are robots and the technologies we’re developing actually doing the job that we want them to do and why are they really not useful in these settings?”. And you’ve got these really exciting case stories of the few cases where these firms are in a position to usher in, implement and scale these technologies. They see a complete host of advantages. They do not lose jobs, they can tackle more work, they’re in a position to bring on more staff, those staff have higher wages, the firm is more productive. So how do you realize this kind of win-win-win situation and why is it that so few firms are in a position to achieve that win-win-win situation?

There’s many various aspects. There’s organizational and policy aspects, but there are literally technological aspects as well that we now are really laser focused on within the lab in aiming to deal with the way you enable those with the domain expertise, but not necessarily engineering or robotics or programming expertise to find a way to program the system, program the duty relatively than program the robot. It is a humbling experience for me to imagine I used to be asking the appropriate questions and fascinating on this research and really understand that the world is a way more nuanced and complicated place and we’re in a position to understand that a lot better through these collaborations across disciplines. And that comes back to directly shape the work we do and the impact we’ve on society.

And so we’ve a extremely exciting program at MIT training the following generation of engineers to find a way to speak across disciplines in this fashion and the longer term generations will likely be a lot better off for it than the training those of us engineers have received previously.

Yeah, I believe Julie you brought such a fantastic point, right? I believe it resonated so well with me. I do not think that is something that you just only see in academia’s type of setting, right? I believe this is strictly the type of change I’m seeing in industry too. I believe how the various roles inside the substitute intelligence space come together after which work in a highly collaborative type of way around this type of amazing technology, that is something that I’ll admit I’d never seen before. I believe previously, AI appeared to be perceived as something that only a small group of deep researchers or deep scientists would find a way to do, almost like, “Oh, that is something that they do within the lab.” I believe that is type of lots of the perception from my clients. That is why so as to scale AI in enterprise settings has been an enormous challenge.

I believe with the recent advancement in foundational models, large language models, all these pre-trained models that enormous tech firms have been constructing, and clearly academic institutions are an enormous a part of this, I’m seeing more open innovation, a more open collaborative type of way of working within the enterprise setting too. I really like what you described earlier. It is a multi-disciplinary type of thing, right? It isn’t like AI, you go to computer science, you get a sophisticated degree, then that is the only path to do AI. What we’re seeing also in business setting is people, leaders with multiple backgrounds, multiple disciplines throughout the organization come together is computer scientists, is AI engineers, is social scientists and even behavioral scientists who’re really, really good at defining different sorts of experimentation to play with this type of AI in early-stage statisticians. Because at the top of the day, it’s about probability theory, economists, and naturally also engineers.

So even inside an organization setting within the industries, we’re seeing a more open type of attitude for everybody to come back together to be around this type of amazing technology to all contribute. We all the time discuss a hub and spoke model. I actually think that this is going on, and everybody is getting enthusiastic about technology, rolling up their sleeves and bringing their different backgrounds and skill sets to all contribute to this. And I believe it is a critical change, a culture shift that we’ve seen within the business setting. That is why I’m so optimistic about this positive sum game that we talked about earlier, which is the last word impact of the technology.

That is a extremely great point. Julie, Lan mentioned it earlier, but in addition this access for everybody to a few of these technologies like generative AI and AI chatbots will help everyone construct latest ideas and explore and experiment. But how does it really help researchers construct and adopt those sorts of emerging AI technologies that everybody’s keeping an in depth eye on the horizon?

Yeah. Yeah. So, talking about generative AI, for the past 10 or 15 years, each 12 months I believed I used to be working in probably the most exciting time possible on this field. After which it just happens again. For me the really interesting aspect, or one among the really interesting features, of generative AI and GPT and ChatGPT is, one, as you mentioned, it’s really within the hands of the general public to find a way to interact with it and envision multitude of the way it could potentially be useful. But from the work we have been doing in what we call positive-sum automation, that is around these sectors where performance matters so much, reliability matters so much. You consider manufacturing, you consider aerospace, you consider healthcare. The introduction of automation, AI, robotics has indexed on that and at the fee of flexibility. And so an element of our research agenda is aiming to attain one of the best of each those worlds.

The generative capability could be very interesting to me since it’s one other point on this space of high performance versus flexibility. This can be a capability that could be very, very flexible. That is the concept of coaching these foundation models and everybody can get a direct sense of that from interacting with it and twiddling with it. This is just not a scenario anymore where we’re very rigorously crafting the system to perform at very high capability on very, very specific tasks. It’s extremely flexible within the tasks you possibly can envision making use of it for. And that is game changing for AI, but on the flip side of that, the failure modes of the system are very difficult to predict.

So, for prime stakes applications, you are never really developing the aptitude of performing some specific task in isolation. You are considering from a systems perspective and the way you bring the relative strengths and weaknesses of various components together for overall performance. The way in which it’s essential to architect this capability inside a system could be very different than other types of AI or robotics or automation because you’ve got a capability that is very flexible now, but in addition unpredictable in how it should perform. And so it’s essential to design the remaining of the system around that, or it’s essential to carve out the features or tasks where failure particularly modes usually are not critical.

So chatbots for instance, by and enormous, for lots of their uses, they may be very helpful in driving engagement and that is of great profit for some products or some organizations. But having the ability to layer on this technology with other AI technologies that haven’t got these particular failure modes and layer them in with human oversight and supervision and engagement becomes really necessary. So the way you architect the general system with this latest technology, with these very different characteristics I believe could be very exciting and really latest. And even on the research side, we’re just scratching the surface on methods to do this. There’s lots of room for a study of best practices here particularly in these more high stakes application areas.

I believe Julie makes such a fantastic point that is super resonating with me. I believe, again, all the time I’m just seeing the very same thing. I really like the couple keywords that she was using, flexibility, positive-sum automation. I believe there are two colours I would like so as to add there. I believe on the pliability frame, I believe this is strictly what we’re seeing. Flexibility through specialization, right? Used with the facility of generative AI. I believe one other term that got here to my mind is that this resilience, okay? So now AI becomes more specialized, right? AI and humans actually turn into more specialized. And in order that we are able to each deal with things, little skills or roles, that we’re one of the best at.

In Accenture, we only in the near past published our perspective, “A brand new era of generative AI for everyone.” Inside the perspective, we laid out this, what I call the ACCAP framework. It mainly addresses, I believe, similar points that Julie was talking about. So mainly advice, create, code, after which automate, after which protect. When you link all these five, the primary letter of those five words together is what I call the ACCAP framework (in order that I can remember those five things). But I believe that is how other ways we’re seeing how AI and humans working together manifest this type of collaboration in other ways.

For instance, advising, it’s pretty obvious with generative AI capabilities. I believe the chatbot example that Julie was talking about earlier. Now imagine every role, every knowledge employee’s role in a company may have this co-pilot, running behind the scenes. In a contact center’s case it might be, okay, now you are getting this generative AI doing auto summarization of the agent calls with customers at the top of the calls. So the agent doesn’t need to be spending time and doing this manually. After which customers will get happier because customer sentiment will improve detected by generative AI, creating obviously the various, even consumer-centric type of cases around how human creativity is getting unleashed.

And there is also business examples in marketing, in hyper-personalization, how this type of creativity by AI is being best utilized. I believe automating—again, we have been talking about robotics, right? So again, how robots and humans work together to take over a few of these mundane tasks. But even in generative AI’s case is just not even just the blue-collar type of jobs, more mundane tasks, also looking into more mundane routine tasks in knowledge employee spaces. I believe those are the couple examples that I keep in mind when I believe of the word flexibility through specialization.

And by doing so, latest roles are going to get created. From our perspective, we have been specializing in prompt engineering as a brand new discipline throughout the AI space—AI ethics specialist. We also imagine that this role goes to take off in a short time simply due to responsible AI topics that we just talked about.

And likewise because all this business processes have turn into more efficient, more optimized, we imagine that latest demand, not only the brand new roles, each company, no matter what industries you’re in, for those who turn into superb at mastering, harnessing the facility of this type of AI, the brand new demand goes to create it. Because now your products are convalescing, you’re in a position to provide a greater experience to your customer, your pricing goes to get optimized. So I believe bringing this together is, which is my second point, it will bring positive sum to the society in economics type of terms where we’re talking about this. Now you are pushing out the production possibility frontier for the society as a complete.

So, I’m very optimistic about all these amazing features of flexibility, resilience, specialization, and in addition generating more economic profit, economic growth for the society aspect of AI. So long as we walk into this with eyes wide open in order that we understand a few of the existing limitations, I’m sure we are able to do each of them.

And Julie, Lan just laid out this improbable, really a correlation of generative AI in addition to what’s possible in the longer term. What are you enthusiastic about artificial intelligence and the opportunities in the following three to 5 years?

Yeah. Yeah. So, I believe Lan and I are very largely on the identical page on nearly all of those topics, which is admittedly great to listen to from the educational and the industry side. Sometimes it will possibly feel as if the emergence of those technologies is just going to kind of steamroll and work and jobs are going to alter in some predetermined way since the technology now exists. But we all know from the research that the info doesn’t bear that out actually. There’s many, many selections you make in the way you design, implement, and deploy, and even make the business case for these technologies that may really kind of change the course of what you see on the earth due to them. And for me, I actually think so much about this query of what is called lights out in manufacturing, like lights out operation where there’s this concept that with the advances and all these capabilities, you’ll aim to find a way to run every little thing without people in any respect. So, you do not need lights on for the people.

And again, as an element of the Work of the Future task force and the research that we have done visiting firms, manufacturers, OEMs, suppliers, large international or multinational firms in addition to small and medium firms internationally, the research team asked this query of, “So these high performers which can be adopting latest technologies and doing well with it, where is all this headed? Is that this headed towards a lights out factory for you?” And there have been quite a lot of answers. So some people did say, “Yes, we’re aiming for a lights out factory,” but actually many said no, that that was not the top goal. And one among the quotes, one among the interviewees stopped while giving a tour and turned around and said, “A lights out factory. Why would I would like a lights out factory? A factory without people is a factory that is not innovating.”

I believe that is the core for me, the core point of this. After we deploy robots, are we caging and kind of locking the people out of that process? After we deploy AI, is actually the infrastructure and data curation process so intensive that it really locks out the power for a site expert to are available in and understand the method and find a way to have interaction and innovate? And so for me, I believe probably the most exciting research directions are those that enable us to pursue this kind of human-centered approach to adoption and deployment of the technology and that enable people to drive this innovation process. So a factory, there is a well-defined productivity curve. You aren’t getting your assembly process if you start. That is true in any job or any field. You never get it exactly right otherwise you optimize it to begin, nevertheless it’s a really human process to enhance. And the way can we develop these technologies such that we’re maximally leveraging our human capability to innovate and improve how we do our work?

My view is that by and enormous, the technologies we’ve today are really not designed to support that and so they really impede that process in quite a few other ways. But you do see increasing investment and exciting capabilities by which you possibly can engage people on this human-centered process and see all the advantages from that. And so for me, on the technology side and shaping and developing latest technologies, I’m most excited concerning the technologies that enable that capability.

Excellent. Julie and Lan, thanks a lot for joining us today on what’s been a extremely improbable episode of The Business Lab.

Thanks a lot for having us.

Thanks.

That was Lan Guan of Accenture and Julie Shah of MIT who I spoke with from Cambridge, Massachusetts, the house of MIT and MIT Technology Review overlooking the Charles River.

That is it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 on the Massachusetts Institute of Technology. You could find us in print, on the internet, and at events every year around the globe. For more details about us and the show, please try our website at technologyreview.com.

This show is out there wherever you get your podcasts. When you enjoyed this episode, we hope you may take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Giro Studios. Thanks for listening.

A human-centric approach to adopting AI

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