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"Anybody who uses digital equipment is put under some form of surveillance. It seems to me that that cannot happen without consent, it cannot happen without the consent of populations. So, my message to the lawmakers is: please protect us.""

"O'Neill recounts an exercise to improve service to homeless families in New York City, in which data-analysis was used to identify risk-factors for long-term homelessness. The problem, O'Neill describes, was that many of the factors in the existing data on homelessness were entangled with things like race (and its proxies, like ZIP codes, which map extensively to race in heavily segregated cities like New York). Using data that reflects racism in the system to train a machine-learning algorithm whose conclusions can't be readily understood runs the risk of embedding that racism in a new set of policies, these ones scrubbed clean of the appearance of bias with the application of objective-seeming mathematics. "

"The US computer programmer came up with the idea of electronic messages that could be sent from one network to another in 1971. His invention included the ground-breaking use of the @ symbol in email addresses, which is now standard."

"The bill will now allow police to access all web browsing records in specific crime investigations, beyond the illegal websites and communications services specified in the original draft bill. It will extend the use of state remote computer hacking from the security services to the police in cases involving a "threat to life" or missing persons. This can include cases involving "damage to somebody's mental health", but will be restricted to use by the National Crime Agency and a small number of major police forces."

"Many people unwittingly sign up to be location-tracked 24/7, unaware that the highly sensitive data this generates is being used and sold on for profit. Campaigners say that if this information were stolen by hackers, criminals could use it to target children as they leave school or homes after occupants have gone out."

"More than 10m tweets sent by state actors attempting to influence US politics have been released to the public, forming one of the largest archives of political misinformation ever collated. The database reveals the astonishing extent of two misinformation campaigns, which spent more than five years sowing discord in the US and had spillover effects in other national campaigns, including Britain's EU referendum."

""Each time we build new ways of doing something close to human level, it allows us to automate or augment human labor," said Jeremy Howard, founder of Fast.ai, an independent lab based in San Francisco that is among those at the forefront of this research. "This can make life easier for a lawyer or a paralegal. But it can also help with medicine." It may even lead to technology that can - finally - carry on a decent conversation. But there is a downside: On social media services like Twitter, this new research could also lead to more convincing bots designed to fool us into thinking they are human, Howard said."

"Eck said machine learning, a powerful form of AI, will be integrated into how humans communicate with each other. He raised the idea of "assistive writing" in the future with Google Docs, the company's online word processing software. This may be based on Google's upcoming Smart Compose technology that suggests words and phrases based on what's being typed. Teachers used to worry about whether students used Wikipedia for their homework. Now they may wonder what part of the work the students wrote themselves, Eck said."

"The researchers, Tero Karras, Samuli Laine, and Timo Aila, came up with a new way of constructing a generative adversarial network, or GAN. GANs employ two dueling neural networks to train a computer to learn the nature of a data set well enough to generate convincing fakes. When applied to images, this provides a way to generate often highly realistic fakery. The same Nvidia researchers have previously used the technique to create artificial celebrities (read our profile of the inventor of GANs, Ian Goodfellow)."

"The app, using Solid's decentralized technology, allows Berners-Lee to access all of his data seamlessly-his calendar, his music library, videos, chat, research. It's like a mashup of Google Drive, Microsoft Outlook, Slack, Spotify, and WhatsApp."

"FOLLOW US Twitter / Facebook / RSS Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. The downside of this powerful approach is that it means machine learning classifiers can be confounded by confusing, out-of-context elements in a scene, as is demonstrated in The Elephant in the Room, a paper from a trio of Toronto-based computer science academics."

"Cybersecurity experts have warned for years that malfeasance, technical breakdown or administrative incompetence could easily wreak havoc with electronic systems and could go largely or wholly undetected. This is a concern made much more urgent by Russia's cyber-attacks on political party servers and state voter registration databases in 2016 and by the risk of a repeat - or worse - in this November's midterms. "

"Amid mounting financial pressure, at least a dozen police forces are using or considering predictive analytics, despite warnings from campaigners that use of algorithms and "predictive policing" models risks locking discrimination into the criminal justice system."

"L&M used a credential stuffing attack: using email addresses gleaned from massive breaches to gain access by repeatedly trying different email/password combinations."

"Automatically produced texts use language models derived from statistical analysis of vast corpuses of human-generated text to produce machine-generated texts that can be very hard for a human to distinguish from text produced by another human. These models could help malicious actors in many ways, including generating convincing spam, reviews, and comments -- so it's really important to develop tools that can help us distinguish between human-generated and machine-generated texts."

""If they are not happy, and not eating well, in some cases you can predict whether the pig is sick," said Jackson He, chief executive officer of Yingzi Technology, a small firm based in the southern city of Guangzhou that has introduced its vision of a "future pig farm" with facial and voice recognition technologies. China's biggest tech firms want to pamper pigs, too. Alibaba, the e-commerce giant, and JD.com, its rival, are using cameras to track pigs' faces. Alibaba also uses voice-recognition software to monitor their coughs."

"Cambridge Analytica sought to influence the Nigerian presidential election in 2015 by using graphically violent imagery to portray a candidate as a supporter of sharia law who would brutally suppress dissenters and negotiate with militant Islamists, a video passed to British MPs reveals."

"One great way to tell the difference is to ask AI recruiting companies what they use artificial intelligence, machine learning and/or deep learning for. Hopefully the hiring firm can what it's using the new technology for and not just that it is. If not it's time to dig a bit deeper."

"Many of the countries buying into that technology, however, still lack the institutions and the legislative oversight to keep it under control. In young, volatile democracies especially, the lure of technological greatness is already coming at a great social cost. "The thing with technology is that it kind of becomes irresistible," says Professor Webster. "It's very tempting when it can do something for us more efficiently. But just because the technology can do something it doesn't mean we should use it.""

"New Yorkers can expect to be fined from $25 to $250 if police officers catch them "using a portable electronic device while crossing a roadway"."