In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology.

In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology. I've been reading Artificial Intelligence in Education, a 2019 publication by Wayne Holmes, Maya Bialik, and Charles Fadel, that explores implications of AI in the realm of teaching and learning.

Artificial Intelligence is now a part of our normal lives. We are surrounded by this technology from automatic parking systems, smart sensors for taking spectacular photos, and personal assistance. Similarly, Artificial Intelligence in education is being felt, and the traditional methods are changing drastically. At the World Innovation Summit for Education global summit in Doha, Qatar, I sat with Professor of Learner Centred Design at the UCL Knowledge Lab in London, whose research involves the design and evaluation of educational technology using theories from the learning sciences and techniques from Artificial Intelligence (AI). "AI has come to stay in our life. So, I think we need the population at large to understand more about Artificial Intelligence (AI). So that they can use it to their benefits. And so that they can keep themselves safe. And we need a small percentage of the population to understand enough about AI. To be the people who develop the next generation of AI technology. And we need a small percentage of the population to understand enough about AI. To develop the next generation of ethical guidelines and regulations for AI. And actually, we don't really know how to regulate and provide people with the right guidelines for development. But we need more people to understand enough about AI to help with the process. And then the third area and that we need to pay attention to, is to change the way that we educate and train the people. Because the world is changing and much of that change is driven by automation. So, we need to think about how we change our education systems. So, these areas are not different. There are areas all of which we need to pay attention to."

Facial recognition tools have significant error rates that differ by race. An AI hiring tool from Amazon "learned" gender bias against women and favored male candidates. We know diversity bias is rampant in artificial intelligence. But decisions made based on prejudiced AI systems aren't just an ethical dilemma; they're a financial one. The more unbiased a system, the more likely it is to maximize profits, make better hiring or selling recommendations and provide accurate risk predictions.

Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. In this blog post, I provide an introduction to the graphical approach to causal inference in the tradition of Sewell Wright, Judea Pearl, and others. We first rehash the common adage that correlation is not causation. We then move on to climb what Pearl calls the "ladder of causal inference", from association (seeing) to intervention (doing) to counterfactuals (imagining). We will discover how directed acyclic graphs describe conditional (in)dependencies; how the do-calculus describes interventions; and how Structural Causal Models allow us to imagine what could have been. This blog post is by no means exhaustive, but should give you a first appreciation of the concepts that surround causal inference; references to further readings are provided below. Messerli (2012) published a paper entitled "Chocolate Consumption, Cognitive Function, and Nobel Laureates" in The New England Journal of Medicine showing a strong positive relationship between chocolate consumption and the number of Nobel Laureates. I have found an even stronger relationship using updated data2, as visualized in the figure below. Now, except for people in the chocolate business, it would be quite a stretch to suggest that increasing chocolate consumption would increase the number Nobel Laureates. Correlation does not imply causation because it does not constrain the possible causal relations enough. If two random variables $X$ and $Y$ are statistically dependent ($X \perp Y$), then either (a) $X$ causes $Y$, (b) $Y$ causes $X$, or (c) there exists a third variable $Z$ that causes both $X$ and $Y$. Further, $X$ and $Y$ become independent given $Z$, i.e., $X \perp Y \mid Z$. An in principle straightforward way to break this uncertainty is to conduct an experiment: we could, for example, force the citizens of Austria to consume more chocolate, and study whether this increases the number of Nobel laureates in the following years.

A new leak of highly classified Chinese government documents has uncovered the operations manual for running the mass detention camps in Xinjiang and exposed the mechanics of the region's Orwellian system of mass surveillance and "predictive policing." The China Cables, obtained by the International Consortium of Investigative Journalists, include a classified list of guidelines, personally approved by the region's top security chief, that effectively serves as a manual for operating the camps now holding hundreds of thousands of Muslim Uighurs and other minorities. The leak also features previously undisclosed intelligence briefings that reveal, in the government's own words, how Chinese police are guided by a massive data collection and analysis system that uses artificial intelligence to select entire categories of Xinjiang residents for detention. The manual, called a "telegram," instructs camp personnel on such matters as how to prevent escapes, how to maintain total secrecy about the camps' existence, methods of forced indoctrination, how to control disease outbreaks, and when to let detainees see relatives or even use the toilet. The document, dating to 2017, lays bare a behavior-modification "points" system to mete out punishments and rewards to inmates. The manual reveals the minimum duration of detention: one year -- though accounts from ex-detainees suggest that some are released sooner. Experts say the platform, which is used in both policing and military contexts, demonstrates the power of technology to help drive industrial-scale human rights abuses. The China Cables reveal how the system is able to amass vast amounts of intimate personal data through warrantless manual searches, facial recognition cameras, and other means to identify candidates for detention, flagging for investigation hundreds of thousands merely for using certain popular mobile phone apps.

In 2010, US authors in top-rated AI journals outnumbered Chinese counterparts by two to one. That ratio has now reversed. Last year, 1,073 AI experts based at Chinese universities were credited in AI journals such as the Institute of Electrical and Electronics Engineers's Transactions on Neural Networks, compared to 492 US authors. Australia and Israel also do well on this metric. When experts are ranked according to their'H-index' – a metric of productivity and the citation impact of the publications of a scientist or scholar – Americans occupy 626 of the 1,000 top spots, including all of the top ten spots at the time of our analysis. New Zealand, Saudi Arabia and Finland's AI academics are also highly ranked.

At Ibotta we train a lot of machine learning models. They make predictions for millions of users as they interact with our mobile app. While we do much of our data processing with Spark, our preferred machine learning framework is scikit-learn. As compute gets cheaper and time to market for machine learning solutions becomes more critical, we've explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

New Delhi: Silicon Valley-based learning platform Udacity with respect to Monday said it intends to double the headcount at its New Delhi office to help its growth student and enterprise customer base in India. The organization trains laborers on cutting edge abilities, for example, Artificial Intelligence, Machine Learning, robotization, profound learning, information investigation, through its Nanodegree programs. "India is quick ascending as a conspicuous advanced first economy. Because of this quick digitisation crosswise over ventures, there is a flooding interest for new-age experts capable in cutting edge innovations, for example, AI, AI, mechanization, profound learning, information investigation, and so forth.," Udacity CEO Gabriel Dalporto said in an announcement.

Chemical process industries (CPI) companies are entering a critical stage in the movement toward digitalization (Industry 4.0), in which the majority of organizations are now initiating pilot projects aimed at improving operations with advanced digital tools. This includes a wide range of technologies, including data analytics, cloud computing, machine learning, artificial intelligence and many others. As the digitalization transformation of the CPI gains momentum, it has become clear that the movement is as much about people as it is about technology. The acceptance and involvement of workers is critical to the successful adoption and expansion of digital tools, as they are asked to adapt to new work practices. He emphasizes: "Companies don't adopt new technologies; people do."