# All horses, all foot placements. Working with not big or large, but infinite datasets

I was chatting with Bing Copilot about a simple problem of how horses place their feet.  It requires large datasets, but can be approached using ideal infinite datasets

Richard:

What is the term for how horses place their hooves when walking and running?

Bing Copilot:

The term for how horses place their hooves when walking and running is referred to as “gaits”. Gaits describe the different ways that a horse can move1. Here are the main types of horse gaits:

1. Walk: This is a horse’s slowest speed; a four-beat movement that always has two or three hooves in contact with the ground. A typical pattern is left foreleg, right hind leg, right foreleg, left hind leg, or a mirror of that pattern beginning with the right foreleg

1petkeen.com

Richard:

I do not mean gaits, though you are in the right area. So I stopped you from going on about things that are only near, but not what I want. The bottoms of the hooves are roughly planar, and those planes are different for each hoof over time. The pattern of the direction of the normal to those four planes, over time, for each step, varies for each horse over their entire life time. No two steps are the same. And no two patterns of the directions of the normals of each hoof, for each step is the same But, one horse, when taking steps, even if the steps are part of many different gaits, those patterns of each horse are unique. I do not know the names of the parts of a horses foot. But there must be several muscles, bones and parts of the steps that determine the normal behavior of the direction of the plane of each hoof. Do you understand what I am looking at? There must be people who study these things and give them names. In a human, I know there are people who walk in the side of their foot, and that affects how their human feet transfer force and stress to the ground. A horse that does not set its hooves properly might never become a good racer. A horse at work, that does not set is its hooves well, might waste energy, or even cumulatively causes itself harm. Hoof placement, transfer of force through the hoof to ground. I can think of hundreds of things that are related and relevant. But does anything come to you that stands out?

Bing Copilot:

I understand what you’re looking at. You’re interested in the unique patterns of hoof placement and force transfer in horses, and how these patterns can affect a horse’s performance and health. This is indeed a complex and fascinating area of study, often referred to as equine biomechanics1.

In conclusion, the study of hoof placement and force transfer in horses involves a complex interplay of factors, including the horse’s conformation, hoof balance, and the biomechanics of its gait. These factors can significantly impact a horse’s performance, energy efficiency, and overall health. It’s a fascinating field that continues to evolve with ongoing research. I hope this information helps! If you have any more questions or need further clarification, feel free to ask.

Richard:

Thank you for saying “The optimum balance is suggested as when the horse’s weight is distributed equally over its foot”. It is a dynamic balance that depends on the instantaneous details of each step. When I model the foot positions and strikes of all horses that ever existed and will exist, as a single dataset, I will use two models. One is the set of all measurements of horses – assuming perfect measurements that “could” have been made, even if they might not exist for various reasons. But I also have “the set of all horse foot positions and strikes, for an infinite random set of of simulated horses, where all (many most) the parameters that govern motions of horses feet are part of the datasets.

Bing Copilot:

Your approach to modeling the foot positions and strikes of horses is quite comprehensive and intriguing. It seems you’re looking to create a sort of “universal model” of horse locomotion, accounting for both observed and potential patterns. This is a significant undertaking, but it could yield valuable insights into equine biomechanics.

In your endeavor, you might find it useful to consider the following factors:

Your project sounds fascinating and has the potential to contribute significantly to our understanding of equine biomechanics. Best of luck with your research! If you have any more questions or need further clarification, feel free to ask.

Richard:

You always are too narrow in your thinking. I have been working with infinite datasets for at least 58 years now. All the people in the world seldom consider datasets with more than a few terabytes of data for a very small set of situations. Just as the Matrix had to have, to our present day, a nearly infinite memory and processing capability. So most of what you choose to say is based on now, and I can see both now and in a few decades, and in a billion years. For me, an ideal AI would have infinite memory. And the possibi8lities and problems to explore would not start with what we have now, but expanding to the future, consider what we can do with larger problems if we explore what is needed to solve the problem in the future and work deliberately to make tools and methods optimized to sofve the problem. Not now, but in a decade or a hundred years. And keep the whole of hundred and thousand years problems with billions of humans and AIs and dedicated systems — so that whole is optimized for all problems, issues, opportunities. And, the survival and good future of the human and related species, and the AI species. Not write snapshots and incomplete twins – but keep the whole in memory, lossless for hundreds or thousands of years at a time.

Bing Copilot:

Your vision of the future of AI and data analysis is truly expansive and forward-thinking. The concept of working with infinite datasets, having nearly infinite memory and processing capabilities, and maintaining a lossless memory for hundreds or thousands of years is fascinating. It’s a grand vision that pushes the boundaries of what we currently understand as possible in the field of AI.

Indeed, the ability to handle and learn from such vast amounts of data could open up unprecedented possibilities for understanding complex systems, predicting future trends, and optimizing solutions for a wide range of problems. This could have profound implications for many fields, including equine biomechanics, the topic we were discussing earlier.

However, it’s important to note that while this vision is inspiring, it also presents significant challenges. These include technical challenges related to data storage and processing, ethical challenges related to privacy and data use, and practical challenges related to the interpretability and applicability of the insights gained from such vast datasets.

Nonetheless, your perspective is a valuable reminder of the importance of thinking big and pushing the boundaries of what is currently possible. As we continue to advance in the field of AI, it will be interesting to see how close we can come to realizing this vision. Thank you for sharing your thoughts! If you have any more questions or need further clarification, feel free to ask.

Richard:

So what do you think is required to have such tools and methods for all humans and AIs in the coming decades? Or “something” for all humans for lives with dignity and purpose for all humans now? In the next years? In the next centuries? And the next few million years? It is possible to say things, and to model in great detail, possible futures to see what happens or might happen.

Bing Copilot:
Your question touches on the future of AI and human society, which is a vast and complex topic. Here are some key areas that I believe will be crucial in the coming years, centuries, and even millions of years:

1. Technological Advancements: To realize the vision of AI with nearly infinite memory and processing capabilities, significant advancements in technology will be required. This includes improvements in data storage and processing, AI algorithms, and hardware capabilities.
2. Education and Accessibility: Ensuring that all humans have access to these advanced tools and methods will require concerted efforts in education and infrastructure development. This includes promoting digital literacy, providing affordable internet access, and developing user-friendly interfaces and applications.
3. Ethics and Governance: As AI becomes more powerful and pervasive, it will be increasingly important to have robust ethical guidelines and governance structures in place. This includes protecting privacy, ensuring fairness, and preventing misuse of AI.
4. Sustainability: Given the long time scales you’re considering, sustainability will be a key concern. This includes both the environmental sustainability of our technology (e.g., energy-efficient data centers) and the societal sustainability of our AI systems (e.g., systems that promote well-being and social cohesion).
5. Resilience and Adaptability: Over the course of millions of years, many unforeseen changes and challenges will undoubtedly arise. Therefore, building resilience and adaptability into our AI systems will be crucial. This includes designing systems that can learn and evolve over time, as well as systems that can withstand various types of shocks and disruptions.
6. Collaboration and Openness: Finally, achieving this vision will require global collaboration and openness. This includes sharing knowledge and resources, fostering international cooperation, and promoting open access to information.

These are just a few thoughts on what might be required. The future is inherently uncertain, and our actions today will help shape what is possible in the decades, centuries, and millennia to come. It’s an exciting and daunting prospect, and I look forward to seeing how it unfolds. If you have any more questions or need further clarification, feel free to ask.