The science behind Alexa’s new interactive story-creation experience

AI models that generate stories, place objects in a visual scene, and assemble music on the fly customize content to children’s specifications.

In September, Amazon senior vice president Dave Limp unveiled Amazon Devices’ new lineup of products and services. Among them was a new Alexa experience that receives customer prompts and uses AI to generate short children’s stories, complete with illustrations and background music.

The experience is slated for general release later this year. It allows children to choose themes for their stories, such as “underwater” or “enchanted forest”; protagonists, such as pirate or mermaid; colors, which will serve as visual signatures for the illustrations; and adjectives, such as “silly” or “mysterious”.

From the prompts, an AI engine generates an original five-scene story. For each scene, it also composes an illustration (often animated) and background music, and it selects appropriate sound effects. Since the experience depends heavily on AI models, it can repeatedly generate different stories from the same set of prompts.

A hybrid approach

To ensure both family-friendly visual content and a consistent visual vocabulary, the Alexa story creation experience uses a library of designed or curated, AI-generated backgrounds and foreground objects. The AI model determines which objects to use and how to arrange them on the screen.

Story creation 1_INGRESS.png
The new Alexa story creation experience uses AI to arrange visual elements on either artist-rendered or AI-generated backgrounds, to illustrate stories produced by a separate AI module. (The images shown in this article are for illustration purposes only.)

Similarly, the background-music module augments composer-created harmonic and rhythmic patterns by automatically generating melodies, which are stored in a library for efficient runtime deployment. An AI model then assembles the background music to follow a hero character and match the moods and themes of the story scenes. Sound effects corresponding to particular characters, objects, and actions are selected in similar fashion.

The core of the story creation experience, however, is the story generator, which takes user prompts as input and outputs a story. The story text, in turn, is the input to the image and music generators.

Story generator

The story generator consists of two models, both built on top of pretrained language models. The first model — the “planner” — receives the customer-selected prompts and uses them to generate a longer set of keywords, allocated to separate scenes. These constitute the story plan. The second model — the text generator — receives the story plan and outputs the story text.

Story creation 2_HERO.png
Choice of character is one of the prompts that the story generator uses to create a text.

To train the story generator, the Alexa researchers use human-written stories, including a set of stories created in-house by Amazon writers. The in-house stories are labeled according to the themes that customers will ultimately choose from, such as “underwater” and “enchanted forest”.

Related content
Amazon yesterday announced its picks for 2022 Best Books of the Year So Far, including its top book within the general-interest science category, “Stolen Focus: Why You Can’t Pay Attention — and How to Think Deeply Again”.

The first step in the training procedure is to automatically extract salient keywords from each sentence of each story, producing keyword lists, which are used to train the text generator. The lists are then randomly downsampled to just a few words each, to produce training data for the planner.

A Transformer-based coherence ranker filters the text generator’s outputs, so that only the stories that exhibit the highest quality in terms of plot coherence (e.g., character and event consistency) are selected. The same model is also used to automatically evaluate the overall quality of generated stories.

Scene generation

Because training data for the scene generation module was scarce, the Alexa researchers use a pipelined sequence of models to compose the illustrations. Pipelined architectures tend to work better with less data.

Before being sent to the scene generation model, the story text passes through two natural-language-processing (NLP) modules, which perform coreference resolution and dependency parsing, respectively. The coreference resolution module determines the referents of pronouns and other indicative words and rewrites the text accordingly. For instance, if the mermaid mentioned in scene one is referred to as “she” in scene two, the module rewrites “she” as “the mermaid”, to make it easier for the scene generator to interpret the text.

The dependency parser produces a graph that represents the relationships between objects mentioned in the text. For instance, if the text said, “The octopus swam under the boat”, nodes representing the objects “octopus” and “boat” would be added to the graph, connected by a directional edge labeled “under”. Again, this makes the text easier for the scene generator to interpret.

Story creation 3_STORY.png
On the basis of the generated text, the scene generator will select a background and place the appropriate figures on it with the appropriate scale and orientation.

The first step in the scene generation pipeline is to select a background image, based on the outputs of the NLP modules and the customer’s choice of theme. The library of background images includes both artist-rendered and AI-generated images.

Next, the NLP modules’ outputs pass to a model that determines which elements from the library of designed objects the scene should contain. With that information in hand — along with visual context — another model chooses the scale and orientation of the objects and places them at specific (x, y) coordinates on the selected background image.

Many of the images in the library are animated: for instance, fish placed on the underwater background will flick their tails. But these animations are part of the image design. The orientations and locations of the fish can change, but the animations are executed algorithmically.

Music

To ensure the diversity and quality of the background music for the stories, the Alexa researchers created a large library of instrumental parts. At run time, the system can automatically combine parts to create a theme and instrumental signature for each hero character.

Related content
From physical constraints to acoustic challenges, learn how Amazon collaborated with NASA and Lockheed Martin to get Alexa to work in space.

The library includes high-quality artist-created chord progressions, harmonies, and rhythms, which an AI melody generator can use to produce melodies of similar quality that match the instrumentation of existing parts. The AI-created melodies are generated offline and stored in the library with the other musical assets.

In the library, the assets are organized by attributes such as chord progression, rhythm, and instrument type. An AI musical-arrangement system ensures that all the pieces fit together.

Like the illustration module, the music generation model processes text inputs in two ways. A text-to-speech model computes the time it will take to read the text, and a paralinguistic-analysis model scores the text along multiple axes, such as calm to exciting and sad to happy. Both models’ outputs serve as inputs to the musical-arrangement system and help determine the duration and character of the background music.

Guardrails

Beyond the compositional approach to scene generation, the researchers adopted several other techniques to ensure that the various AI models’ outputs were age appropriate.

Related content
Eliminating the need for annotation makes bias testing much more practical.

First, they curated the data used to train the models by manually and automatically screening and excluding offensive content. Second, they limit the input prompts for story creation to pre-curated selections. Third, they filter the models’ outputs to automatically identify and remove inappropriate content.

In addition, use of the Alexa story creation experience will require parental consent, which parents will be able to provide through the Alexa app.

Together, all of this means that the new Alexa story creation experience will be both safe and delightful.

[Editor's note: The Create with Alexa service was officially launched on Nov. 29 for Echo Show devices in the United States. In September, Amazon Science explored the science behind the new service, including how scene generation works and how researchers worked to ensure the experience is age appropriate.]

Research areas

Related content

US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * the ability to think big and conceive of new ideas and novel solutions; * the insight to correctly identify those worth exploring; * the hands-on skills to quickly develop proofs-of-concept; * the rigor to conduct careful experimental evaluations; * the discipline to fast-fail when data refutes theory; * and the fortitude to continue exploring until your solution is found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
GB, London
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python or R is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: London, GBR
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. We are open to hiring candidates to work out of one of the following locations: London, GBR
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: London, GBR
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: London, GBR
LU, Luxembourg
Have you ever wished to build high standard Operations Research and Machine Learning algorithms to optimize one of the most complex logistics network? Have you ever ordered a product on Amazon websites and wondered how it got delivered to you so fast, and what kinds of algorithms & processes are running behind the scenes to power the whole operation? If so, this role is for you. The team: Global transportation services, Research and applied science - Operations is at the heart of the Amazon customer experience. Each action we undertake is on behalf of our customers, as surpassing their expectations is our passion. We improve customer experience through continuously optimizing the complex movements of goods from vendors to customers throughout Europe. - Global transportation analytical teams are transversal centers of expertise, composed of engineers, analysts, scientists, technical program managers and developers. We are focused on Amazon most complex problems, processes and decisions. We work with fulfillment centers, transportation, software developers, finance and retail teams across the world, to improve our logistic infrastructure and algorithms. - GTS RAS is one of those Global transportation scientific team. We are obsessed by delivering state of the art OR and ML tools to support the rethinking of our advanced end-to-end supply chain. Our overall mission is simple: we want to implement the best logistics network, so Amazon can be the place where our customers can be delivered the next-day. The role: Applied scientist, speed and long term network design The person in this role will have end-to-end ownership on augmenting RAS Operation Research and Machine Learning modeling tools. They will help understand where are the constraints in our transportation network, and how we can remove them to make faster deliveries at a lower cost. You will be responsible for designing and implementing state-of-the-art algorithmic in transportation planning and network design, to expand the scope of our Operations Research and Machine Learning tools, to reflect the constantly evolving constraints in our network. You will enable the creation of a product that drives ever-greater automation, scalability and optimization of every aspect of transportation, planning the best network and modeling the constraints that prevent us from offering more speed to our customer, to maximize the utilization of the associated resources. The impact of your work will be in the Amazon EU global network. The product you will build will span across multiple organizations that play a role in Amazon’s operations and transportation and the shopping experience we deliver to customer. Those stakeholders include fulfilment operations and transportation teams; scientists and developers, and product managers. You will understand those teams constraints, to include them in your product; you will discuss with technical teams across the organization to understand the existing tools and assess the opportunity to integrate them in your product.You will engage with fellow scientists across the globe, to discuss the solutions they have implemented and share your peculiar expertise with them. This is a critical role and will require an aptitude for independent initiative and the ability to drive innovation in transportation planning and network design. Successful candidates should be able to design and implement high quality algorithm solutions, using state-of-the art Operations Research and Machine Learning techniques. Key job responsibilities - Engage with stakeholders to understand what prevents them to build a better transportation network for Amazon - Review literature to identify similar problems, or new solving techniques - Build the mathematical model representing your problem - Implement light version of the model, to gather early feed-back from your stakeholders and fellow scientists - Implement the final product, leveraging the highest development standards - Share your work in internal and external conferences - Train on the newest techniques available in your field, to ensure the team stays at the highest bar About the team GTS Research and Applied Science is a team of scientists and engineers whom mission is to build the best decision support tools for strategic decisions. We model and optimize Amazon end-to-end operations. The team is composed of enthusiastic members, that love to discuss any scientific problem, foster new ideas and think out of the box. We are eager to support each others and share our unique knowledge to our colleagues. We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
US, CA, Santa Clara
Amazon AI is looking for world class scientists and engineers to join its AWS AI Labs. This group is entrusted with developing core data mining, natural language processing, deep learning, and machine learning algorithms for AWS. You will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore conceptually new solutions. 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. Inclusive Team Culture Here 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. Work/Life Balance Our 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 Growth Our 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. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA
DE, BE, Berlin
Are you excited about developing state-of-the-art computer vision models that revolutionize Amazon’s Fulfillment network? Are you looking for opportunities to apply AI on real-world problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience — at Amazon scale. To this end, we are looking for an Applied Scientist who will build and deploy models that make smarter decisions on a wide array of multi-modal signals. Together, we will be pushing beyond the state of the art in optimizing one of the most complex systems in the world: Amazon's Fulfillment Network. Key job responsibilities In this role, you will build computer vision and multi-modal deep learning models that understand the state of products and packages flowing through Amazon’s fulfillment network. You will build models that solve challenging problems like product identification and damage detection on Amazon's entire retail catalog (billions of different items, thousands of new items every day). You will primarily work with very large real-world vision datasets, as well as a diverse set of multi-modal datasets, including natural language and structured data. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions. A day in the life AFT AI delivers the AI solutions that empower Amazon’s fulfillment network to make smarter decisions. You will work on an interdisciplinary team of scientists and engineers with deep expertise in developing cutting-edge AI solutions at scale. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will adapt state-of-the-art machine learning and computer vision techniques to develop solutions for business problems in the Amazon Fulfillment Network. About the team Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. AFT AI is spread across multiple locations in NA (Bellevue WA and Nashville, TN) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location. Publicly available articles showcasing some of our work: - Damage Detection: https://www.amazon.science/latest-news/the-surprisingly-subtle-challenge-of-automating-damage-detection - Product ID: https://www.amazon.science/latest-news/how-amazon-robotics-is-working-on-new-ways-to-eliminate-the-need-for-barcodes We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU
IN, KA, Bengaluru
Job Description ATE (Analytics, Technology and Engineering) is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in operations for the benefit of our customers. Our team is responsible for creating core analytics, science capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes.. You will work with professional software development managers, data engineers, data scientists, applied scientists, business intelligence engineers and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East. We are on the lookout for an enthusiastic and highly analytical individual to be a part of our journey. Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools. Major responsibilities include: · In this role, you will be responsible for developing and implementing innovative, scalable models and tools aimed at tackling novel challenges within Amazon’s global fulfillment network. Collaborating with fellow scientists from various teams, you will work on integrated solutions to enhance fulfillment speed, reduce costs. Your in-depth comprehension of business challenges will enable you to provide scientific analyses that underpin critical business decisions, utilizing a diverse range of methodologies. You’ll have the opportunity to design scientific tool platforms, deploy models, create efficient data pipelines, and streamline existing processes. Join us in shaping the future of Amazon’s global retail business by optimizing delivery speed at scale and making a lasting impact on the world of e-commerce. If you’re passionate about solving complex problems and driving innovation, we encourage you to apply. About the team This team is responsible for applying science based algo and techniques to solve the problems in operation and supply chain. Some of these problems include, volume forecasting, capacity planning, fraud detection, scenario simulation and using LLM/GenAI for process efficiency We are open to hiring candidates to work out of one of the following locations: Bengaluru, KA, IND