Alexa enters the “age of self”

More-autonomous machine learning systems will make Alexa more self-aware, self-learning, and self-service.

Alexa launched in 2014, and in the more than six years since, we’ve been making good on our promise to make Alexa smarter every day. In addition to foundational improvements in Alexa’s core AI technologies, such as speech recognition and natural-language-understanding systems, Alexa scientists have developed technologies that continue to delight our customers, such as whispered speech and Alexa’s new live translation service.

Prem Natarajan, Alexa AI vice president of natural understanding, giving a presentation
Prem Natarajan, Alexa AI vice president of natural understanding, at a conference in 2018.

But some of the technologies we’ve begun to introduce, together with others we’re now investigating, are harbingers of a step change in Alexa’s development — and in the field of AI itself. Collectively, these technologies will bring a new level of generalizability and autonomy to both the Alexa voice service and the tools available to Alexa developers, ushering in what I like to think of as a new “age of self” in artificial intelligence, an age in which AI systems such as Alexa become more self-aware and more self-learning, and in which they lend themselves to self-service by experienced developers and even end users.

By self-awareness, I mean the ability to maintain an awareness of ambient state (e.g., time of day, thermostat readings, and recent actions) and to employ commonsense reasoning to make inferences that reflect that awareness and prior/world knowledge. Alexa hunches can already recognize anomalies in customers’ daily routines and suggest corrections — noticing that a light was left on at night and offering to turn it off, for instance. Powered by commonsense reasoning, self-awareness goes further: for instance, if a customer turns on the television five minutes before the kids’ soccer practice is scheduled to end, an AI of the future might infer that the customer needs a reminder about pickup.

Smart home.png
In the "age of self", AIs will be able to infer customers’ implicit intentions from observable temporal patterns, such as interactions with smart-home devices like thermostats, door locks, and lights.

Self-learning is Alexa’s ability to improve and expand its capabilities without human intervention. And like self-awareness, self-learning employs reasoning: for example, does the customer’s response to an action indicate dissatisfaction with that action? Similarly, when a customer issues an unfamiliar command, a truly self-learning Alexa would be able to infer what it might mean — perhaps by searching the web or exploring a knowledge base — and suggest possibilities.

Self-service means, essentially, the democratization of AI. Alexa customers with no programming experience should be able to customize Alexa’s services and even create new Alexa capabilities, and skill developers without machine learning experience should be able to build complex yet robust conversational skills. Colloquially, these are the conversational-AI equivalents of no-code and low-code development environments.

To be clear, the age of self is not yet upon us, and its dawning will require the maturation of technologies still under development, at Amazon and elsewhere. But some of Alexa’s recently launched capabilities herald a lightening in the Eastern sky.

Self-awareness

In 2018, we launched Alexa hunches for the smart home, with Alexa suggesting actions to take in response to anomalous sensor data. By early 2021, the science has advanced adequately for us to launch an opt-in service in which Alexa can take action immediately and automatically. In the meantime, we’ve also been working to expand hunches to Alexa services other than the smart home.

Technologies will bring a new level of generalizability and autonomy to both the Alexa voice service and the tools available to Alexa developers, ushering in what I like to think of as a new 'age of self' in artificial intelligence.
Prem Natarajan

But commonsense reasoning requires something more — the ability to infer customers’ implicit intentions from observable temporal patterns. For instance, what does it mean if the customer turns down the thermostat, turns out the lights, locks the front door, and opens the garage? What if the customer initiates an interaction with a query like “Alexa, what’s playing at Rolling Hills Cine Plaza?”

In 2020, we took steps toward commonsense reasoning with a new Alexa function that can infer a customer’s latent goal— the ultimate aim that lies behind a sequence of requests. When a customer asks for the weather at the beach, for instance, Alexa might use that query, in combination with other contextual information, to infer that the customer may be interested in a trip to the beach. Alexa could then offer the current driving time to the beach.

To retrieve that information, Alexa has to know to map the location of the weather request to the destination variable in the route-planning function. This illustrates another aspect of self-awareness: the ability to track information across contexts.

That ability is at the core of the night-out experience we’ve developed, which engages the customer in a multiturn conversation to plan a complete night out, from buying movie tickets to making restaurant and ride-share reservations. The night-out experience tracks times and locations across skills, revising them on the fly as customers evaluate different options. To build the experience, we leveraged the machinery of Alexa Conversations, a service that enables developers to quickly and easily create dialogue-driven skills, and we drew on our growing body of research on dialogue state tracking.

Slot_tracking.png._CB436837753_.png
Dialogue states at several successive dialogue turns

Self-awareness, however, includes an understanding not only of the conversational context but also of the customer’s physical context. In 2020, we demonstrated natural turn-taking on Alexa-enabled devices with cameras. When multiple speakers are engaging with Alexa, Alexa can use visual cues to distinguish between speech the customers are directing at each other and speech they’re directing at Alexa. In ongoing work, we’re working to expand this functionality to devices without cameras, by relying solely on acoustic and linguistic signals.

Finally, self-awareness also entails the capacity for self-explanation. Today, most machine learning models are black boxes; even their creators have no idea how they’re doing what they do. That uncertainty has turned explainable or interpretable AI into a popular research topic.

Amazon actively publishes on explainable-AI topics. In addition, the Alexa Fund, an Amazon venture capital investment program, invested in fiddler.ai, a startup that uses techniques based on the game-theoretical concept of Shapley values to do explainable AI.

Self-learning

Historically, the AI development cycle has involved collection of data, annotation of that data, and retraining of models on the newly annotated data — all of which add up to a laborious process.

In 2019, we launched Alexa’s self-learning system, which automatically learns to correct errors — both customer errors and errors in Alexa’s language-understanding models — without human involvement. The system relies on implicit signals that a request was improperly handled, as when a customer interrupts a response and rephrases the same request.

Absorbing-Markov-chain models for three different sequences of utterances
Alexa's self-learning system models customer interactions with Alexa as sequences of states; different customer utterances (u0, u1, u2) can correspond to the same state (h0). The final state of a sequence, known as the "absorbing state", indicates the success (checkmark) or failure (X) of a transaction.
Stacy Reilly

Currently, that fully automatic system is correcting 15% of defects. But those are defects that occur across a spectrum of users; only when enough people implicitly identify the same flaw does the system address it. We are working to adapt the same machinery to individual customers’ preferences — so that, for instance, Alexa can learn that when a particular customer asks for the song “Wow”, she means not the Post Malone hit from 2019 but the 1978 Kate Bush song.

Customers today also have the option of explicitly teaching Alexa their preferences. In the fall of 2020, we launched interactive teaching by customers, a capability that enables customers to instruct Alexa how they want certain requests to be handled. For instance, the customer can teach Alexa that the command “reading mode” means lights turned all the way up, while “movie mode” means only twenty percent up.

Self-service

Interactive teaching is also an early example of how Alexa is enabling more self-service. It extends prior Alexa features, like blueprints, which let customers build their own simple skills from preexisting templates, and routines, which let customers chain together sequences of actions under individual commands.

In March 2021, we announced the public release of Alexa Conversations, which allows developers to create dialogue-driven skills by uploading sample dialogues. Alexa Conversations’ sophisticated machine learning models use those dialogues as templates for generating larger corpora of synthetic training data. From that data, Alexa Conversations automatically trains a machine learning model.

Alexa Conversations does, however, require the developer to specify the set of entities that the new model should act upon and an application programming interface for the skill. So while it requires little familiarity with machine learning, it assumes some programming experience. 

ambiguous_slots.gif._CB438712971_.gif
An Alexa feature known as catalogue value suggestions suggests entity names to skill developers on the basis of their "embeddings", or locations in a representational space. If the embeddings of values (such as bird, dog, or cat) for a particular entity type are close enough (dotted circles) to their averages (solid circle and square), the system suggests new entity names; otherwise, it concludes that suggestions would be unproductive.
Animation by Nick Little

We are steadily chipping away at even that requirement, by making development for Alexa easier and more intuitive. As Alexa’s repertory of skills grows, for instance, entities are frequently reused, and we already have systems that can inform developers about entity types that they might not have thought to add to their skills. This is a step toward a self-service model in which developers no longer have to provide exhaustive lists of entities — or, in some cases, any entities at all.

Another technique that makes it easier to build machine learning models is few-shot learning, in which an existing model is generalized to a related task using only a handful of new training examples. This is an active area of research at Alexa: earlier this year, for example, we presented a paper at the Spoken Language Technologies conference that described a new approach to few-shot learning for natural-language-understanding tasks. Compared to its predecessors, our approach reduced the error rate on certain natural-language-understanding tasks by up to 12.4%, when each model was trained on only 10 examples.

These advances, along with the others reported on Amazon Science, demonstrate that the Alexa AI team continues to accelerate its pace of invention. More exciting announcements lie just over the horizon. I’ll be stopping back here every once in a while to update you on Alexa’s journey into the age of self.

Research areas

Related content

US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
LU, Luxembourg
The Decision, Science and Technology (DST) team part of the global Reliability Maintenance Engineering (RME) is looking for a Senior Operations Research Scientist interested in solving challenging optimization problems in the maintenance space. Our mission is to leverage the use of data, science, and technology to improve the efficiency of RME maintenance activities, reduce costs, increase safety and promote sustainability while creating frictionless customer experiences. As a Senior OR Scientist in DST you will be focused on leading the design and development of innovative approaches and solutions by leading technical work supporting RME’s Predictive Maintenance (PdM) and Spare Parts (SP) programs. You will connect with world leaders in your field and you will be tackling customer's natural language challenges by carrying out a systematic review of existing solutions. The appropriate choice of methods and their deployment into effective tools will be the key for the success in this role. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and outstanding ability in balancing technical leadership with strong business judgment to make the right decisions about model and method choices. Key job responsibilities • Provide technical expertise to support team strategies that will take EU RME towards World Class predictive maintenance practices and processes, driving better equipment up-time and lower repair costs with optimized spare parts inventory and placement • Implement an advanced maintenance framework utilizing Machine Learning technologies to drive equipment performance leading to reduced unplanned downtime • Provide technical expertise to support the development of long-term spares management strategies that will ensure spares availability at an optimal level for local sites and reduce the cost of spares A day in the life As a Senior OR Scientist in DST you will be focused on leading the design and development of innovative approaches and solutions by leading technical work supporting RME’s Predictive Maintenance (PdM) and Spare Parts (SP) programs. You will connect with world leaders in your field and you will be tackling customer's natural language challenges by carrying out a systematic review of existing solutions. The appropriate choice of methods and their deployment into effective tools will be the key for the success in this role. About the team Our mission is to leverage the use of data, science, and technology to improve the efficiency of RME maintenance activities, reduce costs, increase safety and promote sustainability while creating frictionless customer experiences. We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists in the Forecasting, Macroeconomics & Finance field document, interpret and forecast Amazon business dynamics. This track is well suited for economists adept at combining cutting edge times-series statistical methods with strong economic analysis and intuition. This track could be a good fit for candidates with research experience in: macroeconometrics and/or empirical macroeconomics; international macroeconomics; time-series econometrics; forecasting; financial econometrics and/or empirical finance; and the use of micro and panel data to improve and validate traditional aggregate models. Economists at Amazon are expected to work directly with our senior management and scientists from other fields on key business problems faced across Amazon, including retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. The Forecasting, Macroeconomics & Finance field utilizes methods at the frontier of economics to develop formal models to understand the past and the present, predict the future, and identify relevant risks and opportunities. For example, we analyze the internal and external drivers of growth and profitability and how these drivers interact with the customer experience in the short, medium and long-term. We build econometric models of dynamic systems, using our world class data tools, formalizing problems using rigorous science to solve business issues and further delight customers. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists in the Forecasting, Macroeconomics & Finance field document, interpret and forecast Amazon business dynamics. This track is well suited for economists adept at combining cutting edge times-series statistical methods with strong economic analysis and intuition. This track could be a good fit for candidates with research experience in: macroeconometrics and/or empirical macroeconomics; international macroeconomics; time-series econometrics; forecasting; financial econometrics and/or empirical finance; and the use of micro and panel data to improve and validate traditional aggregate models. Economists at Amazon are expected to work directly with our senior management and scientists from other fields on key business problems faced across Amazon, including retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. The Forecasting, Macroeconomics & Finance field utilizes methods at the frontier of economics to develop formal models to understand the past and the present, predict the future, and identify relevant risks and opportunities. For example, we analyze the internal and external drivers of growth and profitability and how these drivers interact with the customer experience in the short, medium and long-term. We build econometric models of dynamic systems, using our world class data tools, formalizing problems using rigorous science to solve business issues and further delight customers. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Economists in the Forecasting, Macroeconomics & Finance field document, interpret and forecast Amazon business dynamics. This track is well suited for economists adept at combining cutting edge times-series statistical methods with strong economic analysis and intuition. This track could be a good fit for candidates with research experience in: macroeconometrics and/or empirical macroeconomics; international macroeconomics; time-series econometrics; forecasting; financial econometrics and/or empirical finance; and the use of micro and panel data to improve and validate traditional aggregate models. Economists at Amazon are expected to work directly with our senior management and scientists from other fields on key business problems faced across Amazon, including retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. The Forecasting, Macroeconomics & Finance field utilizes methods at the frontier of economics to develop formal models to understand the past and the present, predict the future, and identify relevant risks and opportunities. For example, we analyze the internal and external drivers of growth and profitability and how these drivers interact with the customer experience in the short, medium and long-term. We build econometric models of dynamic systems, using our world class data tools, formalizing problems using rigorous science to solve business issues and further delight customers. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Boston, MA, USA | Los Angeles, CA, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA