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Professor Judea Pearl, Chancellor’s Professor of UCLA, father of Bayesian Networks, Cause and Effect, Turing Award, and World Leader in AIWS, and Mentor of AIWS.net, introduces: “Another exciting paper arriving at my desk reads: Causal Relational Learning which promises to revolutionize causal inference the same way first-order predicate logic has transformed Boolean logic.”

 

Causal Relational Learning    

Babak Salim (University of Washington), Harsh Parikh (Duke University), Moe Kayali (University of Washington), Sudeepa Roy (Duke University), Lise Getoor (University of California at Santa Cruz), and Dan Suciu (University of Washington)

Abstract

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials; unfortunately, these are not always feasible due to ethical, legal, or cost constraints. As an alternative, methodologies for causal inference from observational data have been developed in statistical studies and social sciences. However, existing methods critically rely on restrictive assumptions such as the study population consisting of homogeneous elements that can be represented in a single flat table, where each row is referred to as a unit. In contrast, in many real-world settings, the study domain naturally consists of heterogeneous elements with complex relational structure, where the data is naturally represented in multiple related tables. In this paper, we present a formal framework for causal inference from such relational data. We propose a declarative language called CaRL for capturing causal background knowledge and assumptions and specifying causal queries using simple Datalog-like rules. CaRL provides a foundation for inferring causality and reasoning about the effect of complex interventions in relational domains. We present an extensive experimental evaluation on real relational data to illustrate the applicability of CaRL in social sciences and healthcare.

 

Conclusions and Future Work

We introduced the Causal Relational Learning framework for performing causal inference on relational data. This framework allows users to encode background knowledge using a declarative language called CaRL (Causal Relational Language) using simple Datalog-like rules, and ask various complex causal queries on relational data. CaRL isdesigned for researchers and analysts with a social science, healthcare, academic or legal background who are interested inferring causality from a complex relational data. CaRL adds on to existing causal inference literature by relaxing the unit-homogeniety assumption and allowing the confounders, treatment units and outcome units to be of different kinds.

We evaluated CaRL’s completeness and correctness on real-world and synthetic data from academic and healthcare domains. CaRL is successfully able to recover the treatment effects for complex causal queries that may require multiple joins and aggregates.

In future, we aim to extend CaRL to deal with complex cyclic causal dependencies using stationary distribution of stochastic processes. We plan to study stochastic interventions and complex interventions on relational skeletons, which are assumed to be fixed in this paper. We also plan a theoretical and methodological study the functionality of different types of embeddings. We aim to develop principled learning approach for finding efficient embeddings using graph representation learning and graph embedding.

Recently, it has been shown that causality is foundational to the emerging field of algorithmic fairness [52]. In future work we plan to use causal relational learning to study a causality-based framework for fairness and discrimination in relational domains.

The paper can be found here.

The West – sacrifices at the forefront of mankind

The numbers of post-Wuhan casualties in the West are overwhelming. My worries and prayers go out for the people suffering in these countries. To the author, it feels closer than physical distance would rationalize perhaps because many Vietnamese people have lived throughout these countries. Dreadful deaths. Inexplicably, I have not a slightest thought that their countries are not as well-equipped as China (if there were any reason to believe in the figures given by China) in preventing and fighting with the pandemic.

Perhaps they were insufficiently aware of the risks causing them a non-defensive situation. Western countries might have believed in WHO’s manipulated reports and mitigation claims on Chinese coronavirus crisis which lowered their safeguards. Consequently, many governments lost the golden opportunity to stop the epidemic. Now that their awake and living a nightmare at the mercy of the virus, they fight vigorously and openly. I see them as the blatant opposite of China in the battle with the pandemic. The numbers of infected cases and death tolls increase gradually, sharply and… transparently. There was no suppression, no dumping into trucks of infected persons, no nailing the doors of sick persons’ houses and no unreasonably low numbers. There has been no sign that the governments in these countries worry about losing their face, because people died in their countries more than in the others, instigating the concealing of the data. Instead, people have come together resilient in their resolve and resigned to the fact that in life and death, disease is part of our humanity on the timeline of history.

I see no reason for the West to be perceived as weak.  Impatiently, some argue that the democratic West has failed and that China has succeeded attracting the justification of the brutal way the Chinese government curbed the pandemic.  In an article in the Diplomat, I described China’s approach as the “iron hand” which strikes when “the end justifies the means” as is often the strategy of dictatorial regimes. I find Westerners open, honest and sympathetic to each other. Through their media I have a window to the front line of humanity and it includes me! I see and feel as though they are fighting for the survival of humanity, ours. I pray for them as if I were in the back and they were on the front lines of the pandemic battle. Our pains and victories are shared.

To shift to the rationale side and put aside emotions, I have searched for the rank of the countries that have contributed the most to humanity, realizing that my feelings are aligned with their contributions to progress. The most developed countries, the wounded ones fighting bravely against the Wuhan virus, are the highest ranked nations in the contribution and promotion of human development, especially in science and health (while my country is very low in the rankings). They are the pioneers of mankind in finding the cure from which poor nations have benefited in the past. In the same course of thinking, I went on to look for the top countries with the world’s leading vaccine manufacturing companies, I then got a list of all Western companies from exactly the countries tattered by coronavirus. The company ranked 10 (in the top 10) is a Japanese company, the only Asian company, of Mitsubishi Corporation (*). Along that conclusion when there is a perceived cure who will you trust to administer it? Would you question a Chinese company’s vaccine, are you ready to take it, or will you choose a US or Western vaccine?

Life is important, I once said that the highest ideology is the ideology for human life. But when the sense of life and death in the overall continuum of human existence is considered, the sacrifice at the frontline is the ultimate morality. Fortunately, the frontline to prevent the disease is being fought by the strongest nations – they have been at the forefront of combat and in the search for medication and prevention of diseases to the benefit of all humankind before. It is not a matter of China’s hiding the truth of their losses to make the country seem stronger for a while, but it is Western transparency and sacrifice as solution, the medicine for the world, leading humanity to be stronger and more civilized.

Do many of us think this way?

Instead of thinking that the countries are less capable in fighting against epidemics, I appreciate their audacious struggle, their courageous and scientific commitment as well as their humanity and solidarity. I thank them who are in the forefront of fighting fiercely, and urgently seeking ways to limit the disease to humanity today and tomorrow. What can others do then to honor the sacrifices?

Vietnam – knowing her strength, limitations and understanding China

On the other hand, I appreciate the way Vietnam is coping with this pandemic. Some have called the commitment to containment miraculous. It is the result of the intelligence and prudence of those who know their strength and limitations. However, if I choose between arrogant pride and modesty during this successful period, I think I will choose to be modest.

Why did Vietnam stop the Wuhan pandemic early even though we are close to China while other countries have fared far worse?

Many reasons above apply but one thought I must share (and revel a bit in the reveal). A small but satisfying conclusion: the Vietnamese have never believed in the Chinese government. From the insufficient information from Wuhan, Hubei from the early days, the government’s cover-ups and fraudulent propaganda on the pandemic. Even positive comments from WHO about the coronavirus crisis in China … are all considered with the utmost vigilance of a people whose watchfulness from the North has become part of our survival.  Is it wrong to find joy in our trepidation? It is even more joyful that more and more countries are awakening to the same dangers of the Chinese government’s self-seeking and irresponsible international policy in this human pandemic and its broader implications?

Vietnam acted as an early warning system to the world that China was not to be trusted. Vietnam’s caution and closure of the border has shown to be warranted and wise. Could this modest contribution of Vietnam in the prevention of this pandemic signal a higher standard for global response to future challenges and show that China’s lies and lack of human right protections won’t be tolerated?

We, Vietnam, strive, to shoulder the burden of contributing to humanity to enhance our national prestige in the international arena!

May mankind soon overcome the pandemic and stop the spread of Chinese authoritarianism and crimes which led to the loss of life and chaotic response to the pandemic! In the future, may humanity unite on the frontlines for our continued survival as one species.

Dr. Trien Vinh Le is a lecture at the School of Government, University of Economics, Ho Chi Minh City, Vietnam

(*) https://blog.technavio.com/blog/top-10-vaccine-manufacturers

Deep learning is good at finding patterns in reams of data, but can’t explain how they’re connected. Turing Award winner Yoshua Bengio wants to change that.

In March 2019, Yoshua Bengio received a share of the Turing Award, the highest accolade in computer science, for contributions to the development of deep learning – the technique that triggered a renaissance in artificial intelligence, leading to advances in self-driving cars, real-time speech translation, and facial recognition.

Now, Bengio says deep learning needs to be fixed. He believes it won’t realize its full potential, and won’t deliver a true AI revolution, until it can go beyond pattern recognition and learn more about cause and effect. In other words, he says, deep learning needs to start asking why things happen.

The 55-year-old professor at the University of Montreal, who sports bushy gray hair and eyebrows, says deep learning works well in idealized situations but won’t come close to replicating human intelligence without being able to reason about causal relationships. “It’s a big thing to integrate [causality] into AI,” Bengio says. “Current approaches to machine learning assume that the trained AI system will be applied on the same kind of data as the training data. In real life it is often not the case.”

The original article can be found here.

In the field of causal reasoning, Professor Judea Pearl is a 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).

A new consortium of top scientists will be able to use some of the world’s most advanced supercomputers to look for solutions.

Advanced computers have defeated chess masters and learned how to pick through mountains of data to recognize faces and voices. Now, a billionaire developer of software and artificial intelligence is teaming up with top universities and companies to see if A.I. can help curb the current and future pandemics.

Thomas M. Siebel, founder and chief executive of C3.ai, an artificial intelligence company in Redwood City, Calif., said the public-private consortium would spend $367 million in its initial five years, aiming its first awards at finding ways to slow the new coronavirus that is sweeping the globe.

“I cannot imagine a more important use of A.I.,” Mr. Siebel said in an interview.

Known as the C3.ai Digital Transformation Institute, the new research consortium includes commitments from Princeton, Carnegie Mellon, the Massachusetts Institute of Technology, the University of California, the University of Illinois and the University of Chicago, as well as C3.ai and Microsoft. It seeks to put top scientists onto gargantuan social problems with the help of A.I. — its first challenge being the pandemic.

The original article can be found here.

On March 22, 2020, AIWS Innovation Network (AIWS.net) contributed a solution to bring back normalization to life and society. AIWS.net Solution is based on the MIT app Private Kit: Safe Path.

Everyone knows that the cock’s crowing at dawn does not “cause” the sun to rise. Conversely, we have equal confidence “that flipping a switch will cause a light to turn on or off and that a sultry summer afternoon will cause sales to go up at the local ice cream parlor.” Such intuitions are integral to countless practical and moral judgments that fill our daily lives. And yet, as Prof. Judea Pearl and the science writer Dana Mackenzie note in their illuminating new work, “The Book of Why: The New Science of Cause and Effect,” scientists and statisticians lacked a common language until recently to distinguish between these very different kinds of observation. Indeed, within academia, “causal vocabulary was virtually prohibited for more than half a century.”

The subject of causation has preoccupied philosophers at least since Aristotle. Professor Pearl has deftly used the arc of his own career — first at RCA Laboratories and for the last 50 years at the University of California, Los Angeles (initially in the engineering department and since 1970 in computer science) — to chart the recent history of the subject.

This period broadly coincides with what Professor Pearl terms “the causal revolution.” Three ascending rungs of what he calls the “ladder of causation” serve as the central metaphor driving the narrative of “The Book of Why.” The “revolution” charted in the book, and in which Professor Pearl and his disciples played a crucial role, is what has allowed researchers across a vast range of disciplines to move beyond the first rung of the causal ladder, where they had been perennially stuck.

This lowest rung deal simply with observation — basically looking for regularities in past behavior. Professor Pearl places “present-day learning machines squarely on rung one.” While it is true that the explosion of computing power and accessible deep data sets have yielded many surprising and important results, the mechanics still operate “in much the same way that a statistician tries to fit a line to a collection of points.”

“Deep neural networks have added many layers of complexity of the fitted function, but raw data still drives the fitting process,” according to Professor Pearl. The causal revolution is what has enabled researchers to explore the higher rungs of the ladder.

Despite this well-considered skepticism, Professor Pearl is remarkably optimistic about what artificial intelligence can achieve and even whether we can make machines that are capable of distinguishing good and evil. Regardless of whether one agrees with these provocative conclusions, we can all hope that in any counterfactual world in which that is the case, programmers with the humanity of Professor Pearl will be in charge.

The original article can be found here.

Professor Pearl’s book was highly acclaimed with the praise by Dr. Vint Cerf, Chief Internet Evangelist at Google Inc. and World Leader in AI World Society (AIWS) award, “Pearl’s accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence… and they have redefined the term ‘thinking machine.'” 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”. His work will contribute to AI transparency, which is one of important AIWS topics to identify, publish and promote principles for the virtuous application of AI in different domains including healthcare, education, transportation, national security, and other areas. On March 19, Governor Michael Dukakis, Chairman of Michael Dukakis Institute (MDI), Co-founder of the AIWS Innovation (AIWS.net) announced that MDI honors Professor Judea Pearl with the World Leader in AIWS 2020 Award.

Professor Aleksander Madry, MIT, strives to build machine-learning models that are more reliable, understandable, and robust.

Madry’s research centers largely on making machine learning — a type of artificial intelligence — more accurate, efficient, and robust against errors. In his classroom and beyond, he also worries about questions of ethical computing, as we approach an age where artificial intelligence will have great impact on many sectors of society.

“I want society to truly embrace machine learning,” says Madry, “To do that, we need to figure out how to train models that people can use safely, reliably, and in a way that they understand.”

In the end, he aims to make each model’s decisions more interpretable by humans, so researchers can peer inside to see where things went awry. At the same time, he wants to enable nonexperts to deploy the improved models in the real world for, say, helping diagnose disease or control driverless cars.

“It’s not just about trying to crack open the machine-learning black box. I want to open it up, see how it works, and pack it back up, so people can use it without needing to understand what’s going on inside,” he says.

That’s why Madry seeks to make machine-learning models more interpretable to humans. New models he’s developed show how much certain pixels in images the system is trained on can influence the system’s predictions.

Madry has also been engaging in conversations about laws and policies to help regulate machine learning. A point of these discussions, he says, is to better understand the costs and benefits of unleashing machine-learning technologies on society.

“Sometimes we overestimate the power of machine learning, thinking it will be our salvation. Sometimes we underestimate the cost it may have on society,” Madry says. “To do machine learning right, there’s still a lot still left to figure out.”

The original article can be found here.

AIWS Innovation Network encourages researchers and experts to contribute solutions and models for transparency in AI. It is fundamental to audit AI and create a better world with it.

In this time when all of the world tries to defeat COVID-19, all governments have to create transparency, accountability and collaboration.

MIT Techreview briefs about an ultra-fast image sensor with a built-in neural network that has been developed at TU Wien (Vienna), which can be trained to recognize certain objects and has now been presented in ‘Nature’

 

The news: A new type of artificial eye, made by combining light-sensing electronics with a neural network on a single tiny chip, can make sense of what it’s seeing in just a few nanoseconds, far faster than existing image sensors.

Why it matters: Computer vision is integral to many applications of AI—from driverless cars to industrial robots to smart sensors that act as our eyes in remote locations—and machines have become very good at responding to what they see. But most image recognition needs a lot of computing power to work. Part of the problem is a bottleneck at the heart of traditional sensors, which capture a huge amount of visual data, regardless of whether or not it is useful for classifying an image. Crunching all that data slows things down.

A sensor that captures and processes an image at the same time, without converting or passing around data, makes image recognition much faster using much less power. The design, published in Nature today by researchers at the Institute of Photonics in Vienna, Austria, mimics the way animals’ eyes pre-process visual information before passing it on to the brain.

How it works: The team built the chip out of a sheet of tungsten diselenide just a few atoms thick, etched with light-sensing diodes. They then wired up the diodes to form a neural network. The material used to make the chip gives it unique electrical properties so that the photosensitivity of the diodes—the nodes in the network—can be tweaked externally. This meant that the network could be trained to classify visual information by adjusting the sensitivity of the diodes until it gave the correct responses. In this way, the smart chip was trained to recognize stylized, pixelated versions of the letters n, v, and z.

Limited vision: This new sensor is another exciting step on the path to moving more AI into hardware, making it quicker and more efficient. But there’s a long way to go. For a start, the eye consists of only 27 detectors and cannot deal with much more than blocky 3×3 images. Still, small as it is, the chip can perform several standard supervised and unsupervised machine-learning tasks, including classifying and encoding letters. The researchers argue that scaling the neural network up to much larger sizes would be straightforward.

 

The original article can be found here.

AIWS Innovation Network connects distinguished professors and innovators from top universities to build the social contract 2020, and a monitoring system to supervise the use of AI by governments, large corporations. At Policy Dialogue “Transatlantic Approaches on Digital Governance: A New Social Contract in Artificial Intelligence”, professor Nazli Choucri, MIT, will talk about the Social Contract 2020.

Last week, the European Union issued its long-anticipated white paper on artificial intelligence. The document is a prequel to new legislation and regulations governing the technology that are likely to have global consequences.

That’s because, as with Europe’s privacy law, GDPR, any new A.I. rules are likely to apply to anyone who sells to an EU customer, processes the data of an EU citizen, or has a European employee. And, as with GDPR, any rules Europe enacts may serve as a model for other nations—or even individual U.S. states—looking to regulate A.I.

The paper says that the 27-nation bloc should have strict legal requirements for “high-risk” uses of the technology.

What’s high-risk? Any scenario with “a risk of injury, death or significant material or immaterial damage; that produce effects that cannot reasonably be avoided by individuals or legal entities,” especially in sectors such as healthcare, transportation, energy and government.

The original article can be found here.

AIWS Innovation Network includes distinguished thinkers from top universities such as Harvard, MIT, Stanford, Berkeley, Princeton, Yale, Columbia, Brown, UCLA, Oxford, Cambridge, Carnegie Mellon, and more, and preeminent leaders will be a platform for the Transatlantic Alliance for Digital Governance to collaborate with EU and countries to make AI for good.

Artificial intelligence (AI) has the potential to help every sector of the economy. There is a challenge, though, in sectors that have fuzzier analysis and the potential to train with data that can continue human biases. A couple of years ago, I described the problem with bias in an article about machine learning (ML) applied to criminal recidivism. It’s worth revisiting the sector as time have changed in how bias is addressed. One way is to look at sectors in the legal profession where bias is a much smaller factor.

Tax law has a lot more explicit rules than, for instance, do many criminal laws. As much as there have been issues with ML applied to human resource systems (Amazon’s canceled HR system), employment law is another area where states and nations have created explicit rules. The key in choosing the right legal area. What seems to be the focus, according to conversations with people at Blue J Legal, is the to focus on areas with strong rules as opposed to standards. The former provide the ability to have clear feature engineering while that later don’t have the specificity to train an accurate model.”

Blue J Legal arose from a University of Toronto course started by the founders, combining legal and computer science skills to try to predict cases. The challenge was, as it has always been in software, to understand the features of the data set in the detail needed to properly analyze the problem. As mentioned, the choice of the tax system was picked for the first focus. Tax law has a significant set of rules that can be designed. The data can then be appropriately labeled. After their early work on tax, they moved to employment.

The products are aimed at lawyers who are evaluating their cases. The goal is to provide the attorneys statistical analysis about the strength and weaknesses of each case.

Law is an interesting avenue in which to test the integration between AI and people. Automation won’t be replacing the lawyer any time soon, but as AI evolves it will be able to increasingly assist the people in the industry, to become more educated about their options and to use their time more efficiently. It’s the balance between the two that will be interesting to watch.

The original article can be found here.

According to the Michael Dukakis Institute for Leadership and Innovation (MDI), the Artificial Intelligence World Society (AIWS) has been established for the purpose of promoting ethical norms and practices in the development and use of AI. AI can be an important tool to serve and strengthen democracy, human rights, and the rule of law. Besides, AI World Society Innovation Network (AIWS-IN), which is part of AIWS, is created to identify, publish and promote principles for the virtuous application of AI in different domains including healthcare, education, transportation, national security, and other areas.