Centralized state camera surveillance is but one component of a burgeoning practice of personal data collection paired with artificial intelligence (AI). Camera surveillance is not inherently unlawful and has long been used at border-crossings, airports, and other high-security areas. However, recent technological advances have contributed to the spread of a more intrusive form of video surveillance that includes powerful, if imperfect, facial recognition abilities and AI decision making. While the technology offers states the ability to, among other things, identify lost children, identify criminals, and monitor threats, the new capacity also raises significant human rights issues. The use of camera surveillance has grown with leaps in technology, including the introduction of videocassette recorders in the 1970s and the internet in the 1990s.

Artificial intelligence (AI) can make cybersecurity better than ever – except, of course, that cybercriminals can also leverage these same technologies to identify and exploit vulnerabilities faster and more effectively. Is an AI arms race now inevitable? To some extent, cybersecurity has always resembled an arms race. As new threats are devised, security teams quickly adapt and try to stay one step ahead of the hackers. This is becoming harder to manage, however.

'Deepfakes' is the name given to video and audio developed by artificial intelligence (AI), resembling something, someone-- or someone doing something-- that didn't, in fact, occur. Advances in deep-learning and AI continue to make deepfakes more realistic, to the extent that in many cases it's becoming very difficult to distinguish what is real, and what is generated by AI. Give it a go on this website, and see if you can determine which is a real photo, and which is computer generated. With the presence of deepfakes doubling within the last year, and the technology continuously advancing, there are clear concerns surrounding the various ways they could be used. Many predict that deepfakes could provide a dangerous new medium for information warfare, helping to spread misinformation or'fake news'.

Artificial intelligence is all the rage these days. In the popular media, regular cyber systems seem almost passe, as writers focus on AI and conjure up images of everything from real-life Terminator robots to more benign companions. In intelligence circles, China's uses of closed-circuit television, facial recognition technology, and other monitoring systems suggest the arrival of Big Brother--if not quite in 1984, then only about forty years later. At the Pentagon, legions of officers and analysts talk about the AI race with China, often with foreboding admonitions that the United States cannot afford to be second in class in this emerging realm of technology. In policy circles, people wonder about the ethics of AI--such as whether we can really delegate to robots the ability to use lethal force against America's enemies, however bad they may be.

Is the EU's approach to regulating artificial intelligence misguided? Artificial intelligence (AI) is transforming society for the better. We're using it to improve services and save lives. Applications range from fighting fraud to preventing forest fires and creating new medicines. The EU's desire to be the world leader in AI and to protect citizens is laudable, but its regulatory stance risks having the opposite effect.

Cyberattacks on the likes of several tech giants have brought to the fore the challenge of bridging the skills gap in the cybersecurity space in India. And, artificial intelligence being the latest buzzword of the tech industry, is being touted as one of the key solutions to the cybersecurity skills gap. According to a report, it is estimated that there will be 3.5 million unfilled cybersecurity jobs globally by the year 2021. And therefore, companies are struggling to find adequate qualified people to assist in creating an intelligent cybersecurity framework. The challenge has become apparent in the last five to ten years with a sharp increase in cyberattacks, all the way from ransomware to zero-day malware to now sneaky crypto-mining attacks.

Voice assistants enable us to issue voice commands to our phones and even smart home devices. We can set alarms using our voice, search for a bookstore on Google, and even turn/off the sound system. Some of the most popular voice assistants include Apple's Siri, Google Assistant, Google Duplex, Amazon's Alexa, Samsung's Bixby, and Microsoft's Cortana. On the other hand, voice assistants recognize human speech using Natural Language Processing (NLP). The speech is then converted to numbers using machine language before a response is formulated and returned. Web services such as Email rely on machine learning algorithms to categorize messages into folders. This is an email filtering process that makes it easier for users to access the most important messages. In this case, the subject of the message is assessed and then the message is sent to the appropriate folder. An instance of this is the Gmail app that uses machine learning to arrange messages into folders such as primary, promotion, social.

Across the world, investing in AI innovations is on a steep rise. Artificial intelligence (AI) is indeed opening up the newer phases of technological advancements. Last year, riding the AI wave, Indian Institute of Technology (IIT), across the country, came up with several AI initiatives -- from rolling out courses to launching R&Ds centres and Center of Excellences in various parts of the country. Although we have just stepped in the year 2020, IITians have already started to make headlines with their newer innovations for the year. In fact, Google has recently decided to start six artificial intelligence-based research projects in India by providing funds and computational resources, where two of the prominent IITs have agreed to participate -- IIT Delhi and IIT Madras.

On Monday, the Pentagon announced the official adoption of a series of new principles for ethical use of artificial intelligence in warfare, the Associated Press reports. The principles were formed out of a commission with the (darkly Newspeak-y) name the Defense Innovation Board, which released its recommendations (title: "AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by the Department of Defense") to the Pentagon last October. The board was fronted by former Google CEO Eric Schmidt, an interesting twist given (as pointed out by the AP) due to the way Google seemed to (or: pretended to) drop out of a defense department project involving A.I. in 2018 after internal protests from Google staffers (to say nothing of the way Google's involvement was handled by the Pentagon). The Next Web called the principles "hazy" and "toothless." And Dave Gershgorn of OneZero noted that these supposed ethics are missing "'don't kill somebody with a robot.'"

The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprising. However, propaganda detection, like other tasks that deal with news documents and other forms of decontextualized social communication (e.g. sentiment analysis), inherently deals with data whose categories are simultaneously imbalanced and dissimilar. We show that BERT, while capable of handling imbalanced classes with no additional data augmentation, does not generalise well when the training and test data are sufficiently dissimilar (as is often the case with news sources, whose topics evolve over time). We show how to address this problem by providing a statistical measure of similarity between datasets and a method of incorporating cost-weighting into BERT when the training and test sets are dissimilar. We test these methods on the Propaganda Techniques Corpus (PTC) and achieve the second-highest score on sentence-level propaganda classification.