Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley
Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley
Credit: Flavia Loreto

Artificial Intelligence—The revolution hasn’t happened yet

Michael I. Jordan, Amazon scholar and professor at the University of California, Berkeley, writes about the classical goals in human-imitative AI, and reflects on how in the current hubbub over the AI revolution it is easy to forget that these goals haven’t yet been achieved. This article is reprinted with permission from the Harvard Data Science Review, where it first appeared.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists, and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying use of the phrase. However, this is not the classical case of the public not understanding the scientists—here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us, enthralling us and frightening us in equal measure. And, unfortunately, it distracts us.

There is a different narrative that one can tell about the current era. Consider the following story, which involves humans, computers, data, and life-or-death decisions, but where the focus is something other than intelligence-in-silicon fantasies. When my spouse was pregnant 14 years ago, we had an ultrasound. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. “Those are markers for Down syndrome,” she noted, “and your risk has now gone up to one in 20.” She let us know that we could learn whether the fetus in fact had the genetic modification underlying Down syndrome via an amniocentesis, but amniocentesis was risky—the chance of killing the fetus during the procedure was roughly one in 300. Being a statistician, I was determined to find out where these numbers were coming from. In my research, I discovered that a statistical analysis had been done a decade previously in the UK in which these white spots, which reflect calcium buildup, were indeed established as a predictor of Down syndrome. I also noticed that the imaging machine used in our test had a few hundred more pixels per square inch than the machine used in the UK study. I returned to tell the geneticist that I believed that the white spots were likely false positives, literal white noise.

She said, “Ah, that explains why we started seeing an uptick in Down syndrome diagnoses a few years ago. That’s when the new machine arrived.”

We didn’t do the amniocentesis, and my wife delivered a healthy girl a few months later, but the episode troubled me, particularly after a back-of-the-envelope calculation convinced me that many thousands of people had gotten that diagnosis that same day worldwide, that many of them had opted for amniocentesis, and that a number of babies had died needlessly. The problem that this episode revealed wasn’t about my individual medical care; it was about a medical system that measured variables and outcomes in various places and times, conducted statistical analyses, and made use of the results in other situations. The problem had to do not just with data analysis per se, but with what database researchers call provenance—broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight.

I’m also a computer scientist, and it occurred to me that the principles needed to build planetary-scale inference-and-decision-making systems of this kind, blending computer science with statistics, and considering human utilities, were nowhere to be found in my education. It occurred to me that the development of such principles—which will be needed not only in the medical domain but also in domains such as commerce, transportation, and education—were at least as important as those of building AI systems that can dazzle us with their game-playing or sensorimotor skills.

Whether or not we come to understand ‘intelligence’ any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life. While some view this challenge as subservient to the creation of artificial intelligence, another more prosaic, but no less reverent, viewpoint is that it is the creation of a new branch of engineering. Much like civil engineering and chemical engineering in decades past, this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and to do so safely. Whereas civil engineering and chemical engineering built upon physics and chemistry, this new engineering discipline will build on ideas that the preceding century gave substance to, such as information, algorithm, data, uncertainty, computing, inference, and optimization. Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities.

While the building blocks are in place, the principles for putting these blocks together are not, and so the blocks are currently being put together in ad-hoc ways. Thus, just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans, and the environment. Just as early buildings and bridges sometimes fell to the ground—in unforeseen ways and with tragic consequences—many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws.

Unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. What we’re missing is an engineering discipline with principles of analysis and design.

The current public dialog about these issues too often uses the term AI as an intellectual wildcard, one that makes it difficult to reason about the scope and consequences of emerging technology. Let us consider more carefully what AI has been used to refer to, both recently and historically.

Most of what is labeled AI today, particularly in the public sphere, is actually machine learning (ML), a term in use for the past several decades. ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines (see below) to design algorithms that process data, make predictions, and help make decisions. In terms of impact on the real world, ML is the real thing, and not just recently. Indeed, that ML would grow into massive industrial relevance was already clear in the early 1990s, and by the turn of the century forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical, back-end problems in fraud detection and supply-chain prediction, and building innovative consumer-facing services such as recommendation systems. As datasets and computing resources grew rapidly over the ensuing two decades, it became clear that ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. New business models would emerge. The phrase ‘data science’ emerged to refer to this phenomenon, reflecting both the need of ML algorithms experts to partner with database and distributed-systems experts to build scalable, robust ML systems, as well as reflecting the larger social and environmental scope of the resulting systems.This confluence of ideas and technology trends has been rebranded as ‘AI’ over the past few years. This rebranding deserves some scrutiny.

Historically, the phrase “artificial intelligence” was coined in the late 1950s to refer to the heady aspiration of realizing in software and hardware an entity possessing human-level intelligence. I will use the phrase “human-imitative AI” to refer to this aspiration, emphasizing the notion that the artificially intelligent entity should seem to be one of us, if not physically then at least mentally (whatever that might mean). This was largely an academic enterprise. While related academic fields such as operations research, statistics, pattern recognition, information theory, and control theory already existed, and often took inspiration from human or animal behavior, these fields were arguably focused on low-level signals and decisions. The ability of, say, a squirrel to perceive the three-dimensional structure of the forest it lives in, and to leap among its branches, was inspirational to these fields. AI was meant to focus on something different: the high-level or cognitive capability of humans to reason and to think. Sixty years later, however, high-level reasoning and thought remain elusive. The developments now being called AI arose mostly in the engineering fields associated with low-level pattern recognition and movement control, as well as in the field of statistics, the discipline focused on finding patterns in data and on making well-founded predictions, tests of hypotheses, and decisions.

Indeed, the famous backpropagation algorithm that David Rumelhart rediscovered in the early 1980s, and which is now considered at the core of the so-called “AI revolution,” first arose in the field of control theory in the 1950s and 1960s. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon.

Since the 1960s, much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. Rather, as in the case of the Apollo spaceships, these ideas have often hidden behind the scenes, the handiwork of researchers focused on specific engineering challenges. Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success—these advances have powered companies such as Google, Netflix, Facebook, and Amazon.

One could simply refer to all of this as AI, and indeed that is what appears to have happened. Such labeling may come as a surprise to optimization or statistics researchers, who find themselves suddenly called AI researchers, but labels aside, the bigger problem is that the use of this single, ill-defined acronym prevents a clear understanding of the range of intellectual and commercial issues at play.

The past two decades have seen major progress—in industry and academia—in a complementary aspiration to human-imitative AI that is often referred to as “Intelligence Augmentation” (IA). Here computation and data are used to create services that augment human intelligence and creativity. A search engine can be viewed as an example of IA, as it augments human memory and factual knowledge, as can natural language translation, which augments the ability of a human to communicate. Computer-based generation of sounds and images serves as a palette and creativity enhancer for artists. While services of this kind could conceivably involve high-level reasoning and thought, currently they don’t; they mostly perform various kinds of string-matching and numerical operations that capture patterns that humans can make use of.

Hoping that the reader will tolerate one last acronym, let us conceive broadly of a discipline of “Intelligent Infrastructure” (II), whereby a web of computation, data, and physical entities exists that makes human environments more supportive, interesting, and safe. Such infrastructure is beginning to make its appearance in domains such as transportation, medicine, commerce, and finance, with implications for individual humans and societies. This emergence sometimes arises in conversations about an Internet of Things, but that effort generally refers to the mere problem of getting ‘things’ onto the Internet, not to the far grander set of challenges associated with building systems that analyze those data streams to discover facts about the world and permit ‘things’ to interact with humans at a far higher level of abstraction than mere bits.

For example, returning to my personal anecdote, we might imagine living our lives in a societal-scale medical system that sets up data flows and data-analysis flows between doctors and devices positioned in and around human bodies, thereby able to aid human intelligence in making diagnoses and providing care. The system would incorporate information from cells in the body, DNA, blood tests, environment, population genetics, and the vast scientific literature on drugs and treatments. It would not just focus on a single patient and a doctor, but on relationships among all humans, just as current medical testing allows experiments done on one set of humans (or animals) to be brought to bear in the care of other humans. It would help maintain notions of relevance, provenance, and reliability, in the way that the current banking system focuses on such challenges in the domain of finance and payment. While one can foresee many problems arising in such a system—privacy issues, liability issues, security issues, etc.—these concerns should be viewed as challenges, not show-stoppers.

We now come to a critical issue: is working on classical human-imitative AI the best or only way to focus on these larger challenges? Some of the most heralded recent success stories of ML have in fact been in areas associated with human-imitative AI—areas such as computer vision, speech recognition, game-playing, and robotics. Perhaps we should simply await further progress in domains such as these. There are two points to make here. First, although one would not know it from reading the newspapers, success in human-imitative AI has in fact been limited; we are very far from realizing human-imitative AI aspirations. The thrill (and fear) of making even limited progress on human-imitative AI gives rise to levels of over-exuberance and media attention that is not present in other areas of engineering.

Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. On the sufficiency side, consider self-driving cars. For such technology to be realized, a range of engineering problems will need to be solved that may have little relationship to human competencies (or human lack-of-competencies). The overall transportation system (an II system) will likely more closely resemble the current air-traffic control system than the current collection of loosely coupled, forward-facing, inattentive human drivers. It will be vastly more complex than the current air-traffic control system, specifically in its use of massive amounts of data and adaptive statistical modeling to inform fine-grained decisions. Those challenges need to be in the forefront versus a potentially distracting focus on human-imitative AI.

As for the necessity argument, some say that the human-imitative AI aspiration subsumes IA and II aspirations, because a human-imitative AI system would not only be able to solve the classical problems of AI (e.g., as embodied in the Turing test), but it would also be our best bet for solving IA and II problems. Such an argument has little historical precedent. Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? Should chemical engineering have been framed in terms of creating an artificial chemist? Even more polemically: if our goal was to build chemical factories, should we have first created an artificial chemist who would have then worked out how to build a chemical factory?

A related argument is that human intelligence is the only kind of intelligence we know, thus we should aim to mimic it as a first step. However, humans are in fact not very good at some kinds of reasoning—we have our lapses, biases, and limitations. Moreover, critically, we did not evolve to perform the kinds of large-scale decision-making that modern II systems must face, nor to cope with the kinds of uncertainty that arise in II contexts. One could argue that an AI system would not only imitate human intelligence, but also correct it, and would also scale to arbitrarily large problems. Of course, we are now in the realm of science fiction—such speculative arguments, while entertaining in the setting of fiction, should not be our principal strategy going forward in the face of the critical IA and II problems that are beginning to emerge. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda.

It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. Such systems must cope with cloud-edge interactions in making timely, distributed decisions, and they must deal with long-tail phenomena where there is lots of data on some individuals and little data on most individuals. They must address the difficulties of sharing data across administrative and competitive boundaries. Finally, and of particular importance, II systems must bring economic ideas such as incentives and pricing into the realm of the statistical and computational infrastructures that link humans to each other and to valued goods. Such II systems can be viewed as not merely providing a service, but as creating markets. There are domains such as music, literature, and journalism that are crying out for the emergence of such markets, where data analysis links producers and consumers. And this must all be done within the context of evolving societal, ethical, and legal norms.

Of course, classical human-imitative AI problems remain of great interest as well. However, the current focus on doing AI research via the gathering of data, the deployment of deep learning infrastructure, and the demonstration of systems that mimic certain narrowly defined human skills—with little in the way of emerging explanatory principles—tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the AI revolution it is easy to forget that they are not yet solved.

IA will also remain quite essential, because for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean).

It was John McCarthy (while a professor at Dartmouth, and soon to take a position at MIT) who coined the term AI, apparently to distinguish his budding research agenda from that of Norbert Wiener (then an older professor at MIT). Wiener had coined “cybernetics” to refer to his own vision of intelligent systems—a vision that was closely tied to operations research, statistics, pattern recognition, information theory, and control theory. McCarthy, on the other hand, emphasized the ties to logic. In an interesting reversal, it is Wiener’s intellectual agenda that has come to dominate in the current era, under the banner of McCarthy’s terminology. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than in most fields.)

Beyond the historical perspectives of McCarthy and Wiener, we need to realize that the current public dialog on AI—which focuses on narrow subsets of both industry and of academia—risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA, and II.

This scope is less about the realization of science-fiction dreams or superhuman nightmares, and more about the need for humans to understand and shape technology as it becomes ever more present and influential in their daily lives. Moreover, in this understanding and shaping, there is a need for a diverse set of voices from all walks of life, not merely a dialog among the technologically attuned. Focusing narrowly on human-imitative AI prevents an appropriately wide range of voices from being heard.

While industry will drive many developments, academia will also play an essential role, not only in providing some of the most innovative technical ideas, but also in bringing researchers from the computational and statistical disciplines together with researchers from other disciplines whose contributions and perspectives are sorely needed—notably the social sciences, the cognitive sciences, and the humanities.

On the other hand, while the humanities and the sciences are essential as we go forward, we should also not pretend that we are talking about something other than an engineering effort of unprecedented scale and scope; society is aiming to build new kinds of artifacts. These artifacts should be built to work as claimed. We do not want to build systems that help us with medical treatments, transportation options, and commercial opportunities only to find out after the fact that these systems don’t really work, that they make errors that take their toll in terms of human lives and happiness. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data- and learning-focused fields. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline.

We should embrace the fact that we are witnessing the creation of a new branch of engineering. The term engineering has connotations—in academia and beyond—of cold, affectless machinery, and of loss of control for humans, but an engineering discipline can be what we want it to be. In the current era, we have a real opportunity to conceive of something historically new: a human-centric engineering discipline. I will resist giving this emerging discipline a name, but if the acronym AI continues to serve as placeholder nomenclature going forward, let’s be aware of the very real limitations of this placeholder. Let’s broaden our scope, tone down the hype, and recognize the serious challenges ahead.

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Job summaryAre you excited about cutting-edge deep-learning NLP, NLU, and Conversational AI? If so, then come and join the Alexa Artificial Intelligence (AI) team. We are the science team behind Amazon’s intelligence voice assistance system and are responsible for the deep learning technology that is central to the automated ranking and arbitration to optimize for end-to-end customer satisfaction.Key job responsibilitiesAs an Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. 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 spoken language understanding.A day in the life· Design, build, test and release predictive ML models· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use casesAbout the teamWe are a science and engineering team part of Alexa AI organization. Our mission is to help Alexa decide which action to take in response to customer requests, incorporating a variety of contextual signals including both direct and indirect customer feedback to provide the best response to the customer. Our work directly contributes to improvement in Alexa business and customer metrics.
US, VA, Arlington
Job summaryDo you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. The Robotics AI team at Amazon builds high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. The Robotics AI team invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently with the clutter and uncertainty of real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself.We hire and develop subject matter experts in AI with a focus on computer vision, deep learning, intelligent control, semi-supervised and unsupervised learning. We target high-impact algorithmic unlocks in areas such as scene and activity understanding, simultaneous localization and mapping, closed-loop control, robotic grasping and manipulation—all of which have high-value impact for our current and future fulfillment networks.We are seeking hands-on, Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable ML solutions using deep learning, active learning and computer vision (instance and semantic segmentation, pose estimation, activity understanding). As an Applied Scientist in Robotics AI, you will contribute to the research and development of advanced robotic systems; you will work along with other top-notch scientists and engineers to deliver the world's most scalable and robust robotic systems.In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation. A successful candidate has excellent technical depth, great communication skills, and a drive to achieve results in a collaborative team environment.You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a fearless disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers.Inclusive Team CultureHere at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 87,000 employees across hundreds of chapters around the world. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust.FlexibilityIt isn’t about which hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We offer flexibility and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthWe care about your career growth too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it.
US, WA, Bellevue
Amazon delights customers around the world with millions of products each day. Meet the team that is transforming how quickly and conveniently Amazon delivers that delight to its customers. The Amazon Access Points team designs and builds new ways for people to get the things they order online, through a growing network of hundreds of thousands of physical pickup point locations worldwide. Our locker and counter products offer customers greater security, flexibility, and control, while reducing transportation costs and environmental footprint. Beyond customer convenience, Amazon Access Points network offers brands differentiated advertising opportunities to build awareness and convert new users through our growing advertising products.We are looking for a data scientist to work across our science team to drive the worldwide expansion of Access Points.This position will work with a highly collaborative team, and have direct influence over science roadmaps, establishment of key performance metrics, dashboards and report development. This role will be responsible for working with senior leadership and cross functional teams within Access Points and ensure a customer centric point of view in our models. They will solve real world problems and build science models to improve decision making.A day in the lifeAbout the hiring groupJob responsibilities· Help shape analytics and research roadmap and enable data-driven innovation for Access Points.· Partner closely with product and program owners, as well as scientists and engineers with a clear path to business impact.· Participate in scoping and planning of Access Points science roadmap.· Uncover drivers, impacts, and key influences on Access Points network.· Develop predictive and optimization models for key applications.· Navigate a variety of data sources, such as enterprise data, customize surveys, focus groups, and/or external data sources.· Work in expert cross-functional teams delivering on demanding projects.
US, CA, Sunnyvale
Job summaryAlexa AI is seeking an Applied Science Manager to drive initiatives on the leading edge of Machine Learning (ML), Natural Language Processing (NLP), Question Answering (QA) and Information Retrieval (IR).Working collaboratively with scientists and engineers, you will design and implement automated, scalable NLP/ML/QA/IR models for accessing and presenting information as well as improve products and features within Alexa. This exciting opportunity will impact the customer experience, design, architecture, and implementation of a cutting-edge product.If you are an entrepreneurial, data-driven, innovative, and influential individual who thrives on solving complex ambiguous problems and building innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you.As an Applied Science Manager you will lead the science efforts to develop novel algorithms and modeling techniques to advance the state of the art in ML, NLP, QA and IR. You will also:· Manage a team of high caliber Applied Scientists and Software Engineers working on building world class, scalable systems· Recruit, hire, mentor, and coach scientists and engineers at different levels of experience· Manage and execute against project plans and delivery commitments within an Agile/Scrum environment· Contribute to and lead science, design, architecture, process and development discussions· Serve as a technical lead on demanding and cross-team projects, and effectively collaborating with multiple cross-organizational teams· Apply technical influence on partner teams, increasing their productivity by sharing your deep knowledge.
US, MA, Boston
Job summaryAmazon Alexa is seeking an Applied Science Manager to drive initiatives on the leading edge of Machine Learning (ML), Natural Language Processing (NLP), Information Retrieval (IR), and Speech.Working collaboratively with scientists and engineers, you will design and implement automated, scalable NLP/ML/IR models for accessing and presenting information as well as improve products and features within Alexa. This exciting opportunity will impact the customer experience, design, architecture, and implementation of a cutting-edge product that will be used every day by people you know.If you are an entrepreneurial, data-driven, innovative, and influential individual who thrives on solving complex ambiguous problems and building innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you.Key job responsibilitiesIn this role you will,· Manage a team of high caliber Applied Scientists and Software Engineers working on building world class, scalable systems· Recruit, hire, mentor, and coach scientists and engineers at different levels of experience· Manage and execute against project plans and delivery commitments within an Agile/Scrum environment· Contribute to and lead design, architecture, process and development discussions· Own all operational metrics and support for your team’s software· Drive improvements in software engineering practices across engineering teams
AU, ACT, Canberra
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add talented and experienced Machine Learning Applied Scientists to our already awesome team.We are a diverse team – our team members bring many different experiences to our mission and many different types of leaders succeed here, but have a few things in common:· High level of motivation with a drive to deliver results· Analytical acumen and a passion for solving problems (many of which are complex)· Ability to make decisions in the face of ambiguity· A desire to experiment, innovate and learn from both successes and failures· Excellent communication skills: ability to work independently across all levels of the organization, both locally and globally· Enjoyment for working as a team with a strong sense of ownership and personal achievementWhat will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. 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, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
AU, SA, ADELAIDE
There's never been a more exciting time to join Amazon Australia!Who are we and what do we do?We are a world class ML team based in Adelaide and created in April 2020 with the hire of the Director of Applied Science, Anton van den Hengel.The Amazon ML AU team is developing state-of-the-art large-scale Machine Learning methods and applications involving terabytes of data. We work on applying machine learning, and particularly computer vision, to a wide spectrum of areas such as Amazon Retail, Seller Services, and Online Video. We also publish our research in the best venues internationally.The team is high performing, learning-oriented, motivated to over-achieve, have fun, and make history. We also have access to great data, and the best computing infrastructure.About Anton van den HengelAnton was the founding Director of The Australian Institute for Machine Learning (Australia’s largest machine learning research group), and is currently the Director of the Centre for Augmented Reasoning and a Professor of Computer Science at the University of Adelaide.With over 18,000 citations and an H-index of 67, Anton is one of the worlds’ leading authorities on Computer Vision and ML.About the TeamThe vast majority of the team have PhDs in machine learning (ML) or a related area from some of the best institutions globally, including Oxford, Stanford, Edinburgh, and Imperial College London, and have published in the best places in the field including Science, NeurIPS, IEEE PAMI, and CVPR.The team includes two world-class Principal Scientists and an Amazon Scholar. We value diversity and collaboration to help each other succeed as a team.Where are we based?Although the team is mainly Adelaide based, we support flexible working options blending at home and in office from our offices inAdelaide, Sydney, Melbourne, Canberra or Brisbane.Who are we looking for?We are seeking to add talented and experienced Machine Learning Applied Scientists to our already awesome team.We are a diverse team – our team members bring many different experiences to our mission and many different types of leaders succeed here, but have a few things in common:· High level of motivation with a drive to deliver results· Analytical acumen and a passion for solving problems (many of which are complex)· Ability to make decisions in the face of ambiguity· A desire to experiment, innovate and learn from both successes and failures· Excellent communication skills: ability to work independently across all levels of the organization, both locally and globally· Enjoyment for working as a team with a strong sense of ownership and personal achievementWhat will I be working on?It’s fair to say that no two days are alike – so this position suits someone who enjoys variety and problem-solving:· Use machine learning, computer vision, data mining and statistical techniques to create new, scalable solutions for business problems· Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· Design, develop and evaluate highly innovative models for predictive learning· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· Research, implement and publish novel machine learning and statistical approachesAdditional InformationWe have a number of current employees who split their time between lecturing at University and working for Amazon. Please let us know if this is of interest to you.We provide full visa sponsorship which is a relatively fast process as we have been successful in obtaining Distinguished Talent visas for this team (typically takes weeks rather than months).Full domestic and international relocation is also provided.About Amazon AustraliaAmazon offers great benefits including a competitive compensation and stock plan. We also look after our people with benefits including: subsidized private health and life insurance, commuter benefits and even an Amazon discount. 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, disability, age, or other legally protected status.For up to date news covering diversity and inclusion, sustainability and community engagement, please visit: https://www.aboutamazon.com.au/
CN, 11, Beijing
Job summaryAmazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems.As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times.Your responsibilities include:· Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems.· Analyzing data and metrics relevant to the search experiences.· Working with teams worldwide on global projects.Your benefits include:· Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers· The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact· Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goalsAmazon Search JP负责在Amazon JP购物网站上开发产品搜索功能,并将这些创新扩展到全球。作为这个快速发展中的团队的应用科学家,您将在改善NLP和Amazon产品搜索的体验方面发挥关键作用。我们的最终目标是帮助客户找到他们想要的产品,并发现他们感兴趣的新产品。我们通过开发涵盖多种语言和系统的NLP组件来达成目标。作为Search JP的应用科学家,您将在Amazon网站上设计和实现搜索功能,帮助数百万客户快速找到他们想要的内容。您将基于TB级的产品和流量数据提出NLP和IR领域的创新,构建机器学习模型,并使用离线指标以及A / B测试在线指标进行效果评估,然后将模型集成到面向客户的生产搜索引擎中,从而通过数据,建模,应用,和模型选择完成上线。您的模型同时需要平衡业务指标和毫秒级响应时间的要求。您的职责包括:•设计和实施新功能和机器学习模型,包括应用最先进的深度学习来解决搜索匹配,排名和搜索建议问题。•分析与搜索体验相关的数据和指标。•与全球团队合作开展全球项目。您的收获包括:•通过开发有高度影响力的产品改善数百万客户的体验•有机会使用和创造最新的机器学习方法来解决重要现实问题•作为成长中的团队的一员,我们将共同定义团队的使命,方向以及如何实现目标
IL, Tel Aviv
Job summaryAmazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, Amazon Echo and Halo.What will you help us create?Key job responsibilitiesYou will be part of a world-class Computer Vision team tasked with solving huge business problems through innovative technology and focus on product industrialization.We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company.Major responsibilities:· Research, design, implement and evaluate novel computer vision algorithms· Work on large-scale datasets, focusing on creating scalable and accurate computer vision systems in versatile application fields· Collaborate closely with team members on developing systems from prototyping to production level· Collaborate with teams spread all over the world· Work closely with software engineering teams to drive scalable, real-time implementations· Track general business activity and provide clear, compelling management reports on a regular basis
US, MN, Minneapolis
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Bellevue
Job summaryAmazon AI is looking for world class scientists to join its AI Lab. This group is entrusted with developing core machine learning algorithms for AWS. As a part of the AI Lab you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore novel solutions to new problems at scale. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, IL, Chicago
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, TX, Austin
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, MI, Detroit
Job summaryMachine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.This position involves on-call responsibilities, typically for one week every two months. We don’t like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don’t get paged for the same issue twice.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.