🦾Jepson Taylor

VEOX

VEOX Inc is revolutionizing the AI industry by creating an AI-driven algorithm factory, compressing decades of progress into moments. Our technology automates AI science research, continuously improving algorithm efficiency and outperforming human-made algorithms. VEOX is not just building another AI company; we're catalyzing an explosion of technological advancements that will reshape the entire tech landscape. Join us in accelerating intelligence beyond human limits and leading the next wave of AI breakthroughs.

CEO/founder VEOX

Artificial Intelligence Masterclass, NYU - Intellibus

FYI, I’ll be co-leading an AI Master Class for about 20 senior leaders at NYU’s School of Professional Studies in September and October!  We have a fantastic team of expert AI panelists too, and I’d love to have you join the program. If you’re keen then drop me a note and I’ll make sure a spot is saved for you.  Also, feel free to send this link onto others who will benefit from joining. This course will be the #1 AI course in the US for senior leaders and will change the way you think about AI and how we tackle its opportunities and threats. A big part of this course is the close collaboration between industry and academia. More info here: https://www.aimasterclass.com/

Contributing Faculty

AtomicSoul.ai

I love being the host for the Atomic Soul Podcast where I explore the very things driving humanity forward. This isn’t your average “AI is taking over our civilization” show - it’s something much more rare that’s sure to make you reevaluate your perspective on controversial topics. Each week we’ll hear from the world’s most interesting people, like corporate giant execs and people bending the mold. It’s at this very intersection of tech and the brain where we expand our consciousness.

Podcast Host

Dataiku

I will provide straightforward industry perspectives and insights, thought leadership on the future of AI, and share the stories and best practices of Dataiku’s growing enterprise customers. I will help accelerate the company’s strong recent momentum, including: 
Over 500 customers with 150+ on the Forbes Global 2000 list
95% customer retention rate with Forbes Global 2000 customers 
Industry recognition includes being named to the Forbes Cloud 100 and Forbes AI 50 lists, top 10 in the Data50, and Snowflake’s ML/AI partner of the year.  
Certification as a Great Place to Work, with 94% of Dataikers saying the company is a great place to work (compared to 57% of employees in a typical U.S.-based company)

You can’t have fun if the fundamentals aren’t working and I was impressed with Dataiku’s growth, enterprise customer count, customer satisfaction, and one collaborative platform. 

I want to work for a market leader and believe that Dataiku is indeed the leader that industry analysts and 500+ customers say they are and I want to invest my life force into this company. 

I am very excited about the next year, and my excitement has only continued to increase since joining. This company has the soul I was seeking.

Read more about my WHY here https://blog.dataiku.com/ben-taylor

Chief AI Strategist

DataRobot

As Chief AI Strategist at DataRobot, I work closely with our innovation team, marketing, sales, and product to ensure innovation is aligned with our product roadmap/strategy. I also help nurture strategic partnerships for the innovation team, can we combine like-minded visionaries to accelerate the future? Can we inspire the next generation by combining world-class innovation with brilliant storytelling? I love working with brilliant technologists and amplifying them with genius creatives. Magic happens between these two types of experts. Some of that work was showcased with our 10,000 cast piece which ended up being nominated for a Webby award. Very excited about what we have in the pipeline this year.

Artificial intelligence is already changing our world for the better. Unfortunately, too many homegrown projects are failing giving the rest of the industry a bad rap. Too many executives are naive to the opportunity. The gap of "do-ability" isn't being crossed in the mind of the business executive. AI should inspire, it should transform, everyone should wake up in the morning excited about what AI could do to automate their business and lift our society to new innovative heights.

Chief AI Evangelist

Zeff

co-founder, Chief AI Officer

Working crazy hours to deliver the world's best machine and deep learning to product companies with the least amount of effort. Our goal was to be easiest to use, but surprisingly we are winning on scalability, accuracy, and implementation time. If you need any machine learning in 2017/2018 reach out to see if you are a fit for our current offering. Great press to come.

co-founder & Chief Data Officer

HireVue

Building out HireVue's data science team with the talent needed to execute on our technical data roadmap. We currently have one of the best deep learning teams in the state and by next year it could be one of the top 10 deep learning teams in the country. I love the work we are doing, the opportunity we have, and the success we are seeing. Working with brilliant people is critical to job satisfaction and we have that at HireVue.

Chief Data Scientist

Is it possible to take something as unstructured and complicated as a digital interview and predict the quality of the candidate response? The answer is yes! Imagine what you can do with 100,000x more data than a resume. Leading HireVue's data science team to success on algorithm development for the HireVue insights IRIS product, a new data frontier for sure.

Principal Data Scientist

Metaheuristic Solutions LLC

Metaheuristic Solutions is a optimization consulting company with the world's leading metaheuristic optimization algorithm, the x algorithm. The new algorithm was developed by Ben Taylor and was made available in 2011.

Founder/CTO

IM Flash Technologies

Using Matlab, R, and Julia to develop multivariate and single variate models and analysis (MVA/UVA) for tool fault detection. These models are then used to shut down tools used in the semiconductor manufacturing process, notify stake holders, and mark up lots for real-time detection analysis (RDA). Projects also include building complex yield models from tool data and automating alarm rule setup as well as fab wide tool fingerprinting to identify rogue behavior even if alarms are not present.

I am also very busy on the web app side building PHP/Mysql wrappers to give engineers access to big data solutions. Hoping to switch soon to python/django/postgres. Also using custom built SQL queuing system that I designed on the backend to manage user loading for job requests.

Senior Data Scientist, Fault Detection

launchup.org

observer

I build stuff that doesn’t exist, an AI explorer. 
Unfortunately I have too many connections so please follow me instead. 

人工智能 (Chinese)
人工知能 (Japanese)
कृत्रिम बुद्धिमत्ता (hindi)
الذكاء الاصطناعي (Standard Arabic)
কৃত্রিম বুদ্ধিমত্তা (Bengali)
искусственный интеллект (Russian)

I watch the watchers, I direct the learners. 

I am passionate about machine learning. My life's mission is to further the boundaries of what is possible with data science. I spend as much time as I can working with deep learning, NLP, and all forms of data science. I currently have a passion project I am managing where I am combining the power of genetic programming with deep learning to automate network design. I think there is a trend happening in data science where human expertise and creativity are being replaced by powerful new methods (i.e. deep learning, big data). 

I am most well known for my machine learning work with the data science community and with HireVue. With the community, I enjoy posting opinions on jobs and tutorials on ML methods. I also enjoy speaking and presenting to all types of audiences. 

I've enjoyed working across engineering, product, sales, marketing, and customer success. This wider POV has helped shape the way I think about innovation, from the innovation farm to the customer's table ;)

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

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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Do you think Gemini is the only big AI release from Google?
The truth is, Google is among the few companies that built one of the most complete AI ecosystems in the world and most people don’t even realize how deep, interconnected, and powerful it has become. 

From advanced multimodal models to coding agents, business assistants, creative tools, and enterprise AI platforms, Google’s ecosystem covers nearly every category of modern AI development.

Here’s a breakdown and what each part does:

🔸The Models
Google’s core family of Gemini and Gemma models power reasoning, multimodal understanding, fast inference, and lightweight on-device AI.

🔸Build & Code
A full suite of tools which include App Builder, AI Studio, Code Assist, Antigravity, and Jules. They help developers prototype apps, debug code, automate tasks, and build production-ready agents.

🔸Video & Motion
Tools like Veo, Lumiere, Flow, and VideoFX generate cinematic footage, consistent characters, smooth motion, and AI-powered video storytelling.

🔸Image Creation
Imagen 3, Nano Banana, Google ImageFX, Whisk, and Stitch create photorealistic images, editable UI layouts, artistic styles, and precise controlled-generation outputs.

🔸Assistants & Business
Gemini Live, Gemini Gems, Workspace Gemini, Google PomellI, and NotebookLM act as reasoning assistants, brand agents, workflow automators, and personalized long-term AI personas.

🔸Additional Ecosystem Pieces
Vertex AI, Gemini for Firebase, Gemini Nano, Creative tools, and AI Safety systems bring enterprise orchestration, mobile integrations, on-device intelligence, and responsible AI safeguards.

However, this doesn’t mean this is the only choice you have when it comes to building state of the art AI systems. Would love to know which one you’re a big fan of. 
#AI

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Check out this simple breakdown of the four core LLM architectures shaping most of today’s advanced systems.

Not all LLMs reason the same way. Building reliable AI systems, or choosing the right model for a workflow requires a clear mental model of the architectures that drive them.

Let’s review:

1.🔸Decoder Only (GPT, LLaMA, Grok)
Generates text by predicting one token at a time using previous context.

Best for generative workloads, conversational systems, and multi step reasoning.

2.🔸Encoder Only (BERT, RoBERTa)
Processes text with full bidirectional context to learn dense semantic representations.

Best for classification tasks, retrieval, and embedding generation.

3.🔸Encoder Decoder (T5, FLAN T5, BART)
Separates understanding from generation. The encoder builds meaning, the decoder transforms it.

Best for translation, summarization, and any structured text to text transformation.

4.🔸Mixture of Experts (Mixtral, Gemini 1.5)
Routes each input to a subset of specialized expert networks.

Best for scaling model capacity while controlling compute cost.

These architectures go beyond academic details to determine latency profiles, context handling, fine tuning strategies, and how models behave under real workloads. A clear grasp of them helps you design better pipelines, choose the right foundation models, and understand why two LLMs can produce very different outcomes on the same task.

Let me know your thoughts!

#LLM

I believe the next wave of AI systems will not be defined by bigger models but by smarter orchestration. Salman Paracha got my attention on Arch Router, which introduces a routing approach that aligns model selection with human preferences instead of benchmark scores. You should absolutely check it out, as I know machine alignment is one of the most talked about challenges at enterprise. 

Arch Router treats routing as a semantic matching problem between a user query and a set of natural language policies built from a Domain and Action taxonomy. This design gives teams direct control over how queries should be handled and removes the need to retrain routers whenever new models are added. 

The authors trained a compact 1.5B model that outperforms top proprietary models in multi-turn routing while running at a fraction of their latency. The result is a practical blueprint for multi-model systems that are fast, interpretable, and aligned with real usage rather than synthetic metrics.  

Let me know your thoughts on this one. 

Salman and I will be sharing more on the subject.

#LLM

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The Battle

🦾Jepson Taylor

Greg Coquillo

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taplio

The Battle

🦾Jepson Taylor

Greg Coquillo

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Greg Coquillo's LinkedIn Performance

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