Psibernetix’s methodologies have countless applications in the practically every domain. Psibernetix’s work first began in the field of bioinformatics, in particular we developed systems to predict futures of patients based upon analytical data.

We have been selected to team with University of Cincinnati’s Professor David Fleck, College of Medicine, and Professor Kelly Cohen, College of Engineering & Applied Sciences, to develop an Intelligent System for neuroscience applications.  Our first bioinformatics system was LITHIA, the LITHium Intelligent Agent (below).  Additionally, the joint UC-Psibernetix project titled “Prediction of Symptom-free Traumatic Brain Injury using Standard Clinical MRI” was one of the two 2015 UC Sports Health Innovation Award winners.

Since, Psibernetix has developed systems in fields ranging from shipping, manufacturing, disaster management, and defense.  While our methods can be applied to create systems for practically any problem, problems that contain randomness, uncertainty, and/or noise as well as applications that require real-time, embedded, or assured operations are all areas where we significantly stand out from the competition.

Biomedical AI: LITHIA

Our methods allow applying fuzzy logic based control to problems of extreme complexity. LITHIA, the LITHium Intelligent Agent, was created to apply our fuzzy logic based linguistic control to the prediction of effectiveness for Lithium treatments of Bipolar disorder patients.

Compared to other methods that can be applied to a problem of this complexity, LITHIA is incredibly accurate, robust, and transparent. Most alternative methods are similar to a black-box system in once cannot fully understand why the system provided a certain response. When asked why a patient received a certain prediction of effectiveness, LITHIA responds in an English if-then sentence. This allows LITHIA to be utilized a research tool, allowing medical doctors with little to no Artificial Intelligence expertise to work alongside LITHIA to determine key predictors and any variables that could be excluded.

On a 80% / 20% training / validation split of the patients, LITHIA is 100% accurate in predicting whether symptoms will be considered in remission after the treatment or not. The exact symptom reduction percentage is 94% accurate on this set of patient data.

8 other machine learning methods were tested, all varying between 50-75% accuracy in predicting symptom remission and 61-84% accuracy in exact symptom reduction percentage. These other methods also suffer from the black-box syndrome; their outputs provide extremely minimal insight into the problem compared to LITHIA.

It is clear why a genetic fuzzy system has not been applied to a problem such as this before. While the methodology has many strengths, a standard genetic fuzzy system would take much more than one googolplex years of computation time on the most powerful supercomputer in the United States. LITHIA can finish learning from the training data in under 5 hours, on a laptop. Post-training, running new patient data through LITHIA is nearly instantaneous.

Normal axial T2-weighted MR image of the brain

Defense AI: ALPHA


ALPHA is an Artificial Intelligence that controls flights of Unmanned Combat Aerial Vehicles in aerial combat missions within a high-fidelity simulation environment.  Psibernetix was hired to create ALPHA by the US Air Force Research Laboratory (AFRL).

Our ALPHA System has recently been highlighted by the University of Cincinnati, and published in the Journal of Defense Management!

Just as UAVs represented a revolutionary capability for the USAF in the mid-1990s, Manned-Unmanned Autonomous Teaming in an air combat environment will certainly represent a revolutionary leap in capability of airpower in the near future.  Air combat, as it is performed by human pilots today, is a highly dynamic application of aerospace physics, skill, art, and intuition to maneuver a fighter aircraft and missile against an adversary moving at high speeds in three dimensions.  Today’s fighters close on each other at speeds in excess of 1,500 MPH while flying at altitudes above 40,000 feet.  The selection and application of air-to-air tactics requires assessing a tactical advantage or disadvantage and reacting appropriately in microseconds.  The cost of mistakes is high.  Future aircraft are likely to employ a high level of coordinated autonomous offensive and defensive capabilities, requiring reaction times which surpass that of a human pilot, in order to survive in such hostile environments.

Trained by our EVE learning system, ALPHA has been able to achieve immense success against a baseline controller AFRL had previously been utilizing to control the red forces.  However, ALPHA’s ability to defeat AI-flown enemies is only one measure of success; it must also be able to defeat highly trained and experienced fighter pilots.  ALPHA was assessed by an air combat subject matter expert, Colonel (retired) Gene “Geno” Lee.  As a former USAF Air Battle Manager, Mr. Lee is a United States Air Force Fighter Weapon School graduate and Adversary Tactics (Aggressor) Instructor, and has controlled or flown in thousands of air-to-air intercepts as a Ground Control Intercept officer, as a Mission Commander on AWACS, and in the cockpit of multiple fighter aircraft.

When Geno took manual control of the blue aircraft against the reds controlled by the baseline controller AFRL had previously been utilizing, he could easily defeat it.  However, even after repeated attempts against ALPHA, not only could he not score a kill against it, he was shot out of the air by the reds every time after protracted engagements.  He described ALPHA as “the most aggressive, responsive, dynamic and credible AI (he’s) seen-to-date.”    

Distribution approved for public release; 88ABW Cleared 12/15/2015; 88ABW-2015-6028