Ten university teams selected for Alexa Prize TaskBot Challenge 2

Second iteration features five new teams.

Amazon today announced that ten teams from around the globe have been selected to participate in the Alexa Prize TaskBot Challenge year 2, a university challenge focused on developing multimodal (voice and vision) conversational agents that assist customers in completing tasks requiring multiple steps and decisions.

Alexa Prize is a flagship industry-academic collaboration dedicated to accelerating the science of conversational artificial intelligence (AI) and multimodal human-AI interactions.

“Prize competitions provide an agile science experimentation framework for researchers and students encouraging them to explore transformational ideas at the boundaries of what is achievable,” said Reza Ghanadan, senior principal scientist with Alexa AI and head of Alexa Prize. “We have developed the CoBot platform and tools to lower the barriers to AI innovation for both the academic research community and students interested in conversational AI assistants. These tools allow students to quickly deploy their solutions at scale in the real world with Alexa, then observe, evaluate, and enhance their research results using feedback from Alexa customers.”

Photo of Participants in the Alexa Prize TaskBot Challenge Bootcamp
The Alexa Prize TaskBot Bootcamp was held in Seattle, Washington, with representatives from all ten university teams.

The teams selected for the challenge, which began in January, feature five returning entrants — including the top three finishers in the most recent challenge — and five new universities.

Team

University

Faculty advisor

Returning

TWIZ

NOVA School of Science and Technology

João Magalhães

EvoquerBOT

Penn State University

Rui Zhang

Taco 2.0

The Ohio State University

Huan Sun

GRILL

University of Glasgow

Jeff Dalton

Maruna

University of Massachusetts Amherst

Hamed Zamani

New

BoilerBot

Purdue University

Julia Rayz

DiWBot

Rutgers University

Matthew Stone

Sage

University of California, Santa Cruz

Xin (Eric) Wang

ISABEL

University of Pittsburgh

Malihe Alikhani

PLAN-Bot

Virginia Tech

Ismini Lourentzou

The prizes for overall performance in the competition will be $500,000 for the first-place team, $100,000 for second, and $50,000 for third. Those prizes will be paid out to the students on the teams with the best overall performance.

“I am delighted to see that new teams are joining the second year of the competition together with returning teams, who, by competing again, are signaling to us that they found value in the TaskBot challenge, said Yoelle Maarek, vice president research and science for Amazon Shopping.  

“We expect these talented graduate students to continue surprising us, as well as Amazon customers, this year. Connecting academia, Amazonians, and actual customers experimenting with taskbots, is a winning combination to keep pushing the boundaries of science in conversational AI for Alexa to delight and ease the lives of millions of customers.”

The Alexa Prize is a competition for university students dedicated to advancing the field of conversational AI. Launched in 2016, the program was created to recognize students from around the globe who are changing the way we interact with technology.

TaskBot Challenge 2 teams are working to address one of the hardest problems in conversational AI — creating next-generation conversational AI experiences that delight customers by addressing their changing needs as they complete complex tasks. This challenge builds upon the Alexa Prize’s foundation of providing universities a unique opportunity to test cutting-edge machine learning models with actual customers at scale.

The Alexa Prize TaskBot challenge provides a realistic scenario with real-user multimodal interactions, making this the perfect setting to observe and measure human-bot conversations and AI algorithms in a groundbreaking setting.
rafael_ferreira_twiz.jpg
Rafael Ferreira, NOVA School of Science and Technology, Team TWIZ
Our vision of EvoquerBOT combines improving task completion rates and elevating user satisfaction. To this end, we deliver innovative solutions to fundamental NLP challenges.
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Haoran Zhang, Penn State University, Team EvoquerBOT
We are especially interested in developing innovative ways to achieve successful coordination of multiple modalities, such as visual and verbal elements, and create a more engaging and intuitive user experience.
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Lingbo Mo, The Ohio State University, Team Taco 2.0
The GRILL team is excited to continue bringing cutting-edge AI research to improve people’s lives. Our research team works on new capabilities of foundation models that understand text, images, and the surrounding world.
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Sophie Fischer, University of Glasgow, Team GRILL
The competition lets us create interfaces for the general public in a production environment – it’s a unique opportunity to connect our research with our career goals.
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Baber Khalid, Rutgers University, Team DiWBot
We are very excited to be part of the community and look forward to working with the Alexa team and other teams.
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Anthony Sicilia, University of Pittsburgh, Team ISABEL
The Alexa Prize TaskBot Challenge combines a vast range of tasks over multiple domains with multimodal outputs. This is the ultimate test for any moonshot concept, and we can't wait to see what the real world has in store for us.
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Rey (Alex) Gonzalez, Purdue University, Team BoilerBot
Participating in this competition is an incredible opportunity that will allow us to do applied research and ship it to real users.
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Chris Samarinas, University of Massachusetts Amherst, Team Maruna
Although artificial intelligence has experienced explosive development in the past decade, there is still a gap between research and real-world application. The TaskBot Challenge provides us with a unique opportunity to explore multimodal AI in practical situations.
UCSC Kaishi TB2.png
Kaizhi Zheng Univerisity of California, Santa Cruz-Amherst, Team Sage
Our bot will make adaptable conversation a reality by allowing customers to follow personalized decisions through the completion of multiple, sequential sub-tasks and adapt to the tools, materials, or ingredients available to the user by proposing appropriate substitutes and alternatives.
Afrina Tabassum
Afrina Tabassum

TaskBot is the first conversational AI challenge to incorporate multimodal customer experiences, so in addition to receiving verbal instructions, customers with Echo Show or Fire TV devices, can also be presented with step-by-step instructions, images, or diagrams that enhance task guidance.

This year’s challenge has been expanded to include more hobbies and at-home activities. Participating teams were asked to propose interesting ways to incorporate visual aids into every conversation turn when a screen is available. Innovative ideas on improving the presentation of visual aids, as well as the coordination of visual and verbal modalities, were part of the team selection criteria.

Each university selected for the challenge receives a $250,000 research grant, Alexa-enabled devices, free Amazon Web Services (AWS) cloud computing services to support their research and development efforts, access to Amazon scientists, the CoBot (conversational bot) toolkit and other tools such as automated speech recognition through Alexa, neural detection and generation models, conversational data sets, and design guidance and development support from the Alexa Prize team.

"Alexa, let's work together"

The university teams’ taskbots will be available for Alexa customers to engage with in May 2023 with a finals event being held in September, and winners announced later that month.

As with the previous challenge, Alexa customers can engage in conversation with teams’ taskbots when they become available in May by saying, “Alexa, let’s work together.” Until then, “Alexa, let’s work together” will direct you to conversations with the previous challenge winners of 2022 and the Alexa Prize TaskBot.

After initiating the interaction, Alexa customers then receive a brief message informing them that they are interacting with an Alexa Prize university taskbot before being randomly connected to one of the participating taskbots.

After exiting the conversation with the taskbot, which customers can do at any time, the customer is prompted for a verbal rating, followed by an option to provide additional feedback. The interactions, ratings, and feedback are shared with the teams to help them improve their taskbots. Customer ratings are also used to determine which university teams will move on to the semifinals and finals.

Our goal is to contribute to the multimodal conversational AI field and move it closer to the way humans perceive, reason, and communicate through multimodal information.
joao_magalhaes_twiz.jpg
João Magalhães, associate professor, NOVA School of Science and Technology, Team TWIZ
We look forward to the Challenge because it is the perfect platform to create multimodal, tasked-oriented dialogue systems that elevate user experience and engagement.
rui_zhang.jpeg
Rui Zhang, assistant professor, Penn State University, Team EvoquerBOT
Through this TaskBot Challenge, we hope our work can expand the horizon of conversational AI along dimensions like dialogue depth, multi-modal coordination, commonsense reasoning, and learning from use.
Huan_Sun.png
Huan Sun, associate professor, The Ohio State University, Team Taco 2.0
The GRILL team is creating the next generation of open assistants that understand and use knowledge about the world and can communicate effectively to inform and educate.
jeff.jpeg
Jeff Dalton, associate professor, University of Glasgow, Team GRILL
Our TaskBot will help people get things done through personalized, adaptive, and context-aware conversational interaction by combining our research results with the state-of-the-art capabilities of Alexa devices.
Matthew Stone (Rutgers).jpg
Matthew Stone, professor, Rutgers University, Team DiWBot
We work towards making conversational AI technology more inclusive and collaborative. Inclusive Alexa can collaborate with users from diverse cultures and with different communication capabilities and preferences.
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Malihe Alikhani, assistant professor, University of Pittsburgh, Team ISABEL
We hope to develop a task-oriented system that can interact with users based on their level of knowledge, experience, and communication preference.
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Julia Rayz, professor, Purdue University, Team BoilerBot

Success in the previous TaskBot Challenge required teams to address many difficult AI obstacles. The challenge required the fusion of multiple AI techniques including knowledge representation and inference, commonsense and causal reasoning, and language understanding and generation.

The “GRILLBot” team from University of Glasgow won the TaskBot 1 Challenge, earning a $500,000 prize for its performance. Teams from NOVA School of Science and Technology (Portgual) and The Ohio State University earned second- and third-place prizes, respectively.

Research papers from Amazon’s Alexa Prize team, and each of the competing teams, can be viewed and downloaded here.

Alexa Prize Taskbot Challenge Finals | Amazon Science

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Amazon Web Services (AWS) is assembling an elite team of world-class scientists and engineers to pioneer the next generation of AI-driven development tools. Join the Amazon Kiro LLM-Training team and help create groundbreaking generative AI technologies including Kiro IDE and Amazon Q Developer that are transforming the software development landscape. Key job responsibilities As a key member of our team, you'll be at the forefront of innovation, where cutting-edge research meets real-world application: - Push the boundaries of reinforcement learning and post-training methodologies for large language models specialized in code intelligence - Invent and implement state-of-the-art machine learning solutions that operate at unprecedented Amazon scale - Deploy revolutionary products that directly impact the daily workflows of millions of developers worldwide - Break new ground in AI and machine learning, challenging what's possible in intelligent code assistance - Publish and present your pioneering work at premier ML and NLP conferences (NeurIPS, ICML, ICLR , ACL, EMNLP) - Accelerate innovation by working directly with customers to rapidly transition research breakthroughs into production systems About the team The AWS Developer Agents and Experiences (DAE) team is reimagining the builder experience through generative AI and foundation models. We're leveraging the latest advances in AI to transform how engineers work from IDE environments to web-based tools and services, empowering developers to tackle projects of any scale with unprecedented efficiency. Broadly, AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
IN, KA, Bengaluru
Alexa+ is Amazon’s next-generation, AI-powered virtual assistant. Building on the original Alexa, it uses generative AI to deliver a more conversational, personalised, and effective experience. Alexa Sensitive Content Intelligence (ASCI) team is developing responsible AI (RAI) solutions for Alexa+, empowering it to provide useful information responsibly. The team is currently looking for Senior Applied Scientists with a strong background in NLP and/or CV to design and develop ML solutions in the RAI space using generative AI across all languages and countries. A Senior Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a dynamic, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of Artificial Intelligence (AI), Natural Language Understanding (NLU), Machine Learning (ML), Dialog Management, Automatic Speech Recognition (ASR), and Audio Signal Processing where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities 1. Define and own the scientific vision and roadmap for ML solutions for building end-to-end Responsible AI solutions 2. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 3. Guide model and system design to build innovative ML solutions at Alexa scale using state-of-the-art NLP and CV techniques. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience and trust. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life As an Applied Science Manager on the Alexa Sensitive Content team, you'll lead a team of scientists and ML engineers building AI systems that keep Alexa safe and trustworthy for millions of users worldwide. Your role combines technical leadership with strategic decision-making and collaborating with product teams and policy experts to deliver engaging and safe experiences across Amazon devices. You'll stay current with advances in generative AI to design, develop, and own state-of-the-art NLP solutions. You will be coaching scientists to identify and mitigate risks early, building more robust ML systems. You'll balance near-term delivery with long-term innovation, ensuring solutions are robust, interpretable, and scalable. Your work directly impacts delivery reliability, cost efficiency, and customer experience at massive scale. About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.
US, MA, Boston
**This is an experimental role to support a business pilot and can potentially span up to 12 months** Embark on a transformative journey as our Sr. Domain Expert Lead, where intellectual rigor meets technological innovation. As a Sr. Domain Expert Lead, you will blend your advanced analytical skills and domain expertise to provide strategic oversight to our human-in-the-loop and model-in-the-loop data pipelines. You will also provide mentorship and guidance to junior team members. Your responsibilities will ensure data excellence through strategic oversight of high-quality data output, while delivering expert consultation throughout the pipeline and fostering iterative development. This position directly impacts the effectiveness and reliability of our AI solutions by maintaining the highest standards of data quality throughout the development process while building capability within the broader team. Key job responsibilities • Serve as a trusted domain advisor to cross-functional teams, providing strategic direction and specialized problem-solving support • Champion domain knowledge sharing across multiple channels and teams to maintain data quality excellence and standardization • Drive collaborative efforts with science teams to optimize output of complex data collections in your domain expertise, ensuring data excellence through iterative feedback loops • Foster team excellence through mentorship and motivation of peers and junior team members • Make informed decisions on behalf of our customers, ensuring that selected code meets industry standards, best practices, and specific client needs • Collaborate with AI teams to innovate model-in-the-loop and human-in-the-loop approaches, to ensure the collection of high-quality data, safeguarding data privacy and security for LLM training, and more. • Stay abreast of the latest developments in how LLMs and GenAI can be applied to your area of expertise to ensure our evaluations remain cutting-edge. • Develop and write demonstrations to illustrate "what good data looks like" in terms of meeting benchmarks for quality and efficiency • Provide detailed feedback and explanations for your evaluations, helping to refine and improve the LLM's understanding and output
US, MA, Boston
**This is an experimental role to support a business pilot and can potentially span up to 12 months** Embark on a transformative journey as our Sr. Domain Expert Lead, where intellectual rigor meets technological innovation. As a Sr. Domain Expert Lead, you will blend your advanced analytical skills and domain expertise to provide strategic oversight to our human-in-the-loop and model-in-the-loop data pipelines. You will also provide mentorship and guidance to junior team members. Your responsibilities will ensure data excellence through strategic oversight of high-quality data output, while delivering expert consultation throughout the pipeline and fostering iterative development. This position directly impacts the effectiveness and reliability of our AI solutions by maintaining the highest standards of data quality throughout the development process while building capability within the broader team. Key job responsibilities • Serve as a trusted domain advisor to cross-functional teams, providing strategic direction and specialized problem-solving support • Champion domain knowledge sharing across multiple channels and teams to maintain data quality excellence and standardization • Drive collaborative efforts with science teams to optimize output of complex data collections in your domain expertise, ensuring data excellence through iterative feedback loops • Foster team excellence through mentorship and motivation of peers and junior team members • Make informed decisions on behalf of our customers, ensuring that selected code meets industry standards, best practices, and specific client needs • Collaborate with AI teams to innovate model-in-the-loop and human-in-the-loop approaches, to ensure the collection of high-quality data, safeguarding data privacy and security for LLM training, and more. • Stay abreast of the latest developments in how LLMs and GenAI can be applied to your area of expertise to ensure our evaluations remain cutting-edge. • Develop and write demonstrations to illustrate "what good data looks like" in terms of meeting benchmarks for quality and efficiency • Provide detailed feedback and explanations for your evaluations, helping to refine and improve the LLM's understanding and output