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

TWIZNOVA School of Science and TechnologyJoão Magalhães
EvoquerBOTPenn State UniversityRui Zhang
Taco 2.0The Ohio State UniversityHuan Sun
GRILLUniversity of GlasgowJeff Dalton
MarunaUniversity of Massachusetts AmherstHamed Zamani

New

BoilerBotPurdue UniversityJulia Rayz
DiWBotRutgers UniversityMatthew Stone
SageUniversity of California, Santa CruzXin (Eric) Wang
ISABELUniversity of PittsburghMalihe Alikhani
PLAN-BotVirginia TechIsmini 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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 Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.” Please visit https://www.amazon.science for more information Amazon Business is one of Amazon’s fastest growing businesses, focused on enabling business customers to research, discover and buy business, industrial and scientific products in large catalogs. Our customers include individual professionals, businesses and institutions that buy products in bulk quantities to run their business smoothly. Business customers have different and more complex needs than the traditional Amazon customer base. We operate in the US, EU5, Canada, Japan and India. And we are continually expanding to new countries. We are looking for Applied Scientist with strong technical experience, who are passionate in building scientific driven solutions to be part of the Amazon Business organization. This is a great opportunity to innovate and provide the best customer experience in the entire journey of purchase, right from product discovery to post purchase experiences. This is your chance to make an impact of multi-trillion-dollars on your own. In this role, you will be a technical expert with significant scope and impact. You will work with Product Managers, Software Engineers, Data Engineers, and other Applied Scientists, to build new and enhance existing ML models to optimize customer experience. A successful Applied Scientist at Amazon has extreme bias for action and operates in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. We need great leaders to think big and design new solutions to solve complex problems using machine learning (ML) and natural language processing (NLP) techniques to improve our customers’ experience when using AB. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career." Key job responsibilities - Serve as a technical expert and leader in building new and enhancing existing ML models to optimize customer experience for Amazon Business - Work closely with cross-functional teams including Product Managers, Software Engineers, Data Engineers, and other Applied Scientists - Identify data requirements, build methodology, and develop statistically grounded tools and solutions using machine learning (ML) and LLM - Drive projects from ideation to execution with an emphasis on bias for action. - Stay up-to-date with the latest advancements in AI/ML and proactively identify opportunities to improve the customer experience A day in the life - Collaborate with product and engineering teams to understand business requirements and translate them into technical solutions - Conduct in-depth data analysis and feature engineering to build robust ML models - Prototype and test new ideas, iterate quickly, and deploy models to production - Monitor model performance, troubleshoot issues, and continuously optimize models - Share learnings and best practices with the broader team, contribute to innovation and publish research papers.
IN, KA, Bangalore
“Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.” Please visit https://www.amazon.science for more information Amazon Business is one of Amazon’s fastest growing businesses, focused on enabling business customers to research, discover and buy business, industrial and scientific products in large catalogs. Our customers include individual professionals, businesses and institutions that buy products in bulk quantities to run their business smoothly. Business customers have different and more complex needs than the traditional Amazon customer base. We operate in the US, EU5, Canada, Japan and India. And we are continually expanding to new countries. We are looking for Applied Scientist with strong technical experience, who are passionate in building scientific driven solutions to be part of the Amazon Business organization. This is a great opportunity to innovate and provide the best customer experience in the entire journey of purchase, right from product discovery to post purchase experiences. This is your chance to make an impact of multi-trillion-dollars on your own. In this role, you will be a technical expert with significant scope and impact. You will work with Product Managers, Software Engineers, Data Engineers, and other Applied Scientists, to build new and enhance existing ML models to optimize customer experience. A successful Applied Scientist at Amazon has extreme bias for action and operates in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. We need great leaders to think big and design new solutions to solve complex problems using machine learning (ML) and natural language processing (NLP) techniques to improve our customers’ experience when using AB. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career." Key job responsibilities - Serve as a technical expert and leader in building new and enhancing existing ML models to optimize customer experience for Amazon Business - Work closely with cross-functional teams including Product Managers, Software Engineers, Data Engineers, and other Applied Scientists - Identify data requirements, build methodology, and develop statistically grounded tools and solutions using machine learning (ML) and LLM - Drive projects from ideation to execution with an emphasis on bias for action. - Stay up-to-date with the latest advancements in AI/ML and proactively identify opportunities to improve the customer experience A day in the life - Collaborate with product and engineering teams to understand business requirements and translate them into technical solutions - Conduct in-depth data analysis and feature engineering to build robust ML models - Prototype and test new ideas, iterate quickly, and deploy models to production - Monitor model performance, troubleshoot issues, and continuously optimize models - Share learnings and best practices with the broader team, contribute to innovation and publish research papers.