Archives for February 2017

February 16, 2017 - Comments Off on Leveraging software’s encoded knowledge to create an assistive AI — science podcast 4 of 4

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

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

February 16, 2017 - Comments Off on Equality: The fundamental operation for software as data — science podcast 3 of 4

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

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

February 16, 2017 - Comments Off on The Turing machine, the Halting problem, and Rice’s use of the Turing proof — science podcast 2 of 4

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

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.

February 16, 2017 - Comments Off on Changing the essence of software and creating breakaway efficiency — science podcast 1 of 4

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

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.