Germany is working on implementing a handful of new rules for autonomous cars that address ethical questions that come with the technology. In June, the ethics commission of the Federal Ministry of Transport and Digital Infrastructure -- made up of 14 scientists and legal experts -- released a report with guidelines it believed self-driving vehicles should be designed to follow. This week, the ministry said it would implement and enforce those guidelines. One of the proposed rules says that human life should always have priority over property or animal life and another stipulates that a surveillance system, like a black box, should record the activity so that it can be determined later on who was at fault during an accident -- the driver or the technology. Additionally, drivers should get to decide what personal information is collected from their vehicle, so that data can't be used to customize advertising, for example.

A brain area key to understanding speech has been identified by scientists. The neurons respond to changes in vocal pitch - crucial to conveying the meaning of what is being said. Tone is so important to language the exact same words can have a very different message. For example, 'Anna likes oranges' can be a statement, or if the pitch varies near the end, the phrase can be posed as a question, 'Anna likes oranges?' Pitch is also key to understanding sarcasm and emotions such as anger. This animation highlights pitch-sensing cells in a small brain area known as the superior temporal gyrus (STG).

Data science and machine learning are some of the top buzzwords in the technical world today. Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. Python is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with python, then go for this course.

Do you ever feel overwhelmed by an unending stream of information? It can seem like a barrage of new email and text messages demands constant attention, and there are also phone calls to pick up, articles to read, and knocks on the door to answer. Putting these pieces together to keep track of what is important can be a real challenge. The same information overload is a concern in many computational settings. Telecommunications companies, for example, want to keep track of the activity on their networks, to identify overall network health and spot anomalies or changes in behavior. Yet, the scale of events occurring is huge: many millions of network events per hour, per network element. While new technologies allow the scale and granularity of events being monitored to increase by orders of magnitude, the capacity of computing elements (processors, memory, and disks) to make sense of these is barely increasing. Even on a small scale, the amount of information may be too large to store in an impoverished setting (say, an embedded device) or to keep conveniently in fast storage. In response to this challenge, the model of streaming data processing has grown in popularity. The aim is no longer to capture, store, and index every minute event, but rather to process each observation quickly in order to create a summary of the current state. Following its processing, an event is dropped and is no longer accessible. The summary that is retained is often referred to as a sketch of the data. Coping with the vast scale of information means making compromises: The description of the world is approximate rather than exact; the nature of queries to be answered must be decided in advance rather than after the fact; and some questions are now insoluble. The ability to process vast quantities of data at blinding speeds with modest resources, however, can more than make up for these limitations.

How is computer security different in a high-performance computing (HPC) context from a typical IT context? On the surface, a tongue-in-cheek answer might be, "just the same, only faster." After all, HPC facilities are connected to networks the same way any other computer is, often run the same, typically Linux-based operating systems as are many other common computers, and have long been subject to many of the same styles of attacks, be they compromised credentials, system misconfiguration, or software flaws. Such attacks have ranged from the "wily hacker" who broke into U.S. Department of Energy (DOE) and U.S. Department of Defense (DOD) computing systems in the mid-1980s,42 to the "Stakkato" attacks against NCAR, DOE, and NSF-funded supercomputing centers in the mid-2000s,24,39 to the thousands of probes, scans, brute-force login attempts, and buffer overflow vulnerabilities that continue to plague high-performance computing facilities today. On the other hand, some HPC systems run highly exotic hardware and software stacks. In addition, HPC systems have very different purposes and modes of use than most general-purpose computing systems, of either the desktop or server variety. This fact means that aside from all of the normal reasons that any network-connected computer might be attacked, HPC computers have their own distinct systems, resources, and assets that an attacker might target, as well as their own distinctive attributes that make securing such systems somewhat distinct from securing other types of computing systems. The fact that computer security is context- and mission-dependent should not be surprising to security professionals--"security policy is a statement of what is, and what is not, allowed,"7--and each organization, will therefore have a somewhat distinctive security policy.

From reports Amazon's same-day delivery is less available in black neighbourhoods to Microsoft's'racist' chatbots, signs of online prejudice are becoming increasingly common. Scientists now say they can spot racist and sexist software using a code that finds out if there is implicit bias in algorithms running on websites and apps. By changing specific variables - such as race, gender or other distinctive traits - the online code Themis claims to know if data is discriminating against specific people. Previous research suggests technology is generally becoming racist and sexist as it learns from humans - and as a result, hindering its ability to make balanced decisions. Themis is a freely available code that mimics the process of entering data - such as making a loan application - into a given website or app.

R is considered as lingua franca of Data Science. Candidates with expertise in R programming language are in exceedingly high demand and paid lucratively in Data Science. IEEE has repeatedly ranked R as one of the top and most popular Programming Languages. Almost every Data Science and Machine Learning job posted globally mentions the requirement for R language proficiency. All the top ranked universities like MIT have included R in their respective Data Science courses curriculum.

Can machine intelligence in the form of Amazon Macie andGoogle Cloud DLP API solve once impossible problems? Deep learning AI algorithms have revolutionized natural language processing (NLP) and automated image analysis and enable features that were once the stuff of science fiction that now seem as routine. Whether it's online text translation, consumer chatbots or automatic face detection and tagging in photos, predictive analytics and deep learning enable features once seen as impossible. As I've discussed many times over the past few months, whether for cyber security like malware detection, conversational UIs, or specialized industry applications, AI is reshaping the world of enterprise software, with significant implications for every business. One area of emerging promise for machine intelligence enhancement is a vexing problem facing every organization; data protection and privacy.

What does the future of work look like? Will there still be jobs even if the nature of work is exceptionally different from today? New technologies undoubtedly changed the way we work. Recent studies suggest that unemployment rate today is significant in most developed nations and it's only going to get worse. By 2030, mid-level jobs will be by and large obsolete.

The death of a man strapped to a chair for 46 hours in a San Luis Obispo County jail puts a focus on the conditions for mentally ill inmates in California's county jails. Here are the stories you shouldn't miss today: Andrew Holland's legs and arms were shackled to a chair in a jail observation cell, where he sat in his own filth, eating and drinking almost nothing, for nearly two days in January. He was naked except for a helmet and mask covering his face and a blanket that slipped off his lap. San Luis Obispo County jail officials say Holland, who had schizophrenia, was restrained because he had been hitting himself in the head and was kept there because he refused to not harm himself further. Within 40 minutes of being unbound, he had stopped breathing. Holland's death has provoked outrage, a $5-million legal settlement and questions about the way California jails handle a growing number of mentally ill inmates.