| Table of Contents |
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Key Points
- 2 contexts: in-process analytics and stand alone analytics
Learning Roadmap
Stanford ML AI Basics Course With Labs. GDF
Free AI course list
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
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| Table of Contents |
|---|
Key Points
- 2 contexts: in-process analytics and stand alone analytics
Learning Roadmap
Stanford ML AI Basics Course With Labs. GDF
Free AI course list
https://www.linkedin.com/posts/transformpartnerdirk-zee_thefree-ai-courses-andsave-digital1000s-transformationof-competencyhours-ugcPost1-7153356976977059840activity-7125048945776451586-Ee7iMDg?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
https://www.youtube.com/watch?v=h2FDq3agImI
Summary of "How I'd Learn AI in 2024 (if I could start over)"
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.
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.
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.
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.
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
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https://hbr.org/2020/10/a-practical-guide-to-building-ethical-ai
Practical Guide to Building Ethical AI ** HBR pdf
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https://www.quantinsti.com/blog/machine-learning-basics
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Machine Learning Basics
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https://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/
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Executive summary 1 on ML
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Linux Foundation Open AI
Deep Learning Solutions
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Machine Learning Math Course free ***
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file:///C:/Users/Jim%20Mason/Google%20Drive/_docs/howto/data/mlearn/Guide_to_Open_Source_AI.pdf
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Linux F Guide to OS AI frameworks - good starting point
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https://objectcomputing.com/files/5715/6095/8662/Slide_Deck_Groovy_for_Data_Science_Webinar.pdf
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Groovy for Data Science
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DataCamp course - Python for Algorithmic Trading
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https://www.datasciencecentral.com/profiles/blogs/new-books-and-resources-for-dsc-members
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datasciencecentral.com free ebooks
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https://drive.google.com/open?id=1oa3bMB6KHDbo6ftgZMWSfnj8FpwQWCrm
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https://d2wvfoqc9gyqzf.cloudfront.net/content/uploads/2018/09/Ng-MLY01-13.pdf
https://drive.google.com/open?id=1wDgmF9V3A7zeBXslgmRube2_cQ0XzxrM
...
. << 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
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
https://www.youtube.com/watch?v=h2FDq3agImI
Summary of "How I'd Learn AI in 2024 (if I could start over)"
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.
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.
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.
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.
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
...
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 verificationsadd 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.
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:
- Perception: It gathers data from the world around it.
- Reasoning: It processes this data to understand what’s going on.
- Action: It decides what to do based on its understanding.
- Learning: It improves and adapts over time, learning from feedback and experience.
Difference between AI Agents and Agentic AI
Agentic AI use cases
- Tesla, Waymo - self driving cars
- 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.
- Agentic AI can detect threats and vulnerabilities by analyzing network activity and automatically responding to potential breaches. Darktrace, an AI cybersecurity company
- 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
- Simple Chatbots that don't give good answers to most questions - Zendesk etc
- Phone assistants
- Google email response composer
- Github Co-Pilot
Keys to AI success
- Good Use Cases >> GAPS can create well defined use cases with metrics for clear value-add opportunities ( see VCRST )
- DATES focus >> Data, Decisions, Automations, Trusts, Events, Security engineering model focus including ( AI Security )
- 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
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“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 )
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The architecture of an emerging startup, Modguard, in the accompanying diagram, gives us a path towards compliance with these laws
EU AI Regulations
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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
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
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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
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