Posts Tagged ‘natural language’

Podcast: How Amazon’s Alexa Learns

Much of the ballyhoo around intelligent home assistance devices is that they make life easier for us: from regulating our thermostats to freeing our hands while we check a favorite recipe for roast turkey, to playing that favorite jam to get us pumped up in the morning. And it turns out that because these devices are designed to learn from our patterns and habits, they become more helpful the longer we live with them.

In this podcast, Ashwin Ram, one of the minds behind Amazon Alexa, describes how the company is balancing privacy concerns with natural language recognition to design a more effective device.

KelloggInsight podcast with Jennifer Cutler, Northwestern University’s Kellogg School of Management



TED: Imagine a world of AI

Ashwin Ram works on the AI behind Alexa, one of several new bots that might change the way your home and your life function within the next few years. Imagine a bot that turns on your lights, shops for you, even helps you make decisions. Learn more about a bot-enabled future that might have you saying (like Shah Rukh Khan does): “Alexa, I love you!”

#TomorrowsWorld made easier with Artificial Intelligence. #TEDTalksIndiaNayiSoch

Innovator and entrepreneur, Ashwin Ram believes AI will changes our lives in future. #TomorrowsWorld #TEDTalksIndiaNayiSoch

Can you imagine what #TomorrowsWorld will be like? Shah Rukh Khan introduces.

#TEDTalksIndiaNayiSoch is a groundbreaking TV series showcasing new thinking from some of the brightest brains in India and beyond and hosted by “The King of Bollywood,” Shah Rukh Khan.

On Evaluating and Comparing Conversational Agents

Conversational agents are exploding in popularity. However, much work remains in the area of non goal-oriented conversations, despite significant growth in research interest over recent years. To advance the state of the art in conversational AI, Ama- zon launched the Alexa Prize, a 2.5-million dollar university competition where sixteen selected university teams built conversational agents to deliver the best social conversational experience. Alexa Prize provided the academic community with the unique opportunity to perform research with a live system used by millions of users. The subjectivity associated with evaluating conversations is key element underlying the challenge of building non-goal oriented dialogue systems.

In this paper, we propose a comprehensive evaluation strategy with multiple metrics de- signed to reduce subjectivity by selecting metrics which correlate well with human judgement. The proposed metrics provide granular analysis of the conversational agents, which is not captured in human ratings. We show that these metrics can be used as a reasonable proxy for human judgment. We provide a mechanism to unify the metrics for selecting the top performing agents, which has also been applied throughout the Alexa Prize competition. To our knowledge, to date it is the largest setting for evaluating agents with millions of conversations and hundreds of thousands of ratings from users. We believe that this work is a step towards an automatic evaluation process for conversational AIs.


On Evaluating and Comparing Conversational Agents
by A Venkatesh, C Khatri, A Ram, F Guo, R Gabriel, A Nagar, R Prasad, M Cheng, B Hedayatnia, A Metallinou, R Goel, S Yang, A Raju
NIPS-2017 Workshop on Conversational AI


Topic-based Evaluation for Conversational Bots

Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle. To detect conversation topics per utterance, we adopt Deep Average Networks (DAN) and train a topic classifier on a variety of question and query data categorized into multiple topics. We propose a novel extension to DAN by adding a topic-word attention table that allows the system to jointly capture topic keywords in an utterance and perform topic classification. We compare our proposed topic based metrics with the ratings provided by users and show that our metrics both correlate with and complement human judgment. Our analysis is performed on tens of thousands of real human-bot dialogs from the Alexa Prize competition and highlights user expectations for conversational bots.

Topic-based Evaluation for Conversational Bots
by F Guo, A Metallinou, C Khatri, A Raju, A Venkatesh, A Ram
NIPS-2017 Workshop on Conversational AI

Announcing Winners of 2017 Alexa Prize

Earlier today, Rohit Prasad, vice president and head scientist, Alexa Machine Learning, and I had the pleasure of announcing the winner of the inaugural Alexa Prize competition for university students dedicated to accelerating the field of conversational artificial intelligence (AI).

Congratulations to team Sounding Board, an inspiring group of students from the University of Washington, whose socialbot earned an average score of 3.17 on a 5-point scale from our panel of independent judges and achieved an average conversation duration of 10:22. As the winner of our inaugural competition, team Sounding Board earned our $500,000 first-place prize, which will be shared among the students.

We also had the privilege of honoring and surprising our other finalists on stage. Our runner up was team Alquist from Czech Technical University in Prague. We presented them with a $100,000 prize for their efforts. We also awarded our third-place winner, team What’s Up Bot from Heriot-Watt University in Edinburgh, Scotland, with a $50,000 prize.


Just say “Alexa, let’s chat” to any Alexa-enabled device. (If you’re outside the U.S., set your Amazon Preferred Marketplace (PFM) to U.S. or use a U.S. based Amazon account.)




Conversational AI: The Science behind the Alexa Prize

Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents, known as “socialbots”, to converse coherently and engagingly with humans on popular topics such as Sports, Politics, Entertainment, Fashion and Technology for 20 minutes.

The Alexa Prize offered the academic community a unique opportunity to perform research with a live system used by millions of users. The competition provided university teams with real user conversational data at scale, along with the user-provided ratings and feedback augmented with annotations by the Alexa team. This enabled teams to effectively iterate and make improvements throughout the competition while being evaluated in real-time through live user interactions.

To build their socialbots, university teams combined state-of-the-art techniques with novel strategies in the areas of Natural Language Understanding, Context Modeling, Dialog Management, Response Generation, and Knowledge Acquisition. To support the teams’ efforts, the Alexa Prize team made significant scientific and engineering investments to build and improve Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice User Experience, and tools for traffic management and scalability.

This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational AI.

Conversational AI: The Science behind the Alexa Prize

by Ashwin Ram, Rohit Prasad, Chandra Khatri, Anu Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, Art Pettigrue

Proceedings of the 2017 Alexa Prize
Invited talk at NIPS-2017 Workshop on Conversational AI
Invited talk at re:Invent 2017 (with Spyros Matsoukas)




Conversational News Experiences

News consumption is a passive experience—reading print or online newspapers, listening to radio shows and podcasts, watching television broadcasts. News producers create, curate, and organize  content which consumers absorb passively. With the advent of interactive conversational technologies ranging from chatbots to voice-based conversational assistants such as Amazon Alexa, there is an opportunity to engage consumers in more interactive experiences around news.

At the Computation+Journalism symposium held at Northwestern University this year, Emily Withrow, editor at Quartz Bot Studio and assistant professor at Northwestern’s Medill School of Journalism and I had a fireside chat to share recent technological developments in this area and explore what kinds of conversational news experiences these technologies might enable.

Panel at the 2017 Computation+Journalism Symposium, Northwestern University, Evanston, IL. #cj2017