February 4, 2019

IEEE magazine publishes Phase Change research scientist co-authored paper

January 31, 2019

by Todd Erickson*

Phase Change research scientist Rahul Pandita’s co-written paper, “A Conceptual Framework for Engineering Chatbots,” was recently published in the November-December 2018 issue of IEEE Internet Computing^.

The industry magazine is published bi-monthly by the Institute for Electrical and Electronics Engineers (IEEE) Computer Society for evaluating and reviewing Internet-based computer applications and enabling technologies. It focuses on technologies and applications that enable practitioners to utilize Internet-based applications and tools, instead of having to build their own.

The paper

The use of chatbots as virtual assistants is becoming more widespread as companies strive to increase community engagement online and on social-media platforms.

The problem is that most commercially available bots are engineered with If-This-Then-That (IFTTT) frameworks from the 1980s. These decades-old frameworks often create inflexible chatbots that are difficult to maintain.

The bots can be monolithic and may mix dialog-managing rules with business-execution logic and response-generation rules. And when these chatbots must interact with third-party services to orchestrate workflows, the orchestration logic becomes entwined with the IFTTT rules.

Additionally, IFTTT tends to be order sensitive. As chatbots’ capabilities increase, their implementation rules grow more complex, and even simple modifications can require substantial effort.

The paper, “A Conceptual Framework for Engineering Chatbots,“ outlines a high-level conceptual framework founded upon agent-oriented abstractions – goals, plans, and commitments.

It theorizes that well-studied abstractions of goals and commitments from the area of artificial intelligence (AI) and multiagent systems allow for more flexible chatbots. Goals capture an agent’s intentions, and commitments capture meaningful business relationships between agents.

The paper describes how employing goals and commitments can enable a model chatbot that can be verified at design time or runtime, offers flexible enactments, and provides a basis for judging correctness.


In addition to Pandita, the paper is written by:

It is available free online for IEEE members, and can be purchased through the IEEE Xplore Digital Library.

*Todd Erickson is a tech writer with Phase Change Software. You can reach him at terickson@phasechange.ai.

^The figure represented in the featured image and the IEEE Internet Computing magazine cover are copyrighted by the Institute of Electrical and Electronics Engineers Inc..

December 19, 2018

Phase Change unveils COBOL Colleague product website

December 18, 2018

by Todd Erickson

Phase Change announces the launch of its initial product website – CodeCatalyst.ai. The website will support the company's market entry product, COBOL Colleague, the first cognitive tool for software development, by targeting organizations that rely on COBOL-based applications for critical business operations.

The CodeCatalyst.ai website details how COBOL Colleague will assist COBOL reliant organizations with their unique issues, such as a vanishing workforce, lost application knowledge, and lagging productivity.

COBOL Colleague reads-in the source code, extracts the embedded concepts, discovers the dependencies, reveals the buried knowledge, and becomes an expert that never tires and never leaves.

Natural-language-interaction enables developers and stakeholders with limited COBOL experience to collaborate with the cognitive agent and work productively with their COBOL applications.

Find bugs and dead code in seconds, not minutes or hours. Make changes with full knowledge of the downstream impact. Confidently add new features, products, and services. Empower anyone with a basic understanding of COBOL to interact and engage with your COBOL applications.

Everything you dreamed of in COBOL-based environments is now a reality. Visit CodeCatalyst.ai.

Todd Erickson is a tech writer with Phase Change Software. You can reach him at terickson@phasechange.ai.

March 21, 2018

Phase Change scientists present natural language chat interface paper at AAAI Conference – blog

March 20, 2018

by Rahul Pandita and Todd Erickson

Research Scientist Aleksander Chakarov, Ph.D., presented a recently published Phase Change workshop paper at the 32nd AAAI Conference on Artificial Intelligence in February.

The AAAI conference is held each spring by the Association for the Advancement of Artificial Intelligence (AAAI) nonprofit and scientific society to promote research in artificial intelligence (AI) and scientific discussion among researchers, practitioners, scientists, and engineers in related fields.

The paper, Towards J.A.R.V.I.S. for Software Engineering: Lessons Learned in Implementing a Natural Language Chat Interface, was co-written by Chakarov and fellow research scientists Rahul Pandita and Hugolin Bergier.

"We're excited about the opportunity to share our work with researchers and get their feedback," Pandita remarked. "We consider it the first of many stepping stones to present the science behind Phase Change's technology."

Phase Change is developing a ground-breaking cognitive platform and an AI-based collaborative agent called Mia that will dramatically improve software development productivity and efficiency. Mia utilizes a natural-language chat interface so users can get up-and-running quickly.

Aleksander presented the paper on during the February 2 AAAI Workshop on NLP for Software Engineering in New Orleans, Louisiana.

The paper

Mia uses a natural language chat interface, much like the virtual assistants in other industries that have demonstrated the potential to significantly improve users' digital experiences.

The paper relates the lessons our developers learned during the first iteration of the Mia chat interface implementation, including:

  • Reusing components to quickly prototype
  • Gradually migrating from rule-based to statistical approaches
  • Adopting recommendation systems

The paper describes these lessons and others, including our experiences applying subliminal priming and the benefits of data-driven prioritization, in more detail.

The workshop

"I feel like we did a good job of setting up the context – what problems we are solving, what our approach is – and then we moved to the takeaways very quickly," Aleksander said about his experience presenting the paper. "People were engaged."

He also described two comments made during his session's brief Q&A time. The first commentator explained how current scientific research supports the paper's findings about subliminal priming and how conversations change over time.

The second commentator discussed our use of rules-based approach at first to develop an optimal work environment and then gradually moving towards a statistical approach. He suggested that there is also a third tactic that uses simulations to quickly gather data and hasten the inclusion of statistical approaches. We will investigate his suggestions for further use.

We welcome your comments and observations.

Rahul Pandita is a senior research scientist at Phase Change. He earned his Ph.D. in computer science from North Carolina State University. You can reach him at rpandita@phasechange.ai.

Todd Erickson is a tech writer with Phase Change. You can reach him at terickson@phasechange.ai.

March 6, 2017

An Analogy: Software AI and Natural Language — blog

March 6, 2017

Today's AI technology is amazing.

Only a few short years ago, only humans could interpret the meaning of text and speech. Now our cell phones understand our voices and language well enough to distinguish accents, metaphors, and sarcasm.

IBM's Watson supercomputer even understood Alex Trebek well enough to beat some of Jeopardy!'s® best players.

Computers achieve natural-language understanding through a series of logically consistent normalization steps -- starting with the processing of basic sounds to recognizing words and then understanding sentences.

If computers can understand natural language using logically consistent processes, shouldn't we be able to use similar processes to break down and normalize software?

In fact, shouldn't software be easier to normalize than the messy ambiguity of human communication?

The answer is yes.

Phase Change normalizes software source code into formal data types and organizes them into hierarchical structures that are probabilistically linked (horizontally and vertically). Our technology unlocks the vast domain and system knowledge embedded in software and makes it available to anyone involved in creating and supporting software.

To learn more about how Phase Change's revolutionary technology transforms chaotic code into coherent data and intractable software into artificially intelligent agents, read Steve Bucuvalas' paper: "An Analogy: Software AI and Natural Language."


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