Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

AI Agents are task-specific, autonomous systems that integrate large language models with structured tools, APIs, and real-time data sources. 

They operate across domains such as cybersecurity, supply chain logistics, and healthcare by executing workflows that traditionally required human-in-the-loop decision making. 

These agents leverage vector databases, retrieval-augmented generation, and fine-tuned embeddings to enable contextual reasoning and dynamic response generation. 

As orchestration frameworks mature, multi-agent systems are increasingly capable of handling end-to-end processes like demand forecasting, patient triage, and adaptive tutoring with minimal supervision.

The below chart shows just how broad their impact is:

1.🔹 IT & Security : Phishing filters, threat detection, patch suggestions
2.🔹Healthcare : Patient alerts, medical chatbots, symptom matching
3.🔹 Education : Flashcards, concept explainers, AI tutors
4.🔹 Sales & Marketing : Lead scoring, campaign ideas, email outreach
5.🔹Logistics : Fleet tracking, demand forecasting, inventory updates
6.🔹Manufacturing : Predictive maintenance, robotic control, energy monitoring
7.🔹 Research : Academic writing, data cleaning, topic expansion
8.🔹 Customer Support : FAQ bots, emotion detection, chat summaries
9.🔹 Smart Environments : Digital twins, voice commands, access control
10.🔹Ops Automation : Shift scheduling, system alerts, order tracking

What used require significant manual effort, now takes a few smart agents.

I believe it’s a great time to start exploring and experimenting in this space…

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed.

People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently.
Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills.

This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms.

Here’s the full breakdown, day by day:

Day 1 - AI Automation Fundamentals
Learn what automation really means, how it differs from AI and agents, and see real examples.

Day 2 - Automation Thinking
Break work into steps, triggers, and outcomes - the mindset behind every good automation.

Day 3 - APIs & Webhooks Basics
Understand how apps communicate and how events trigger workflows.

Day 4 - No-Code Automation Platforms
Explore Zapier, Make, n8n - and how no-code tools actually run workflows.

Day 5 - Build Your First Automation
Create a simple trigger-action workflow and connect two apps.

Day 6 - Data Handling
Pass data between steps, map fields, and work with text, numbers, and dates.

Day 7 - Logic & Error Handling
Add filters, conditional logic, retries, and fallbacks to keep automations reliable.

Day 8 - AI Model Basics
Learn prompts vs system instructions, tokens, limits, and LLM behavior.

Day 9 - Using AI Inside Automations
Insert AI steps into workflows and parse structured AI outputs.

Day 10 - Prompt Design for Automation
Write consistent prompts and reduce hallucinations with JSON outputs.

Day 11 - Text-Based Task Automation
Automate email replies, summaries, CRM updates, and document tasks.

Day 12 - Knowledge Automation (RAG Basics)
Connect AI to internal documents and fetch accurate answers from real data.

Day 13 - AI Agents Basics
Understand agent planning, tools, and identify use cases for agents.

Day 14 - Business Use Case Automation
Automate lead qualification, ticket routing, and internal processes.

Day 15 - Sales & Marketing Automation
Personalize outreach, repurpose content, and automate follow-ups.

Day 16 - Operations Automation
Manage approvals, notifications, and repetitive operational tasks.

Day 17 - Monitoring & Optimization
Track workflow success, cut costs, and improve performance.

Day 18 - Build & Ship Your System
Design, test, document, and finalize a complete end-to-end automation.

You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution.

Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

Data ➝ AI ➝ Value.
That’s still what companies think AI Looks like

But in the real world…

AI inside companies looks nothing like that.

Because value doesn’t come from “building a model”.

Value comes from building a working system around the model.

This guide explains what AI actually looks like in companies 👇

It’s a full production chain, not a single step:

1) Data
Selection, sourcing, synthesis - getting usable data is already 50% of the work.

2) Data Science
Exploration → cleaning → normalization → feature engineering → scaling
Then modeling: selection → training → evaluation → tuning

3) Value (Operationalizing)
Real value starts only after:
registration → deployment → monitoring → retraining

So ML is not “AI work”.

It’s data engineering + modeling + MLOps, all together.

What GenAI-enabled AI actually looks like

GenAI doesn’t remove complexity.

It shifts complexity from “models” to workflows + control.

The real GenAI path is:

- Understand the decision process
What decisions are being made today? Where does judgment happen?

- Workflow experimentation
Iterate with prompts, APIs, tools, retrieval, automation, coding — until it works reliably.

- Guardrails
Policies, safety filters, access control, cost limits, prompt rules, grounding.

- Monitoring
Track quality, drift, failure modes, user behavior, data leakage, hallucinations.

- Human in the loop
Because the highest-value workflows still need supervision, approvals, and feedback.

The takeaway?

AI isn’t a “feature.”

AI is an operating system change.

And companies that understand this early…

ship faster, safer, and with real ROI.

If you're building enterprise AI, stop chasing “Data → AI → Value”.

Start building: Workflows → Guardrails → Monitoring → Value.

#AI

Knowing RAG is easy. Measuring it is the real skill.

Everyone can plug in a vector DB and call it a RAG system.
But only a few can actually evaluate whether the system retrieves the right knowledge, avoids hallucinations, and answers with confidence.

That’s where retrieval metrics matter, they turn guesswork into engineering.

Here’s a simple breakdown of the 20 metrics that actually show whether your RAG system is good… or just lucky.

1. Precision@K - Checks how many retrieved chunks are truly relevant.
2. Recall@K - Measures whether all important chunks were found.
3. Hit Rate - Confirms if at least one correct chunk appears.
4. MRR - Rewards results where the correct chunk ranks high.
5. NDCG@K - Evaluates ranking quality, not just correctness.
6. Top-1 Accuracy - Tests whether the first chunk is the best one.
7. Query Coverage - Checks if retrieval answers the entire question.
8. Evidence Sufficiency - Asks: “Is this enough to answer fully?”
9. Context Relevance Score - Measures alignment between query meaning and chunk content.
10. Chunk Redundancy Rate - Tracks repeated or duplicate chunks.
11. Chunk Diversity Score - Shows whether results come from varied, reliable sources.
12. Source Coverage - Checks if retrieval pulls from the right documents.
13. Citation Accuracy - Confirms the final answer cites real, correct evidence.
14. Groundedness Score - Tests how strongly the answer stays rooted in retrieved context.
15. Faithfulness - Ensures the model doesn’t distort the evidence.
16. Hallucination Rate - Measures unsupported claims the model invents.
17. Retrieval Latency - How fast chunks reach the model.
18. Token Efficiency - How much useful context fits into the token limit.
19. Freshness - Checks if the retrieved info is up-to-date.
20. Retrieval Failure Rate - Tracks how often retrieval returns irrelevant or misleading chunks.

Anyone can build RAG.
Only skilled teams can measure retrieval, and that’s what decides whether your system scales or breaks.

Which metric do you think is the most underrated right now?

Single agents are powerful.
But multi-agent systems?
That’s where AI starts behaving like a real team.

As AI moves from “answering questions” to doing actual work, the real unlock isn’t a bigger model, it’s how multiple agents collaborate.

Different patterns fit different types of tasks, and choosing the right one can be the difference between a smart system and a chaotic one.

Here are 8 multi-agent collaboration patterns every builder should understand:

1. Manager–Worker Pattern
One agent breaks down the task, others execute the pieces.
Great for large, structured work like research + writing + QA.

2. Specialist Agents (Role-Based Team)
Each agent has a defined role - researcher, analyzer, writer.
Best when accuracy and expertise matter.

3. Planner–Executor Pattern
A planner decides the steps; an executor runs tools and actions.
Ideal for automation tasks, workflows, and system integrations.

4. Critic / Reviewer Agent
One agent generates, another fixes mistakes.
Useful for RAG responses, code checks, compliance-sensitive tasks.

5. Debate Pattern
Agents challenge each other with alternative solutions until the best idea wins.
Good for strategy, architecture, and trade-off decisions.

6. Voting / Consensus Pattern
Multiple agents propose answers, the system picks the best one.
Great for open-ended, ambiguous, or high-risk queries.

7. Swarm / Parallel Exploration
Many agents explore multiple sources at once.
Perfect for fast research and time-sensitive retrieval.

8. Tool Router / Dispatcher Pattern
A router agent decides which tool or agent should handle the request.
Works best in enterprise setups with many databases, APIs, and systems.

AI isn’t becoming powerful because models are getting bigger.
It’s becoming powerful because agents are learning to work together.

This is what turns AI experiments into real products.

Teams get excited about the model… 
but the model is only one slice of the stack.

Real AI products work because every layer around it is engineered with intent.

This breakdown shows what a modern, production-grade AI product stack actually looks like: 

1. Application Layer
The user-facing shell - authentication, UI, APIs, monitoring, analytics. This is where reliability, speed, and trust are felt long before AI runs.

2. Agentic AI Layer
The intelligence engine - reasoning, retrieval, tool use, orchestration, and memory. This layer decides whether your system simply answers… or actually acts.

3. GenAI Ops Layer
The guardrails - tracing, evaluation, experiments, and quality checks. This is how you keep an AI system stable as it learns and evolves in production.

4. Tools & Services Layer
The essentials - model APIs, data warehouses, and external integrations. These services give your AI the inputs, structure, and capabilities it needs to function.

5. Infrastructure Layer
The backbone - compute, storage, GPUs, networking, and security. If this layer is weak, nothing above it scales safely.

AI products don’t succeed because of one great model.
They succeed because every layer, from UI to GPUs - works together as one system.

RAG is not just “upload PDFs + chat with them.”

But that’s like calling every vehicle a “car.”

Because in real AI systems, there are 3 completely different architectures hiding behind the same word:

Naive RAG vs Agentic RAG vs Agentic Memory.

And if you don’t know the difference, you’ll build the wrong system… and blame the LLM later.

Here’s what’s actually happening : 

1. Naive RAG (Basic Retrieval)
Naive RAG is the most common setup: documents get indexed once, stored in a vector database, and whenever a user asks a question, the system fetches the closest chunks and sends them to the LLM. It works well for simple document Q&A, but the accuracy completely depends on retrieval quality  - if the wrong chunk is retrieved, hallucinations become inevitable.

2. Agentic RAG (Tool-based Retrieval + Reasoning)
Agentic RAG is not a fixed pipeline - it’s a decision-making system. Instead of blindly retrieving from a single database, an agent dynamically decides which sources to search, which tools to use, whether the answer needs verification, and when to retry retrieval. This is what enables enterprise-grade search across documents, CRMs, knowledge bases, and even the web - but it also introduces more complexity and cost.

3. Agentic Memory (Long-term Learning Loop)
Agentic Memory is not about pulling knowledge from documents - it’s about building continuity. The system stores useful signals from past interactions (preferences, decisions, repeated tasks, user context) and retrieves them later to give better responses over time. This turns the assistant into something that evolves with usage but if memory isn’t governed properly, it can quickly become noisy, irrelevant, or even risky.

The easiest way to remember it:

Naive RAG = answers from stored docs
Agentic RAG = answers using tools + multiple sources
Agentic Memory = answers that improve over time

If you’re building agents in 2026…
understanding this difference is not optional, it’s architecture.

81k | Director, Data & Analytics/ AI @ Gartner

Summary

Making state-of-the-art Artificial Intelligence concepts accessible and profitable to professionals from any background for over 35 years!

Highlights
-  Veteran innovator with 3 decades of experience leading state-of-the-art AI and Analytics ventures
-  98% of students surveyed would recommend me to peers
-  Generative AI/ Agentic AI expert-  ChatGPT, Claude, Copilot, Perplexity, Lovable, n8n, more
-  Experienced AI instructor for Cisco, LinkedIn Learning, University of Pennsylvania, & Simplilearn
-  Senior AI Curriculum Advisor for Graduate School of Education at Ivy League university UPenn
-  Winner, International Society of Data Scientists Outstanding Voice Award
-  Social media leader with over 107k LinkedIn followers


Selected Clients
 
-  3M
-  Audi
-  Biogen 
-  Booz Allen Hamilton
-  Bristol-Myers Squibb
-  Cisco
-  Citicorp
-  Franklin Templeton
-  Gartner/ Evanta
-  Harvard Innovation Labs’ Experfy
-  IBM
-  International Olympic Committee
-  Los Angeles Times
-  Mercedes
-  Morgan Stanley
-  Royal Bank of Canada
-  St. Jude’s Children’s Hospital
-  Wells Fargo
-  US Homeland Security
-  US Veterans Administration 
-  Yale University

Partial Skills Inventory
 
-  AIOps
-  Accountable AI/ Ethics
-  Anomaly Detection
-  Anthropic/ Claude
-  Artificial Intelligence
-  AWS
-  Brokerage Firms
-  Client Management
-  Consulting
-  Compelling Presentations
-  Compliance Regs and Frameworks
-  Curriculum Development
-  Dashboards
-  Data Storytelling
-  Data Visualization
-  Education
-  Explainable AI
-  Feature Engineering
-  Financial Services
-  Fraud Detection
-  Gap Analysis
-  Generative AI
-  GitHub Models
-  Google Cloud
-  Google Gemini
-  Insurance
-  KPI's and Metrics
-  Lovable AI
-  Machine Learning
-  Microsoft Azure
-  MLOps
-  Model Ensembles
-  Nano Banana
-  OpenAI/ ChatGPT
-  Orange Data Mining
-  Perplexity
-  Postgres SQL
-  Professional Development
-  Research
-  Retail
-  Retention and Attrition
-  SPSS
-  SQL Server
-  Underwriting

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗛𝗶𝗿𝗶𝗻𝗴- 𝗝𝘂𝘀𝘁 𝗪𝗵𝗮𝘁 𝗔𝗥𝗘 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗿𝘀 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿?

The Terrible Facts:
- Artificial Intelligence needs a lot more experts.
- Tons of qualified professionals aren't getting hired.
- The expectations of hiring managers are just out of control.

If you're a hiring manager, please STOP looking for an entire department in just one employee.  It simply doesn't work that way.  You need a team.

Personally, I find the job search to be truly dreadful. It is exhausting, soul-crushing, depressing, dispiriting- actually, I will run out time before I run of negative emotions on this topic!  

But I hold out hope that one day everyone else will figure this out!

Please follow me for more analysis! 

- T. Scott Clendaniel

#AI Training for Executives | 108K LinkedIn Followers

It’s been a while since I started my role at Nautilus-AI.com as a Chief Learning Officer, but I wanted to share this update with everyone.

🔬 #ArtificialIntelligence: 𝗪𝗵𝗲𝗿𝗲 𝗗𝗼 𝗬𝗢𝗨 "𝗔𝗜"?

Our friends at GiGadgets researched something extraordinarily fun!

AI isn't the only place where innovation occurs.  Sometimes, the very desks we use to work with GenAI, Machine Learning, or any of the rest of AI occurs can show us remarkable creativity.

Take a look and smile!

- T. Scott Clendaniel

🔬 True Wisdom via Michael Tchuindjang!

Cybersecurity Thought Leader | Bridging Academia & Industry | Mentor for Startups & Aspiring Cyber Pros | Board Member Driving Cyber Impact | Associate Lecturer in Cybersecurity

Boardroom AI Strategy Meeting (2026 Edition) 



CEO: Who are we?
Boardroom: CEOs

CEO: What do we want?
Boardroom: AI!

CEO: AI to do what exactly?
Boardroom: We don’t know

CEO: When do we want it?
Boardroom: RIGHT NOW

If you’ve ever sat in a boardroom lately, this probably feels uncomfortably familiar.

AI has become the new magic word:

Replace “digital transformation”

Upgrade from “blockchain”

Stronger than “cloud-first”

Don’t get me wrong, AI is here to stay.

But AI without a clear problem, data readiness, risk assessment, or ethics is just… expensive enthusiasm.

Real innovation starts when we ask better questions:

What problem are we solving?
What risks are we introducing?
Who is accountable when AI fails?
Are humans still in the loop?

Otherwise, we’re not building AI strategies, we’re just speed-running confusion with a GPU 😄

Credits to @Cyber Writes ✍.

#ai #cybersecurity #security #leadership #aihype #responsibleai #jobs #hiringandpromotion #hiring #techhumor #humor #memes #klex

🔬 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜/ 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴, 𝗦𝘁𝗲𝗽-𝗕𝘆-𝗦𝘁𝗲𝗽:
𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗔 𝗖𝘂𝘀𝘁𝗼𝗺 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁 𝗔𝗽𝗽!

What happens when you're building Artificial Intelligence training and a process for execution?  You need to be able to manage all you video assets.  

But what if you want to go further?  What if you want to go beyond Google Sheets and Microsoft Excel and create your own app?

Well it helps if you're a genius like Tianyu Xu!  🤣 

However, I don't have that kind of luck.  So instead, I'm going to share his amazing video.  Take notes.  

Then #SAVE and #SHARE this with your teams and peers!

Happy AI-ing!

T. Scott Clendaniel

🎉 𝗚𝗲𝗻𝗔𝗜 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 𝗧𝗵𝗲 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗧𝗮𝗯𝗹𝗲! 

Here is the magic of video!

Generative AI can build entire new worlds.  This is one I absolutely love.  Mini planets made from elements in the period table:


𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀

▶ 1.  Copper
▶ 2.  Mercury
▶ 3.  Calcium

▶ 4.  Magnesium
▶ 5.  Palladium
▶ 6.  Uranium

▶ 7.  Arsen
▶ 8.  Lead
▶ 9.  Chlorine

Happy Chemistry

Please SAVE and SHARE!  You can follow me: T. Scott Clendaniel

🔬 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴 #AI: 𝗦𝘁𝗲𝗮𝗹𝗶𝗻𝗴 𝗮 𝗩𝗶𝗱𝗲𝗼 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗳𝗿𝗼𝗺 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆!

Picture this: you’re holding a cube. It’s got 8 corners, 12 edges, and 6 faces. 

Add the corners and faces, subtract the edges, and you always get 2. Every time. That’s Euler’s Polyhedron Formula—V - E + F = 2. It works for a cube, a pyramid, even a soccer ball.

So why does that matter? Because it’s a reminder that under all complexity, there’s structure. Euler didn’t just find a number—he found a pattern that connects everything.

That’s how I think about explaining AI. The goal isn’t to drown people in details; it’s to show the structure beneath the surface. When you connect data (edges), decisions (vertices), and outcomes (faces) just right, people get it.

The best teachers—whether they’re explaining geometry or generative AI—make the complex feel simple and the simple feel profound. That’s how real understanding sticks.

Happy Learning,

T. Scott Clendaniel

🔬 #ArtificialIntelligence Innovation: Human-Powered Helicopter!  

Human-powered helicopter hovers for more than a minute, defying sceptics' claims it was scientifically impossible!


𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘀𝘁

The Igor I Sikorsky Human Powered Helicopter Competition was launched in 1980 by the American Helicopter Society (AHS). The rules stated that the $250,000 prize could only be won by a human-powered machine capable of hovering for at least 60 seconds, reach at least 3 metres in height, and stay within an area 10 metres by 10 metres.

On Thursday, the award was handed over to the AeroVelo project, led by Todd Reichert and Cameron Robertson. Their winning flying machine, called Atlas, has a set of 20-metre rotors at each corner of a square frame 50 metres on each side, and is powered by a bicycle at the centre of the square.

Thanks to the use of lightweight materials, such as carbon fibre, the whole setup only weighs only 55kg despite its huge size. The team describe the Atlas machine in detail in a claim for a world record to the FAI (Fédération Aéronautique Internationale).

"This isn't something that you're going to commute to work in any time soon, but it's an exercise in really pushing the limits on what's physically possible, and what you can do with lightweight materials and really creative design," Reichert told the Canadian Press. "Winning this competition really is a catalyst to keep doing the things we love. Our goal is to take on projects that really inspire people to follow big dreams."


𝗧𝗵𝗲 𝗪𝗶𝗻𝗻𝗲𝗿𝘀

The AeroVelo team started work on Atlas in January 2012 and flight testing began in August. They refined the design over the following months and, despite two big crashes that destroyed much of their work, Reichert piloted the machine to make the record-breaking flight on 13 June (if you're reading on a mobile device click here to see the flight).

At the presentation of the prize on Thursday, AHS executive director, Mike Hirschberg, said that many in the world's vertical flight community had thought the Sikorsky challenge was impossible. "Several studies 'proved' that it was in fact scientifically impossible," he said. "A human being simply could not generate enough power for long enough to keep a human-powered helicopter in the air for 60 seconds, much less reach 3 metres. And when you add the weight of a control system to keep it hovering over a 10-metre box, it made it even more impossible.

"Well, it took a third of a century to prove those sceptics wrong. It took that long for the state of the art of vertical flight to see significant technological advances in lightweight structures, computer-aided design, aeromechanics, and multidisciplinary design optimisation."

Gotta love innovation!

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗶𝗻 𝗗𝗲𝘀𝗶𝗴𝗻: 𝗣𝗿𝗼𝘀𝘁𝗵𝗲𝘁𝗶𝗰 𝗟𝗶𝗺𝗯𝘀!

SoftFoot Pro is a cutting-edge, motorless prosthetic foot developed by Italian researchers, mimicking human foot anatomy with flexible chains and titanium to adapt passively to terrain, offering better stability, natural movement (like bending), and improved energy efficiency for amputees, and also serving as a robotic component. 


---
𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 & 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀
---

▶▶ 𝗛𝘂𝗺𝗮𝗻-𝗜𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝗗𝗲𝘀𝗶𝗴𝗻
Emulates the shape and structures of the human foot, including the plantar fascia, using flexible components.

▶▶ 𝗣𝗮𝘀𝘀𝗶𝘃𝗲 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻
Its flexible sole and chain system allow it to deform and widen its contact area, naturally adapting to rough or slippery surfaces without motors.

▶▶ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗠𝗼𝗯𝗶𝗹𝗶𝘁𝘆
Enhances stability, allows for natural actions like bending down, and makes navigating stairs easier for users.

▶▶ 𝗟𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 & 𝗦𝘁𝗿𝗼𝗻𝗴
Weighs about 450g and supports up to 100kg, using titanium/aluminum and high-strength plastic chains.

Have a great week!

Follow me for more tips: T. Scott Clendaniel

🔬  #GenAI- 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀!

CNN's are the basis for many tools, including most of the computer vision, image, and video generation for Generative AI.  Here's how to teach it to your team.


𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀

A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio.

Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently have been replaced -- in some cases -- by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections.

For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution (or cross-correlation) kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.


𝗦𝗼𝗺𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗖𝗡𝗡'𝘀

▶▶   image and video recognition
▶▶   recommender systems
▶▶   image classification
▶▶   image segmentation

▶▶   medical image analysis
▶▶   natural language processing
▶▶   brain–computer interfaces
▶▶   financial time series

CNNs are also known as shift invariant or space invariant artificial neural networks, based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps.  Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input.

Via Wikipedia ==> https://bit.ly/4126GDT

Happy Learning,

T. Scott Clendaniel

🔬 "𝗜𝗳 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗜𝘀 𝗕𝗼𝗿𝗶𝗻𝗴, 𝗜𝘁'𝘀 𝗕𝗲𝗶𝗻𝗴 𝗧𝗮𝘂𝗴𝗵𝘁 𝗕𝘆 𝗧𝗵𝗲 𝗪𝗿𝗼𝗻𝗴 𝗣𝗲𝗿𝘀𝗼𝗻!"

Okay, so Richard Feynman wrote the original quote about Science in general, not about Artificial Intelligence specifically.  So I confess.  I took a wee bit of dramatic license!

But UNILAD captured this fantastic video of Tatiana Erukhimova in here element teaching Physics.

And quite frankly, AI has a lot more possibilities for interactive content!

So no excuses!

And if you need help, reach out to me directly!  🤗 

- T. Scott Clendaniel




🎥 credits UNILAD/ Texas A&M University

JLL - Jones Lang LaSalle - Group Manager Top 50 Most Influential Leaders of 2025

“Our similarities bring us to common ground; Our differences allow us to be fascinated by each other.”

~ Tom Robbins ~

🔬 #AI #Education:
Introducing 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 demo- with 𝗣𝗨𝗣𝗣𝗜𝗘𝗦! 🐶🐶🐶

In the world of AI, Reinforcement Learning is truly a star, and the basis of things like self-driving cars/ autonomous vehicles.

It contains a few elements:

▶  Problem to be solved
▶  An agent, who is observed trying to solve the problem
▶  Reward/ penalty functions
▶  Scoring system

Just like these adorable puppies, who serve as our agents, trying to learn how to reach the treats, our agents try to figure out the problem.  Each puppy's success or failure is recorded, and the reinforcement learning system eventually solves the puzzle!

Enjoy!

T. Scott Clendaniel

🤗 𝗦𝗼𝗺𝗲 𝗚𝗼𝗼𝗱 𝗡𝗲𝘄𝘀- 𝗪𝗮𝘀𝘁𝗲 𝗪𝗮𝘁𝗲𝗿 𝗕𝗼𝘁𝘁𝗹𝗲𝘀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗙𝗿𝗲𝗲 𝗟𝗶𝗴𝗵𝘁𝗶𝗻𝗴!


𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄

Liter of Light is an open source design for a low-cost light tube (or deck prism or vault light) that refracts solar light to provide daytime interior lighting for dwellings with thin roofs. 

Daylighting is cheaper than using indoor electric lights during the day. The device is simple: a transparent two-liter bottle is filled with water plus a little bleach to inhibit algal growth and fitted into a hole in a roof. 

The device functions like a deck prism: during daytime the water inside the bottle refracts sunlight, delivering about as much light as a 40–60 watt incandescent bulb to the interior. A properly installed solar bottle can last up to 5 years.


𝗛𝗶𝘀𝘁𝗼𝗿𝘆

The use of plastic bottles in this way to provide indoor lighting from daylight was developed by Alfredo Moser of Brazil. Using the technology as a social enterprise was first launched in the Philippines by Illac Diaz under the My Shelter Foundation in April 2011. In order to help the idea to grow sustainably, Diaz implemented a "local entrepreneur" business model whereby bottle bulbs are assembled and installed by local people, who can earn a small income for their work. 


𝗥𝗲𝘀𝘂𝗹𝘁𝘀

Within months, the organization expanded from one carpenter and one set of tools in one community in San Pedro, Laguna to 15,000 solar bottle bulb installations in twenty cities around the Philippines, and began to inspire local initiatives around the world. MyShelter Foundation also established a training center that conducts workshops with youth, business companies, and other groups who are interested in volunteering their time to build lights in their communities.

In less than a year since inception, over 200,000 bottle bulbs were installed in communities around the world. Liter of Light had a goal to light up 1 million homes by the end of 2015.

Enjoy,

T. Scott Clendaniel

🔬 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗖𝗼𝗺𝗲 𝗔𝗹𝗶𝘃𝗲!

The art of surprise will help your learners stay intrigued as you go through your training!


𝗖𝗮𝗻 𝗬𝗢𝗨 𝗳𝗶𝗴𝘂𝗿𝗲 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲 𝗕𝗘𝗙𝗢𝗥𝗘 𝘆𝗼𝘂 𝗰𝗹𝗶𝗰𝗸 𝗽𝗹𝗮𝘆?  Tell me in the comments below!

Please make sure to #SAVE and #REPOST so your followers can take advantage of the advice!

- T. Scott Clendaniel

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