Phase Change announces management changes in anticipation of market entry

March 10, 2021

March 10, 2021

By Todd Erickson

Phase Change Software announced a number of executive changes in anticipation of bringing its first product, COBOL Colleague, to market.

Founder and Inventor Steve Bucuvalas is stepping away from his roles as CEO and Chairman of the Board to enable him to focus all of his attention on product development and innovation, including Phase Change’s planned second product, a Java version of the company’s revolutionary platform. Steve will continue to serve on the Board of Directors.

Former President and current Board Member Gary Brach has assumed the role of CEO and will be focused on bringing COBOL Colleague to market. Former COO Steve Brothers was appointed President and will continue to be responsible for the company’s day-to-day operations.

In addition to the executive changes, the Board of Directors also elected long-time Member Don Peskin as the Chairman of the Board.

Bios

Steve Bucuvalas founded Phase Change in 2005 and has held many titles over the years, including Founder, President, CEO, and Chairman of the Board of Directors. Steve is the Chief Inventor of Phase Change's software digitization. He brings over 40 years of experience to software productivity. Steve graduated from Harvard University in 1977 and began his software career the same year as a Bank of Boston assembly language programmer. He has led corporate advanced technology groups, specialized database management systems, and much more over the course of his career. Steve brings an interdisciplinary perspective to solving the software digitization problem. Steve currently resides in Bernalillo, New Mexico.

Gary Brach, CEO, joined Phase Change as President and Board Member in 2016. He is focused on introducing Phase Change to the market. In the software industry, Gary has over 25 years of experience primarily as a software entrepreneur in the storage and insurance industries. Gary studied at Brown University and received his MBA at the University of Chicago. Gary currently resides in Boston and is an avid tennis player.

Steve Brothers, President, joined Phase Change as the COO in 2018, bringing over 30 years of experience in technology-related organizations with leadership, technical and sales roles in industries such as financial services, healthcare and services. Previously, Steve held positions as CEO at Ajubeo and Executive Vice President and CIO for Urban Lending Solutions. Steve graduated from the University of Colorado at Boulder and holds a B.A. in Philosophy and a B.S. in Information Systems. Steve is a proud father of two boys, is a mentor at Galvanize and resides in Golden, CO.

Don Peskin is the Chairman of the Phase Change Board of Directors. He has been a Phase Change Board Member since November 2007. Don has more than 25 years of Wall Street and investment experience, most notably as a Managing Director and Principal at Donaldson, Lufkin & Jenrette, which he left in 1997 to pursue private investment opportunities in finance, technology, and related industries. He is the Founder and President of Short Hills Capital LLC, a privately held investment company. In addition to his principal investment activities, Don is also a Managing Member of the real-estate development firm Chatham Hills Development LLC, and he recently served as a Managing Member of Cognitive Capital Management, which was the General Partner of the Cognitive Strategic Fund.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Phase Change CEO Steve Bucuvalas featured on the InfluenceNow! podcast

February 11, 2019

February 7, 2019

by Todd Erickson1

Phase Change’s Inventor, Founder, and CEO, Steve Bucuvalas, was featured in the January 31, 2019, episode of the InfluenceNow! podcast, hosted by Justin Craft2.

The InfluenceNow! podcast highlights startups, exceptional business influencers, and ideas from a variety of industries that influence the world.

Steve and Justin discussed how Phase Change and the technology behind Mia, the first cognitive agent for software development, became a reality.

The interview begins with Steve describing his career leading technology and artificial intelligence (AI) groups in financial services and insurance companies, and his subsequent entrepreneurial career starting and selling two different companies. He tells the story of how a single conversation with the buyer of his second company led to his interest in applying AI technology to the problem of software-development productivity.

At the closing, the buyer said to me, 'What's wrong with you guys in software? AI has changed financial services extraordinarily - increased our productivity 100 times,' which is accurate. 'Why can’t you do that with your own industry?'

That moment led Steve to research the barriers to applying AI to software development, and the development of the human-centric principles that led to the creation of the Mia cognitive agent.

The podcast continues with Steve and Justin discussing why organizations that rely on applications written in the Common Business-oriented Language (COBOL) programming language are Phase Change’s first target market.

COBOL is this 40-50 year-old language that has atrocious legacy problems. Because the code has been around [so long], it runs 85% of the world’s financial transactions and [there’s] 220 billion lines of [active COBOL] code. The programmers are all in their 60’s and they all want to retire, but they keep getting incentives to work a few more years because no one wants to learn COBOL. In fact, some of the kids in computer science [college courses] have never heard of it.

Justin and Steve conclude the interview discussing the productivity gains realized by Mia and Phase Change’s technology, and when it will be generally available.

To learn more about how Steve and Phase Change Software will radically improve software productivity, watch the podcast video below or listen to the audio podcast.


1Todd Erickson is a tech writer with Phase Change Software. You can reach him at [email protected].
2Justin Craft is the Founder and CEO of Cast Influence, a Denver, Colorado,-based turnkey marketing agency. Phase Change Software is a client of Cast Influence.

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

March 21, 2018

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 [email protected].

Todd Erickson is a tech writer with Phase Change. You can reach him at [email protected].

Phase Change scientists publish paper on lessons learned implementing a natural-language chat interface – blog

March 8, 2018

Our research scientists recently published a workshop paper on the lessons learned implementing the company's natural-language chat interface. This post summarizes the key lessons learned and identifies the open questions we faced during our initial implementation.

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 natural-language processing (NLP) chatbot capabilities so new users can use the technology immediately with little or no training.

towards jarvis, lessons learned implementing NL chat interface paper

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 research scientists Rahul Pandita, Aleksander Chakarov, Hugolin Bergier, and inventor and company founder Steve Bucuvalas. The full paper text is available here.

The paper

Virtual assistants have demonstrated the potential to significantly improve information technology workers' digital experiences. Mia will help software developers radically improve program comprehension. Then we will gradually expand its capabilities to include program composition and verification.

Here are a few things we learned during the first iteration of the Mia chat interface implementation.

Reuse components to quickly prototype

Instead of building everything from scratch, consider reusing existing frameworks and libraries to quickly prototype and get feedback.

Gradually migrate from rule-based to statistical approaches

With the ever-increasing popularity and efficacy of statistical approaches, teams are often tempted to implement them without enough data to design an optimal work environment.

We have noticed that recent advances in transfer learning require only a small amount of data to begin reaping the benefits of statistical approaches. However, rule-based approaches still allow prototypes to get up-and-running with only a small amount of set-up time.

A rule-based-approach also allowed us to collect more data for a better understanding of the chatbot requirements, and future positioning to effectively leverage statistical approaches.

Adopt recommendation systems

In our testing phase, we learned that although users appreciated honesty when our chatbot did not understand a request, they didn't take it well (to put it mildly) when the chatbot did not provide a way to remedy the situation.

There can be many causes for the chatbot failing to understand a request. For instance, the request might actually fall outside the chatbot's capabilities, or, in our case, one class of incomprehensible requests were due to implementation limitations.

While we can't do much about the former, building a recommendation system for the later class of requests almost always proves beneficial and vastly improves user experience.

For example, the noise in a speech-to-text (STT) component is a major cause of incomprehensible requests. In our fictional banking system, we've created software that allows pets to interact with ATMs, and a Mia user might form a query to discover all of the uses cases in which the actor "pet" participates.

If the user says: "filter by actor pet," we could expect the following transcript from the STT component, which, unfortunately, caused the subsequent pipeline components to misfire:

  • filter boy actor pet
  • filter by act or pet
  • filter by act or pad
  • filter by a store pet
  • filter by actor pass
  • filter by active pet
  • filter by actor Pat

While users will most likely be more deliberate in their subsequent interactions with the STT component, we noticed that these errors are commonplace and very negatively affected user experience.

To remedy the situation, we used a light-weight, string-similarity-based method to provide recommendations. Subsequent observations indicated that users almost always liked recommendations - except when they were too vague.

To avoid annoying users, we came up with two heuristics. First, we provided no more than three recommendations. Second, to be considered as a candidate query for recommendation, the query's similarity measure had to score higher than an empirically determined threshold with respect to incoming requests.

Over time users stop using fully formed sentences

The novelty of using a natural language interface quickly wears off. We observed that most users began sessions by forming requests with proper English sentences, but the conversation was quickly reduced to keyword utterances. Chatbot designers should plan for this eventuality. 😉

Actually, I find this quite fascinating and the natural evolution of conversation. I think of this phenomena as mirroring our natural conversations. When we first meet someone new, we are deliberate in our conversation. However, overtime, conversations are more informal. But that is a topic for future posts.
~Rahul Pandita, Phase Change research scientist
Subliminal priming

In formal conversation study, the entrainment effect is informally defined as the convergence of the vocabulary of conversation participants over a period of time to achieve effective communication. We stumbled on this effect when we observed that users employed an affected accent to get better mileage out of the STT component.

In psychology and cognitive science, subliminal priming is the phenomenon of eliciting a specific motor or cognitive response from a subject without explicitly asking for it.

We decided to see if subliminal priming would expedite entrainment. We began playing back a normalized version of a query with the query responses. That simple change led users to quickly converge to our chatbot vocabulary.

Consider the frequencies of following user request variations in our system:

Query # of users by
Test Subjects
list computations with a negative balance 30
filter for computations where output concept Balance is less than 0 17
filter by balance Less Than 0 16
filter by output concept balance is less than 0 09
show computations where output concept balance is less than 0 01
filter by output balance less than 0 224

By playing back "our system found following instances where output concept balance is less than 0," to each of these request responses, we observed that users began using the phrase "output balance less than 0," more, as shown in the frequency counts.

For the keen-eyed, notice that the repeated proper phrase, "filter by output concept balance is less than 0" is used less. However, remember that over time, users stop using fully formed sentences.

We also observed that talking with affected American or British accents works. This may be a product of an unbalanced training set used during creation of the speech-to-text models. That's why fairness testing is important. But that is yet another topic for future posts.

~Rahul Pandita
Data-driven prioritization

We also realized the benefits of leveraging data to prioritize engineering tasks as opposed to going with your gut.

A pipeline design is often a used for chatbot realization. Like most pipeline designs, the efficacy of the final product is a function of how well the individual components work in tandem within the pipeline. Thus, optimizing the design involves iteratively tuning and fixing various individual components.

So how does one decide which components to tune first? This is where data-driven prioritization can really help. For instance, in our setting, a light-weight error analysis helped on more than one occasion to identify the components we needed to focus on.

I only imagine that data-driven prioritization will become more useful in the future as we experiment with statistical approaches that often have a pipeline design.
~Rahul Pandita

We hope that our observations will be helpful for those embarking on the journey to build virtual assistants. We would love to hear your experiences.

An Analogy: Software AI and Natural Language — blog

March 6, 2017

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."

Leveraging software’s encoded knowledge to create an assistive AI — science podcast 4 of 4

February 16, 2017

February 16, 2017

This is the fourth and final in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

Using a whimsical example of dog banking, Steve discusses how the knowledge that’s encoded in software is normalized into a data structure, which enables us to create an assistive AI and solve the learning curve problem.

Podcast Slides and References

Time Stamps Slides and References
00:11 Steve Bucuvalas Podcast – Equality: The fundamental operation for software as data -- science podcast 3 of 4
05:15 PowerPoint Slide #1: Black-box view of Dog banking application -- the user (dog) view
05:21 PowerPoint Slide #2: White-box view of Dog Banking application -- the developer view
08:30 PowerPoint Slide #3: Merging the black-box and white-box views -- Dog Banking source code sliced into functional segments

Equality: The fundamental operation for software as data — science podcast 3 of 4

February 16, 2017

February 16, 2017

This is the third in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

In this podcast, Steve addresses the fundamental operation for software to be treated as data, which is equality, and begins by asking how we know when a fundamental unit of software is equal to something else? The first talk in this series introduces the idea of compiling programs into an AI representation. In the second talk, the Turing and Rice proofs are shown that they only apply to the mental domain of computation.

Podcast Slides and References

Time Stamps Slides and References
00:28 Steve Bucuvalas Podcast – Changing the essence of software and creating breakaway efficiency — science podcast 1 of 4
00:36 Steve Bucuvalas Podcast – The Turing machine, the Halting problem, and Rice’s use of the Turing proof — science podcast 2 of 4
02:50 PowerPoint Slide #1: Using C-language functions to show functional equivalence determination method
09:05 PowerPoint Slide #2: Stack Overflow thread about Turing's Halting problem -- Online Thread
10:34 Steve Bucuvalas Podcast – Leveraging software’s encoded knowledge to create an assistive AI — science podcast 4 of 4

The Turing machine, the Halting problem, and Rice’s use of the Turing proof — science podcast 2 of 4

February 16, 2017

February 16, 2017

This is the second in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

Steve reviews Turing's Halting problem and Rice's theorem, which have influenced computational theory for years. He shows how their abstract theories about infinity and an infinite number of programs do not apply to finite software programs in the real world.

Changing the essence of software and creating breakaway efficiency — science podcast 1 of 4

February 16, 2017

February 16, 2017

This is the first in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

In keeping with the physics' definition of the term ‘phase change,’ we are changing the essence of software. Taking something that is chaotic and turning it into something coherent. Taking something that is intractable and hard to understand and making it into an AI that actively helps every person in the software development process.

Math and science make the difference — video

January 5, 2017

January 5, 2017

Founder and CEO Steve Bucuvalas explains why Phase Change is well-founded in science and how it is overturning historical assumptions about computational theory.

 

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