Screenshot shows a portion of the what should I watch experience
The new What Should I Watch (WSIW) experience, released in mid-September, combines Alexa AI and Fire TV recommendations to turn Alexa into an entertainment expert who provides relevant suggestions with a conversational customer experience.

The science behind the new “Alexa, what should I watch?” Fire TV experience

The phrase launches a feature built to help customers navigate an increasingly complex and diverse world of content.

"What should I watch?"

In an entertainment universe filled with a rapidly expanding catalog of shows across myriad channels and apps, this might be one of the most common questions to pop up in many households. And if you are among those who have trouble keeping up with all the latest shows and pinpointing which ones are worth your time, you are not alone.

In fact, more than half of respondents in a recent survey from the consulting firm Deloitte found it difficult to access content across multiple services, and 49% were frustrated if a service failed to provide them with good recommendations. Viewers find themselves surfing … and surfing. It takes the average smart TV owner 12 minutes to land on a show, according to a 2020 survey by Tivo — and for some viewers that can take up to half an hour.

"It's kind of shocking how much time customers have to spend on finding content instead of just sitting down on the couch and jumping into a TV show or a movie that they really enjoy," said Cosmin Laslau, a technical program manager who works on spoken language understanding as part of the Amazon Alexa Entertainment team. "We wanted to leverage new technology to help solve that problem for customers."

Image shows the new Fire TV Cube, left, the Fire TV Omni QLED Series, middle, and the Alexa Voice Remote Pro, right
The What Should I Watch experience works with many Fire TV devices, including the new Fire TV Cube, left, the Fire TV Omni QLED Series, middle, and the Alexa Voice Remote Pro announced at the 2022 Devices and Services event.

The team did that by launching What Should I Watch (WSIW). The new experience, released in mid-September, combines Alexa AI and Fire TV recommendations to turn Alexa into an entertainment expert who provides relevant suggestions with a conversational customer experience. The experience also works with the new Fire TV Cube, the Fire TV Omni QLED Series, and the Alexa Voice Remote Pro announced at the 2022 Devices and Services event.

“We built WSIW to rapidly experiment with new Alexa technologies and push the envelope on discovery experiences to address the core customer need to find something interesting to watch,” explained Parthasarathi Dutta Sharma, a product manager who helped bring WSIW to customers.

WSIW displays personalized recommendations when customers ask, “Alexa, what should I watch?” or a variant of that phrase. Customers can then customize the recommendations using voice prompts (for example, “just the ones that are free to me”) or by using their remote to select filters on the screen, watch trailers, view additional information (eg genre, ratings), and initiate playback.

Related content
Rohit Prasad on the pathway to generalizable intelligence and what excites him most about his re:MARS keynote.

The experience combines innovation for both Fire TV, with its extensive catalog, search and recommendation features, and the conversational AI that drives Alexa.

"We wanted to layer on these new innovations that have been developed around Alexa Conversations specifically," Laslau said. "We've given customers a broad range of natural ways to interact with Alexa, without being limited to a single utterance."

Since previewing WSIW last fall and beginning beta testing with customers, teams have worked to refine the customer experience.

“We used beta testing to closely observe how customers interacted with WSIW and to validate our core hypotheses on what works for customers,” explained Dutta Sharma. “A prime hypothesis we validated was viewers naturally gravitate to using natural language, with variability in inputs, while interacting with Alexa.”

Related content
Dialogue simulator and conversations-first modeling architecture provide ability for customers to interact with Alexa in a natural and conversational manner.

For example, to customize recommendations, the team found that initially customers might say, “I am in the mood for something funny”. They would then follow that by asking, “Which of these are on Prime Video?” or simply stating, “free to me”. So, the team worked to ensure WSIW could support those types of interactions with Alexa. It proved to be a feature customers responded to enthusiastically.

The team also responded to early feedback by introducing more gradual introductions to autoplay trailers and swapped an intro video on how to use the WSIW feature with on-screen contextual hints.

“Another insight was that customers wanted to be able to view only the titles they were already entitled to — versus those for rent or purchase — so we added a permanent free-to-me filter. Customers routinely call that out as a highlight,” Dutta Sharma said.

Building AI for the entertainment space

The What Should I Watch experience builds upon existing Alexa natural language understanding and automatic speech recognition capabilities.

"But bringing natural conversation to the entertainment domain has its own set of unique challenges," Laslau explained. Maybe a show, like The Boys or The Expanse, is ambiguously named, or a movie starts to trend that wasn't in the catalog a week or two ago. Optimizing the feature required combining core advances in AI around natural, multi-turn conversations with a fast-changing catalog.

"We are making sure those natural conversations are intelligent enough to reflect the very latest of what's happening in entertainment," he said.

The team also worked to ensure a mix of personalization based on your preferences— those British detective series you always gravitate toward — and something new that you might not have seen otherwise.

They did this by customizing Fire TV's existing recommender technology, mixing personalization with popular titles and randomizing subsets of these lists so that viewers encounter fresh ideas each time they turn on the TV.

A flywheel effect on innovation

The deep-learning-based Alexa Conversations makes it far simpler to develop the thousands of potential dialogue turns that a “What Should I Watch?” utterance might generate.

Alexa Conversations comprises three models: entity recognition (identifying Tom Cruise as an actor, for example), action prediction (utilizing the “movie searching” API to find movies), and argument filling (indicating the movies to be those with Tom Cruise).

“Alexa Conversations is designed to reduce the burden on developers, generating variations of dialogue automatically. The team has added several new features recently,” said Jiun-Yu Kao, an applied scientist within the Alexa AI Natural Understanding organization.

The WSIW experience is the first to launch with enhanced understanding of screen context.
Jiun-Yu Kao

Those include conversational Q&A which allow customers to ask broad questions about the recommended titles, such as which movies won an Oscar; a context reset function that allows a user to "start over" with a blank slate; and visual context, which enhances Alexa’s ability to respond correctly when a viewer says something like, "play the one on the left,” referencing what’s on the screen instead of naming the movie title.

“The WSIW experience is the first to launch with enhanced understanding of screen context,” Kao said. “It is also the first to combine all above-listed features for improved customer experience.”

Alexa and Fire TV science, engineering, and product teams collaborated to build the different components of the new feature.

Related content
A behind-the-scenes look at the unique challenges the engineering teams faced, and how they used scientific research to drive fundamental innovation to overcome those challenges.

“What’s super cool is that we are tapping into so many different services in parts of Alexa and Fire TV,” said Carlos Mattoso, a Fire TV software development engineer. “We are using a lot of the domain knowledge and capabilities that Fire TV has built around the recommendation space, for instance. But where we do that, we’re also trying to raise the bar: How can we use the information we’re gleaning from usage of What Should I Watch back into the system so that we have this flywheel that continuously improves?”

Mattoso noted that work with the Alexa team enabled not just suggestions but new in-context commands for Fire TV playback and volume changes, for example, that weren’t previously available.

“For instance, when we were building the first beta, we did not really have a way of initiating playback of a title from within an Alexa skill for Fire TV,” he explained. “So, we worked together with the Alexa Video team to extend the existing capability and then add support for that feature so that we could use it on WSIW.”

Looking ahead

Teams continue to work on making What Should I Watch faster and smarter.

One possibility is for users to explicitly guide Alexa by saying something like, "I'm a big sci-fi fan," or "I don't like horror movies." This type of interaction represents an opportunity for Alexa to adapt to customer engagement preferences, with some preferring to guide the service directly, and others wanting to lean back and take in recommendations.

As collaboration on the experience continues, both Alexa and Fire TV are becoming more capable. That could have a broader effect, particularly for the Alexa skill development community.

“We’re really trying to raise the bar,” Mattoso said, “and the capabilities we develop may eventually benefit third-party skill developers. Those might include improved long-term memory, better context resetting, and better visual context understanding.”

Research areas

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
The Global Cross-Channel and Cross- Category Marketing (XCM) org are seeking an experienced Economist to join our team. XCM’s mission is to be the most measurably effective and creatively breakthrough marketing organization in the world in order to strengthen the brand, grow the business, and reduce cost for Amazon overall. We achieve this through scaled campaigning in support of brands, categories, and audiences which aim to create the maximum incremental impact for Amazon as a whole by driving the Amazon flywheel. This is a high impact role with the opportunities to lead the development of state-of-the-art, scalable models to measure the efficacy and effectiveness of a new marketing channel. In this critical role, you will leverage your deep expertise in causal inference to design and implement robust measurement frameworks that provide actionable insights to drive strategic business decisions. Key Responsibilities: Develop advanced econometric and statistical models to rigorously evaluate the causal incremental impact of marketing campaigns on customer perception and customer behaviors. Collaborate cross-functionally with marketing, product, data science and engineering teams to define the measurement strategy and ensure alignment on objectives. Leverage large, complex datasets to uncover hidden patterns and trends, extracting meaningful insights that inform marketing optimization and investment decisions. Work with engineers, applied scientists and product managers to automate the model in production environment. Stay up-to-date with the latest research and methodological advancements in causal inference, causal ML and experiment design to continuously enhance the team's capabilities. Effectively communicate analysis findings, recommendations, and their business implications to key stakeholders, including senior leadership. Mentor and guide junior economists, fostering a culture of analytical excellence and innovation.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create 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, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques
IL, Haifa
We’re looking for a Principal Applied Scientist in the Personalization team with experience in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problem Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
US, WA, Seattle
Are you a brilliant mind seeking to push the boundaries of what's possible with intelligent robotics? Join our elite team of researchers and engineers - led by Pieter Abeel, Rocky Duan, and Peter Chen - at the forefront of applied science, where we're harnessing the latest advancements in large language models (LLMs) and generative AI to reshape the world of robotics and unlock new realms of innovation. As an Applied Science Intern, you'll have the unique opportunity to work alongside world-renowned experts, gaining invaluable hands-on experience with cutting-edge robotics technologies. You'll dive deep into exciting research projects at the intersection of AI and robotics. This internship is not just about executing tasks – it's about being a driving force behind groundbreaking discoveries. You'll collaborate with cross-functional teams, leveraging your expertise in areas such as deep learning, reinforcement learning, computer vision, and motion planning to tackle real-world problems and deliver impactful solutions. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied robotics and AI, where your contributions will shape the future of intelligent systems and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available in San Francisco, CA and Seattle, WA. The ideal candidate should possess: - Strong background in machine learning, deep learning, and/or robotics - Publication record at science conferences such as NeurIPS, CVPR, ICRA, RSS, CoRL, and ICLR. - Experience in areas such as multimodal LLMs, world models, image/video tokenization, real2Sim/Sim2real transfer, bimanual manipulation, open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, and end-to-end vision-language-action models. - Proficiency in Python, Experience with PyTorch or JAX - Excellent problem-solving skills, attention to detail, and the ability to work collaboratively in a team Join us at the forefront of applied robotics and AI, and be a part of the team that's reshaping the future of intelligent systems. Apply now and embark on an extraordinary journey of discovery and innovation! Key job responsibilities - Develop novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of LLMs and generative AI for robotics - Tackle challenging, groundbreaking research problems on production-scale data, with a focus on robotic perception, manipulation, and control - Collaborate with cross-functional teams to solve complex business problems, leveraging your expertise in areas such as deep learning, reinforcement learning, computer vision, and motion planning - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment, and communicate effectively with diverse stakeholders
US, WA, Seattle
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers like Pieter Abbeel, Rocky Duan, and Peter Chen to lead key initiatives in robotic intelligence. As a Senior Applied Scientist, you'll spearhead the development of breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence in areas such as perception, manipulation, scence understanding, sim2real transfer, multi-modal foundation models, and multi-task learning, designing novel algorithms that bridge the gap between cutting-edge research and real-world deployment at Amazon scale. In this role, you'll combine hands-on technical work with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. You'll leverage Amazon's vast computational resources to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Lead technical initiatives in robotics foundation models, driving breakthrough approaches through hands-on research and development in areas like open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Guide technical direction for specific research initiatives, ensuring robust performance in production environments - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus A day in the life - Develop and implement novel foundation model architectures, working hands-on with our extensive compute infrastructure - Guide fellow scientists in solving complex technical challenges, from sim2real transfer to efficient multi-task learning - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within your team and with key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster - Mentor team members while maintaining significant hands-on contribution to technical solutions Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team, led by pioneering AI researchers Pieter Abbeel, Rocky Duan, and Peter Chen, is building the future of intelligent robotics through groundbreaking foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, WA, Seattle
The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists and Engineers, who incubate and build disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey. Our solutions are crucial for the success of Amazon’s own brands and serve as a beacon for discovery solutions across Amazon. This is a high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and engineers. As a scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams. Key job responsibilities - 5+ yrs of relevant, broad research experience after PhD degree or equivalent. - Advanced expertise and knowledge of applying observational causal interference methods - Strong background in statistics methodology, applications to business problems, and/or big data. - Ability to work in a fast-paced business environment. - Strong research track record. - Effective verbal and written communications skills with both economists and non-economist audiences.
US, WA, Seattle
The AWS Marketplace & Partner Services Science team is hiring an Applied Scientist to develop science products that support AWS initiatives to grow AWS Partners. The team is seeking candidates with strong background in machine learning and engineering, creativity, curiosity, and great business judgment. As an applied scientist on the team, you will work on targeting and lead prioritization related AI/ML products, recommendation systems, and deliver them into the production ecosystem. You are comfortable with ambiguity and have a deep understanding of ML algorithms and an analytical mindset. You are capable of summarizing complex data and models through clear visual and written explanations. You thrive in a collaborative environment and are passionate about learning. Key job responsibilities - Work with scientists, product managers and engineers to deliver high-quality science products - Experiment with large amounts of data to deliver the best possible science solutions - Design, build, and deploy innovative ML solutions to impact AWS Co-Sell initiatives About the team The AWS Marketplace & Partner Services team is the center of Analytics, Insights, and Science supporting the AWS Specialist Partner Organization on its mission to provide customers with an outstanding experience while working with AWS partners. The Science team supports science models and recommendation systems that are deployed directly to AWS Customers, AWS partners, and internal AWS Sellers.
CA, ON, Toronto
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Device organization where our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful science leader in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have solid technical background and extensive experience in leading projects and technical teams. The ideal candidate would also have experiences in developing natural language processing systems (particularly LLM based systems) for industry applications, enjoy operating in highly dynamic and ambiguous environments, be self-motivated to take on challenging problems to deliver customer impact. In this role, you will lead a team of scientists to fine tune and evaluate the LLM to improve instruction following capabilities, align human preferences with RLHF, enhance conversation responses with RAG techniques, and various other. You will use your management, research and production experience to develop the team, communicate direction and achieve the results in a fast-paced environment. You will have significant influence on our overall LLM strategy by helping define product features, drive the system architecture, and spearhead the best practices that enable a quality product. Key job responsibilities Key job responsibilities Build a strong and coherent team with particular focus on sciences and innovations in LLM technologies for conversation AI applications Own the strategic planning and project management for technical initiatives in your team with the help of technical leads. Provide technical and scientific guidance to your team members. Collaborate effectively with multiple cross-organizational teams. Communicate effectively with senior management as well as with colleagues from science, engineering and business backgrounds. Support the career development of your team members.
DE, Aachen
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.