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Ai4life aiml-xops-sig
- 1. Brainstorming Session / Thought Workshop Research and experimentation approaches/avenues for AI/ML XOP’s
- 3. Vision
- 4. life is guided by ‘..feeling and emotions..’ SEED – Potential ‘Creative Reasoning’ Inspiration TREE ( Progress ) Evidence ( Fruits ) SEED
- 5. ai human discovery Complex machines machine Simple machines nature envision for .. new potential Conserve.. WHERE WHAT FOR? Scaling.. Autonomous..
- 6. Wearing an “Intelligence HAT” .. Artificial Intelligence Intelligence Automated YOU
- 7. Comparison of AI guidelines – Trigger for Exploration Values to be respected AI Utilization Guidelines Draft AI R&D guidelines for international discussions Social Principles of Human-centric AI Ethics Guideline for Trustworthy AI Recommendati on of the Council on Artificial Intelligence Ethically Aligned Design Asilomar AI Principles Tenets by The Conference toward AI Network Society (MIC) /Japan The Conference toward AI Network Society (MIC) /Japan Integrated Innovation Strategy Promotion Council.（Social Principles of Human-centric AI） /Japan European Commission (High Level Expert Group on AI (AI HLEG)) OECD IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems Future of Life Institute (FLI) Partnership on AI Human Centered Human dignity Diversity and Inclusiveness Sustainable society International Cooperation Proper Utilization Education/literacy Human intervention and Controllability Proper data Collaboration among AI systems Safety Security Privacy Fairness, equity, removal of Discrimination Transparency, Explainability Accountability … https://www.soumu.go.jp/main_content/000637845.pdf
- 8. BIAS • Simpson Paradox • Longitudinal Data Fallacy • Behavioural • Content Production Linking • Temporal • Popularity • Algorithmic • User Interaction • Presentation • Ranking • Social • Emergent • Self-Selection • Omitted Variable • Cause-Effect • Observer • Funding FAIRNESS • Direct • Indirect • Systemic • Statistical • Explainable • Unexplainable • Equalized Odds • Equal Opportunity • Demographic Parity • Through Awareness • Through Unawareness • Treatment Equality • Test • Counterfactual • Relational Domains • Conditional Statistical Parity • Individual • Group • SubGroup govern https://arxiv.org/pdf/1908.09635.pdf Survey on AI Bias and Fairness – Trigger for Experimentation
- 9. Mission
- 10. learning from data .. lifecycles of “above”.. sell gpu.. adopt Op’s ai structured image, text unstructured GPU’s ( DL ) data is both the representation of worlds problem and its inherent solutions .. approach.. learning from data.. evolution Data - Key Influence Solutions data algorithms architecture
- 11. prediction.. unknown value.. historical evidence.. Tasks.. • Classification, Regression supervised • grouping unsupervised • Trend, seasonality Timeseries • matrix factorization Recommender • delayed rewards, optimize for policy Reinforcement Approaches… • navie, regressions.. statistical • SVM, Tress, XGBoost.. classical • Convolution, Strides, Transformer’s, Spikes, Hybrid neural What how
- 12. human reasoning… • similarities patterns and make associations ( neighbour, grouping ) Analogical • involves reasoning from a specific case or cases to derive a general rule ( trees ) Inductive • making best guesses, dealing with uncertainties ( prior, naive) Abductive • independent analysis of parts (trustworthiness ) Decompositional • linkage between two events (interpretability, explainability) Cause-and-effect • definitive ( data, meta data of different stages ) Critical thinking • logical certainty (checks and bounds , context ) Deductive
- 14. Challenges ( flip – side ) Opportunities fuzzy prospective promises unaddressable constraint pragmatic approaches Unprepared failure scenarios Unrealistic expectation Unfamiliar approach challenges opportunities
- 15. How to conceive experimentation ideas.. ? Brainstorming •Metaphors •Mockery •Problem •Failures •Survey Technology Curiosity •Stats vs ML VS DL •DL & Decision Trees •Disassociate Implicit Steps – SVM DT •Splitting Units Question Terms and Terminologies •Ground Truth •Cross Validation •Weaker Signatures •Boosting •Bagging •Dropout Inspiration from Failures •Stability in Human Recognition •Which Stride, Which Convolution •Which is more Fair ? Alarming Failures •Cyber Security National Emergency Progress •Curiosity •Specificity •Finer Understanding
- 16. Deployment Model Concern Data Knobs for Audit, Regulations Class •Labelled ? •Ranges ? •Consistent Correlation ? •Where Mis- Classification? •Left out Binary •Fuzzier Boundary Multi-Label •Multi –Class •What more • Framewor k for Data Governan ce Insights Simplify Which Model Splitting Units Hyper parameter Feedbacks Framework for Model De- Mystifying Bounded, Context, Constructs Generalization Errors Drift in Data, Model, Mathematical • Framewor k for • Heuristic • Extraction Secure Attack Adversity Framework for AI Testing Weaker Signatures Boosting Bagging Dropout Framework for AI Governance Take 1 problem at time.. to explore .. the dot’s XOP’s based approach quantify observation How to progress on experimentation ideas.. ? problem observation solutions Observability
- 17. xop’s as augmenting the ai .. journey.. How to onboard AI into practice ..?
- 18. cater 2 life … scenarios .. purpose .. Making Sense life • Afghan<>Taliban • Giving birth under the Taliban • https://www.bbc.co.uk/newsround/24118241 Task Nature • Amazon Forest • Task Identify
- 19. How do you go about reading ..? Data Engineer Data Analyst Visualization Story Telling Statistician Descriptive Inferential Data Scientist Mining XOPS Continuous https://www.bbc.co.uk/newsround/24118241 Driving Factors Purpose Question Evidences Answers
- 20. Data Capture Age No of Child Pain Relief Medic ine Food Temp Power Fuel 3 No No No 43C No No https://www.bbc.co.uk/newsround/24118241
- 21. Data Capture Age No of Child Pain Relief Medic ine Food Temp Power Fuel 3 No No No 43C No No https://www.bbc.co.uk/newsround/24118241 Correlation? ( Association ) Causation ?
- 22. Cli Hos Supply Medical Data https://www.bbc.co.uk/newsround/24118241 What are Missing Dimensions? – Age of Mother, Her other Two Childs Data Diversity – How many Data Cleaning ? Time – Patient Age No of Child Pain Relief Medic ine Food Temp Power Fuel 3 No No No 43C No No
- 23. Feature Types Feature Variable Age No of Child Pain Relief Medic ine Food Temp Power Fuel 3 No No No 43C No No
- 24. Feature Types Feature Variable Categorical Nominal lable ? Is Mother? Ordinal Order Which child? Numerical discrete continious dosen’t have mathematic meaning Age No of Child Pain Relief Medic ine Food Temp Power Fuel 3 No No No 43C No No
- 25. Visualization Methods • If you were to • Tell your Story • Analyze the Data • Organize an Information • OLAP Model Distribution Histogram Sparse/Dense Probable Range Skew or Centered Comparison Bar Chart (Multi) Category vs Numerical Box Plot N-tile, Multi-Group Composition Part of Whole Correlation Scatter Plot Making sense of Data scale Normali zing Bucket ing
- 26. stats Descriptive Inferential differences associative predictive Approaches .. ml and stats.. Extract patterns Extract model
- 27. Data Recognition • Most Real-World Data are Wide , many Dimensions - Sparse • Most Machine Data are Deep Few Dimensions • Storage I/O .. less signatures • Most Systems Data • Chemical Factory.. Are more stable.. • Neural Data • Image are Dense.. Wide D E E P
- 28. methodological approaches stats •What expect? ml •What more ? ai •How can it augment?
- 29. policies Where is it Addressed ? How well its addressed ? What are areas not addressed ? What is Addressed ? What are even unknown?
- 30. does data driven decision recommendation make sense? Exploratory Mining Insight Does it expose a New reality ? Does it Justify a Call for Action? How strong is the reporting ? How does it Vary from History to Present ? Does it even require
- 31. XOP’s Bridging the Human <>AI towards building a better future with greater power comes greater responsibility.. let’s take some.. steps towards.. make it eco-friendly.. incorporate ethics.. construct explainable knobs.. Process for governance.. ensure it’s secure.. make it approachable.. consumable. . understandable.. repeatable.. debuggable.. amenable.. whatever is your vision
Editor's Notes
- https://www.quora.com/What-is-the-difference-between-feelings-and-emotions-1 Feelings, are generated in the heart and are related to one’s higher truth or Dharma. It is due to feelings that one does certain actions that uphold his true nature. Feelings are something you can choose from the inner depths of your soul. Feelings are the inner compass that help develop intuition. Emotions are egoic.
- News reporters : Live News of What's happening, Various aspects, Humanitarian, Pandemic, Travel, Medical ( Childbirth ) , Education Historical Data : Taliban ( How it was handled and what it has impacted ?) General Data : Refugee, War, Conflict
- Question Needing Answers : Near Terms, ( Weeks ), Short Term ( Months), Mid Term( Quarter), Long Terms( Year) UN : Release Fund or Not ?
- Does it expose a New reality ? Does it Justify a Call for Action? How strong is the reporting ? How does it Vary from History to Present ? Does it even require