Study seeks to explore if Artificial Intelligence Machines can Judge at Reining Horse Shows

In a small room, not far from some of the largest reining horse ranches in America, a computer study is underway to determine the effectiveness and potential application of using a form of artificial intelligence to judge reining horse exhibitors that could be applied at all levels of competition worldwide. If the study reflects a reasonable probability of success resources required for writing and testing the algorithms and programming could be expensed in expectation of creating a working model of the theory.

Artificial intelligence (AI) serves two critical components of intelligence: predication and comparative judgement. Since 2012, a specialized field within AI labeled “Deep Learning” has attracted great minds to explore that thru object recognition one enables the machines not only to see, but to make predictions and judgements.

In 2014 Google purchased UK-based DeepMind for $600 million, not for the revenue but for their demonstration thru AI that their machine had learned how to play some Atari games with superhuman performance without being programmed to do that. In 2015 machines crossed the threshold and could identify images better than people. Since than the opportunity has grown for “Deep Learning” algorithms and programing to collect data from video. Today the image analysis breaks down to as little as two pixels. The renowned physicist Stephen Hawking claimed that “Success in creating AI would be the biggest event in human history”. Today anyone speeding along in a Tesla with the handsoff autopilot navigating traffic is using AI. Now Elon Musk is changing the engineering approach to AI, in the Tesla, from radar based to camera based, the same technology, programming and algorithm system discussed for application here.

What if it is possible to use AI at reining horse shows? Imagine an AI machine sitting in the judge’s chair for the first-time at a NRHA show. Prior to the event, the machine has collected and analyzed thousands of minutes of reining videos, and pictures from past NRHA Futurities, Derbies, and countless shows worldwide. Now, at the event, the AI machine operates in real-time. Engineers have given the AI its own eyes and ears by linking to the event’s live-feed video, and other cameras. The AI machine observes the incoming video data as the exhibitor rides the pattern simultaneously observing the horse, and the human’s actions to the smallest detail.

Each horse and rider can be judge singularly with each measured against their own prediction standard, and the collective of the two. The horse and the rider each end up with separate scores, and a combined score. However, to begin with the AI machine will study the horse and rider for scoring purposes.

Since the AI machine has already observed hundreds, or thousands of minutes of identical maneuvers from the top rides at reining competitions, for example slide-stops, it can compare each physical action by the horse and rider against what is the “Perfect Slide Stop Maneuver”. It has learned what is perfect over-time and created the prediction using the machine’s in depth database of videos and pictures. The comparison between the actual maneuver and the “Perfect Maneuver” is judged and scored for the

performance of that move. An improvement is that the AI machine scores each maneuver from minus-one to plus-one or a fraction thereof. The AI machine scores maneuvers on the same scale from minus-one to plus-one but reflects those judgement over 200 fractional points within those two points. This results in less of or total elimination of ties.

The AI machine’s vast memory of historical horse and rider performances comes into play when it uses that knowledge when judging each move of horse and rider. It could learn to alter the prediction of that particular horse and rider based on the breeding, or win and loss record. Thus, giving prior champion horses or riders a little edge. Which variables to value would be determined in the programing. So, if one desired, just like real judges the AI machine could be influenced by external variables, or new variables. Each time it sees new show entries it adds to the knowledge base and becomes better at predicting the specific action the horse and rider should be performing and then judging their actions against the prediction standard. The AI learns to measure the prediction of the “Perfect Ride” against the actual video data coming in from the live-feed cameras already deployed at the events. This keeps the system up to date on subtle changes in styles and trends in the industry, and over time this effects the prediction of what is the “Perfect Ride”. The AI machine is constantly learning and updating the process of judging reining performances.

If it works, the AI machine could eventually supplement or replace humans of the predictive tasks of judging reining horse entries, thereby lowering entry fees as event producers pass on the high costs related to judges. As the machines increase their knowledge base of video and data the predictions improve resulting in higher decision-making quality. In effect, with experience, the AI machines become better judges. At some point, the AI machine may become so accurate and reliable that it changes how organizations do things. In theory, the AI judging machines could improve the quality and consistency of NRHA judging, worldwide. Once in use the AI machine collects more and more data on the horses and riders and then uses that data to improve its judgements and predictions. It would learn and know all the significant sires and dams and their show records, plus their offspring’s earnings and more. All this data from the past and present improves the AI’s judgement and prediction accuracy. The increasing depth of knowledge adds to the influences that create the predictions that the actual event of showing the horses is measured against. The machine is learning how to improve the judging quality outcomes all the time.

For example, Amazon AI collects data on users’ preferences and purchases and uses that date to improve predictions. The more one uses Amazon the better it becomes at predicting what you would like to purchase or see and promotes those products on Amazona pages you visit.

The huge library of NRHA performance videos, (data), in reining and the advancement in computers are at the core of predictions in artificial intelligence that make this work. Recent advances in machine learning have transformed how we collect video and other data for creating predictions and to make judgements. The

system sees the moves in realtime and judges that against the standard derived from the knowledge base to define the Plus or Minus points for each maneuver. As the rider’s performance progresses every segment of the performance is measured, judged, and scored against the prediction of the perfect ride.

“Deep Learning” relies on an approach called “back propagation”. The machine learns thru example. If a reining horse performs a maneuver labeled a “Plus One” the technology recognizes that and learns to recognize and judge “Plus One” maneuvers. It’s repetitive learning. Same result with a “Minus One” maneuver in design and programing so that by the time it makes judgements in the show pen it’s seen thousands of the same maneuver. The AI analyzes the horse and rider’s maneuver and recognizes that it is to be judged a zero, plus or minus or a fraction thereof.

The technology learns by primarily absorbing data from videos and pictures with no limit of capacity. It could view hundreds of thousands of reining videos and pictures with those variations and labels “Plus One, Minus One, etc.” They are all fed into the machine which then develops more associations and learns to distinguish between good and bad reining maneuvers. It forms its own decisionmaking process with the results supported by quantitative analysis.

Machine learning creates the process of prediction, a key component of intelligence. Measuring the actual horse and rider performance against the prediction of the “Perfect Ride” is the judgement. And the more it performs it improves learning and increases the level of accuracy in judging thus enabling the AI machines to perform tasks that, until now, were associated with human

intelligence.

There are four steps in the process to determining the viability of using AI machines to judge reining horse shows. The first step is to write the programing and algorithms required. Second is to input enough videos, and pictures into the system that it can establish the ability of recognition for the “Perfect Rides” which are used as a standard for judging an

exhibitor’s performances. The third step is training the system to make judgements on the reining maneuvers and start scoring performances. The fourth step is for the system to be deployed as a proof of concept at an existing reining show, and have those results measured against the actual judges scores.

The estimated expense to execute the four step process is $25,000.

Data has real value; the key is to unlock it with prediction machines. With the current generation of AI technology, the machines learning combines the videos of both good and bad rides when measured against the standard, “Perfect Ride”. So, the prediction machine needs video and picture data from many people and performances from terrible to excellent. It needs the entire spectrum of performances to execute the comparison that enables the prediction, or precise judgement to the finest detail.