Key Points
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| Reference_description_with_linked_URLs_________________________ | Notes_________________________________________________________ |
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| http://ciml.info/ | Machine Learning overview focused on algorithms for different problem types |
| Math Foundations for AI ML |
| Statistics Foundations for AI ML |
http://ciml.info/ https://github.com/hal3/ciml/ | ML Course 1 |
https://www.datacamp.com/courses/preparing-for-statistics-interview-
questions-in-python?fbclid=IwAR29QSzZqoJEaarVtOoSRimPGOFbPbVYGLQ3
j2nyqt4PZ74AmkcJqILip94 | Interview questions for statistics in python |
| https://courses.edx.org/dashboard | edX courses for ML |
| https://thenextweb.com/podium/2019/11/11/machine-learning-algorithms-and-the-art-of-hyperparameter-selection/ | ML Algorithm concepts |
| https://www.linkedin.com/feed/update/urn:li:activity:6617271768493191168/ | ML Intro Concepts links - Linkedin |
| Guide_to_Open_Source_AI.pdf | Guide_to_Open_Source_AI.pdf |
| machine-learning-for-dummies-w_wile255.pdf | machine-learning-for-dummies-w_wile255.pdf |
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| ML use cases |
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| The Big Book of Data Science Use Cases.pdf | The Big Book of Data Science Use Cases.pdf |
| https://www.gartner.com/smarterwithgartner/top-3-benefits-of-ai-projects/ | Gartner - 3 key AI benefits |
| https://alyce.ai/application/files/7615/6933/9987/ALYCE_Survey_.pdf | Alyce.AI - object computing survey with use cases |
| AI_2019-news-from-the-batch-includes-AV-info-jmason.pdf | AI status - 2019 - The Batch |
https://www.forbes.com/sites/janakirammsv/2019/01/01/an-executives-guide-to-
understanding-cloud-
based-machine-learning-services/#6e7c6c043e3e | Forbes - overview of cloud machine learning services |
https://hbr.org/2020/10/a-practical-guide-to-building-ethical-ai Practical Guide to Building Ethical AI ** HBR pdf | Practical Guide to Building Ethical AI ** HBR |
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| Machine Learning Basics |
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https://www.quantinsti.com/blog/machine-learning-basics | Machine Learning Basics |
https://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/ | Executive summary 1 on ML |
https://lfdl.io/projects/ | Linux Foundation Open AI Deep Learning Solutions |
http://ciml.info/ | Machine Learning Math Course free *** |
| https://www.udemy.com/hands-on-introduction-to-artificial-intelligenceai/learn/lecture/12130906#overview | Udemy free ML course ** |
file:///C:/Users/Jim%20Mason/Google%20Drive/_docs/howto/data/mlearn/Guide_to_Open_Source_AI.pdf | Linux F Guide to OS AI frameworks - good starting point |
https://objectcomputing.com/files/5715/6095/8662/Slide_Deck_Groovy_for_Data_Science_Webinar.pdf | Groovy for Data Science |
| http://glaforge.appspot.com/article/machine-learning-apis-with-apache-groovy | ML apis from Groovy ( Java ) integrating Google AI - ML services ** |
| machine-learning-with-python-v2-2024.pdf. link | machine-learning-with-python-v2-2024 ** |
https://www.datacamp.com/community/tutorials/finance-python-trading?fbclid=IwAR1n33gRfaNvZv02RR5wCrSNkS4RqxJQqipBG
QuqMyWc6_akSPQ2hAkFp6c | DataCamp course - Python for Algorithmic Trading |
https://www.datasciencecentral.com/profiles/blogs/new-books-and-resources-for-dsc-members | datasciencecentral.com free ebooks |
https://www.slideshare.net/carologic/ai-and-machine-learning-demystified-by-carol-smith-at-midwest-ux-2017 https://drive.google.com/open?id=1oa3bMB6KHDbo6ftgZMWSfnj8FpwQWCrm | AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017 |
| https://drive.google.com/open?id=1sPBXhjtvEt54BlyAcW_Mz1PMETzCTLdv | machine learning for dummies ebook |
https://d2wvfoqc9gyqzf.cloudfront.net/content/uploads/2018/09/Ng-MLY01-13.pdf https://drive.google.com/open?id=1wDgmF9V3A7zeBXslgmRube2_cQ0XzxrM | Deep Learning Strategies and Project mgt - Andrew Ng |
| https://drive.google.com/open?id=1H3ivxDSLUxsw2CQAXUhdyzMPGZdnAj6j | Python Tensorflow Tutorial - datacamp 2018 |
| python-tensorflow-tutorial1-datacamp-Convolutional Neural Networks with TensorFlow | python-tensorflow-tutorial1-datacamp-Convolutional Neural Networks with TensorFlow |
https://www.datacamp.com/community/tutorials/cnn-tensorflow-python https://drive.google.com/open?id=1H3ivxDSLUxsw2CQAXUhdyzMPGZdnAj6j | Tutorial: Tensorflow neural network example in Python: Datacamp |
https://www.datacamp.com/community/tutorials/tensorflow-tutorial https://drive.google.com/open?id=1pC1805LpYe4el3mCT7qw0wzqV-vP3sv4 | Tutorial: Tensorflow basics : Datacamp |
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| https://drive.google.com/open?id=1b2bh7XJ_MkVG0C1kx7IITOqK0XfFh8WK | Containers for ML workloads |
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| billable courses |
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| https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ | Python ML covers Python basics - $12 - RECOMMENDED first course |
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| https://university.cloudera.com/instructor-led-training/cloudera-data-scientist-training | Cloudera - $3200 - 4 days - pyspark - sparklr - spark2 env |
| https://www.udemy.com/course/pytorch-for-deep-learning-with-python-bootcamp/ | Pytorch bootcamp - Udemy - $12 - sharp focus on Pytorch -requires Python |
| https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ | Python ML covers Python basics - $12 |
| https://www.udemy.com/course/the-complete-neural-networks-bootcamp-theory-applications/ | Complete Neural Networks bootcamp - $12 - more ML models, examples - requires Python, math |
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Google ML |
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| GCP platform - free account |
https://www.kaggle.com/ | Google Kaggle - free ML test account |
https://www.kaggle.com/learn/overview | Kaggle learning resources |
https://techcrunch.com/2019/06/24/google-brings-together-bigquery-and-
kaggle-in-new-integration/?yptr=yahoo | Link Kaggle notebook to BigQuery results |
https://www.datacamp.com/community/blog/keras-cheat-sheet?fbclid=IwAR2x0PgQEjsALKSIrlC_ZkADzXgqUyG9dJ_zAeh7h1c
VFrQQzcEjRxtWB98 | Keras cheat sheet |
| https://keras.io/ | Keras Basics |
| https://drive.google.com/open?id=1Y5jf0CLBlhieO85Er0n8bsYnScnXEIvG | Tf.keras - tensor flow Keras gen 2 |
https://medium.com/sciforce/understanding-tensor-processing-units-10ff41f50e78 https://drive.google.com/open?id=1lFzNJcXvyuD41vkB1K_Gkftgz9o4DHaJ | Tensorflow concepts |
https://cloud.google.com/blog/products/gcp/understanding-neural-networks-with-tensorflow-playground https://drive.google.com/open?id=13_fj-IB2jwKIdBHquGm43gpYq4GhsCnl | Tensorflow exampe overview - playground - 2015 |
| Tensorflow Tutorial |
| https://cloud.google.com/tpu/ | Google TPU AI processor overview |
| https://drive.google.com/open?id=1UWwyoHEJZi1f6mQo3SvpxgQz8WBDjuIm | AI hardware- GPU or TPU? |
| https://cloud.google.com/products/ai/building-blocks/ | Google AI building blocks |
| https://cloud.google.com/automl/ | Google auto translation bots |
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| MLFlow - Linux Foundation - open source platform for the machine learning lifecycle | open source platform for the machine learning lifecycle |
| https://mlflow.org | MLFlow |
https://mlflow.org/#:~:text=MLflow%20is%20an%20open%20source,
and%20a%20central%20model%20registry. | MLFlow model registry |
| https://databricks.com/blog/2018/06/05/introducing-mlflow-an-open-source-machine-learning-platform.html | Overview |
| https://mlflow.org/docs/latest/quickstart.html | Quickstart |
| https://towardsdatascience.com/getting-started-with-mlflow-52eff8c09c61 | Getting Started article |
| https://github.com/mlflow/mlflow | MLFlow on github |
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| AWS ML |
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https://aws.amazon.com/machine-learning/e-guide/?trk=FB_AI_DeepLens_eGuide_v3&sc
_channel=PSM&sc_campaign=field&sc_publisher=
FB&sc_category=DeepLens&sc_country=US&sc_geo=NAMER&sc_outcome=aware&sc_
medium=FIELD-P%7CFB%7CSocial-P%7CAll%7CAW%7CMachine+Learning%7CDeepLens%7CUS%7CEN%7CImage&fbclid=
IwAR2h6I83BZsUuK0quB2bgT1uz6ux229cEDCQtlNpLwNQfvgpSybvuskc5p4 | Business View of AI services on AWS |
| ML Training |
| https://aws.amazon.com/machine-learning/ | ML Overview |
| ML Apps, Services |
| ML Sagemaker - Deep Learning Utility |
| ML Tools - Keras, Tensorflow, Kubeflow |
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| Azure ML |
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| IBM ML |
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https://www.ibm.com/cloud/watson-studio/resources?cm_mmc=PSocial_Linkedin-_-Hybrid Cloud_
Data Science-_-WW_NA-_-1074735077_Tracking Pixel&cm_mmca1=000000RE&cm_mmca2=10009788&cm_mmca4=1074735077&cm_mmca5=
1078561140&cm_mmca6=d3617d3d-e5a9-4451-ba40-18c00fc18384 | ibm watson free access, ar |
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| Facebook ML |
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https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ | ML Recommendation Mgr open sourced |
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| ML Tutorials |
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https://github.com/cloud-annotations/training https://www.linkedin.com/feed/update/urn:li:activity:6496499508409561088 | real-time image object recognition customizable in your browser with TensorFlow.js and Python demo real-time image recognition |
https://rasa.com/ https://rasa.com/docs/ | open source framework to build smart chatbots in python w RNN, NLU, NLP |
| http://ciml.info/ | Machine Learning overview focused on algorithms for different problem types |
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| Data Science Tools |
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https://drive.google.com/drive/u/0/folders/1e5WecKq_87ahkcL3UM7EdOz73qviLyhW https://objectcomputing.com/files/5715/6095/8662/Slide_Deck_Groovy_for_Data_Science_Webinar.pdf | Groovy Data Science - Paul King - OCI |
| https://campus.datacamp.com/courses/building-chatbots-in-python/chatbots-101?ex=1 | DataCamp - Python Chatbots course |
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| https://www.cloudera.com/products/data-science-and-engineering/data-science-workbench.html | Cloudera DS workbench |
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Other ML providers and services |
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| https://www.h2o.ai/products/h2o-driverless-ai/ | H2O AI services - platform with consulting services |
https://rasa.com/ https://github.com/RasaHQ/rasa | Rasa - open-source smart assistant framework for chatbots see Smart Assistant Integration for more on bulding assistants for NLP domains |
| https://www.nextplatform.com/2019/03/05/one-step-closer-to-deep-learning-on-neuromorphic-hardware/ | Deep Learning neural networks have a software training environment ( Whetstone ) that can work on multiple neural network platforms when ported. Efficient, mimics human neuron behavior. Goal of this work to be able to leverage standard deep learning technologies to produce inferencing software capable of running in a much lower power envelope when deployed. |
| https://www.sage.com/en-us/blog/wp-content/uploads/sites/2/2018/09/Artificial_Intelligence_In_2019_Sage_Handbook.pdf | AI business concepts handbook |
| https://www.forbes.com/insights-intelai/ | Very good series of articles on AI impacts across many industries including AI concepts as well |
| https://blockchain.ieee.org/technicalbriefs/march-2019/towards-advanced-artificial-intelligence-using-blockchain-technologies | AI and blockchain together article |
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AI Regulation Concepts |
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EUBOF on convergence of AI and Blockchain report - 2021 url EUBOF on convergence of AI and Blockchain meeting recording |
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| EU Regulatory Framework Proposal on AI - 2021 url |
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EU AI Regulatory Concepts today - 2022 url EU AI Regulatory Concepts today - 2022 url.pdf file |
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US AI Bill of Rights Proposal 2022 url US-2022-Blueprint-for-an-AI-Bill-of-Rights.pdf file |
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UK AI Regulation Approach doc - 2022 url UK-AI-regulation-approach-2022-faegredrinker.com-AI Regulation in the UK New Government Approach.pdf file | U.K. Government intends to “regulate the use of AI rather than the technology itself,” |
| UK establishing an approach to regulating AI - 2022 url |
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| UK on EU AI regulations as risky 2022 url |
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| ai-governance-for-citiies-2023-UN-MILA.pdf. link |
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| AI-goverenance-2021-The_ghost_of_AI_governance_past_present_and_future.pdf file |
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| EU AI Regulation flawed - 2022 article |
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G7 2023 Statement on AI Governance. url G7 2023 Statement on AI Governance. linkj |
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...
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
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:
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- 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
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- 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
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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 )
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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
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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
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
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