Over the last few months, the usual debate around unicorns and bubbles seems to have been put on hold a bit, as fears of a major crash have thankfully not materialized, at least for now. Instead another discussion has emerged, one that's actually probably more fundamental. Which areas will produce the Googles and Facebooks of the next decade? What's prompting the discussion is a general feeling that we're on the tail end of the most recent big wave of innovation, one that was propelled by social, mobile and cloud. A lot of great companies emerged from that wave, and the concern is whether there's room for a lot more "category-defining" startups to appear.

A centuries-old Yemeni city stands alongside the gleaming towers of Hong Kong as one of the world's most beautiful architectural views, writes Jonathan Glancey. And, like the faces of those we hold dear, we hold certain skylines in our minds' eyes even when far … This election has been particularly noisy. Ask not what the government can do for Silicon Valley; ask what Silicon Valley can do for the government. How a tiny Florida community could influence the way we fight Zika around the world.By Graphics by Ella KoezePhotography by Erika LarsenIllustrations … The Harry Potter books have sold more than 400 million copies worldwide and been translated in over 60 languages. The books are filled with a tricky mix of wordplay, invented words, songs, allusions, British cultural references, and more.

It is difficult to predict when this might happen, but most artificial intelligence (AI) specialists estimate that it is more likely than not within this century. The leading AI researcher Stuart Russell suggests that, for better or worse, it would be'the biggest event in human history'. Stuart Russell and Martin Rees are affiliated with the new Leverhulme Centre for the Future of Intelligence at the University of Cambridge, where Huw Price is the academic director. Martin Rees and Jaan Tallinn are co-founders of the Centre for the Study of Existential Risk at the University of Cambridge, where Huw Price is the academic director.

Fifty civil rights groups have signed a letter asking the U.S. Department of Justice to investigate police use of facial-recognition databases following a report that half of America's adults have their images stored in at least one searchable facial-recognition database used by local, state and federal authorities They argue the technology disproportionately affects minorities and has minimal oversight. Researchers even found The Maricopa County Sheriff's Office in Arizona has enrolled all of Honduras' driver's licenses and mug shots into its database. States in dark blue use drivers license photos in police facial recognition databases. Red dots represent other jurisdictions using facial recognition. Of the 52 agencies that acknowledged using face recognition, only one obtained legislative approval for its use and only one agency provided evidence that it audited officers' face recognition searches for misuse. Not one agency required warrants, and many agencies did not even require an officer to suspect someone of committing a crime before using face recognition to identify her.

Updated 10-18-16: We've updated our recommendation for best Phablet (5.5 inches or greater) to the excellent Pixel XL. We haven't yet reviewed the standard-size Pixel, but one of the two is a likely candidate for best overall phone. The Android universe is teeming with options, from super-expensive flagship phones, to affordable models that make a few calculated compromises, to models expressly designed for, say, great photography. Chances are that whichever phone you buy, you'll keep it for at least two years. So choosing the best Android phone for you isn't a decision you should take lightly.

SAN FRANCISCO - As handy as all our voice recognition friends are, conversing with them still feels you're talking to a foreign relative. Whether its Siri (Apple) or Alexa (Amazon) or Google Assistant or Cortana (Microsoft), each requires the human to speak in slow, articulated phrases to increase the odds of comprehension. But researchers at Microsoft say they've reached a milestone that promises a future where machines can transcribe us as well as another person. In a paper published Monday called "Achieving Human Parity in Conversational Speech Recognition," engineers with Microsoft Artificial Intelligence and Research announced they'd developed a speech recognition system that makes the same or fewer errors as professional transcriptionists. The team hit a word error rate of 5.9 percent, down from the 6.3 percent WER the team reported just last month.

Photographs of nearly half of all U.S. adults--117 million people--are collected in police facial recognition databases across the country with little regulation over how the networks are searched and used, according to a new study. Along with a lack of regulation, critics question the accuracy of facial recognition algorithms. Meanwhile, state, city, and federal facial recognition databases include 48 percent of U.S. adults, said the report from the Center on Privacy & Technology at Georgetown Law. The search of facial recognition databases is largely unregulated, the report said. "A few agencies have instituted meaningful protections to prevent the misuse of the technology," its authors wrote.

When humans try to transcribe a spoken conversation all in one go, they manage to miss 5.9 percent of what they hear on average. Microsoft announced on Tuesday that, for the first time, they've managed to get a computer to perform that same transcription task just as well as a person. "We've reached human parity," Microsoft's chief speech scientist Xuedong Huang, said in a statement. To accomplish the 5.9 percent error rate, which beats a 6.3 percent record set just last month, the Microsoft team leveraged neural language models resembling associative word clouds. That is, a word like "fast" resides much closer to "fast" than it does to "slow".

Losing customers means losing revenue. Not surprisingly, then, companies strive to detect potential customer churn through predictive modeling, allowing them to implement interventions aimed at retaining customers. This might sound easy, but it can actually be very complicated: Customers leave for reasons that are as divergent as the customers themselves are, and products and services can play an important, but hidden, role in all this. What's more, merely building models to predict churn for different customer segments--and with regard to different products and services--isn't enough; we must also design interventions, then select the intervention judged most likely to prevent a particular customer from departing. Yet even doing this requires the use of analytics to evaluate the results achieved--and, eventually, to select interventions from an analytical standpoint.

Many important problems can be represented and studied using graph. If we accept graphs as a basic mean of structuring and analyzing data about the world, we shouldn't be surprised to see it being widely use in Machine Learning as a powerful tool that can enable intuitive properties and power a lot of useful features. Graph-based machine learning is destined to become this resilient piece of logic transcending a lot of other techniques. This post explores the tendencies of nodes in a graph to spontaneously form clusters of internally dense linkage (hereby termed community); a remarkable and almost universal property of biological networks. This is particularly interesting knowing that a lot of information can be extrapolated from a node's neighbor (e.g.