The historical evolution of Artificial Intelligence (AI) dates back to the year 1996 when Deep Blue AI defeated the then world chess champion. Garry Kasparov The year 2019 witnessed geopolitical paradigm where there was a race for technological supremacy between superpowers. It is estimated that by 2034-40, 50 per cent of the jobs would be automated in United States i.e.; within the next 15 years (Lee Kai Fu, AI Superpowers). Also majority of researchers predict singularity by 2045 -- a stage where machines become more advanced than human beings. This necessitates one to understand AI, its benefits, its major issues and its implications on government and social order.

New Delhi: Artificial intelligence is going to completely change agriculture and farming in the 21st century, Prime Minister Narendra Modi said on Thursday, as he stressed on the use of new technologies to boost farm incomes. Addressing a webinar on agriculture and the Union Budget, the prime minister said over 700 agri startups have been launched in the last couple of years, adding that newer technologies will drive growth. Modi also said the PM-KISAN had entered its third anniversary. "This scheme has become a strong support for the small farmers of the country. Under the scheme, almost ₹1.75 lakh crore has been given to 11 crore farmers," he said.

Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has been made. Such an ability is especially important at a time when machine learning is being used to automate decision processes which concern sensitive factors and legal outcomes. Indeed, it is even a requirement according to EU law. Furthermore, researchers concerned with imposing overly restrictive functional form (e.g., as would be the case in a linear regression) may be motivated to use machine learning algorithms in conjunction with explainability techniques, as part of exploratory research, with the goal of identifying important variables which are associated with an outcome of interest. For example, epidemiologists might be interested in identifying `risk factors' - i.e. factors which affect recovery from disease - by using random forests and assessing variable relevance using importance measures. However, and as we demonstrate, machine learning algorithms are not as flexible as they might seem, and are instead incredibly sensitive to the underling causal structure in the data. The consequences of this are that predictors which are, in fact, critical to a causal system and highly correlated with the outcome, may nonetheless be deemed by explainability techniques to be unrelated/unimportant/unpredictive of the outcome. Rather than this being a limitation of explainability techniques per se, we show that it is rather a consequence of the mathematical implications of regression, and the interaction of these implications with the associated conditional independencies of the underlying causal structure. We provide some alternative recommendations for researchers wanting to explore the data for important variables.

Last November, the tech giant Yandex unveiled Chervonenkis, Russia's most powerful supercomputer and the 19th most powerful commercial computer on the planet. Chervonenkis, which Yandex uses to train artificial intelligence algorithms for applications like web search and translation, was built by linking together more than 1,500 chips from the US company Nvidia. Earlier this week, Russia's Ministry of Internal Affairs said that it was having trouble sourcing the home-grown chips it's required to use, and as a result was considering returning to chips made by Intel, according to CNews, a Russian outlet. Russia's reliance on Western technology, particularly for chips, is at the heart of the sanctions announced Thursday by President Biden and US allies in response to Russia's invasion of Ukraine. Biden said in an address to the nation that restrictions on Russia's imports of key technology, including semiconductors, would squeeze its "access to finance and technology for strategic areas of its economy, and degrade its industrial capacity for years to come."

Artificial intelligence is revolutionary, but it's not without its controversies. Some believe it can take us down a dangerous path, potentially arming governments with dangerous Orwellian surveillance and mass control capabilities. We have to remember that any technology is only as'good' or'bad' as the people who use it. Consider the EU's hailed'blueprint for AI regulation' and China's proposed crackdown on AI development; these instances seek to regulate AI as if it were already an autonomous, conscious technology. The U.S. must think wisely before following in their footsteps and consider addressing the actions of the user behind the AI.

Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization.10 To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books, or other products) to customers. While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations. These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underlie how customers react to visual information displays and make decisions. As another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the Earth's surface is changing over time.50 These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of a6utomatically detected and annotated objects and information. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels. The breadth and complexity of these and many other data science scenarios means the modern data scientist requires broad knowledge and experience across a multitude of topics. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise. Considering this bottleneck, it is not surprising there is increasing interest in automating parts, if not all, of the data science process.

Analyzing data is now essential to success in education, employment, and other areas of activity in the knowledge society. Even though several frameworks describe the competencies and skills needed to meet current and future challenges, no data analytics competency framework exists to describe the importance of specific skills to succeed in data analytics assignments.

Urs Gasser (ugasser@cyber.harvard.edu) is the Dean of the new TUM School of Social Sciences and Technology at the Technical University of Munich, Germany, and a Faculty Director of the Berkman Klein Center for Internet & Society at Harvard University, Cambridge, MA, USA. Virgílio Almeida (virgilio@dcc.ufmg.br) is a Professor Emeritus of Computer Science at the Federal University of Minas Gerais (UFMG), Brazil, and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University, Cambridge, MA, USA.

George Orwell's novel 1984 got one thing wrong. A surveillance state will not have people watching people, as the Stasi did in East Germany. Computers will be the ones watching people. Technology lets you perform surveillance at an industrial scale. This is already happening in China, where facial recognition software is being used by law enforcement for catching relatively minor offenders such as jaywalkers to enabling much more disturbing activities such as tracking Uyghurs.

Digital technologies for learning, health, politics, and commerce have enriched the world. Digital heroes like Sir Tim Berners-Lee, Batya Friedman, Alan Kay, JCR Licklider, and Joe Weizenbaum have blazed trails. We depend upon software that nobody totally understands. We are vulnerable to cyberterrorism. Privacy is overrun by surveillance capitalism.7 Totalitarian control advances. Daily Internet news matching our beliefs makes it difficult to tell true from false.