Key Points

1. 2 contexts: in-process analytics and stand alone analytics

Learning Roadmap 

TLearn-AI-v1.    GD

Stanford ML AI Basics Course With Labs.  GDF

Free AI course list

https://www.linkedin.com/posts/dirk-zee_free-ai-courses-save-1000s-of-hours-1-activity-7125048945776451586-iMDg?utm_source=share&utm_medium=member_desktop

data-science-4-dummies-v3-2022.pdf.   link.  << good beginner concepts on data science engineering / solutions./ concepts

https://objectcomputing.com/files/5715/6095/8662/Slide_Deck_Groovy_for_Data_Science_Webinar.pdf  - Groovy for Data Science - migrate v3x

http://glaforge.appspot.com/article/machine-learning-apis-with-apache-groovy - article on Groovy ML

https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ - basic ML and Python programming course covers Spark etc as well

http://ciml.info/ - ML algorithms

More Advanced

https://www.udemy.com/course/pytorch-for-deep-learning-with-python-bootcamp/

ML use cases & Models > Descriptive, Predictive, Prescriptive

_ml-use-cases-The Big Book of Machine Learning Use Case.pdf

Executive Guide to AI Best Practices - Snowflake

AI and Digital Transformation Competencies for Civil Servants

...

Key Points

1. 2 contexts: in-process analytics and stand alone analytics

Learning Roadmap 

TLearn-AI-v1.    GD

Stanford ML AI Basics Course With Labs.  GDF

Free AI course list

https://www.linkedin.com/posts/dirk-zee_free-ai-courses-save-1000s-of-hours-1-activity-7125048945776451586-iMDg?utm_source=share&utm_medium=member_desktop

aidata-science-skills-Artificial Intelligence and Digital Transformation Competencies-study-2022,4-dummies-v3-2022.pdf.   link

ai-skills-Artificial Intelligence and Digital Transformation Competencies-study-2022.pdf. file

...

I'll argue Trust is actually a capability not an attitude

Learning Strategies for AI

How I'd Learn AI in 2024 (if I could start over)

https://www.youtube.com/watch?v=h2FDq3agImI

Summary of "How I'd Learn AI in 2024 (if I could start over)"

1. Background and Context:

   • The speaker began studying AI in 2013 and has since worked as a freelance data scientist.
   • They have a YouTube channel with over 25,000 subscribers, sharing their AI journey and knowledge.
   • Emphasis on the growing AI market (expected to reach nearly $2 trillion by 2030) and the ease of entry into the field, especially with pre-trained models from OpenAI.

2. Understanding AI and Choosing a Path:

   • AI is a broad term, encompassing machine learning, deep learning, and data science.
   • The speaker encourages understanding AI beyond popular misconceptions and choosing between coding and no-code/low-code tools.
   • They stress the importance of technical understanding for those aspiring to build reliable AI applications.

3. Technical Roadmap and Learning Approach:

   • The roadmap focuses on learning by doing and reverse engineering, rather than solely theoretical understanding.
   • Key areas include setting up a working environment, learning Python, and understanding essential libraries like NumPy, pandas, and Matplotlib.
   • Emphasis on practical learning through projects and portfolio building, with resources like Kaggle and Project Pro mentioned.

4. Specialization and Knowledge Sharing:

   • After gaining foundational knowledge and experience, the speaker advises choosing a specialization in AI.
   • Sharing knowledge and teaching others is highlighted as a way to deepen one's understanding and contribute to the AI community.

5. Final Steps and Community Engagement:

   • The importance of applying knowledge in real-world scenarios, embracing challenges, and continuous learning.
   • Encourages joining communities of like-minded individuals and staying updated with the rapidly evolving field of AI and data science.
   • Offers a free resource with a complete AI learning roadmap, including training videos and instructions.

References

...

http://ciml.info/

https://github.com/hal3/ciml/

...

.  << good beginner concepts on data science engineering / solutions./ concepts

https://objectcomputing.com/files/5715/6095/8662/Slide_Deck_Groovy_for_Data_Science_Webinar.pdf  - Groovy for Data Science - migrate v3x

http://glaforge.appspot.com/article/machine-learning-apis-with-apache-groovy - article on Groovy ML

https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ - basic ML and Python programming course covers Spark etc as well

http://ciml.info/ - ML algorithms

More Advanced

https://www.udemy.com/course/pytorch-for-deep-learning-with-python-bootcamp/

ML and AI use cases & Models > Analytic, Descriptive, Predictive, Prescriptive, Reactive, Responsible, Smart

SWT custom definitions for >> Reactive, Responsible, Smart

_ml-use-cases-The Big Book of Machine Learning Use Case.pdf

Executive Guide to AI Best Practices - Snowflake

AI and Digital Transformation Competencies for Civil Servants

https://www.linkedin.com/posts/transformpartner_the-ai-and-digital-transformation-competency-ugcPost-7153356976977059840--Ee7?utm_source=share&utm_medium=member_desktop

ai-skills-Artificial Intelligence and Digital Transformation Competencies-study-2022,pdf.   link

ai-skills-Artificial Intelligence and Digital Transformation Competencies-study-2022.pdf. file

there is an unmet need to develop comprehensive digital competency frameworks that can:

 

1. Clearly identify the internal challenges a government faces in its digital transformation journey;

2. Propose specific competencies that can address those challenges,

The Artificial Intelligence and Digital Transformation Competency Framework includes three major Competency Domains:

 

1.        #DigitalPlanning and Design

2.        Data Use and Governance

3.        #DigitalManagement and Execution

 

The competency framework also includes five complementary Attitudes that enable civil servants to pursue digital transformation effectively:

1. Trust


2. Creativity


3. Adaptability


4. Curiosity


5. Experimentation

 

Each Competency Domain is structured around three Proficiency Levels: Basic, Medium and Advanced, and includes an ‘AI-specific level’ that aims to identify and unpack the major AI elements.

I'll argue Trust is actually a capability not an attitude

Learning Strategies for AI

How I'd Learn AI in 2024 (if I could start over)

https://www.youtube.com/watch?v=h2FDq3agImI

Summary of "How I'd Learn AI in 2024 (if I could start over)"

1. Background and Context:

   • The speaker began studying AI in 2013 and has since worked as a freelance data scientist.
   • They have a YouTube channel with over 25,000 subscribers, sharing their AI journey and knowledge.
   • Emphasis on the growing AI market (expected to reach nearly $2 trillion by 2030) and the ease of entry into the field, especially with pre-trained models from OpenAI.

2. Understanding AI and Choosing a Path:

   • AI is a broad term, encompassing machine learning, deep learning, and data science.
   • The speaker encourages understanding AI beyond popular misconceptions and choosing between coding and no-code/low-code tools.
   • They stress the importance of technical understanding for those aspiring to build reliable AI applications.

3. Technical Roadmap and Learning Approach:

   • The roadmap focuses on learning by doing and reverse engineering, rather than solely theoretical understanding.
   • Key areas include setting up a working environment, learning Python, and understanding essential libraries like NumPy, pandas, and Matplotlib.
   • Emphasis on practical learning through projects and portfolio building, with resources like Kaggle and Project Pro mentioned.

4. Specialization and Knowledge Sharing:

   • After gaining foundational knowledge and experience, the speaker advises choosing a specialization in AI.
   • Sharing knowledge and teaching others is highlighted as a way to deepen one's understanding and contribute to the AI community.

5. Final Steps and Community Engagement:

   • The importance of applying knowledge in real-world scenarios, embracing challenges, and continuous learning.
   • Encourages joining communities of like-minded individuals and staying updated with the rapidly evolving field of AI and data science.
   • Offers a free resource with a complete AI learning roadmap, including training videos and instructions.

References

Key Concepts

AI Terms

https://www.linkedin.com/posts/dirk-zee_%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F-%3F%3F-dont-miss-activity-7137821904437948416-m_t_?utm_source=share&utm_medium=member_desktop

...

Image Removed

AI platforms

...

Recent hardware investments from Amazon, Google, Microsoft and Facebook, made ML infrastructure cheaper and efficient. Cloud providers are now offering custom hardware that’s highly optimized for running ML workloads in the cloud. Google’s TPU and Microsoft’s FPGA offerings are examples of custom hardware accelerators exclusively meant for ML jobs. When combined with the recent computing trends such as Kubernetes, ML infrastructure becomes an attractive choice for enterprises.

...

Gen AI Platforms

Generative AI concepts

Generative AI: ChatGPT and more

Google AI: Gemini, Vertex, ai studio

LLMS >> 

Meta LLama

Claude.ai - Anthropic

Openai 

Gemini and Vertex

SLMs >>

Granite LLMS

AI Commercial Construction Tools Cheatsheet

ai-architecture-cheatsheet_aifi150.pdf   link

ai-architecture-cheatsheet_aifi150.pdf. file

Key Questions

1. what is data science?
2. how is it different from data analysis?
3. what questions do data scientists answer?
4. what questions do data analysts answer?
5. how are both different from data architects and engineers?
6. what are key AI use cases by industry?
7. what are key benefits for AI and ML ?  VCR3S

ML / AI Operations Models

User Type - Human or Automated System for decision making ( eg a vehicle recognizing people crossing a road )

Operations Type - Standalone research ( which stock to buy long term ) or Embedded Operations ( decide when to buy power on a grid automatically )

4 Types of AI

https://www.govtech.com//computing/understanding-the-four-types-of-artificial-intelligence.html?utm_campaign=Re-engagement%20-%20GT&utm_medium=email&_hsmi=223352446&_hsenc=p2ANqtz--J8naIOn0aB0TgrqNWDE3kqq3yFEH5eQBWk7YF3DznZ8_0G9B8oKoXge_75XP67FNxYFcu89SblNADJRYFKjoXw0h7DA&utm_content=223352168&utm_source=hs_automation

govtech.com-Understanding the Four Types of Artificial Intelligence.pdf  link

Generative AI creates novel content

https://finance.yahoo.com/news/china-building-parallel-generative-ai-150129822.html

After text-to-image tools from Stability AI and OpenAI became the talk of the town, ChatGPT's ability to hold intelligent conversations is the new obsession in sectors across the board.

Generative AI has trust and quality issues: needs better trust, transparency, verifications with SLT

https://www.cnet.com/tech/google-lets-people-start-trying-bard-its-own-ai-chatbot/

SLT - Shared Ledger Technology - can make Generative AI better

It can add better: trust, transparency and verifications

AI Agents 

Agentic AI 

...

Key Concepts

AI Terms

https://www.linkedin.com/posts/dirk-zee_%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F-%3F%3F-dont-miss-activity-7137821904437948416-m_t_?utm_source=share&utm_medium=member_desktop

Don't miss the Essential Concepts:



- 𝗔𝗜: The overarching world of Artificial Intelligence, transforming industries.

- 𝗠𝗟: Machine Learning's role in shaping intelligent systems.

- 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Neural networks for human-like thinking.

- 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸: Mimicking human brain functions for learning.

- 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Teaching computers with labelled examples.

- 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Machines finding patterns without labels.

- 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Trial-and-error learning for machines.

- 𝗡𝗟𝗣: Tech enabling computers to understand human language.

- 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻: Machines interpreting visual information.

- 𝗖𝗵𝗮𝘁𝗯𝗼𝘁: Conversational AI for customer support and more.

- Hallucination - when Gen AI produces the wrong answer but is convinced it's correct

- 𝗜𝗢𝗧: Devices connected, sharing data for smart applications.

- 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Remote storage, management, and data processing.

- 𝗕𝗶𝗮𝘀 𝗶𝗻 𝗔𝗜: Addressing unintentional biases in algorithms.

- 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺: Core of AI, the building block for intelligent systems.

- 𝗗𝗮𝘁𝗮 𝗠𝗶𝗻𝗶𝗻𝗴: Extracting patterns and insights from vast datasets.

- 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮:Navigating challenges with massive and diverse data.

- 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀: Merging AI with physical machines for automation.

- 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗙𝗮𝗶𝗿𝗻𝗲𝘀𝘀: Ensuring fairness and avoiding bias in AI.

- 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Applying knowledge for enhanced AI efficiency.

- 𝗘𝗱𝗴𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Localised AI implementation for efficiency.

- 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜: Making AI decisions transparent and understandable.

- 𝗚𝗔𝗡𝘀: AI creating realistic data through adversarial networks.

- 𝗘𝗱𝗴𝗲 𝗔𝗜: Localised AI for reduced reliance on centralised servers.

- 𝗔𝗜 𝗘𝘁𝗵𝗶𝗰𝘀: Guiding principles for responsible AI development.

Image Added

AI platforms

1. Cloud ML platforms engineered for ML performance
   

   Recent hardware investments from Amazon, Google, Microsoft and Facebook, made ML infrastructure cheaper and efficient. Cloud providers are now offering custom hardware that’s highly optimized for running ML workloads in the cloud. Google’s TPU and Microsoft’s FPGA offerings are examples of custom hardware accelerators exclusively meant for ML jobs. When combined with the recent computing trends such as Kubernetes, ML infrastructure becomes an attractive choice for enterprises.

   Amazon EC2 Deep Learning AMI backed by NVIDIA GPU, Google Cloud TPU, Microsoft Azure Deep Learning VM based on NVIDIA GPU, and IBM GPU-based Bare Metal Servers are examples of niche IaaS for ML.
   
   
   
   

2. Deep learning systems can learn new information, improve their own performance
   
   The complexity of the steps needed in a given decision making process all depend on the skills of the person or system making the decision. Just like we teach people basic skills before advanced, we teach systems the same way. Extending the metaphor, just like people, systems can also learn on their own:  for a given goal, collect relevant data and update their understanding ( the model ) of how best to solve a problem.
   In a driving example, once the system knows how to brake, how to steer, how to follow a digital map, how to sense objects within a range, asking the system to drive me to work is not a complex set of instructions and decisions. Systems, just like people, can build skills, knowledge and decision making capability leveraging what they already know. (edited)

Gen AI Platforms

Generative AI concepts

Generative AI: ChatGPT and more

Google AI: Gemini, Vertex, ai studio

LLMS >> 

Meta LLama

Claude.ai - Anthropic

Openai 

Gemini and Vertex

SLMs >>

Granite LLMS

AI Commercial Construction Tools Cheatsheet

ai-architecture-cheatsheet_aifi150.pdf   link

ai-architecture-cheatsheet_aifi150.pdf. file

Key Questions

1. what is data science?
2. how is it different from data analysis?
3. what questions do data scientists answer?
4. what questions do data analysts answer?
5. how are both different from data architects and engineers?
6. what are key AI use cases by industry?
7. what are key benefits for AI and ML ?  VCR3S

ML / AI Operations Models

User Type - Human or Automated System for decision making ( eg a vehicle recognizing people crossing a road )

Operations Type - Standalone research ( which stock to buy long term ) or Embedded Operations ( decide when to buy power on a grid automatically )

4 Types of AI

https://www.govtech.com//computing/understanding-the-four-types-of-artificial-intelligence.html?utm_campaign=Re-engagement%20-%20GT&utm_medium=email&_hsmi=223352446&_hsenc=p2ANqtz--J8naIOn0aB0TgrqNWDE3kqq3yFEH5eQBWk7YF3DznZ8_0G9B8oKoXge_75XP67FNxYFcu89SblNADJRYFKjoXw0h7DA&utm_content=223352168&utm_source=hs_automation

govtech.com-Understanding the Four Types of Artificial Intelligence.pdf  link

Generative AI creates novel content

https://finance.yahoo.com/news/china-building-parallel-generative-ai-150129822.html

After text-to-image tools from Stability AI and OpenAI became the talk of the town, ChatGPT's ability to hold intelligent conversations is the new obsession in sectors across the board.

Generative AI has trust and quality issues: needs better trust, transparency, verifications with SLT

https://www.cnet.com/tech/google-lets-people-start-trying-bard-its-own-ai-chatbot/

SLT - Shared Ledger Technology - can make Generative AI better

It can add better: trust, transparency and verifications

AI Agents vs Agentic AI article

AI Agents 

 AI Agents are typically built to do specific tasks. They’re designed to help you with something — like answering questions, organizing your calendar, or even managing your email inbox. AI Agents are great at automating simple, repetitive tasks but don’t have the autonomy or decision-making abilities that Agentic AI does. Think of them as virtual helpers that do exactly what you tell them to do, without thinking for themselves.

Image Added

Agentic AI 

At its core, Agentic AI is a type of AI that’s all about autonomy. This means that it can make decisions, take actions, and even learn on its own to achieve specific goals. It’s kind of like having a virtual assistant that can think, reason, and adapt to changing circumstances without needing constant direction. Agentic AI operates in four key stages:

1. Perception: It gathers data from the world around it.
2. Reasoning: It processes this data to understand what’s going on.
3. Action: It decides what to do based on its understanding.
4. Learning: It improves and adapts over time, learning from feedback and experience.

Image Added

Difference between AI Agents and Agentic AI

Image Added

Agentic AI use cases

1. Tesla, Waymo - self driving cars
2. Supply Chain Management: Agentic AI is also helping companies optimize their supply chains. By autonomously managing inventory, predicting demand, and adjusting delivery routes in real-time, AI can ensure smoother, more efficient operations. Amazon’s Warehouse Robots, powered by AI, are an example — these robots navigate complex environments, adapt to different conditions, and autonomously move goods around warehouses.
3. Agentic AI can detect threats and vulnerabilities by analyzing network activity and automatically responding to potential breaches. Darktrace, an AI cybersecurity company
4. IBM’s Watson Health uses AI to analyze massive amounts of healthcare data, learning from new information to offer insights that help doctors and healthcare professionals.

AI Agent use cases 

1. Simple Chatbots that don't give good answers to most questions - Zendesk etc
2. Phone assistants 
3. Google email response composer
4. Github Co-Pilot

Keys to AI success

1. Good Use Cases >> GAPS can create well defined use cases with metrics for clear value-add opportunities ( see VCRST )
   
   
2. DATES focus >> Data, Decisions, Automations, Trusts, Events, Security engineering model focus including ( AI Security )
   
   
3. STEAR Goverannce framework >> Automated Governance and Regulatory Compliance using the STEAR solution architecture governance framework and VITAC 

Enterprise AI Solutions & Operations in the Cloud

ai-devops-ENTERPRISE AI in the Cloud-2024.pdf

...

https://www.forbes.com/sites/vipinbharathan/2023/06/25/guardrails-for-ai-what-is-possible-today/?sh=150eef613a0d

“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” The Center for AI Safety published this single statement as an open letter on May 30, 2023.

OWASP and AI Governance of LLM ( current technology )

...

https://www.forbes.com/sites/vipinbharathan/2023/06/25/guardrails-for-ai-what-is-possible-today/?sh=150eef613a0d

The architecture of an emerging startup, Modguard, in the accompanying diagram, gives us a path towards compliance with these laws

Image Modified

EU AI Regulations

...

Secretary of Commerce has a lead role in defining rules for AI model and service management

" The Secretary of Commerce, in consultation with the Secretary of State, the Secretary of Defense, the Secretary of Energy, and the Director of National Intelligence, shall define,"

Assignments > Objectives with normal time limits less than 1 year for reporting on impacts, goals, strategies by agency

Identifies responsibity concepts for businesses in building, using AI in commerce

Fact Sheet on Executive Order for Responsible AI

https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/

New Standards for AI Safety and Security

developers of the most powerful AI systems share their safety test results and other critical information with the U.S. government

 standards, tools, and tests to help ensure that AI systems are safe, secure, and trustworthy

Protect against risks to people, organizations, ecosystems, systems

Reduce fraud, deception, misinformation 

Protect Privacy and Rights

Promote Innovation

Promote American Leadership ( or better partnerships ?? )

Effective governance for Responsible AI

NIST Proposed AI Risk Mgt Framework

...

AI Notes including Governance

AI-notes1 gdoc

DeepSeek R1 - LLM - 2025 - notes

DeepSeek’s R1, the new Chinese large-language model that’s more powerful and incurred 95% less development costs than its American competitors, is the best thing to happen to artificial intelligence in a decade

flaws in framing this with Cold War-era militarist logic. <<  a problem since BOTH sides do this now and common governance is the only real solution to mistrust

previous Trump Administration’s hostility led Beijing to double-down on developing strategically important technologies. << probably was on their roadmap anyway

difficult to keep these ideas locked down when they are constantly being shared << yes technology advantages have a short life

Potential Value Opportunities

...