We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The recent developments in AI are the result of increased processing power, improvements in algorithms and the exponential growth in the volume and variety of digital data. Many applications of AI have started entering into our every-day lives, from machine translations, to image recognition, and music generation, and are increasingly deployed in industry, government, and commerce. Connected and autonomous vehicles, and AI-supported medical diagnostics are areas of application that will soon be commonplace.

The rise of populist politicians across the rich world has led to a profound rethinking of the way developed economies work. In particular, the impact of automation on the labor market, and the disappearance of routine manufacturing jobs, has been blamed for the electoral successes of leaders, such as US President Donald Trump and Italy's Matteo Salvini. Yet, there are profound differences in what determines the economic winners and losers on the two sides of the Atlantic. In the US, the main factor deciding whether a worker can prosper in the age of robots appears to be education. Conversely, in the European Union, it seems to be whether staff have strong protection in their employment contracts--as many older industrial workers do here. It would be foolish for any government to dissuade companies from investing in machines that are more productive.

Since 2006, improvised explosive devices have killed more than 1,000 U.S. troops in Iraq as small groups of U.S. soldiers routinely travel in convoys on bomb-ridden roads, according to Congressional Research Service data. But General Motors is developing commercial vehicle technology that could dramatically lower the casualty count from IEDs. In fact, GM is gambling that it has a lot of technology that the military will want to buy. For example, "GM has demonstrated leader-follower capability," GM Defense President David Albritton told the Free Press. Leader-follower means a manned vehicle leads a dozen unmanned vehicles using GM's self-driving vehicle technology.

COIMBATORE: The nation's constitution is what prevails and has to prevail and it was the duty of every citizen to see that the constitutional values, norms and nuances remain sacrosanct, said former Chief Justice of India, Dipak Misra on Saturday. He was speaking at the inaugural session of the fourth edition of the Indian Cyber Congress held at a private college in the city. "Question arises that who is sovereign amongst the three wings of the state -- executive, legislative or judiciary. On occasions there you will find there are certain utterances that there is'parliamentary sovereignty'. But the Supreme Court time and again has reiterated that there is'constitutional sovereignty', meaning thereby it's the Constitution that prevails and that has to prevail. It's the duty of every citizen to see that the constitutional values, norms and nuances are remain sacrosanct," said Misra.

It's now widely acknowledged that artificial intelligence has made rapid progress in recent years. Many applications of AI are now outperforming humans at specific tasks – such as game playing and diagnostic systems. Most of this has been achieved within the last decade through rapid progress using data-driven approaches that are centered on machine learning technologies and algorithms. However, despite all the euphoria, many AI researchers believe that machine learning alone is not enough to produce human-level intelligence. Human-level intelligence has come to be known as Strong AI or Artificial General Intelligence (AGI).

Facial recognition systems can produce wildly'inaccurate results', especially for Asian and African Americans, a new US government study has found. The new research comes amid widespread deployment of facial recognition technology for law enforcement, airports, banking, retailing and smartphones. The study found that facial recognition software would confused two people 100 times more often for Asian and African American faces than it did for white ones. Failures could lead to the'wrong people being arrested' and'lengthy interrogations' according to Jay Stanley of the American Civil Liberties Union. The National Institute of Standards and Technology (NIST) study also found two algorithms assigned the wrong gender to black females 35 per cent of the time.

The first rule about the mall robot appears to be: "Don't talk about the mall robot." Stanford Shopping Center in Palo Alto had mall robots, one of which was famously accused of knocking over a small child. How many robots did the posh mall have at the time? How many does it have now? When and why did the shopping center rid itself of autonomous patrollers? The shopping center declined to say.

The robot uprising forged in the "Terminator" movies is one step closer to reality. On Thursday, Toyota debuted its new, upgraded humanoid robot, the T-HR3, which is controlled remotely by someone wearing a headset and wiring on their arms. Toyota claims that in the future, this machine, which is smoother, lighter and easier to use than past models, could be used "to perform surgery in a distant place where a doctor cannot travel. It also might allow people to feel like they're participating in events they can't actually attend," according to the Associated Press. That same day, it was announced that Swiss researchers developed a light, quick robotic bug called the DEAnsect, which can withstand several whacks from a flyswatter and can survive being stepped on by a shoe.

Although numerous ethics courses are available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on software development or data analysis. Technical students often consider these courses unimportant and a distraction from the "real" material. To develop instructional materials and methodologies that are thoughtful and engaging, we must strive for balance: between texts and coding, between critique and solution, and between cutting-edge research and practical applicability. Finding such balance is particularly difficult in the nascent field of responsible data science (RDS), where we are only starting to understand how to interface between the intrinsically different methodologies of engineering and social sciences. In this paper we recount a recent experience in developing and teaching an RDS course to graduate and advanced undergraduate students in data science. We then dive into an area that is critically important to RDS -- transparency and interpretability of machine-assisted decision-making, and tie this area to the needs of emerging RDS curricula. Recounting our own experience, and leveraging literature on pedagogical methods in data science and beyond, we propose the notion of an "object-to-interpret-with". We link this notion to "nutritional labels" -- a family of interpretability tools that are gaining popularity in RDS research and practice. With this work we aim to contribute to the nascent area of RDS education, and to inspire others in the community to come together to develop a deeper theoretical understanding of the pedagogical needs of RDS, and contribute concrete educational materials and methodologies that others can use. All course materials are publicly available at https://dataresponsibly.github.io/courses.

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.