As artificial intelligence (AI) becomes ubiquitous in fields such as medicine, education and security, there are significant ethical and technical challenges to overcome. While the credits to Star Wars drew to a close in a 1970s cinema, 10-year-old Cynthia Breazeal remained fixated on C-3PO, the anxious robot. "Typically, when you saw robots in science fiction, they were mindless, but in Star Wars they had rich personalities and could form friendships," says Breazeal, associate director of the Massachusetts Institute of Technology (MIT) Media Lab in Cambridge, Massachusetts. "I assumed these robots would never exist in my lifetime." A pioneer of social robotics and human–robot interaction, Breazeal has made a career of conceptualizing and building robots with personality.

Data science is one of the top skills in demand as jobs in the field sees an upward trend, especially after the pandemic. As a matter of fact, many online platforms are providing online data science courses. These programs also offer certifications on their completion, making aspiring data scientists more employable. Analytics India Magazine has collated some of the top subscription-based ed-tech platforms that provide learning data science in various formats. DataCamp is an ed-tech platform purely made only for data science.

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common? They are all masters of deep learning. We often hear about AI, or self-driving cars, or the'algorithmic magic' at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.

Now that companies are moving beyond the basics of artificial intelligence, IT leaders are looking for people with experience in integrating artificial intelligence (AI) with other technologies such as automation. Expertise in machine learning operations will also be in high demand as companies deploy more algorithms into production which requires ongoing care and feeding. Leah Belsky, chief enterprise officer at Coursera, said many of the in-demand technology skills today will remain the same in 2021. This includes artificial intelligence, Python programming, and data storytelling. She predicts that what will shift significantly is who is learning technology skills and how they are learning them.

Beijing – Muslims in China's Xinjiang were "arbitrarily" selected for arrest by a computer program that flagged suspicious behavior, activists said Wednesday, in a report detailing big data's role in repression in the restive region. The U.S.-based Human Rights Watch said leaked police data that listed over 2,000 detainees from Aksu prefecture was further evidence of "how China's brutal repression of Xinjiang's Turkic Muslims is being turbocharged by technology." Beijing has come under intense international criticism over its policies in the resource-rich territory, where rights groups say as many as 1 million Uighurs and other mostly Muslim minorities have been held in internment camps. China defends the camps as vocational training centers aimed at stamping out terrorism and improving employment opportunities. Surveillance spending in Xinjiang has ballooned in recent years, with facial recognition, iris scanners, DNA collection and artificial intelligence deployed across the province in the name of preventing terrorism.

CSE Assistant Professor Rose Yu, who recently arrived from Northeastern University in Boston, is developing physics-guided machine learning techniques to model spatiotemporal data. She investigates traffic flows, human mobility and fluid dynamics, but her passion for computer science began more humbly. "I think it was because of my love for computer video games," said Yu. "I played a lot of World of Warcraft in high school." That pastime sparked an early interest in computers and later in machine learning. Yu earned her PhD at USC, where she was honored for best dissertation.

A new artificial intelligence (AI)-powered platform is set to supplement school education with live and inclusive learning opportunities for students, not only in the UAE but even across the world. Launched by Sheikh Mohammed bin Hamad bin Mohammed Al Sharqi, Crown Prince of Fujairah, Online Learning World (OLW) comes after extensive market research in the country revealed that the percentage of students taking private classes rose through the grades – and in Grade 12, around 37 per cent of all Emirati and 35 per cent of all non-Emirati students sought online learning. OLW aims to break down complicated chapters into simple and interesting lesson plans which clarify and enhance concept-based learning as per a specific board's curriculum. Subjects like Maths, English, Science, Coding, Arabic, Hindi and French, will be taught in a fun and interactive way with masterclasses for well being, including fitness, culinary, life skills and other short certificate courses also being high on the agenda. "This is a phenomenal platform that empowers the students seeking support to learn complex subjects in a simple and efficient manner. This is also revolutionary because it is on-demand – thereby empowering students and parents to select specific and tailored tutoring and plan their learning days," said Amreesh Chandra, president of OLW.

A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. This setting nevertheless integrates a number of the central challenges of artificial intelligence (AI) research: complex visual perception and goal-directed physical control, grounded language comprehension and production, and multi-agent social interaction. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. However, this is presently impractical. Therefore, we approximate the role of the human with another learned agent, and use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour. Rigorously evaluating our agents poses a great challenge, so we develop a variety of behavioural tests, including evaluation by humans who watch videos of agents or interact directly with them. These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone. Further, we demonstrate that agent capabilities generalise beyond literal experiences in the dataset. Finally, we train evaluation models whose ratings of agents agree well with human judgement, thus permitting the evaluation of new agent models without additional effort. Taken together, our results in this virtual environment provide evidence that large-scale human behavioural imitation is a promising tool to create intelligent, interactive agents, and the challenge of reliably evaluating such agents is possible to surmount.

Suresh Venkatasubramanian (University of Utah), Nadya Bliss (Arizona State University), Helen Nissenbaum (Cornell University), and Melanie Moses (University of New Mexico) Overview Long gone are the days when computing was the domain of technical experts. We live in a world where computing technology--especially artificial intelligence--permeates every aspect of our daily lives, playing a significant role in augmenting and even replacing human decision-making in a broad range of situations. AIenabled technologies can adjust to your child's level of understanding by processing a pattern of mistakes; AI systems can leverage combinations of sensor inputs to choose and carry out braking actions in your car; web browsers with AI capabilities can reason from past observations of your searches to recommend a new cuisine in a new location. Innovations in AI have focused primarily on the questions of "what" and "how"--algorithms for finding patterns in web searches, for instance--without adequate attention to the possible harms (such as privacy, bias, or manipulation) and without adequate consideration of the societal context in which these systems operate. As a result of this tight technical focus, and the rapid, worldwide explosion in its use, AI has come with a storm of unanticipated socio-technical problems, ranging from algorithms that act in racially or gender-biased ways, get caught in feedback loops that perpetuate inequalities, or enable unprecedented behavioral monitoring surveillance that challenges the fundamental values of free, democratic societies.

Matrix completion has received vast amount of attention and research due to its wide applications in various study fields. Existing methods of matrix completion consider only nonlinear (or linear) relations among entries in a data matrix and ignore linear (or nonlinear) relationships latent. This paper introduces a new latent variables model for data matrix which is a combination of linear and nonlinear models and designs a novel deep-neural-network-based matrix completion algorithm to address both linear and nonlinear relations among entries of data matrix. The proposed method consists of two branches. The first branch learns the latent representations of columns and reconstructs the columns of the partially observed matrix through a series of hidden neural network layers. The second branch does the same for the rows. In addition, based on multi-task learning principles, we enforce these two branches work together and introduce a new regularization technique to reduce over-fitting. More specifically, the missing entries of data are recovered as a main task and manifold learning is performed as an auxiliary task. The auxiliary task constrains the weights of the network so it can be considered as a regularizer, improving the main task and reducing over-fitting. Experimental results obtained on the synthetic data and several real-world data verify the effectiveness of the proposed method compared with state-of-the-art matrix completion methods.