“Talking to the public about AI”

The University of Oxford’s Michael Wooldridge and Amazon’s Zachary Lipton on the topic of Wooldridge’s AAAI keynote — and the road ahead for AI research.

Mike Wooldridge portrait.png
Michael Wooldridge, a professor of computer science at the University of Oxford and a program director at the Alan Turing Institute.

This morning, at the annual meeting of the Association for the Advancement of Artificial Intelligence (AAAI), Michael Wooldridge, a professor of computer science at the University of Oxford and a program director at the Alan Turing Institute, gave a talk entitled “Talking to the Public about AI”. It’s a subject that Wooldridge knows a lot about, having appeared frequently on TV and radio, testified before the House of Lords, and written three popular-science books (in addition to coauthoring seven technical books). His most recent bookA Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going, was published on January 19.

Zachary Lipton.jpeg
Zachary Lipton, an AWS scientist and the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University.

Talking to the public about AI is also a passion of Zachary Lipton, an Amazon Web Services scientist and the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University. In 2016, Lipton created the Approximately Correct blog as a forum for examining both social and technical questions surrounding AI, and in 2018, he delivered a talk at MIT Technology Review’s Emerging Technologies conference titled “Machine Learning: The Opportunities and the Opportunists”.

As AAAI 2021 approached, Lipton and Wooldridge joined Amazon Science to share their perspectives on the topic of public communication around AI.

Amazon Science: What are the difficulties in talking about AI? 

Mike Wooldridge: The reality of AI is a long, long, long way away from how it’s often portrayed. The portrayal often divides neatly into either dystopia or utopia. I think there's tons to be excited about; this is clearly the most exciting time that I've seen, and I've been in the game since the 1980s. There are also things that we should be worried about. One of the reasons that I decided to write A Brief History of Artificial Intelligence was to try to reframe the narrative a little bit.

Zack Lipton: There's a distinction between the nature of the technology and the capabilities of technology, and I think there's a lot of confusion about both of them. 

Amazon at AAAI

Learn more about Amazon's participation at AAAI — research papers, workshop involvement, and tutorials.

Neural-network algorithms, together with very large data sets and parallel computation, are really good at something called function fitting — learning to make predictions or to infer statistical relationships. And it turns out that there are a lot of tasks, even ones that might not be so intuitive, that you can frame as prediction problems. For example, machine translation: given some representation of a sentence in English, you could try to predict what's most likely to be the first word of the corresponding translation in French; then, given the input sentence and the first word, try to predict the second word; and so forth. 

But if you have what we thought was a human capacity for doing translation, people start making leaps to all sorts of other things that humans do that are not necessarily as easily cast as prediction problems. A very natural one is making decisions. So people start saying, Well, machine learning is going to completely change medicine. But it turns out that decision making involves all kinds of considerations that aren't just straight-up prediction problems. You need to learn something about causal effects. And this is an area where we're trailing far behind where we are in prediction. 

A Brief History of Artificial Intelligence, the newest book from Michael Wooldridge.

AS: Mike, what is the reframing you're attempting in the book? 

MW: Where the book came from is, in 2014, I was sat at my desk in my office in Oxford, and the phone rang, and it was a news station looking for an expert on AI, because Stephen Hawking says AI might be the end of humanity. And I declined the interview, because I just assumed there was somebody smarter and more eloquent out there who was going to answer the call. But I started to see these stories appearing, and they were in a vacuum. There was no response.

So I finally decided I wanted to be one of the people who responded. Reframing the narrative is trying to say, Look, AI isn't necessarily what you think it is. The stuff that is so exciting from the movies and books is kind of on the fringes. The stuff that you lose sleep about is not necessarily what you should be losing sleep about.

But that doesn't mean there isn't anything you should be worried about. The famous one, which is going to be with us for a while, is bias. I think the AI community and machine learning community are genuinely working very hard to try to understand how bias arises and how to mitigate the risks of those biases. 

ZL: I think, arguably, as much as the interest has ballooned, it topped out around 2018 and has been somewhat level since then. At the same time, I think there's also a lot more supply. It's not like there's only 300 people out there really working in deep learning. I think if you were active in the space in 2013, 2014, you probably started getting a lot of attention, where someone with a comparable level of experience and accomplishment in 2020 might have considerably less focus on them.

There is something interesting that the things we're excited about in 2020 and 2021 are not really qualitatively different than in 2015, 2016, 2017, right? I think it is a telling sign that the things that we're excited about are more or less the stuff we already got to work in 2016, just trained on bigger datasets. 

I started to see these stories appearing, and they were in a vacuum. There was no response. So I finally decided I wanted to be one of the people who responded.
Mike Wooldridge

MW: Neural-net research really hit tough times by the mid-’90s. But the story there is, it just hit the limits of what computers could do. So there is a theory that the progress that we've seen will plateau for exactly that reason: it will just hit the limits. Not of the science, but just of the technology. And without fundamental new ideas to drive it forward — and it would require some quite big ideas in terms of training — we might just well hit the plateau in the next few years.

AS: If we are on a plateau, what do you see on the horizon? 

ZL: I’ll give an analogy: Arguably, before AI was a big commercial interest, databases were, and after databases, an early kind of data mining. But databases never stopped being important. Maybe they weren't generating as revolutionary developments as in the past, but that doesn't mean we stop using them. They just stopped being where the action was.

I don't think neural networks are going away. I don't think we have a rival technology that is vying to replace neural networks as the best way for estimating things like functions that assign categories to images or make complicated predictions based on language data.

What I do see happening is it just not being where the action is. And I think this is already starting to happen. So I could say where I think I think the action is. 

Almost all machine learning, including deep learning, proceeds under this idea that you have some fixed, static world that's throwing off data, and you're collecting the data and trying to find a function that does something useful. And that's just not the world that we live in. In the world that we live in, data is constantly coming in. It's coming from a variety of sources. It's becoming obsolete. The world is changing in various ways, and how to function in the world — even just making predictions — is actually a whole different kind of concern that requires that we think about this outer loop and what's going on in the environment.

Almost all machine learning ... proceeds under this idea that you have some fixed, static world that's throwing off data .... And that's just not the world that we live in.
Zack Lipton

In my work at CMU with my lab, this is where we have been driving a lot of our attention: to consider whether it's just a passively changing world or actually a world that's responding. For example, if you have a policy for making decisions, people will be strategic, and they'll start behaving differently. How you build technology that is suitable for a changing world and accounts for the fact that you are part of a dynamic environment, to me, that's where the action is already moving. 

MW: I think we're overlapping in our answers in at least one respect, which is, what I'm disappointed about is not having enough AI in the physical world, the world that we all inhabit. And there's a number of reasons for that. For example, reinforcement learning is one of the technologies that underpins the breakthrough Atari-playing programs. The thing is just playing endless games against itself. When you're playing against space invaders, it doesn't matter if you make a mistake.

In the real world, it matters. So you can't do driverless-car technology, for example, with reinforcement learning. The natural answer to that is, well, you need high-fidelity simulators. That's what everybody's doing, naturally enough, but it will only take you so far. I want to see programs that could really learn how to do things in the physical world. That for me would be exciting. 

The other thing — and again, it’s overlapping with what you said — is we know from experience with adversarial examples how brittle this technology is. We can only trust the technology so far until we understand where that brittleness lies and what the limits of it are. Understanding that is going to be quite crucial. If we don't get to that, then we're always going to be nervous about this technology whenever it's used outside scenarios like game playing. So those are the two things that I'm really excited about. At least this afternoon.

Research areas

Related content

US, Virtual
The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Key job responsibilities • Identifying creative solutions for challenging research problems in robotics and computer vision • Developing software solutions to test hypotheses and demonstrate new functionality • Prototyping concepts to collect data and measure performance • Writing code and unit tests and integrating code with other software and hardware components • Utilizing Amazon Robotics and Amazon engineering tools, processes and technologies • Delivering a final presentation to managers and engineers on the successes and challenges of their internship and the business value they have contributed
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. We’re seeking a Principal Scientist with a deep expertise in Search Science. Your responsibilities will include everything from developing and prototyping innovative machine learning, and deep learning algorithms to implementing, testing, and supporting full solutions in a production environment. We are looking for innovators who can contribute to advancing search technology on what’s scientifically possible while remaining committed to creating world-class products. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), Earth's most customer-centric company one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Key job responsibilities As a hands-on leader of this team, you’ll be responsible for defining key research questions, identifying relevant data, adopting or proposing innovative machine learning solutions conducting rigorous experiments, publishing results and working with the engineering team to deploy these solutions. As a strategic leader, you will identify investment opportunities, develop long term strategies, and propose, prioritize and deliver on goals. You’ll also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team Starting in 2009, the Visual Search & Augmented Reality team has thus far launched many visual search solutions on the Amazon App that use computer vision and machine learning/deep learning to help customers complete their shopping missions more easily; multiple internal teams at Amazon (devices, Kindle, Seller services, etc.) also use our libraries and APIs to deliver solutions to their own customers. We are a full stack shop, and our team capabilities cover the whole solution spectrum, ranging across applied science, large scale engineering services, product management, UX design, and mobile app development for iOS and Android.
US, MN, Minneapolis
AWS Central Economics is an interdisciplinary team on the cutting edge of economics, statistical analysis, and machine learning whose mission is to solve problems that have high risk with abnormally high returns. Our team leverages the strengths of our scientists to build solutions for some of the toughest business problems here at Amazon AWS. We are looking for an exceptionally talented, seasoned, and motivated Economist to manage a team of economists and data scientists to drive the science for AWS. Key job responsibilities Manage a team of economists and data scientists to deliver actionable economic analyses to business leaders, provide leadership on the economics and science used in the analyses, and engage with business leaders to identify challenges AWS faces that call for in-depth economic analyses and to ensure the analyses have their intended impact.
LU, Luxembourg
&ltHire Relocation Requisition - not for posting> Provides insights to leadership on improving Supply Chain cost and Speed by using Data Science and Analytics techniques. Build Dashboards and models to industrialize these findings at scale.
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
Amazon is looking for talented Postdoctoral Scientists to join our global Science teams for a one-year, full-time research position. Postdoctoral Scientists will innovate as members of Amazon’s key global Science teams, including: AWS, Alexa AI, Alexa Shopping, Amazon Style, CoreAI, Last Mile, and Supply Chain Optimization Technologies. Postdoctoral Scientists will join one of may central, global science teams focused on solving research-intense business problems by leveraging Machine Learning, Econometrics, Statistics, and Data Science. Postdoctoral Scientists will work at the intersection of ML and systems to solve practical data driven optimization problems at Amazon scale. Postdocs will raise the scientific bar across Amazon by diving deep into exploratory areas of research to enhance the customer experience and improve efficiencies. Please note: This posting is one of several Amazon Postdoctoral Scientist postings. Please only apply to a maximum of 2 Amazon Postdoctoral Scientist postings that are relevant to your technical field and subject matter expertise. Key job responsibilities * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.