A blue plaque at Kings College in Cambridge commemorating former student and computer pioneer Alan Turing
A blue plaque at Kings College in Cambridge, UK, commemorating former student and computer pioneer Alan Turing.
chrisdorney/Getty Images

Does the Turing Test pass the test of time?

Four Amazon scientists weigh in on whether the famed mathematician's definition of artificial intelligence is still applicable, and what might surprise him most today.

On Oct. 1, 1950, the journal Mind featured a 27-page entry authored by Alan Turing. More than 70 years later, that paper — "Computing Machinery and Intelligence" — which posed the question, “Can machines think?” remains foundational in artificial intelligence.

However, while the paper is iconic, the original goal of building a system comparable to human intelligence has proved elusive. In fact, Alexa VP and Head Scientist Rohit Prasad has written, “I believe the goal put forth by Turing is not a useful one for AI scientists like myself to work toward. The Turing Test is fraught with limitations, some of which Turing himself debated in his seminal paper.”

Clockwise from top left: Yoelle Maarek, vice president of research and science for Alexa Shopping; Alex Smola, AWS vice president and distinguished scientist; Gaurav Sukhatme, the USC Fletcher Jones Foundation Endowed Chair in Computer Science and Computer Engineering and an Amazon Scholar; Nikko Strom, Alexa AI vice president and distinguished scientist.
Clockwise from top left: Yoelle Maarek, vice president of research and science for Alexa Shopping; Alex Smola, AWS vice president and distinguished scientist; Gaurav Sukhatme, the USC Fletcher Jones Foundation Endowed Chair in Computer Science and Computer Engineering and an Amazon Scholar; Nikko Ström, Alexa AI vice president and distinguished scientist.

In light of the 2021 AAAI Conference on Artificial Intelligence, we asked scientists and scholars at Amazon how they view that paper today. We spoke with Yoelle Maarek, vice president of research and science for Alexa Shopping; Alex Smola, AWS vice president and distinguished scientist; Nikko Ström, Alexa AI vice president and distinguished scientist; and Gaurav Sukhatme, the USC Fletcher Jones Foundation Endowed Chair in Computer Science and Computer Engineering and an Amazon Scholar.

We asked them whether Turing’s definition of artificial intelligence still applies, what they think Turing would be surprised by in 2020, and which of today’s problems researchers will still be puzzling over 70 years from now.

Q. Does Turing’s definition of AI (essentially “a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human”) still apply, or does it need to be updated?

Smola: “The core of the question remains as relevant as it was 70 years ago. That said, I would argue that rather than seeking binary (yes/no) tests for AI we should have something more gradual. For instance, the argument could be about how long a machine can fool a human. Alexa and others by now do a pretty good job for many queries for single turn, and there are even multi-turn systems that are pretty capable. In fact, you can test out some of them as part of the Alexa Prize (‘Alexa, let’s chat’). Using time, you can measure progress more finely, e.g., by the number of minutes (or turns) it takes to uncover the imposter, rather than a fixed time limit.”

Evaluating AI on the basis of being indistinguishable from human intelligence makes as much sense as evaluating airplanes based on being indistinguishable from birds.
Nikko Strom

Maarek: “It is clear it is not a perfect definition. First, I doubt there exists a universally agreed-upon definition of intelligence, and it is not clear what ‘a human’ refers to. Is that any human? Can a machine be indistinguishable from some humans and not from others? It is, however, a simplifier that can still be used for inspiration. And it does bring inspiration, see for instance the outstanding progress in chess or Go. There are, of course, so many other areas where machines still require learning, and these challenges keep inspiring scientists. Two such areas, among others, on which we are focusing in Alexa Shopping Research are to make advancements in conversational shopping (as a subfield of conversational AI) and computational humor. With even small progress in these hard AI challenges, I am sure we will bring tremendous value to our customers and even make them smile.”

Ström: “Evaluating AI on the basis of being indistinguishable from human intelligence makes as much sense as evaluating airplanes based on being indistinguishable from birds. We may never have a single definition, but a common thread is generalizability, i.e., the ability to be successful in novel situations, not considered during the design of the system. To achieve such generalization, an AI needs the ability to reason and plan, have a representation of world-knowledge, an ability to learn and remember, and an ability to regulate and integrate those cognitive capabilities toward goals.

"The AI also needs to be an active participant in the world, and when evaluating intelligence, one needs to consider not just whether goals are met, but how efficiently goals are reached based on efficacy metrics that depend on the application — e.g., cost, energy use, speed, et cetera. My prediction is that once one or several successful such systems exist, a standard model will emerge that becomes a de facto definition of AI.”

Sukhatme: “I think the idea that we want a machine to have the ‘ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human’ still applies when thinking about AI. However, this idea has over the years been interpreted very narrowly when it comes to the ‘test’ – i.e. people look for human-like performance on some narrow task. I think we need to remind people that intelligence is very broad set of capabilities and we need to acknowledge that humans have deep understanding of the world, are social, have empathy, can and do learn continually and can do a very broad range of things. If we are to say that we’ve built a machine or system that exhibits AI, I would want to see it exhibit behavior indistinguishable from humans on a similar breadth of abilities.”

Q. In terms of AI, what do you think would surprise Turing today?

I think he'd be surprised at how far we’ve come in terms of the technological artifacts we’ve produced. And he’d be disappointed in how un-intelligent they are
Gaurav Sukhatme

Sukhatme: “I think he’d be surprised at how far we’ve come in terms of the technological artifacts we’ve produced. And he’d be disappointed in how un-intelligent they are.”

Maarek: “Hard to answer, as this is pure speculation. But I would like to believe that computational humor would be one of them, simply because it makes us all smile.”

Ström: “The resolution of Moravec's paradox. Machine learning and, in particular, deep learning, is now enabling us to solve sensorimotor tasks in robotics, and sensory tasks such as object recognition and speech recognition. Yet general intelligence is still a hard, largely unsolved, problem. I also think Turing would be fascinated by quantum computers.”

Smola: “The thing that would surprise Turing the most is probably the amount of data and its ready availability. The fact that we can build language models on more than 1 trillion characters of text, or that we have hundreds of millions of images available, is probably the biggest differentiator. It’s only thanks to these mountains of data that we’ve been able to build systems that generate speech (e.g. Amazon Polly), that translate text (e.g. Amazon Translate), that recognize speech (e.g. Transcribe), that recognize images, faces in images, or that are able to analyze poses in video.

"At the same time, it’s unclear whether he would have anticipated the exponential growth in computation. The UNIVAC was capable of performing around 4,000 floating point operators (FLOPS) per second. Our latest P4 servers can carry out around 1-2 PetaFLOPS, so that’s 1,000,000,000,000,000 multiply-adds — and you can rent them for around $30 an hour.”

Q. Which of today’s theoretical questions will scientists still be puzzling about in 2090?

Sukhatme: “How do human brains do what they do in such an energy efficient manner? What is consciousness?”

Maarek: “In terms of theoretical computer science problems, I believe that hard AI problems like Winograd Schema Challenge, will be resolved. But I want to believe that other AI challenges, like giving a true sense of humor to machines, won’t be solved yet. It's humbling to think that in 1534 the French writer François Rabelais said, 'le rire est le propre de l’homme' — which can be translated as 'the laugh is unique to humans'. It’s probably why my team is researching computational humor — it’s fun and hard.”

Ström: “In 70 years, I predict that AI has been solved for practical purposes and is used for cognitive tasks, small and large. So that is not it. Some long-standing profound questions like NP=P will still be unsolved. The physics model of time, space, energy and matter will still not be complete, and the question about how life spontaneously emerges from lifeless building-blocks will still puzzle both human and synthetic scientists. Unless we get lucky, 70 years will also not be enough to determine if there is alien intelligent life in our galaxy.”

In the foreground, a welcome to Bletchley Park offers a guide, in the background a group of tourists get a guided tour. This area was used in World War 2 to break the German Enigma Codes.
A group of tourists get a guided tour of the grounds of Bletchley Park. This area was used in World War 2 to break the German Enigma Codes.
NeonJellyfish/Getty Images

Smola: “That’s really difficult since most projections don’t hold up well, even for a decade or so. In 2016, when I interviewed for a job and was deciding between Amazon and another major company, I was told at that other company that I was making a mistake in betting on AI in the cloud. Problems that will keep us awake, probably forever, are how to appropriately balance innovation while also protecting individual liberties. Those challenges will require continuous and careful consideration by multiple stakeholders in academia, industry, government, and our society. Likewise, we will never be able to have a full characterization of the empirical power of our statistical tools. In simple terms, we’ll likely always encounter algorithms that work way better than they should in theory. Lastly, there’s the issue of actually gaining causal understanding from data as to how the world works. This is hard and has been vexing (natural) scientists for centuries.

"Areas where we will likely see a lot of progress include autonomous systems. There’s so much economic promise in self-driving vehicles that I think we will eventually deliver something that works. The algorithms used for cars can also be adapted for a wide variety of other problems such as manufacturing, maintenance, et cetera. The next decade or two will be amazing — and we’ll likely also see great progress on the Turing test itself.”

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Passionate about books, reading and Demand Science? Kindle/Books Demand Science team is seeking an experienced Applied Scientist to unlock the power of the information stored in the 1B+ searches, clicks, purchases, reading and borrows. A successful candidate will bring deep technical expertise, desire to positively impact Books customer experience, and passion for Science.Please visit https://www.amazon.science for more informationThe Kindle/Books Demand Science team is developing our Demand Science arm to optimize supply chain outcomes for Books. We look to optimize these outcomes by applying an understanding of our customer’s preferences and behaviors through initiatives including:· Building and refining demand forecasts which are explainable and actionable· Better serving our customers by understanding their content preferences amongst our various reading formats and programs· Developing a deep understanding of content substitutability and complementarity and its impact on demandCore Responsibilities· Leverage the latest advancements in ML to lead the research efforts in adopting/creating ML science for books· Develop and deploy (in partnership with Engineering, Science and Product Management teams) ML models that power specific applications· Develop, explain, and socialize evaluation metrics for ML models· Mentor and collaborate with other scientists on basic and applied research
US, WA, Seattle
Amazon’s commitment to The Climate Pledge motivates us to work both inside and outside of our business operations to enact carbon emissions mitigation. Along with the emissions reduction initiatives within our business operations, we have a robust strategy aligned with climate science for tackling climate change outside of our business, beginning with nature-based solutions. For example, in April 2021 Amazon announced its leading role in the LEAF Coalition – an initiative that aims to raise global climate ambition and contribute to halting tropical and subtropical deforestation and forest degradation by 2030. As we continue to develop and innovate, Amazon is seeking an experienced Research Scientist to help us identify, evaluate, and guide research efforts in nature-based solutions as well as emerging areas. The ideal candidate will have a deep understanding of carbon cycles and be able to evaluate projects and initiatives across a wide range of natural and technological climate mitigation measures from a first-principles perspective. They will have expertise in one or more types of climate solutions, but more importantly will understand the broad field of solutions.The successful candidate will be part of the team executing on Amazon’s commitment to The Climate Pledge. They will need to work with others to create a system to evaluate nature-based and technological climate solution projects, and make recommendations to senior leadership. This will require strong writing skills and an ability to translate technical material for a general audience.Key Responsibilities:· Evaluate nature-based and technological climate solution projects using a scientifically rigorous first-principles approach.· Evaluate project and research proposals.· Collaborate with internal and external teams to scope, execute, and evaluate highly technical initiatives related to nature-based and technological climate solution projects.· Translate findings into reports and presentations that can be shared internally and externally.· Respond to time-critical questions from multiple business teams and communicate professionally with senior business leaders.
US, WA, Seattle
Would you like to join a fast-paced team creating cutting edge econometrics and machine learning tools used to enhance the lives of Amazon Prime members worldwide? If so, the Prime Economics and Machine learning team is looking for you! We are seeking a talented economist to help us build the next generation of counterfactual modeling tools in Prime. This team is comprised of economists, applied scientists, software/data engineers, and product managers. We work closely with senior Prime leadership to model the implications of company-wide strategic decisions and expect our models to increasingly power customer-facing software systems.A day in the lifeYou build econometric models of Prime customer behavior from scratch. This involves exploring data to motivate modeling assumptions and proposing identification strategies, statistical estimators, and finite/large sample inference strategies. This science usually combines tools from machine learning, reduced-form causal inference, and structural econometrics, often leading to the creation of novel estimators. You vet your work with a team of econometricians, PhD computer scientists/applied mathematicians/statisticians, and well-known academics who serve as Amazon Scholars. You build science using the latest distributed computing infrastructures (e.g., PySpark), partnering with our data/software engineers and product managers to turn this science into scalable, system-facing, high-impact software.About the hiring groupWhat makes our team unique is three things. First, it has a broad mandate to model customer behavior (given that Prime is Amazon’s flagship membership program). Second, we build cutting-edge customer-level simulations using a variety of statistical tools (ML/econometrics/causal inference). This simulation capability is fairly unique across economics/ML-building teams at Amazon, creating green-field scientific and engineering challenges to invent solutions for. Third, given Prime's central role within the company, we have extensive access to senior company leaders, since we often provide timely insights for important strategic decisions. In other words, Prime science serves a "central brains" function for Amazon.Job responsibilitiesMULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services LLC, an Amazon.com CompanyTitle: EconomistWorksite: Seattle, WAPosition Responsibilities:Work directly with a team of economists and senior management on key business problems faced by Amazon Prime and Prime benefit teams. Apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. Build econometric models using company data. Apply economic or econometric theory to solve business problems. Develop new techniques to process large data sets, address quantitative problems, and contribute to the design of automated systems. Apply tools from applied micro-econometrics (e.g. experimental design, difference-in-difference, regression discontinuity, and IV) or forecasting (essential time series models). Leverage big data tools for data extraction. Write up and present analyses for distribution to various levels of management at Amazon.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Do you want to know about new movies and TV shows that you'll enjoy the day they come out? So do millions of our customers, world wide. Did you know we have a wide range of niche content including Natural Park documentaries, Kung-fu movies, or Korean dramas on our service? No? Well, we're looking to change that. Come be part of history, as we fulfill Prime Video's vision of being customer's first place to find something to watchA day in the lifeWe're using cutting edge approaches such as graph convolutional networks (GCNs) to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external ML conferences (e.g. https://dl.acm.org/citation.cfm?id=3292500.3330675).About the hiring groupPrime Video Recommendation science team owns different aspects of personalization algorithms, from user-item relevance, item-item relevance, and page composition, to name a few. We work closely with the engineering teams to put our models in production.Job responsibilities· Develop ML models for various recommendation systems using deep learning, online learning, and optimization methods· Work closely with engineers and product managers to expand the depth of our product insights with data, create a variety of experiments, and determine the highest-impact projects to include in planning roadmaps· Provide technical and scientific guidance to your team members· Stay up-to-date with advancements and the latest modeling techniques in the field· Publish your research findings in top conferences and journalsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Bellevue
The Last Mile Global Fleet and Products (GFP) team’s mission is to provide the vehicles and services that enable Amazon to delight customers through our Last Mile operations. Amazon is continually striving to innovate and provide best-in-class delivery experiences through the introduction of pioneering new products. GFP is on track to purchase and deploy 100,000 electric vehicles (EVs) by 2030 as part of Amazon’s Climate Pledge.Are you inspired by...· The rapid growth and expansion of electric vehicles into the market?· Being part of a effort to drastically reduce carbon emissions?· Working on tough problems at a scale and pace never done before?Then come join our team and help us make history!As a Sr. Research Scientist on the GFP Energy Systems team, you’ll apply your knowledge of electric vehicles (EVs) and couple that with a background in modeling, data science, and software development to help us predict how our EVs will perform in the field. You’ll develop a deep understanding of how our EVs use energy while in use, and provide guidance and recommendations to our deployment and business teams to help ensure our fleet is successful as we scale. This role is inherently cross-functional and requires working closely with software teams, research teams, operations teams, and product management teams to help deploy our EVs into an electrified Last Mile network.#lastmileAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Amazon’s Sustainability Science and Innovations (SSI) organization creates the tools and data to map, measure, and model Amazon's environmental and social impacts at enormous scale.The Role:We're looking for an outstanding Science Manager who combines technical, research, and team management capabilities with a demonstrated ability to get the right things done quickly and effectively.In this role you will lead a team of scientists and will also be responsible for conducting assessments of environmental and social issues across Amazon, evaluating the sustainability impacts of supply chains, from manufacturing, to transportation, to consumer use, to end-of-life. This candidate will also possess excellent communication, negotiation and influencing skills to drive consensus across multiple stakeholders.This is a unique opportunity for someone who wants to combine their passion for sustainability, solving customer problems using data, and building cutting edge machine learning solutions. You will be expected to dive deep into large-scale economic problems, enable measurable actions on the Sustainable economy, and work closely with a large team of scientists and economists. We are particularly interested in candidates with experience building predictive models with Amazon data.Key Responsibilities:· Manage sustainability-related research projects through all stages: framing, data collection, analysis, modeling and visualization, and reporting.· Lead and develop a team of scientists.· Create tools and methods to harvest and continuously update data to provide sustainability insights at scale.· Respond to time critical questions from multiple business teams.· Professionally communicate to senior business leaders.· Work closely with software engineering teams to drive real-time model implementations and new feature creations.· Lead the early investigative / inception phase of strategic sustainability initiatives and effectively influence, negotiate, and communicate with stakeholders to enable hand off of those projects for implementation.
US, VA, Arlington
Amazon’s Global Learning & Development (GLD) Learning Science and Engineering team is growing quickly and is looking for a skilled, driven, applied scientist to develop solutions and innovate at the intersection of human and machine learning.The Learning Science and Engineering org is reinventing workplace learning by building the programs, products, technologies and mechanisms that make learning effective and scalable for Amazon employees. We use machine learning to augment human decision-making to accelerate and personalize learning and skill development. We have a passion for raising the bar on learning and learning design at Amazon, and simultaneously contributing to the science of human and machine learning. We partner with multiple businesses across Amazon to explore ways to help Amazon employees grow their knowledge and capabilities. We publish research and present our work at internal and external conferences.We are looking for someone with a passion for learning and experience applying machine learning to augment human decision-making. The role will continuously improve learning programs using an array of creative approaches which drive productivity, innovation, and operational excellence. As an Applied Scientist, you will be working with scientists, engineers, learning designers, product managers, and stakeholders to deliver products or features of products that accelerate learning.Specific responsibilities include:· Design, develop, and implement models and solutions to support collaborative learning environments that can help accelerate human learning.· Deliver Machine Learning solutions to improve learning experiences that can help learners learn more effectively and efficiently.· Deliver data-driven solutions to assist learning designers to scale up best design practices.· Deliver innovative and impactful solutions that can solve customer pain points.· Deliver scientific findings about human learning to the broader science communities.
US, WA, Seattle
Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions including HBO and Showtime, and live events like Thursday Night Football and Major League Baseball. We are a premier provider of digital entertainment worldwide and we continue to grow very quickly! We need your passion, innovative ideas, and creativity to help continue to deliver on our ambitious goals.Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience ? Do you want to build advanced algorithmic systems that help millions of customer every day? If yes, then you may be a great fit to join our Amazon Prime Video team.A day in the lifeAs an applied scientist, you will apply your knowledge of deep learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models.You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels.About the hiring groupWe are expanding our scene understanding team to drive compliance automation and exceptional customer experience using machine learning, computer vision, audio processing, and natural language understanding. Automation of video understanding at scale is our mission and passion. We need to solve problems across many cultures and languages. we have a huge amount of human-labelled data, and operation team to generate labels across many languages. Our team innovates, with many novel patents, inventions, and papers in the motion picture and television industry. We are highly motivated to extend the state of the art.Job responsibilitiesAs an applied scientist, you will apply your knowledge of deep learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. This is a greenfield with no "off-the-shelf algorithms" that can perform the job. We experiment a lot and it is a must to learn and be curios. You will be encouraged to see the big picture, be innovative, and positively impact millions of customersWe embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.