Posts Tagged ‘natural language’
13
Oct
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Talks. Tagged: interactive drama, natural language, semantic memory, social learning. Leave a comment
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
29
Aug
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory. Leave a comment
We’ve hit another milestone in the Alexa Prize, a $2.5 million university competition to advance conversational AI. University teams from around the world have been hard at work to create a socialbot, an AI capable of conversing coherently and engagingly with humans on popular topics and news events for 20 minutes.
I am now excited to announce the university teams that will be competing in the finals! After hundreds of thousands of conversations, the two socialbots with the highest average customer ratings during the semifinal period are Alquist from the Czech Technical University in Prague and Sounding Board from the University of Washington in Seattle. The wildcard team is What’s Up Bot from Heriot-Watt University in Edinburgh, Scotland.
READ MORE:
developer.amazon.com/blogs/alexa/post/783df492-4770-4b11-81ac-59e009669d56/announcing-the-2017-alexa-prize-finalists
1
Jun
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Talks, Web / Web 2.0. Tagged: believable agents, cognitive media, goal-driven learning, information retrieval, interactive drama, Learning, natural language, personality models, problem solving, semantic memory. 3 comments
As we moved from the age of the keyboard, to the age of touch, and now to the age of voice, natural conversation in everyday language continues to be one of the ultimate challenges for AI. This is a difficult scientific problem involving knowledge acquisition, natural language understanding, natural language generation, context modeling, commonsense reasoning and dialog planning, as well as a complex product design problem involving user experience and conversational engagement.
I will talk about why Conversational AI is hard, how conversational agents like Amazon Alexa understand and respond to voice interactions, how you can leverage these technologies for your own applications, and the challenges that still remain.
Variants of this talk presented (click links for video):
Keynote talks at The AI Conference (2017), O’Reilly AI Conference (2017), The AI Summit (2017), Stanford ASES Summit (2017), MLconf AI Conference (2017), Global AI Conference (2016).
Distinguished lectures at Georgia Tech/GVU (2017), Northwestern University (2017).
Keynote panel at Conversational Interaction Conference (2016).
Lightning TED-style talks at IIT Bay Area Conference (2017), Intersect (2017).
18
May
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory. Leave a comment
On September 29, 2016, Amazon announced the Alexa Prize, a $2.5 million university competition to advance conversational AI through voice. In April, university teams from around the world assembled at the appropriately named Day 1 building in Seattle for the Alexa Prize Summit. The event was a base camp for teams to share learnings and make preparations for the most challenging leg of their journey: to build and scale an AI capable of conversing coherently and engagingly with humans for 20 minutes.
As they build their “socialbots,” they will encounter esoteric problems like context modeling and dialog planning as well as exoteric problems like user experience and conversational engagement. And they will need all the help they can get.
We invite you to join the students on their journey and help them along the way. You can interact with their socialbots simply by saying, “Alexa, let’s chat” on any device with Alexa.
READ MORE:
developer.amazon.com/blogs/alexa/post/e4cc64d1-f334-4d2d-8609-5627939f9bf7/join-the-alexa-prize-journey-and-test-the-socialbots
23
Feb
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Talks. Tagged: believable agents, cognitive media, creativity, goal-driven learning, information retrieval, Learning, meta-reasoning, natural language, personality models, problem solving. Leave a comment
No longer is AI solely a subject of science fiction. Advances in AI have resulted in enabling technologies for computer vision, planning, decision making, robotics, and most recently spoken language understanding. These technologies are driving business growth, and releasing workers to engage in more creative and valuable tasks.
I’ll talk about the moved from the age of the keyboard, to the age of touch, and are now entering the age of voice. Alexa is making this future possible. Amazon is committed to fostering a robust cloud-based voice service, and it is this voice service that the innovators of today, tomorrow, and beyond will be building. It is this voice service—and the ecosystem around it—that awaits the next generation of AI talent.
Keynote at Udacity Intersect Conference, Computer History Museum, Mountain View, CA, March 8, 2017.
READ MORE:
blog.udacity.com/2017/02/dr-ashwin-ram-intersect-2017-speaker.html
VIEW THE TALK:
linkedin.com/feed/update/urn:li:activity:6286681682187812864
14
Nov
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, interactive drama, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory. Leave a comment
On September 29, 2016, Amazon announced the Alexa Prize, a $2.5 million university competition to advance conversational AI through voice. We received applications from leading universities across 22 countries. Each application was carefully reviewed by senior Amazon personnel against a rigorous set of criteria covering scientific contribution, technical merit, novelty, and ability to execute. Teams of scientists, engineers, user experience designers, and product managers read, evaluated, discussed, argued, and finally selected the ten teams who would be invited to participate in the competition. Wait, make that twelve; we received so many good applications from graduate and undergraduate students that we decided to sponsor two additional teams.
Today, we’re excited to announce the 12 teams selected to compete with an Amazon sponsorship.
READ MORE:
developer.amazon.com/blogs/post/Tx1UXVV4VJTPYTL/announcing-the-sponsored-teams-for-the-2016-2017-alexa-prize
29
Sep
Posted by cognitivecomputing in Agents, Language, Learning, Opinion, Web / Web 2.0. Tagged: believable agents, cognitive media, creativity, information retrieval, interactive drama, Learning, meta-reasoning, natural language, personality models, planning, problem solving, semantic memory. Leave a comment
Artificial intelligence (AI) is becoming ubiquitous. With advances in technology, algorithms, and sheer compute power, it is now becoming practical to utilize AI techniques in many everyday applications including transportation, healthcare, gaming, productivity, and media. Yet one seemingly intuitive task for humans still eludes computers: natural conversation. Simple and natural for humans, voice communication in everyday language continues to be one of the ultimate challenges for AI.
Today, we are pleased to announce the Alexa Prize, a $2.5 million university competition to advance conversational AI through voice. Teams of university students around the world are invited to participate in the Alexa Prize (see contest rules for details). The challenge is to create a socialbot, an Alexa skill that converses coherently and engagingly with humans on popular topics for 20 minutes. We challenge teams to invent an Alexa socialbot smart enough to engage in a fun, high quality conversation on popular topics for 20 minutes.
Are you up to the challenge?
READ MORE:
developer.amazon.com/public/community/post/Tx221UQAWNUXON3/Are-you-up-to-the-Challenge-Announcing-the-Alexa-Prize-2-5-Million-to-Advance-Co
15
Apr
Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0. Tagged: case-based reasoning, cognitive media, healthcare, information retrieval, natural language, semantic memory. Leave a comment
We develop an innovative approach to delivering relevant information using a combination of socio-semantic search and filtering approaches. The goal is to facilitate timely and relevant information access through the medium of conversations by mixing past community specific conversational knowledge and web information access to recommend and connect users and information together. Conversational Information Access is a socio-semantic search and recommendation activity with the goal to interactively engage people in conversations by receiving agent supported recommendations. It is useful because people engage in online social discussions unlike solitary search; the agent brings in relevant information as well as identifies relevant users; participants provide feedback during the conversation that the agent uses to improve its recommendations.
Socio-Semantic Conversational Information Access
by Saurav Sahay, Ashwin Ram
WWW-2012 Workshop on Community Question Answering on the Web (CQA-12).
15
Nov
Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0. Tagged: case-based reasoning, cognitive media, healthcare, information retrieval, natural language, semantic memory, text cbr. Leave a comment
The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is ‘aware’ of users’ preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users’ verbal intentions in conversations while making recommendation decision.
One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain.
Cobot leverages these interactions to maintain users’ episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user’s interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation.
The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.
Read the dissertation:
Socio-Semantic Conversational Information Access
by Saurav Sahay
PhD dissertation, College of Computing, Georgia Institute of Technology, November 2011.
smartech.gatech.edu/handle/1853/42855
24
Feb
Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0. Tagged: healthcare, information retrieval, natural language. Leave a comment
With an explosion in the proliferation of user-generated content in communities, information overload is increasing and quality of readily available online content is deteriorating. There is an increasing need for intelligent systems that make use of implicit user-generated knowledge in communities for community engagement. We describe our approach based on modeling user utterances in communities to proactively target the community for exchange of questions and answers. We envision a system that automatically encourages user engagement and participation by routing relevant conversations to users based on individual and community activity levels.
In this paper, we analyze health forum conversations from WebMD, a popular health portal consumer site, and classify them in different acts of speech using Verbal Response Modes (VRM) theory. We describe our approach for modeling an intelligent community recommender to engage participants based on observations from our analysis.
Read the paper:
Intentional analysis of medical conversations for community engagement
by Saurav Sahay, Hua Ai, Ashwin Ram
FLAIRS-11 International Conference on Artificial Intelligence
www.cc.gatech.edu/faculty/ashwin/papers/er-11-01.pdf
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