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The dramatic success in machine learning has led to an explosion of AI applications and increasing expectations for autonomous systems that exhibit human-level intelligence. These expectations, however, have met with fundamental obstacles that cut across many application areas. One such obstacle is adaptability or robustness. Machine learning researchers have noted that current systems lack the capability of recognizing or reacting to new circumstances they have not been specifically programmed or trained for. Another obstacle is explainability, that is, “machine learning models remain mostly black boxes” [Ribeiro et al. 2016] unable to explain the reasons behind their predictions or recommendations, thus eroding users trust and impeding diagnosis and repair. A third obstacle concerns the understanding of cause-effect connections.

According to Professor Judea Pearl from UCLA Computer Science, causal reasoning is an indispensable component of human thought that should be formalized and algorithimitized toward achieving human-level machine intelligence. He has explicated some of the impediments toward that goal in the form of a three-level hierarchy including Association (level 1), Intervention (level 2) and Counterfactual (level 3), and showed that inference to levels 2 and 3 require a causal model of one’s environment.

In addition, Professor Pearl has also described seven cognitive tasks that require tools from those two levels of inference and demonstrated how they can be accomplished in the Structural Causal Models (SCM) framework including:

• Tool 1: Encoding Causal Assumptions – Transparency and Testability
• Tool 2: Do-calculus and the control of cofounding
• Tool 3: The Algorithmization of Counterfactuals
• Tool 4: Mediation Analysis and the Assessment of Direct and Indirect Effects
• Tool 5: Adaptability, External Validity and Sample Selection Bias
• Tool 6: Recovering from Missing Data
• Tool 7: Causal Discovery

It is important to note that the models used in accomplishing these tasks are structural (or conceptual), and requires no commitment to a particular form of the distributions involved. On the other hand, the validity of all inferences depend critically on the veracity of the assumed structure. If the true structure differs from the one assumed, and the data fits both equally well, substantial errors may result, which can sometimes be assessed through a sensitivity analysis. It is also important to keep in mind that the theoretical limitations of model-free machine learning do not apply to tasks of prediction, diagnosis and recognition, where interventions and counterfactuals assume a secondary role.

However, the model-assisted methods by which these limitations are circumvented can nevertheless be applicable to problems of opacity, robustness, explainability and missing data, which are generic to machine learning tasks. Moreover, given the transformative impact that causal modeling has had on the social and medical sciences, it is only natural to expect a similar transformation to sweep through the machine learning technology, once it is enriched with the guidance of a model of the data-generating process. Professor Pearl expected this symbiosis to yield systems that communicate with users in their native language of cause and effect and, leveraging this capability, to become the dominant paradigm of next generation AI.

It is also useful to note that Professor Pearl won the Turing Award in 2011 for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.” In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, AIWS.net is working with Professor Pearl to develop Decision Making System based on Causal Inference Methodology. This AIWS.net system will be a significant contribution to Democratic Alliance on Digital Governance, which is a part of Social Contract 2020 – A New Social Contract in the Age of AI.

The original article can be found here.

Professor Judea Pearl wrote a note on the case for a whiggish account of the history of causation, which was originally written for The Book of Why: The new science of cause and effect (Pearl and Mackenzie, 2018).

Thomas Kuhn’s classic book The Structure of Scientific Revolutions, published in 1962, was one of the most unlikely bestsellers in history. It sold only 919 copies in its first year, yet by its golden anniversary it had reached more than 1.4 million. Structure introduced a new idiom into our language – “paradigm shift”. Before Kuhn, “paradigm” was a philosophical term that referred to rhetorical devices such as parables and fables. Now everyone from feminists to New Age spiritual healers calls for paradigm shifts, and there is even a “Paradigm Shift” quilt pattern!

Kuhn’s book humanized science and perhaps took it off a lofty pedestal where it didn’t belong in the first place. But it also created its own kind of paradigm shift among historians of science. Suddenly history written from the viewpoint of a present-day scientist became unfashionable. “Whig history” was the epithet used to ridicule history written with hindsight, which focuses on the successful theories and experiments while ignoring failed theories and dead ends. Instead a new democratic style of history writing came into fashion, which treats chemists and alchemists with equal respect, viewing the latter merely as being temporarily out of favor.

This historiographic debate between the Whiggish and the priggish came to mind as Professor Pearl was preparing to write about one of the most bizarre paradigms of twentieth-century science: statistics and its attitude toward causality. Like all of Kuhn’s paradigms, statistics restricted the questions that its acolytes could ask and made Causality one of the proscribed questions, maybe even the Ur-question whose name could not be spoken. As a foot soldier in the causal revolution of the 21^st century, Professor Pearl finds it both curious and compelling to ask: How did this paradigm take shape?

As you know, The Book of Why, co-authored by the computer scientist Judea Pearl and the science writer Dana Mackenzie, sets out to give a new answer to this old question, which has been around—in some form or another, posed by scientists and philosophers alike—at least since the Enlightenment. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.” In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Pearl is a leading contributor on Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.

On March 10, 2020, during the AIWS Roundtable “Transatlantic Approach on Digital Governance: the Social Contract 2020”, co-organized by the Boston Global Forum, the World Leadership Alliance – Club de Madrid, and United Nations 2045, Mr. Tuan Nguyen met and discussed with Professor Judea Pearl at his office at UCLA and recognized great ideas from “The Book of WHY” that can shape the futures of AI, and Mr. Tuan conceived using causal inference methodology to build decision-making systems for AI-Government and Democratic Alliance on Digital Governance, a part of the Social Contract 2020.

The link to the paper can be found here.

Nguyen Anh Tuan

While writing a proposal for the Democratic Alliance on Digital Governance (DADG) and developing the Social Contract 2020, a New Social Contract in the Age of AI, I conceived an idea: to upgrade civil society to intellectual society.

It will function like civil society, however Intellectual Society will be a watchdog, monitor society by intellectuals and will contribute social services by intellectuals, with assistance from AI. People of intellectual society have the knowledge, intellect, and strength to contribute to society, as well as monitoring and judging governments, companies, congress, and courts. In the Age of AI, we have great tools to support this mission, and intellectual society must monitor and manage the risks and perils of AI. Furthermore, intellectual society can contribute new economic models by using AI to improve problems in the distribution of income, voice and influence of big brains, thinkers and notable personalities in society.

In the Age of AI, citizens need to survive with new knowledge and ways of thinking. AI World Society can provide educational apps, as well as, easy and quick learning for every individual. AI can assist citizens in becoming intellectuals and when citizens become intellectuals, they will make an intellectual society.

With good education, citizens can understand common values, values of people, standards, etc., and develop critical thinking. They can make more well informed decisions and they can identify fake news and disinformation, thus making it difficult to lie to them.

Governments should design policy with the following goal in mind: all citizens are educated through a basic program for a citizen in the Age of AI.

1. What is an Intellectual Society?
– An upgrade from civil society: organizations of civil society with good education, knowledge, and intellect so that they can contribute good actions, initiatives, and solutions to solve social issues.
– They are thinktanks, intellectual networks, actionable organizations who respect and apply the Social Contract 2020, A New Social Contract in the Age of AI, all of them have intellect and knowledge, and are assisted by AI in decision-making. Influencers who have intellect can see an independent organization of intellectual society.

2. Role and contribute in society, what is its power?
– As House of Representative: vote and raise voice about political decisions and submit bill and laws to Senate.
– Monitor governments and other power centers.
– Generate concepts, models, initiatives, and solutions to solve social issues.
– Take actions to address social problems.
– Intellectual Society is one of seven branches of power, and will do close a congress, or national assembly. 7 Centers of power: 3 centers are three branches of Government (Executive Branch, Legislative Branch, Judicial Branch), Business Sector, Intellectual Society, Influencers, AI Assist Coop.

3. How do they operate, how to evaluate their effectiveness?
– Register by laws and regulations.
– Evaluate by results: transparency of their work and results.
– They have the right to connect and contribute to society and governments; business sector has to be monitored by intellectual society.
– Monitoring and Intellectual Board: Supervisor of laws, accords, standards; generate initiatives as the legislative branch. Create laws and accords.
– Intellectual Society will join to make and run Monitoring and Intellectual Board (AI-Congress)

4. How to build the Intellectual Society?
– Education: all citizens have basic intellectual education. Governments have to design policies with the following goal in mind: all citizens are educated through a basic program for a citizen in the Age of AI.
– Issue laws and regulations to punish people and organizations that deliver fake news, disinformation, and cyberattacks as serious criminals.
– Finance and exchange values: organizations with initiatives, solutions, or projects can get financial support and have their values recognized which would lead to the exchange of values and use of new services and products.
– New AI economy model to improve unreasonable distribution of income, voice, influence of big brains, thinkers, and notable personalities in society.

5. How can AI support Intellectual Society?
– Education to citizens to help them have basis for intellect.
– Assist in decision-making by using causal inference methodology.
– Help to create systems for Global Monitoring, Global AI-Citizen using Causal Inference Methodology.
– New model of AI economy: make the economy more equitable, everyone who has contributed to society or the economy should be able to lead a life with dignity and values should be exchanged directly. AI can make a system to recognize contributions, a system can directly deliver value of innovations, creativities, products, services to people, including financial services. Financial people cannot get rich more easily than innovators, creators, founders, inventors, or thought leaders
– A system that recognizes noble, honest, and good people, but also dishonest people. Now, in social media, and civil society, everyone can contribute to discussions but with this come many problems. Intellectual Society needs to build an AI system that can identify valuable voice and help to identify noble, honest, good people, as well immoral and dishonest people. The Casual Inference Methodology of Judea Pearl can also be applied.
– Directly deliver and develop products and services of innovators, creators, and founders to users; and to make a mechanism that governments cannot use huge markets to make it difficult for companies, eg. ask companies to meet unfair requirements, and abuse huge markets to foment totalitarian power such as what China is doing. Through this method, governments can promote innovations, creativities, inventions, and contributions.

This is one of the first ideas and concepts of the Intellectual Society. I will continue develop with more details, and I am open to feedback from your comments to create a better version in July 2020.

The 2019–20 coronavirus pandemic originates from a virus, referred to as the 2019 novel coronavirus, or as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which induces the infectious disease called Covid-19 (Coronaviridae Study Group of the International Committee on Taxonomy of Viruses, 2020). After an outbreak was identified in Wuhan, China, in December 2019, cases started being reported across multiple countries all over the world, ultimately leading to the World Health Organization (WHO) declaring it a pandemic on the 11th of March 2020 (WHO, 2020). As of 12 May 2020, the pandemic led to more than 287,800 confirmed deaths and more than 4.2 million confirmed cases, spreading across 187 countries.

One of the most cited indicators regarding the disease is the reported case fatality rate (CFR), which indicates the proportion of confirmed cases which end fatally. In the paper, the authors illustrate how tools from causal inference, and in particular mediation analysis, can help interpret data related to the epidemic and better compare CFRs across different countries. The paper has also good analysis based on the observation of a peculiar statistical paradox involving data from China and Italy. Using the contemporary example of comparing Covid-19 CFRs between China and Italy, the authors have illustrated how methods from causal inference, in particular mediation analysis, can be used to resolve apparent statistical paradoxes and answer various causal questions from data regarding the current pandemic.

The paper main accomplishment is to illustrate some fundamental and useful concepts in causal inference to facilitate reasoning about different causal hypothesis regarding the ongoing SARS-CoV-2 pandemic, relating to the attribution of mortality to different factors. The paper is also received a highly-praised comment by Professor Judea Pearl as “This paper is the first COVID-19 analysis I read that goes beyond data-fitting and tells society: Yes! AI can be trusted to extract meaning from data, and can do so rigorously and practically if properly directed and wisely supported.”

It is also useful to note that Professor Judea Pearl is one of the pioneers for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl received the Turing award from Association for Computing Machinery (ACM), which is the highest distinction in computer science, “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning”. In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Pearl is a leading contributor on Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.

The original article can be found here.

In a recent paper Decision-theoretic foundations for statistical causality, Dr. Philip Dawid (Emeritus Professor of Statistics of the University of Cambridge) has  developed a mathematical and interpretative foundation for the enterprise of decision-theoretic statistical causality (DT), which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as “assisted decision-making”, and aims to understand when, and how, I can make use of external data, typically observational, to help me solve a decision problem by taking advantage of assumed relationships between the data and my problem.

The relationships embodied in any representation of a causal problem require deeper justification, which is necessarily context-dependent. Here we clarify the considerations needed to support applications of the DT methodology. Exchangeability considerations are used to structure the required relationships, and a distinction drawn between intention to treat and intervention to treat forms the basis for the enabling condition of “ignorability”. We also show how the DT perspective unifies and sheds light on other popular formalisations of statistical causality, including potential responses and directed acyclic graphs (DAGs).

Dr. Philip Dawid also consider the relationships between DT and alternative current formulations of statistical causality, including potential outcomes (Rubin 1974; Rubin 1978), Pearlian DAGs (Pearl 2009), and single world intervention graphs (Richardson and Robins 2013a; Richardson and Robins 2013b). In specific, Professor Judea Pearl has popularised graphical representations of causal systems based on DAGs. In his work (2009), Dr. Pearl describes what he terms a “Causal Bayesian Network” (CBN), which we shall call a “Pearlian DAG”. This is intended to represent both the conditional independencies between variables in observational circumstances, and how their joint distributions changes when interventions are made on some or all of the variables: specifically, for any node not directly intervened on, its conditional distribution given its parents is supposed the same, no matter what other interventions are made.

In the field of causal inference, Professor Judea Pearl is one of  pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl also received the Turing award from Association for Computing Machinery (ACM), which is the highest distinction in computer science, “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning”. In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF).

Professor Judea Pearl recommended this paper: “Philip Dawid has written a comprehensive overview of his approach to CI. The do-operator is simulated by a decision variable in a Bayesian Network. The paper illuminates what can be done without counterfactuals, a topic of my paper”

AIWS.net is applying the causal inference model to build a national decision-making system for governments.

Which is why more and more programmers are adopting other languages — the top players being Julia, Go, and Rust. Julia is great for mathematical and technical tasks, while Go is awesome for modular programs, and Rust is the top choice for systems programming.

Since data scientists and AI specialists deal with lots of mathematical problems, Julia is the winner for them. And even upon critical scrutiny, Julia has upsides that Python can’t beat.

The below are some highlights on Julia programming language for your reference:

Versatility
Julia can be used for everything from simple machine learning applications to enormous supercomputer simulations. To some extent, Python can do this, too — but Python somehow grew into the job.

In contrast, Julia was built precisely for this stuff. From the bottom up.

Speed
Julia’s creators wanted to make a language that is as fast as C — but what they created is even faster. Even though Python has become easier to speed up in recent years, its performance is still a far cry from what Julia can do.

In 2017, Julia even joined the Petaflop Club — the small club of languages who can exceed speeds of one petaflop per second at peak performance. Apart from Julia, only C, C++ and Fortran are in the club right now.

Code conversion
You don’t even need to know a single Julia-command to code in Julia. Not only can you use Python and C code within Julia. You can even use Julia within Python!
Needless to say, this makes it extremely easy to patch up the weaknesses of your Python code. Or to stay productive while you’re still getting to know Julia.

The original article can be found here.

Forty years ago, artificial intelligence (AI) was nothing but a niche phenomenon. The industry and investors didn’t believe in it, and many technologies were clunky and hard to use. But those who learned it back then are the giants of today — those that are so high in demand that their salary matches that of an NFL player. Similarly, Julia is still very niche now. But when it grows, the big winners will be those who adopted it early.

To support AI application and development for the society and community, Artificial Intelligence World Society Innovation Network (AIWS.net) also created AIWS Young Leaders program including Young Leaders and Experts from Australia, Austria, Belgium, Britain, Canada, Denmark, Estonia, France, Finland, Germany, Greece, India, Italy, Japan, Latvia, Netherlands, New Zealand, Norway, Poland, Portugal, Russia, Spain, Sweden, Switzerland, United States, and Vietnam.

Professor Judea Pearl, UCLA, Turing Award, 2020 World Leader in AIWS Award. He is a mentor of AIWS.net and member of the History of Artificial Intelligence Board. He wrote on his Twitter: “A fairly harsh indictment of AI: AI can’t solve this: The coronavirus could be highlighting just how overhyped the industry is. Why hasn’t AI had more impact? I have asked my colleagues at Stanford HAI the same question: ucla.in/2JEhGyv, but I am not sure they took notice. People blame the noisy data; shouldn’t AI outsmart the noise-makers?”

The article is below:

The world is facing its biggest health crisis in decades but one of the world’s most promising technologies — artificial intelligence (AI) – isn’t playing the major role some may have hoped for.

Renowned AI labs at the likes of DeepMind, OpenAI, Facebook AI Research, and Microsoft have remained relatively quiet as the coronavirus has spread around the world.

“It’s fascinating how quiet it is,” said Neil Lawrence, the former director of machine learning at Amazon Cambridge.

“This (pandemic) is showing what bulls–t most AI is. It’s great and it will be useful one day but it’s not surprising in a pandemic that we fall back on tried and tested techniques.”

The full article can be found here.

Professor Judea Pearl, Chancellor’s Professor, UCLA, World Leader in AIWS Award recipient, and Mentor of AIWS.net, have just publish the paper “Generalizing Experimental Results by Leveraging Knowledge of Mechanisms”:

“We show how experimental results can be generalized across diverse populations by leveraging knowledge of mechanisms that produce the outcome of interest. We use Structural Causal Models (SCM) and a refined version of selection diagrams to represent such knowledge, and to decide whether it entails conditions that enable generalizations. We further provide bounds for the target effect when some of these conditions are violated. We conclude by demonstrating that the structural account offers a more reliable way of analyzing generalization than positing counterfactual consequences of the actual mechanisms.”

Continue to read here.