Posts Tagged ‘creativity’
28
Nov
Posted by cognitivecomputing in Agents, Language, Learning, Talks. Tagged: believable agents, cognitive media, creativity, information retrieval, Learning, multistrategy learning, natural language, personality models, planning, problem solving, semantic memory, social learning, text cbr. Leave a comment
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)
READ THE PAPER:
arxiv.org/abs/1801.03604
WATCH THE TALK:
youtu.be/pn5QJQZjGpM
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
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
19
Aug
Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: case-based reasoning, creativity, games, interactive drama, meta-reasoning, problem solving, virtual worlds. Leave a comment
Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC’s in computer games is that construction of high quality AI behaviors is a hard problem.
There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process restricts it to certain individuals who have a certain expertise in this area. There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments so that novice users (without programming and design experience) can carry out the behavior authoring task. Second, the constructed AI behaviors have problems and bugs in them which cause a break in player expe- rience when the problematic behaviors repeatedly fail. It is harder for novice users to identify, modify and correct problems with the authored behavior sets as they do not have the necessary debugging and design experience.
The two issues give rise to a couple of interesting questions that need to be investigated: a) How can the AI behavior construction process be simplified so that a novice user (without program- ming and design experience) can easily conduct the authoring activity and b) How can the novice users be supported to help them identify and correct problems with the authored behavior sets? In this thesis, I explore the issues related to the problems highlighted and propose a solution to them within an application domain, named Second Mind(SM). In SM novice users who do not have expertise in computer programming employ an authoring interface to design behaviors for intelligent virtual characters performing a service in a virtual world. These services range from shopkeepers to museum hosts. The constructed behaviors are further repaired using an AI based approach.
To evaluate the construction and repair approach, we conduct experiments with human subjects. Based on developing and evaluating the solution, I claim that a design solution with behavior timeline based interaction design approach for behavior construction supported by an understandable vocabulary and reduced feature representation formalism enables novice users to author AI behaviors in an easy and understandable manner for NPCs performing a service in a virtual world. I further claim that an introspective reasoning approach based on comparison of successful and unsuccessful execution traces can be used as a means to successfully identify breaks in player experience and modify the failures to improve the experience of the player interacting with NPCs performing a service in a virtual world.
The work contributes in the following three ways by providing: 1) a novel introspective reasoning approach for successfully detecting and repairing failures in AI behaviors for NPCs performing a service in a virtual world.; 2) a novice user understandable authoring environment to help them create AI behaviors for NPCs performing a service in a virtual world in an easy and understandable manner; and 3) Design, debugging and testing scaffolding to help novice users modify their authored AI behaviors and achieve higher quality modified AI behaviors compared to their original unmodified behaviors.
Read the dissertation:
Construction and Adaptation of AI Behaviors in Computer Games
by Manish Mehta
PhD dissertation, College of Computing, Georgia Institute of Technology, August 2011.
smartech.gatech.edu/handle/1853/42724
7
Aug
Posted by cognitivecomputing in Learning. Tagged: creativity, goal-driven learning, problem solving. Leave a comment
This paper identifies goal handling processes that begin to account for the kind of processes involved in invention. We identify new kinds of goals with special properties and mechanisms for processing such goals, as well as means of integrating opportunism, deliberation, and social interaction into goal/plan processes. We focus on invention goals, which address significant enterprises associated with an inventor. Invention goals represent “seed” goals of an expert, around which the whole knowledge of an expert gets reorganized and grows more or less opportunistically. Invention goals reflect the idiosyncrasy of thematic goals among experts. They constantly increase the sensitivity of individuals for particular events that might contribute to their satisfaction.
Our exploration is based on a well-documented example: the invention of the telephone by Alexander Graham Bell. We propose mechanisms to explain: (1) how Bell’s early thematic goals gave rise to the new goals to invent the multiple telegraph and the telephone, and (2) how the new goals interacted opportunistically. Finally, we describe our computational model, ALEC, that accounts for the role of goals in invention.
Invention as an Opportunistic Enterprise
by Marin Simina, Janet Kolodner, Ashwin Ram, Michael Gorman
19th Annual Conference of the Cognitive Science Society, Stanford, CA, August 1997
www.cc.gatech.edu/faculty/ashwin/papers/git-cs-97-04.pdf
15
May
Posted by cognitivecomputing in Language, Learning. Tagged: creativity, multistrategy learning, natural language, semantic memory, text cbr. Leave a comment
Reading is a complex cognitive behavior, making use of dozens of tasks to achieve comprehension. As such, it represents an important aspect of general cognition; the benefits of having a theory of reading would be far-reaching. Additionally, there is an aspect of reading which has been largely ignored by the research, namely, reading appears to encompass a creative process. In this dissertation, I present a theory capable of explaining creative reading. There are not separate reading behaviors, some mundane and some creative; instead, all of reading must be understood as a creative process. Therefore, a comprehensive theory of reading and creativity is needed. Unfortunately, although the scientific study of reading has been undertaken for almost a century, it is often done in a piecemeal fashion–that is, the research has often concentrated on a narrow aspect of reading behavior. This is due, to some degree, to the fact that reading is a huge process–however, it is my belief that failing to consider the complete reading process will limit the research, Thus, in my work, I identify a set of tasks which sufficiently covers the reading process for short narratives. Together, these tasks form the basis of a functional theory of reading.
Using the reading framework to support the research, I produced a theory of creative understanding, which is the process by which novel concepts come to be understood by a reasoner. To accomplish this, I created a taxonomy of novelty types, I produced a knowledge representation and ontology of sufficient flexibility to permit the representation of a wide-range of conceptual forms, and I created an interlocking set of four tasks which act together to produce the behavior–memory retrieval, analogical mapping, base-constructive analogy, and problem reformulation. My technique for base-constructive analogy is one of the more unique features of my work; it permits existing concepts to be combined in ways which enable novel concepts to be understood. In addition to that, the theory provides for reasonable bounding to occur on the process of creative understanding through a set of heuristics associated with the ontology. This allows reasonable bounding to occur while greatly reducing the possibility of non-useful understandings.
The theory of creative reading is instantiated in a computer model, the ISAAC system, which reads and comprehends short science fiction stories. The model has allowed me to perform empirical evaluation, providing an important stage in the overall theory revision cycle. The evaluation demonstrated that ISAAC can answer independently-generated comprehension questions about a set of science fiction stories with skill comparable to a group of college students. This result, along with an analysis of the internal workings of the model enables me to claim that my theory of creative reading is sufficient to explain important aspects of the behavior.
Read the thesis:
A functional theory of creative reading: Process, knowledge, and evaluation
by Kenneth Moorman
PhD Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1997
www.cs.transy.edu/kmoorman/Dissertation/
13
Jul
Posted by cognitivecomputing in Language. Tagged: case-based reasoning, creativity, natural language, semantic memory. Leave a comment
Successful creative understanding requires that a reasoner be able to manipulate known concepts in order to understand novel ones. A major problem arises, however, when one considers exactly how these manipulations are to be bounded. If a bound is imposed which is too loose, the reasoner is likely to create bizarre understandings rather than useful creative ones. On the other hand, if the bound is too tight, the reasoner will not have the flexibility needed to deal with a wide range of creative understanding experiences. Our approach is to make use of a principled ontology as one source of reasonable bounding. This allows our creative understanding theory to have good explanatory power about the process while allowing the computer implementation of the theory (the ISAAC system) to be flexible without being bizarre in the task domain of reading science fiction short stories.
Read the paper:
The Role of Ontology in Creative Understanding
by Kenneth Moorman, Ashwin Ram
18th Annual Conference of the Cognitive Science Society (CogSci-96), San Diego, CA, July 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-01.pdf
View my profile.svg){kind=logo}
Read my bio
Follow me
OpenStudy with me
Cognitive Computing 