This document discusses brainstorming ideas for experimentation approaches in AI/ML. It covers various topics such as the vision and mission for using AI, challenges and opportunities of AI, different types of human and machine reasoning, biases and fairness in AI, how to conceive experimentation ideas, how to onboard AI into practice, different types of data features, visualization methods, statistical and machine learning methodological approaches, and how XOPs can bridge humans and AI to build a better future.
Machine learning and pattern recognitionsureshraj43
In a very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to artificial intelligence and machine learning. Pattern Recognition is an engineering application of Machine Learning.
Design Insights for the Next Wave Ontology Authoring ToolsMarkel Vigo
We provide a systematic attempt to understand what users really require to build successful ontologies. To do so, we present the insights from an interview study with 15 ontology authors in which we identify the problems reported by authors, and the strategies they employ to solve them. We map the data to a set of design recommendations, which describe how tools can support ontology authoring going forward.
Machine learning and pattern recognitionsureshraj43
In a very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to artificial intelligence and machine learning. Pattern Recognition is an engineering application of Machine Learning.
Design Insights for the Next Wave Ontology Authoring ToolsMarkel Vigo
We provide a systematic attempt to understand what users really require to build successful ontologies. To do so, we present the insights from an interview study with 15 ontology authors in which we identify the problems reported by authors, and the strategies they employ to solve them. We map the data to a set of design recommendations, which describe how tools can support ontology authoring going forward.
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Artificial Intelligence lecture notes. AI summarized notes for introduction to machine learning, symbol based and constructionist learning, also deep learning organized here for reading and may be for self-learning, I think.
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!Sri Ambati
This meetup took place in Mountain View on January 24th, 2019.
Description:
With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by
1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.
2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions
3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)
4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.
Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.
Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)
A set of practical strategies and techniques for tackling vagueness in data modeling and creating models that are semantically more accurate and interoperable.
An Introduction to XAI! Towards Trusting Your ML Models!Mansour Saffar
Machine learning (ML) is currently disrupting almost every industry and is being used as the core component in many systems. The decisions made by these systems may have a great impact on society and specific individuals and thus the decision-making process has to be clear and explainable so humans can trust it. Explainable AI (XAI) is a rather new field in ML in which researchers try to develop models that are able to explain the decision-making process behind ML models. In this talk, we'll learn about the fundamentals of XAI and discuss why we need to start to integrate XAI with our ML models!
Presented in Edmonton DataScience Meetup on October 2nd, 2019. Learn more: https://youtu.be/gEkPXOsDt_w
Unboxing the black boxes (Deprecated version)BLECKWEN
A new version of these slides are avaible: https://www.slideshare.net/BLECKWEN-AI/unboxing-the-black-boxes-updated-version-november-18
As machine learning has become more widely adopted across many industries and involved in many aspects of decision making, machine learning interpretability is therefore becoming an integral part of the data scientist workflow and can no longer just be an afterthought. Ultimately, it’s reasonable to wonder whether we can understand and trust decisions made by a predictive model.
However, in an increasingly competitive environment, data scientists are using ever-complex machine learning algorithms like XGBoost or Deep Learning to deliver more accurate models to businesses. Unfortunately, there is a fundamental tension between accuracy and interpretability: the most accurate models are often the hardest to understand. Opaque and complicated nonlinear models limit trust and transparency, slowing adoption of machine learning models in high regulated industries like banking, healthcare and insurance. But things needn't be that way!
In this talk, Leonardo Noleto, senior data scientist at Bleckwen, will explore the vibrant area of machine learning interpretability and explain how to understand the inner-workings of black-box models, thanks to interpretability techniques. Along the way, Leonardo offers an overview of interpretability and the trade-offs among various approaches of making machine learning models interpretable. Leonardo concludes with a demonstration of open source tools like LIME and SHAP.
Overview of recent developments in datascience and machine learning from Google Cloud and Amazon Web Services will accelerate the digital ingestion data. This includes an overview of Frameworks that convert Data to Insights to Action.
Coding qualitative data for non-researchersKelley Howell
We were pleasantly surprised by the success of a Net Promoter Survey. Thus, our good problem to have was: a lot more qualitative data to sift through than we expected. Our contingency plan was to gather product managers, interns, and analysts and teach them how to code (label) qualitative data. We did this by running two "war room" session. We grabbed our laptops and tackled the coding all together in two day-long sustained sessions.
From Human Intelligence to Machine IntelligenceNUS-ISS
This in an introductory talk to get ready for the AI era, and will talk about human intelligence, the model view of intelligence and machine/artificial intelligence. There will be some coverage of AI roots and subfields.
The presentation by Klaus Gottlieb highlights human thinking tools that maintain advantages over AI, focusing on critical thinking, creativity, and problem-solving strategies. It showcases how these cognitive skills enable humans to interpret, innovate, and navigate complex scenarios more effectively than current AI capabilities, underscoring the importance of leveraging human intellect alongside technological advancements.
Keywords: Critical Thinking, Creativity, Problem-Solving, Human Intellect, Cognitive Skills, Innovation, AI Limitations.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Artificial Intelligence lecture notes. AI summarized notes for introduction to machine learning, symbol based and constructionist learning, also deep learning organized here for reading and may be for self-learning, I think.
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!Sri Ambati
This meetup took place in Mountain View on January 24th, 2019.
Description:
With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by
1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.
2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions
3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)
4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.
Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.
Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)
A set of practical strategies and techniques for tackling vagueness in data modeling and creating models that are semantically more accurate and interoperable.
An Introduction to XAI! Towards Trusting Your ML Models!Mansour Saffar
Machine learning (ML) is currently disrupting almost every industry and is being used as the core component in many systems. The decisions made by these systems may have a great impact on society and specific individuals and thus the decision-making process has to be clear and explainable so humans can trust it. Explainable AI (XAI) is a rather new field in ML in which researchers try to develop models that are able to explain the decision-making process behind ML models. In this talk, we'll learn about the fundamentals of XAI and discuss why we need to start to integrate XAI with our ML models!
Presented in Edmonton DataScience Meetup on October 2nd, 2019. Learn more: https://youtu.be/gEkPXOsDt_w
Unboxing the black boxes (Deprecated version)BLECKWEN
A new version of these slides are avaible: https://www.slideshare.net/BLECKWEN-AI/unboxing-the-black-boxes-updated-version-november-18
As machine learning has become more widely adopted across many industries and involved in many aspects of decision making, machine learning interpretability is therefore becoming an integral part of the data scientist workflow and can no longer just be an afterthought. Ultimately, it’s reasonable to wonder whether we can understand and trust decisions made by a predictive model.
However, in an increasingly competitive environment, data scientists are using ever-complex machine learning algorithms like XGBoost or Deep Learning to deliver more accurate models to businesses. Unfortunately, there is a fundamental tension between accuracy and interpretability: the most accurate models are often the hardest to understand. Opaque and complicated nonlinear models limit trust and transparency, slowing adoption of machine learning models in high regulated industries like banking, healthcare and insurance. But things needn't be that way!
In this talk, Leonardo Noleto, senior data scientist at Bleckwen, will explore the vibrant area of machine learning interpretability and explain how to understand the inner-workings of black-box models, thanks to interpretability techniques. Along the way, Leonardo offers an overview of interpretability and the trade-offs among various approaches of making machine learning models interpretable. Leonardo concludes with a demonstration of open source tools like LIME and SHAP.
Overview of recent developments in datascience and machine learning from Google Cloud and Amazon Web Services will accelerate the digital ingestion data. This includes an overview of Frameworks that convert Data to Insights to Action.
Coding qualitative data for non-researchersKelley Howell
We were pleasantly surprised by the success of a Net Promoter Survey. Thus, our good problem to have was: a lot more qualitative data to sift through than we expected. Our contingency plan was to gather product managers, interns, and analysts and teach them how to code (label) qualitative data. We did this by running two "war room" session. We grabbed our laptops and tackled the coding all together in two day-long sustained sessions.
From Human Intelligence to Machine IntelligenceNUS-ISS
This in an introductory talk to get ready for the AI era, and will talk about human intelligence, the model view of intelligence and machine/artificial intelligence. There will be some coverage of AI roots and subfields.
The presentation by Klaus Gottlieb highlights human thinking tools that maintain advantages over AI, focusing on critical thinking, creativity, and problem-solving strategies. It showcases how these cognitive skills enable humans to interpret, innovate, and navigate complex scenarios more effectively than current AI capabilities, underscoring the importance of leveraging human intellect alongside technological advancements.
Keywords: Critical Thinking, Creativity, Problem-Solving, Human Intellect, Cognitive Skills, Innovation, AI Limitations.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Provides a basic overview automated decision systems and recent attempts to guarantee their allocations are fair. It then examines 4 key papers in the field that suggest that this cannot be guaranteed and offers a brief sketch of a different direction.
We've been taught that "data science" is the esoteric domain of PhDs,
but like anything else, it's easy once you understand it. This talk
explains the basics of data science, covering concepts in supervised
learning (including a detailed explanation of decision trees and
random forests) as well as examples of unsupervised learning
algorithms. Far from being a dry and academic topic, data science and machine learning are useful and practical analytical tools. (This talk is intended for a general audience.)
Topics will include:
1) An introduction to supervised learning using the popular decision
tree algorithm
2) The concepts of training and scoring, and the meaning of "real time"
machine learning
3) Model validation using holdout sets
4) Model complexity and overfitting; understanding bias and variance;
using ensembles to reduce variance
5) An overview of unsupervised learning models including clustering,
topic modeling and anomaly detection
and more!
This presentation focuses on Feature Engineering and the Heuristics which can be extracted pre modelling whihc can be used post modelling for change detectors, explain ability etc.
Learning from Data - Various Approaches - Postermadhucharis
Big Data, Thick Data, Wide Data, Structured Neural Learning
Knowledge Representation, Graph and Neural Learning
Collage from different Images, to emphasize the Theme
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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
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
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
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
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 ?
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
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
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
