I don’t think, as other people might, that we don’t know how to do [AGI] and we’re waiting for some enormous breakthrough. I don’t think that’s the case, I think we do know how to do it, we just need to prove that.
FOUNDER, ELEMENTAL COGNITION DIRECTOR OF APPLIED AI, BRIDGEWATER ASSOCIATES
David Ferrucci built and led the IBM Watson team from its inception to its landmark success in 2011 when Watson defeated the greatest Jeopardy! players of all time. In 2015 he founded his own company, Elemental Cognition, focused on creating novel AI systems that dramatically accelerate a computer’s ability to understand language.
MARTIN FORD: How did you become interested in computers? What’s the path that led you to AI?
DAVID FERRUCCI: I started back before computers were an everyday term. My parents wanted me to become a medical doctor, and my dad hated the fact that I would be home during the school holidays without anything to do. In the summer of my junior year at high school, my dad looked in the paper and found a math class for me at a local college. It turned out that it was actually a programming class using BASIC on DEC computers. I thought it was phenomenal because you could give this machine instructions, and if you could articulate the procedure or the algorithm that you’re going through in your head you could get the machine to do it for you. The machine could store the data AND the thought process. I imagined this was my way out! If I could get the machine to think and memorize everything for me, then I wouldn’t have to do all of that work to become a doctor.
It got me interested in what it meant to store information, to reason over it, to think, and to systematize or to turn into an algorithm whatever process was going on in my brain. If I could just specify that in enough detail, then I could get the computer to do it, and that was enthralling. It was just a mind-altering realization.
I didn’t know the words “artificial intelligence” at the time, but I got very interested in the whole notion of coordinated intelligence from a mathematical, algorithmic, and philosophical perspective. I believed that modeling human intelligence in the machine was possible. There was no reason to think that it wasn’t.
MARTIN FORD: Did you follow that with computer science at college?
DAVID FERRUCCI: No, I had no idea about careers in computer science or AI, so I went to college and majored in biology to become a medical doctor. During my studies, I got my grandparents to buy me an Apple II computer, and I just started programming everything I could think of. I ended up programming a lot of software for my college, from graphing software for experimental lab work, to ecology simulation software, to analog-to-digital interfacing for lab equipment. This, of course, was before any of this stuff even existed, never mind being able to just download it form the internet. I decided to do as much computer science as I could in my last year of college, so I did a minor in it. I graduated with the top biology award and I was ready to go to medical school, when I decided it just wasn’t for me.
Instead, I went to graduate school for computer science, and AI in particular. I decided that was what I was passionate about, and that’s what I wanted to study. So, I did my master’s at Rensselaer Polytechnic Institute (RPI) in New York, where I developed a semantic network system as part of my thesis. I called it COSMOS, which I am sure stood for something related to cognition and sounded cool, but I can’t remember the precise expansion. COSMOS represented knowledge and language, and could perform limited forms of logical reasoning.
I was giving a presentation of COSMOS at a sort of industrial science fair at RPI in 1985 when some folks from the IBM Watson Research Center, who had just started their own AI project, saw me presenting and they asked me if I wanted a job. My original plan had been to stay on and get my PhD, but a few years before this I’d seen an ad in a magazine to become an IBM Research Fellow where you could research whatever you want with unlimited resources—that sounded like my dream job, so I’d cut that ad out and pinned it on my bulletin board. When these people from IBM’s Research Center offered me that job, I took it.
So, in 1985 I started working on an AI project at IBM Research, but then a couple of years later, the 1980s’ AI winter had hit, and IBM was going around canceling every project that was associated with AI. I was told that they would be able to put me to work on other projects, but I didn’t want to work on other projects, I wanted to work on AI, so I decided to quit IBM. My dad was mad at me. He was already pissed I didn’t become a doctor, then by some miracle I had gotten a good job anyway and now I was quitting two years later. That just did not sound like a good thing to him.
I went back to RPI and did my PhD on non-monotonic reasoning. I designed and built a medical expert system called CARE (Cardiac and Respiratory Expert) and just learned a lot more about AI during that period. To support my studies, I also worked on a government contract building an object-oriented circuit design system at RPI. After completing my PhD, I needed to look for work. My dad had gotten pretty sick and he lived down in Westchester, where IBM was also based. I wanted to be near him, so I called some people I knew from my earlier IBM days and ended up going back to IBM Research.
IBM was not an AI company at that point, but 15 years later, with Watson and other projects, I had helped to shape it in that direction. I never gave up my desire to work on AI, and I built a skilled team over the years and engaged in every opportunity to work in areas like language processing, text and multimedia analytics, and automatic question answering. By the time there was this interest in doing Jeopardy!, I was the only one in IBM who believed it could be done and had a team capable of doing it. With Watson’s huge success, IBM was able to transform itself into an AI company.
MARTIN FORD: I don’t want to focus much on your work with Watson, as that’s already a very well-documented story. I’d like to talk about how you were thinking about AI, after you left IBM.
DAVID FERRUCCI: The way I think about AI is that there’s perception—recognizing things, there’s control—doing things, and there’s knowing—building, developing, and understanding the conceptual models that provide the foundation of communication, and the development of theories and ideas.
One of the interesting things I learned working on the Watson project was that pure statistical approaches were limited in the “understanding” part, that’s their ability to produce casual and consumable explanations for their predictions or their answers. Purely data-driven or statistical approaches to prediction are very powerful for perception tasks, such as pattern recognition, voice recognition, and image recognition, and control tasks, such as driverless cars and robotics, but in the knowledge space AI is struggling.
We’ve seen huge advances in voice and image recognition and in general, perception-related stuff. We’ve also seen huge advances in the control systems that you see driving drones and all kinds of robotic driverless cars. When it comes to fluently communicating with a computer based on what it has read and understood, we’re not even close to there yet.
MARTIN FORD: More recently in 2015 you started a company called Elemental Cognition. Could you tell us more about that?
DAVID FERRUCCI: Elemental Cognition is an AI research venture that’s trying to do real language understanding. It’s trying to deal with that area of AI that we still have not cracked, which is, can we create an AI that reads, dialogs, and builds understanding?
A human being might read books and develop rich models of how the world works in their head, and then reason about it and fluently dialog about it and ask questions about it. We refine and compound our understanding through reading and dialoging. At Elemental Cognition, we want our AI to do that.
We want to look beyond the surface structure of language, beyond the patterns that appear in word frequencies, and get at the underlying meaning. From that, we want to be able to build the internal logical models that humans would create and use to reason and communicate. We want to ensure a system that produces a compatible intelligence. That compatible intelligence can autonomously learn and refine its understanding through human interaction, language, dialog, and other related experiences.
Thinking about what knowing and understanding means is a really interesting part of AI. It’s not as easy as providing labeled data for doing image analysis, because what happens is that you and I could read the same thing, but we can come up with very different interpretations. We could argue about what it means to understand that thing. Today’s systems do more text matching and looking at the statistical occurrences of words and phrases, as opposed to developing a layered and logical representation of the complex logic that is really behind the language.
MARTIN FORD: Let’s pause to make sure people grasp the magnitude of this. There are lots of deep learning systems today that can do great pattern recognition and could, for example, find a cat in a picture and tell you there’s a cat in the image. But there is no system in existence that really understands what a cat is, in the way that a person does.
DAVID FERRUCCI: Well yes, but you and I could also argue about what a cat is. That’s the interesting part because it asks what does it mean to actually understand. Think about how much human energy goes into helping each other to develop shared understandings of things. It’s essentially the job of anyone compiling or communicating information, any journalist, artist, manager, or politician. The job is to get other people to understand things the way they understand them. That’s how we as a society can collaborate and advance rapidly.
That’s a difficult problem because in the sciences we’ve developed formal languages that are completely unambiguous for the purposes of producing value. So, engineers use specification languages, while mathematicians and physicists use mathematics to communicate. When we write programs, we have unambiguous formal programming languages. When we talk, though, using natural language, which is where we’re absolutely prolific and where our richest and most nuanced things happen, there it’s very ambiguous and it’s extremely contextual. If I take one sentence out of context, it can mean lots of different things.
It’s not just the context in which the sentence is uttered, it’s also what is in that person’s mind. For you and I to confidently understand each other, it is not enough for me just to say things. You have to ask me questions, and we have to go back and forth and get in sync and align our understandings until we are satisfied that we have a similar model in our heads. That is because the language itself is not the information. The language is a vehicle through which we communicate the model in our heads. That model is independently developed and refined, and then we align them to communicate. This notion of “producing” an understanding is a rich, layered, highly contextual thing that is subjective and collaborative.
A great example was when my daughter was seven years old and doing some school work. She was reading a page in a science book about electricity. The book says that it’s energy that’s created in different ways, such as by water flowing over turbines. It ends by asking my daughter a simple question, “How is the electricity produced?” She looks back at the text, and she’s doing text matching, saying well it says electricity is created and “created” is a synonym of “produced,” and then it has this phrase, “by water flowing over turbines.”
She comes to me and says, “I can answer this question by copying this phrase, but I have no understanding of what electricity is or how it is produced.” She didn’t understand it at all, even though she could get the question right by doing text matching. We then discussed it and she gained a richer understanding. That is more-or-less how most language AI works today—it doesn’t understand. The difference is that my daughter knew she didn’t understand. That is interesting. She expected much more from her underlying logical representation. I took that as a sign of intelligence, but I may be have been biased in this case. Ha!
It’s one thing to look at the words in a passage and take a guess at the answer. It’s another thing to understand something enough to be able to communicate a rich model of your understanding to someone and then discuss, probe, and get in sync to advance your understanding as a result.
MARTIN FORD: You’re imagining a system that has a genuine understanding of concepts and that can converse and explain its reasoning. Isn’t that human-level artificial intelligence or AGI?
DAVID FERRUCCI: When you can produce a system that can autonomously learn, in other words, it can read, understand, and build models then converse, explain, and summarize the models to a person that it’s talking to, then you’re approaching more of what I would call holistic intelligence.
As I said, I think there are three parts to a complete AI, perception, control, and knowing. A lot of the stuff that’s going on with deep learning is remarkable regarding the progress that we’re making on the perception and the control pieces, the real issue is the final piece. How do we do the understanding and the collaborative communication with humans so that we can create a shared intelligence? That’s super powerful, because our main means for building, communicating, and compounding knowledge is through our language and building human-compatible models. That’s the AI that I’m endeavoring to create with Elemental Cognition.
MARTIN FORD: Solving the understanding problem is one of the holy grails of AI. Once you have that, other things fall into place. For example, people talk about transfer learning or the ability to take what you know and apply it in another domain, and true understanding implies that. If you really understand something, you should be able to apply it somewhere else.
DAVID FERRUCCI: That’s exactly right. One of the things that we’re doing at Elemental Cognition is testing how a system understands and compounds the knowledge that it reads in even the simplest stories. If it reads a story about soccer, can it then apply that understanding to what’s going on in a lacrosse game or a basketball game? How does it reuse its concepts? Can it produce analogous understandings and explanations for things, having learned one thing and then doing that reasoning by analogy and explaining it in a similar way?
What’s tricky is that humans do both kinds of reasoning. They do what we might think of as statistical machine learning, where they process a lot of data points and then generalize the pattern and apply it. They produce something akin to a trendline in their head and intuit new answers by applying the trend. They might look at some pattern of values and when asked what is next, intuitively say the answer is 5. When people are doing that, they’re doing more pattern matching and extrapolation. Of course, the generalization might be more complicated than a simple trend line, as it certainly can be with deep learning techniques.
But, when people sit down and say, “Let me explain to you why this makes sense to me—the answer is 5 because...,” now they have more of a logical or causal model that they’ve built up in their head, and that becomes a very different kind of information that is ultimately much more powerful. It’s much more powerful for communication, it’s much more powerful for an explanation, and it’s much more powerful for extension because now I could critique it and say, “Wait, I see where your reasoning is faulty,” as opposed to saying “It’s just my intuition based on past data. Trust me.”
If all I have is inexplicable intuition, then how do I develop, how do I improve, and how do I extend my understanding of the world around me? That’s the interesting dilemma I think we face when we contrast these two kinds of intelligences. One that is focused on building a model that is explicable, that you can inspect, debate, explain, and improve on, and one that says, “I count on it because it’s right more often than it’s wrong.” Both are useful, but they’re very different. Can you imagine a world where we give up agency to machines that cannot explain their reasoning? That sounds bad to me. Would you like to give agency up to humans that cannot explain their reasoning?
MARTIN FORD: Many people believe that deep learning, that second model that you describe, is enough to take us forward. It sounds like you think we also need other approaches.
DAVID FERRUCCI: I’m not a fanatic one way or the other. Deep learning and neural networks are powerful because they can find nonlinear, very complex functions in large volumes of data. By function, I mean if I want to predict your weight given your height, that could be a very simple function represented by a line. Predicting the weather is less likely to be represented by a simple linear relationship. The behavior of more complex systems is more likely represented by very complex functions over many variables (think curvy and even discontinuous and in many dimensions).
You can give a deep learning system huge amounts of raw data and have it find a complex function, but in the end, you’re still just learning a function. You might further argue that every form of intelligence is essentially learning a function. But unless you endeavor to learn the function that outputs human intelligence itself (what would be the data for that?), then your system may very well produce answers whose reasons are inexplicable.
Imagine I have a machine called a neural network where if I load in enough data, it could find an arbitrarily complex function to map the input to the output. You would think, “Wow! Is there any problem it can’t solve?” Maybe not, but now the issue becomes, do you have enough data to completely represent the phenomenon over all time? When we talk about knowing or understanding, we have first to say, what’s the phenomenon?
If we’re talking about identifying a cat in a picture, it’s very clear what the phenomenon is, and we would get a bunch of labeled data, and we would train the neural network. If you say: “How do I produce an understanding of this content?”, it’s not even clear I can get humans to agree on what an understanding is. Novels and stories are complex, multilayered things, and even when there is enough agreement on the understanding, it’s not written down enough for a system to learn the immensely complex function represented by the underlying phenomenon, which is human intelligence itself.
Theoretically, if you had the data you needed that mapped every kind of English story to its meaning, and there was enough there to learn the meaning mapping—to learn what the brain does given an arbitrary collection of sentences or stories—then could a neural network learn it? Maybe, but we don’t have that data, we don’t know how much data is required, and we don’t know what it takes to learn it in terms of the complexity of the function a neural network could potentially learn. Humans can do it, but that’s because the human brain is constantly interacting with other humans and it’s prewired for doing this kind of thing.
I would never take a theoretical position that says, “I have a general function finder. I can do anything with it.” At some levels, sure, but where’s the data to produce the function that represents human understanding? I don’t know.
The methodology for engaging and acquiring that information is something I don’t know how to do with a neural network right now. I do have ideas on how to do that, and that doesn’t mean I don’t use neural networks and other machine learning techniques as part of that overarching architecture.
MARTIN FORD: You had a part in a documentary called Do You Trust This Computer? and you said “In three to five years, we’ll have a computer system that can autonomously learn to understand and how to build understanding, not unlike the way a human mind works.” That really struck me. That sounds like AGI, and yet you’re giving it a three- to five-year time frame. Is that really what you’re saying?
DAVID FERRUCCI: It’s a very aggressive timeline, and I’m probably wrong about that, but I would still argue that it’s something that we could see within the next decade or so. It’s not going to be a 50- or a 100-year wait.
I think that we will see two paths. We will see the perception side and the control side continue to get better in leaps and bounds. That is going to have a dramatic impact on society, on the labor market, on national security, and on productivity, which is all going to be very significant, and that’s not even addressing the understanding side.
I think that will lead to a greater opportunity for AI to engage humans, with things like Siri and Alexa engaging humans more and more in language and thinking tasks. It’s through those ideas, and with architectures like we’re building at Elemental Cognition, that we will start to be able to learn how to develop that understanding side.
My three- to five-year estimate was a way of saying, this is not something that we have no idea how to do. This is something we do have an idea how to do, and it’s a matter of investing in the right approach and putting in the engineering necessary to achieve it. I would make a different estimate if it was something I thought was possible, but that I had no idea how to get there.
However long the wait is depends a lot on where the investment goes. A lot of the investment today is going into the pure statistical machine learning stuff because it’s so short-term and so hot. There are just a lot of low-hanging fruit returns. One of the things I’m doing is getting investment for another technology that I think we need in order to develop that understanding side. It all depends on how the investment gets applied and over what time frame. I don’t think, as other people might, that we don’t know how to do it and we’re waiting for some enormous breakthrough. I don’t think that’s the case, I think we do know how to do it, we just need to prove that.
MARTIN FORD: Would you describe Elemental Cognition as an AGI company?
DAVID FERRUCCI: It’s fair to say we’re focused on building a natural intelligence with the ability to autonomously learn, read, and understand, and we’re achieving our goals for fluently dialoging with humans in that way.
MARTIN FORD: The only other company I’m aware of that is also focused on that problem is DeepMind, but I’m struck by how different your approach is. DeepMind is focused on deep reinforcement learning through games and simulated environments, whereas what I hear from you is that the path to intelligence is through language.
DAVID FERRUCCI: Let’s restate the goal a little bit. Our goal is to produce an intelligence that is anchored in logic, language and reason because we want to produce a compatible human intelligence. In other words, we want to produce something that can process language the way humans process language, can learn through language, and can deliver knowledge fluently through language and reason. This is very specifically the goal.
We do use a variety of machine learning techniques. We use neural networks to do a variety of different things. The neural networks, however, do not alone solve the understanding problem. In other words, it’s not an end-to-end solution. We also use continuous dialog, formal reasoning, and formal logic representations. For things that we can learn efficiently with neural networks, we do. For the things we can’t, we find other ways to acquire and model that information.
MARTIN FORD: Are you also working on unsupervised learning? Most AI that we have today is trained with labeled data, and I think real progress will probably require getting these systems to learn the way that a person does, organically from the environment.
DAVID FERRUCCI: We do both. We do corpus and large corpus analysis, which is unsupervised. We do unsupervised learning from large corpora, but we also do supervised learning from annotated content as well.
MARTIN FORD: Let’s talk about the future implications of AI. Do you think there is the potential for a big economic disruption in the near future, where a lot of jobs are going to be deskilled or to disappear?
DAVID FERRUCCI: I think it’s definitely something that we need to pay attention to. I don’t know if it’ll be more dramatic than in previous examples of when a new technology has rolled in, like in the Industrial Revolution, but I think this AI revolution will be significant and comparable to the industrial revolution.
I think there will be displacements and there will be the need to transition the workforce, but I don’t think it’s going to be catastrophic. There’s going to be some pain in that transition, but in the end, my guess is that it’s likely to create more jobs. I think that’s also what has happened historically. Some people might get caught in that and they have to retrain; that certainly happens, but it doesn’t mean there’ll be fewer jobs overall.
MARTIN FORD: Do you think there’s likely to be a skill mismatch problem? For instance, if a lot of the new jobs created are for robotics engineers, deep learning experts, and so forth?
DAVID FERRUCCI: Certainly, those jobs will get created, and there’ll be a skills mismatch, but I think other jobs will be created as well where there’ll be greater opportunities just for refocusing and saying, “What do we want humans doing if machines are doing these other things?” There are tremendous opportunities in healthcare and caregiving, where things like human contact are important.
The future we envision at Elemental Cognition has human and machine intelligence tightly and fluently collaborating. We think of it as thought-partnership. Through thought-partnership with machines that can learn, reason, and communicate, humans can do more because they don’t need as much training and as much skill to get access to knowledge and to apply it effectively. In that collaboration, we are also training the computer to be smarter and more understanding of the way we think.
Look at all the data that people are giving away for free today, that data has value. Every interaction you have with a computer has value because that computer’s getting smarter. So, to what extent do we start paying for that, and paying for that more regularly? We want computers to interact in ways that are more compatible with humans, so why aren’t we paying humans to help us achieve that? I think the economics of the human-machine collaboration is interesting in and of itself, but there will be big transitions. Driverless cars are inevitable, and there are quite a few people who have decent blue-collar jobs driving, and I think that’ll evolve. I don’t know if that will be a trend, but that will certainly be a transition.
MARTIN FORD: How do you feel about the risks of superintelligence that Elon Musk and Nick Bostrom have both been talking about?
DAVID FERRUCCI: I think there’s a lot of cause to be concerned anytime you give a machine leverage. That’s when you put it in control over something that can amplify an error or the effect of a bad actor. For instance, if I put machines in control of the electrical grid, over weapon systems, or over the driverless car network, then any mistake there can be amplified into a significant disaster. If there’s a cybersecurity problem or an evil actor hacks the system, it’s going to amplify the impact of the error or the hack. That’s what we should be super concerned about. As we’re putting machines in control of more and more things like transportation systems, food systems, and national security systems, we need to be super careful. This doesn’t have anything specifically to do with AI, only that you must design those systems with concern about error cases and cybersecurity.
The other thing that people like Nick Bostrom talk about is how the machine might develop its own goals and decide it’s going to lay waste to the human race to achieve its goals. That’s something I’m less concerned about because there are fewer incentives for machines to react like that. You’d have to program the computer to do something like that.
Nick Bostrom talks about the idea that you could give the machine a benign goal but because it’s smart enough it will find a complex plan that will have unintended circumstances when it executes that plan. My response to that is simple, why would you do that? I mean, you don’t give a machine that has to make paper clips leverage over the electrical grid, it comes back to thoughtful design and design for security. There are many other human problems I would put higher on the list of concerns than the notion that an AI would suddenly come up with its own desires and goals, and/or plan to sacrifice the human race to make more paper clips.
MARTIN FORD: What do you think about the regulation of AI, is there a need for that?
DAVID FERRUCCI: The idea of regulation is something we do have to pay attention to. As an industry, we have to decide broadly who’s liable for what when we have machines making decisions that affect our lives. That’s the case whether it’s in health care, policymaking, or any of the other fields. Are we, as individuals who are affected by decisions that are made by machines, entitled to an explanation that we can understand?
In some sense, we already face these kinds of things today. For example, in healthcare we’re sometimes given explanations that say, “We think you should do this and we highly recommend it because 90% of the time this is what happens.” They’re giving you a statistical average rather than particulars about an individual patient. Should you be satisfied with that? Can you request an explanation as to why they’re recommending that treatment based on this individual patient? It’s not about the probabilities, it’s about the possibilities for an individual case. It raises very interesting questions.
That is one area where governments will need to step in and say, “Where does the liability fall and what are we owed as individuals who are potential subjects of machine decision-making?”
The other area, which we talked a little bit about, was, what are the criteria when you design systems that have dramatic leverage, where negative effects like errors or hacking can be dramatically amplified and have broad human societal impact? You don’t want to slow down the advancement of technology, but at the same time, you don’t want to be too casual about the controls around deploying systems like that.
Another area for regulation that’s a little dicey is the labor market. Do you slow things down and say, “you can’t put machines in this job because we want to protect the labor market”? I think there’s something to be said for helping society transition smoothly and avoiding dramatic impacts, but at the same time, you don’t want to slow down our advance as a society over time.
MARTIN FORD: Since you departed IBM, they’ve built a big business unit around Watson and are trying to commercialize that with mixed results. What do you think of IBM’s experience and the challenges they’ve faced, and does that relate to your concern about building machines that can explain themselves?
DAVID FERRUCCI: I’m many miles away from what’s going on there nowadays, but my sense of that from a business perspective, is that they seized Watson as a brand to help them get into the AI business, and I think it’s given them that opportunity. When I was at IBM, they were doing all kinds of AI technology, it was very spread out throughout the company in different areas. I think that when Watson won the Jeopardy! competition and demonstrated to the public a really palpable AI capability, all that excitement and momentum helped IBM to organize and integrate all their technology under a single brand. That demonstration gave them the ability to position themselves well, both internally and externally.
With regard to the businesses, I think IBM is in a unique place regarding the way they can capitalize on this kind of AI. It’s very different than the consumer space. IBM can approach the market broadly through business intelligence, data analytics, and optimization. And they can deliver targeted value, for example in healthcare applications.
It’s tough to measure how successful they’ve been because it depends on what you count as AI and where you are in the business strategy. We will see how it plays out. As far as the consumer mindshare these days it seems to me like Siri and Amazon’s Alexa are in the limelight. Whether or not they’re providing good value on the business side is a question I can’t answer.
MARTIN FORD: There are concerns that China may have an advantage given that they have a larger population, more data, and fewer concerns about privacy. Is that something we should worry about? Do we need more industrial policy in the United States in order to be more competitive?
DAVID FERRUCCI: I think that there is a bit of an arms race in the sense that these things will affect productivity, the labor markets, national security, and consumer markets, so it matters a lot. To stay competitive as a nation you do have to invest in AI to give a broad portfolio. You don’t want to put all your eggs in one basket. You have to attract and maintain talent to stay competitive, so I think there’s no question that national boundaries create a certain competition because of how much it affects competitive economics and security.
The challenging balancing act is how do you remain competitive there and at the same time, think carefully about controls, regulation, and other kinds of impacts, such as privacy. Those are tough issues, and I think one of the things that the world’s going to need is more thoughtful and knowledgeable leaders in this space who can help set policy and make some of those calls. That’s a very important service, and the more knowledgeable you are, the better, because if you look under the hood, this is not simple stuff. There’s a lot of tough questions, a lot of technology issues to make choices on. Maybe you need AI for that!
MARTIN FORD: Given these risks and concerns, are you optimistic with regard to the future of artificial intelligence?
DAVID FERRUCCI: Ultimately, I’m an optimist. I think it’s our destiny to pursue this kind of thing. Step back to what interested me when I first started on my path in AI: Understanding human intelligence; understanding it in a mathematical and systematic way; understanding what the limitations are, how to enhance it, how to grow it, and how to apply it. The computer provides us with a vehicle through which we can experiment with the very nature of intelligence. You can’t say no to that. We associate our sense of self with our intelligence, and so how do we not do everything we can to understand it better, to apply it more effectively, and to understand its strengths and its weaknesses? It’s more our destiny than anything else. It’s the fundamental exploration—how do our minds work?
It’s funny because we think about how humanity wants to explore space and beyond to find other intelligences, when in fact, we have one growing right next to us. What does it even mean? What’s the very nature of intelligence? Even if we were to find another species, we’ll know more about what to expect and what’s both possible and impossible as we explore the very fundamental nature of intelligence. It’s our destiny to cope with this, and I think that ultimately, it will dramatically enhance our creativity and our standard of living in ways we can’t even begin to imagine today.
There is this existential risk, and I think it’s going to impact a change in how we think about ourselves, and what we consider unique about being human. Coming to grips with that is going to be a very interesting question. For any given task, we can get a machine that does it better, so where does our self-esteem go? Where does our sense of self go? Does it fall back into empathy, emotion, understanding, and things that might be more spiritual in nature? I don’t know, but these are the interesting questions as we begin to understand intelligence in a more objective way. You can’t escape it.
DAVID FERRUCCI is the award-winning AI researcher who built and led the IBM Watson team from its inception in 2006 to its landmark success in 2011 when Watson defeated the greatest Jeopardy! players of all time.
In 2013, David joined Bridgewater Associates as Director of Applied AI. His nearly 30 years in AI and his passion to see computers fluently think, learn, and communicate inspired him to found Elemental Cognition LLC in 2015 in partnership with Bridgewater. Elemental Cognition is focused on creating novel AI systems that dramatically accelerate automated language understanding and intelligent dialog.
David graduated from Manhattan College, with a BS degree in biology and from Rensselaer Polytechnic Institute with a PhD degree in computer science specializing in knowledge representation and reasoning. He has over 50 patents and has published papers in the areas of AI, automated reasoning, NLP, intelligent systems architectures, automatic story generation, and automatic question-answering.
David was awarded the title of IBM Fellow (fewer than 100 of 450,000 hold this technical distinction) and has won many awards for his work creating UIMA and Watson, including the Chicago Mercantile Exchange’s Innovation Award and the AAAI Feigenbaum Prize.