“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.

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.

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.

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.

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.

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. 

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, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
US, CA, Santa Clara
Amazon Q Business is an AI assistant powered by generative technology. It provides capabilities such as answering queries, summarizing information, generating content, and executing tasks based on enterprise data. We are seeking a Language Data Scientist II to join our data team. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Q Business. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users. As part of our diverse team—including language engineers, linguists, data scientists, data engineers, and program managers—you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence. In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions. Key job responsibilities * oversee end-to-end evaluation data pipeline and propose evaluation metrics and methods * incorporate your knowledge of linguistic fundamentals, NLU, NLP to the data pipeline * process and analyze diverse media formats including audio recordings, video, images and text * perform statistical analysis of the data * write intuitive data generation & annotation guidelines * write advanced and nuanced prompts to optimize LLM outputs * write python scripts for data wrangling * automate repetitive workflows and improve existing processes * perform background research and vet available public datasets on topics such as long text retrieval, text generation, summarization, question-answering, and reasoning * leverage and integrate AWS services to optimize data collection workflows * collaborate with scientists, engineers, and product managers in defining data quality metrics and guidelines. * lead dive deep sessions with data annotators About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.