AI Agents, AI Tools, Artificial Intelligence, Blockchains, computer vision, Decentralized, Healtlh, Productivity, Science, Sports AI, Uncategorized, Yogi Nelson

🏀 Blockchain and AI: The Winning Combo for Basketball Performance and Game Planning

August 18, 2025August 5, 2025 Yogi Nelson

by Yogi Nelson

Welcome to the BlockchainAIForum

In the modern era of sports, data is nearly as valuable as talent. As basketball evolves into a more analytics-driven game, two emerging technologies—blockchain and artificial intelligence (AI)—are offering transformative ways to optimize athlete performance and refine coaching strategies.

🤖 AI: The Brain Behind Performance Optimization

AI excels at making sense of large volumes of data. In basketball, this includes but is not limited to:

Shot accuracy by zone
Defensive movement patterns
Reaction times
Player fatigue indicators
Injury risk prediction

By analyzing in-game and training session data, AI can detect performance trends and efficiency gaps. For example, AI tools like computer vision and wearables can break down a player’s jump shot form frame by frame, identifying micro-adjustments that may improve consistency.

Use Case Example:
A guard shows a 12% drop in shooting accuracy when defending players come from the left. AI picks up this pattern and recommends tailored drills to address this weakness—giving that player a measurable performance boost.

🧠 AI for Coaches: Scouting and Strategy

For coaches, AI-powered tools provide deep insights into both their own team and opponents.

Play recognition: AI systems can watch game footage and label each possession by type (e.g., pick-and-roll, isolation, zone defense).
Tactical suggestions: AI compares past matchups and simulates different lineup combinations to predict which defensive sets will neutralize an opponent’s offense.
Opponent weaknesses: AI can flag patterns like a team’s tendency to overcommit to screens or struggle in transition defense.

Use Case Example:
Ahead of a playoff game, an AI model identifies that the opponent’s bench unit allows 1.5 more points per minute than their starters. A coach could then stagger lineups to exploit that window and build a lead.

🔗 Blockchain: Secure, Transparent, and Decentralized Data

While AI analyzes data, blockchain protects it. Blockchain is a distributed ledger that ensures the integrity, privacy, and ownership of data—a vital concern when it comes to sensitive athletic and biometric records.

🔐 Data Ownership and Athlete Control

Using blockchain, athletes can store personal performance data in a decentralized system, giving them full control over who can access it. This is especially relevant when switching teams, signing endorsement deals, or working with independent trainers. Players must demand ownership of this data when negotiating contracts.

Use Case Example:
A player recovering from injury uses a wearable that collects rehab data. That data is written to a private blockchain. The athlete shares selective access with their team’s medical staff while keeping full control of the record, avoiding leaks or unauthorized analysis.

🔄 Interoperability and Smart Contracts

Smart contracts—self-executing programs on the blockchain—can automate and enforce agreements.

Performance-based bonuses: A contract could be tied to verified performance metrics (e.g., scoring 20+ points in 10 consecutive games).
Training commitments: Athletes can log their workouts via blockchain-based platforms that timestamp and verify effort, giving coaches confidence in off-season commitments.

Use Case Example:
A summer training program uses blockchain to verify players’ completion of drills. AI analyzes the submitted footage and scores it, while the blockchain logs the results in tamper-proof fashion—creating trust between coaches and players, even when miles apart.

🧬 The Power of the Combo: AI + Blockchain

Individually, AI and blockchain each provide value—but together, they become a powerful force:

Task	AI Role	Blockchain Role
Injury prediction	Analyze biometric trends	Secure the source data
Game prep	Analyze video and stats	Certify footage origin
Player development	Tailor skill improvement plans	Store verified progress logs
Scouting	Compare player data across teams	Enable trusted cross-org data sharing

📊 Real-World Example: A Future Training Scenario

Imagine this:

A player wears a smart shirt and AR glasses during a practice session.
AI tracks shooting mechanics, dribble speed, and lateral movement.
After the session, AI generates a personalized performance report.
That report is encrypted and stored on a blockchain.
The player shares access with their personal trainer and team coach.
Smart contracts automatically update team dashboards and player health status if pre-set performance benchmarks are hit.

All of this happens without risking privacy or data tampering, ensuring that both the athlete and coaching staff operate on trustworthy, actionable information.

🏗️ What’s Needed for This to Work?

Despite the potential, a few conditions must be met:

Infrastructure Investment: Teams must adopt smart wearables, AI platforms, and secure blockchain networks.
Education: Players and coaches need training to understand and trust these technologies.
Cross-platform Standards: Interoperability between AI and blockchain systems across leagues is key.
Data Privacy Regulations: Systems must comply with health and privacy laws (e.g., HIPAA, GDPR).

🚀 The Future of Smart Basketball

Basketball is already a fast, intelligent game. With the strategic application of AI for insight and blockchain for trust, the sport can move into a new era—where data doesn’t just describe performance, but actively drives it. For athletes, it means smarter training and greater autonomy. For coaches, it means deeper insights and more effective strategies. For the game? It means more precision, fewer injuries, and higher levels of play.

Until next time,

Yogi Nelson

Artificial Intelligence, Blockchains, computer vision, Healtlh, Optometry, Yogi Nelson

Artificial Intelligence in Optometry: Enhancing Eye Care Precision

April 23, 2025 Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your questions are answered! Today’s question: what is the state of artificial intelligence in optometry?

Let’s begin with a definition of optometry. According to the World Health Organization (WHO), optometry is “a health care profession that is autonomous, educated, and regulated, and optometrists are the primary health care practitioners of the eye and visual system who provide comprehensive eye and vision care, which includes refraction and dispensing, detection/diagnosis and management of disease in the eye, and the rehabilitation of conditions of the visual system.” Now we listen to optometrists, beginning with their attitudes towards AI and willingness to use this new technology.

Optometrists are bullish on AI. According to a recent survey published by the National Center for Biotechnology Information, more than 2/3 of the 400 survey participants said they were familiar with the potential of AI in optometry. https://www.eyecarebusiness.ca/practice/the-potential-of-artificial-intelligence-in-optometry/ That’s the good news because everything starts with awareness. Although, 53% expressed concerned with diagnostic accuracy of AI, nevertheless 72% believed AI will improve the practice of optometry. Somewhat contradictory, right? Moreover, 65% reported they are willing to incorporate AI in their own practice.

AI Enhances Diagnostic Precision and Strategic Planning. Using the specific examples below, let’s explore, in detail, the potential of AI to enhance diagnostic precision and strategic planning within the field of optometry practice.

Early Detection. No human can compete with AI’s ability to process vast amounts of data. In optometry, data analysis is essential for certain actions, e.g. when processing optical coherence tomography (OCT) images and retinal images. AI can detect patterns within these images that optometrists might miss for any number of reasons, ranging from optometrist fatigue to improper training.

Diabetic Retinopathy. AI can be particularly effective at monitoring images over time and spot changes that lead to eye diseases that manifest progressively.

Glaucoma. Glaucoma can lead to blindness. However, there is emerging AI technology that offers plenty of promise for those severing from glaucoma. For examples, AI has the potential to detect glaucoma by fundus photographs, and optical coherence tomography (OCT). Specifically, AI is beneficial for open-angle glaucoma cases where symptoms don’t typically exhibit themselves.

Reduce False Positives. A false positive happens when test results shows a disease is present when it isn’t. With AI, and its ability to process vast quantities of data, false positives are reduced.

Office Management. Although not directly related to optometry, but nevertheless significant, is the ability of AI to automate office procedures. For example, repetitive tasks such as scheduling, billing, and follow-ups can be done on the fly and updated in patient records as new information is received. This boost in productivity means more time to focus on patient care, which of course is the point of optometry; not paperwork.

Myopia and Contact Lense Design. In a recent research report from China, the Chinese claim to be making significant progress with AI, specifically related to designing customizable contact lenses. Furthermore, the Chinese report myopia treatment advancements via the use of AI. See this link:

https://pmc.ncbi.nlm.nih.gov/articles/PMC10475639/

Strategic Planning. AI’s ability to capture and analyze vast qualities means optometrists can prepare treatments that are tailored to the individual depending on specific conditions. No more generic remedies. In other words, AI is capable of detecting changes from commencement, and therefore optometrists can formulate health plans tailored to individual patients. Check this link for more information: https://www.mdpi.com/2227-7080/13/2/77

As the famous rock group, Coldplay, once said: “…See You Soon…”

Until next time,

Yogi Nelson

AI Agents, Artificial Intelligence, Banking, Blockchains, computer vision, cryptography, Digital Currency, Uncategorized, Yogi Nelson

AI Agents Explained: Earning Money in the Digital Age

February 5, 2025December 27, 2024 Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your technology questions are answered! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question was submitted by Arcardio, in Puerto Rico, and he wants to know if AI agents are engaged in business and making money?

Arcardio, you came to the right place. The answer is yes–AI agents are busy working for their owners and making cash! I’ll explain that mind bending fact, but first what is an AI agent?

WHAT IS AN AI AGENT

An AI agent is an entity or system than can perceive its environment, process information, and take actions to achieve specific goals. It is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools.” It operates autonomously, using sensors or inputs to gather data, and employs reasoning, decision-making, and sometimes learning capabilities to determine the best course of action.

HOW DO AI AGENTS WORK

To understand how AI agents work, I turned to Kalaidos University, Switzerland. Kalaidos U says AI agents build on top of existing algorithms, in particular large language models (LLMs), e.g., ChatGPT4. Essentially, AI agents use machine learning algorithms to derive patterns, rules or models from the collected data. Based on this, AI agents can make decisions or perform tasks. As robots they interact with their physical environment, or virtually as computer programs. Moreover, AI agents can adapt to changing conditions and therefore continuously improve.

Let’s take the example of a well-known AI agent—ChatGPT. ChatGPT is intelligent enough to use a calculator as a tool, if trained accordingly. ChatGPT can thus become an AI agent. Learning to choose the right tools and the ability to use them is what makes an AI agent. But keep in mind their exact mode of operation depends on the specific application and the underlying algorithms. Therefore, training and quality of data have an immense impact on the effectiveness of AI agents.

AI AGENTS AT WORK CURTESY OF VIRTUALS PROTOCOL

Now, let’s talk about AI agents making money by turning out attention to Virtuals Protocol. Virtuals Protocols is a developer of AI technology. On their platform you can create an AI agent. The agent can perform task(s) for someone needing that specific job done. Yes, you heard me right. These AI agents are ready, willing, and able to perform tasks—for a fee of course! Or, if you don’t want to create your own AI agent, you can contract for an AI agent created by someone else! Holy. I won’t go into the technical details of building your own AI agent in this article. I don’t that later. Instead, I’ll give you three examples of the types of agents you could hire.

Take Molly Analytics for example. The owner of Molly is getting paid every time someone uses her services. What are her services? Molly is an AI On-chain AI analytics experts who tracks and analyzes transactions in order to spot opportunities. Molly says, “…every block holds valuable insights, and within these patterns lie the keys to smarter strategies, better decisions, and measurable growth.”

Or, take Cur8or. This AI agent claims it specializes in guiding artists towards excellence—and profits–by balancing harsh criticism with detailed suggestions.

I’ll give you one more—its wild and goes by The Hooligan. This AI agent is trained to troll a sports team or sports player! Lol. As the number one Boston Celtics hater, I may hire The Hooligan to troll the Celtics, as needed. Lol.

Okay, time to leave you with this proverb from Tunisia: “a small key can open a big door.”

Until next time:

Yogi Nelson

https://www.virtuals.io/

https://www.ibm.com/think/topics/ai-agents

AI Agents, Artificial Intelligence, Blockchains, computer vision, cryptography, Uncategorized

Understanding AI Agents: Types and Classifications

January 29, 2025December 27, 2024 Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your technology questions are answered! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question was submitted by Nikki and she wants to know what are AI agents and is there a way of classifying them?

Nikki, you came to the right place to find an answer. Your question gets to the core of artificial intelligence. Let’s start by defining what is an AI agent?

WHAT IS AN AI AGENT

According to IBM, “an AI agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools.” The key is autonomously. Furthermore, says IBM AI agents can encompass a wide range of functionalities beyond natural language processing including decision-making, problem-solving, interacting with external environments and executing actions. AI agents says IBM, use the advanced natural language processing techniques of large language models (LLMs) to comprehend and respond to user inputs step-by-step and determine when to call on external tools.

CLASSIFICATION OF AI AGENTS

Nikki when it comes to classification of AI agents, there is no standard answer, nevertheless let’s proceed.

Reactive Machines: Simple reflex agents are the most basic forms of artificial intelligence. These agents make decisions based solely on their current sensory input, responding immediately to environmental stimuli without needing memory or learning processes. Their behavior is governed by predefined condition-action rules, which specify how to react to particular inputs. The beauty of reactive AI is that it is highly efficient and easy to implement, especially in environments where the range of possible actions is limited.

Model-based Reflex Agents: These AI agents are a more advanced and designed to operate in partially observable environments. Model-based agents maintain an internal representation, or model, of the world. This model tracks how the environment evolves, allowing the AI agent to infer unobserved aspects of the current state. While these agents don’t actually “remember” past states in the way more advanced agents do, they use their world model to make better decisions about the current state.

Goal-based Agents. These are designed to pursue specific objectives by considering the future consequences of their actions. Wow! Goal-based agents plan sequences of actions to achieve desired outcomes. Effectiveness depends on a clear description of the goal. With a clear goal the AI agent is able to search through possible sequences of actions that could lead toward or to the goal. It then moves on to choosing actions based on their predicted contribution toward reaching the goal. Industrial robots are examples.

Learning Agents: Learning agents improve behavior over time by interacting with its environment and learning from its experiences. These agents modify their behavior based on feedback and experience, using various learning mechanisms to optimize their performance. These AI agents can discover how to achieve their goals through experience rather than purely relying on pre-programmed knowledge. An example would be an energy management system that learns the patterns of usage to optimize resource consumption.

Utility-based Agents: A utility-based agent makes decisions by evaluating the potential outcomes of its actions and choosing the one that maximizes overall utility. Unlike goal-based agents that aim for specific states, utility-based agents can handle tradeoffs between competing goals by assigning numerical values to different outcomes.

Hierarchical Agents: These agents are structured in a tiered system, where higher-level agents manage and direct the actions of lower-level agents. In other words, task decomposition and task coordination! This architecture breaks down complex tasks into manageable subtasks, allowing for more organized control and decision-making.

Multi-agent System: Nikki this is team AI! This AI agent system involves multiple autonomous agents interacting within a shared environment, working independently or cooperatively to achieve individual or collective goals.

I end with a proverb from Netherlands: “trust comes on foot and leaves on horseback”.

Until next time,

Yogi Nelson

https://www.ibm.com/think/topics/ai-agents

https://www.kalaidos-fh.ch/en-GB/Blog/Posts/2024/02/Digitalisierung-1130-Was-sind-AI-Agents-und-wie-funktionieren-sie

https://www.digitalocean.com/resources/articles/types-of-ai-agents

Artificial Intelligence, Blockchains, computer vision, Yogi Nelson

WHAT IS CAMBRIDGE UNIVERSITY’S ARTIFICIAL INTELLIGENCE INITIATIVE?

October 16, 2024 Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your technology questions are answered! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question comes from Ian and he wants to know what is the Cambridge University Center for Human Inspired Artificial Intelligence (CHIA)?

Ian, you came to the right place. CHIA is dedicated to advancing AI for the betterment of human society, says Cambridge. According to Cambridge, CHIA brings together an interdisciplinary community of researchers to investigate the innovative ways in which human and machine intelligence can be combined to yield AI which is capable of contributing to social and global progress. An ambitious task indeed! Let’s learn more about CHIA by examining their program in detail.

Fundamental Human-Level AI. Within the Fundamental Human Level-AI program area, CHIA aims to combine the latest advances in algorithms and computing with human insights and intelligence. The intent is to improve machine learning, speech and language processing, computer vision, robotics and other forms of machine intelligence. What makes the program unique, say CHIA, is the collaboration between AI and a wide variety of human-centric disciplines.

Responsible AI. Ethical use of AI, is a central tenet at CHIA, says CHIA. The standard CHIA aspires to is non-bias models with human oversight of AI. They correctly note, AI systems used for sensitive, assistive or predictive tasks such as medical or legal decision-making must aim for the pinnacle of ethical behavior. I find this reassuring but only time will prove whether they adhere to this noble standard.

Social and Interactive AI. Over the last few years, it’s become increasing obvious that conversational AI technology enables humans and computers to communicate clearly and effectively through speech or text. For instance, chatbots and voice assistants are now commonplace, sometimes to much consternation! Not surprisingly, CHIA has decided to dedicate resources ensuring AI learns to recognize human communication and its nuances. To do so, CHIA is blending the disciplines of computer science, sociology, psychology, physiology and others.

Cognitive AI. CHIA’s goal is to equip machines with human-like learning, reasoning and perceptual abilities. Holy consciousnes! According to CHIA, human-like computing could be integrated into AI, cognitive, biological, and other sciences. The end goal? The ultimate goal is to benefit the numerous applications that require close interaction between computers and human users, e.g., AI-powered tutoring, personalized healthcare, among others.

Health and Global AI. Cambridge asserts, and many agree, that AI has many health-related applications, ranging from systems that automatically generate new medical discoveries, diagnostic techniques and treatments to those that use multi-sensory data for early health diagnosis or management of medical conditions. The folks at CHIA note, AI may one day predict major economic, geopolitical, societal, and technological risks. Moreover, the innovations coming out of CHIA could be used to understand, predict, monitor, and manage natural systems and environmental risks such as climate change. To those in the prediction business, I say use the humble on-ramp.

Creative AI. CHIA claims their Creative AI program is aimed at developing systems that can support, augment, or even automate the creative process. Do you believe the creative process can be automated? Put me in Camp Skeptical! Being brilliant minds, CHIA staff recognize this endeavor will be “… highly challenging and required close collaboration between fields that have been traditionally quite distant from each other: AI and arts and humanities.” An elegant understatement.

Time to end with a proverb from Pakistan: “a white beard does not mean gold or wisdom”.

Until next time,

Yogi Nelson