Ben Dickson is a software engineer and the founder of TechTalks. As artificial intelligence and machine learning mature and manifest their potential to take on complicated tasks, we've become somewhat expectant that robots can succeed where humans have failed -- namely, in putting aside personal biases when making decisions. But as recent cases have shown, like all disruptive technologies, machine learning introduces its own set of unexpected challenges and sometimes yields results that are wrong, unsavory, offensive and not aligned with the moral and ethical standards of human society. While some of these stories might sound amusing, they do lead us to ponder the implications of a future where robots and artificial intelligence take on more critical responsibilities and will have to be held responsible for the possibly wrong decisions they make. At its core, machine learning uses algorithms to parse data, extract patterns, learn and make predictions and decisions based on the gleaned insights.

Anthony Foxx waits for the countdown, then hits the plunger. The catapult releases its bungee cord, slinging the drone from to a standstill to 50 mph in half a second. The drone spins up its twin propellers and flies a few hundred feet up, circling overhead. "That's amazing," Foxx says, as the UAV drops its package within a few feet of the practice delivery zone, then belly flops onto a brown landing pad that resembles the base of a jumping castle. During the closing months of his four-year run as US Secretary of Transportation, Foxx has come to California on a fact finding mission.

Jefferson Graham looks back at the last 8 years and how tech changed since President Obama was elected in 2008. LOS ANGELES -- In 2024, the year the next president -- if he sticks around for the maximum two terms -- waves goodbye, technology is expected to have rocketed us into a future where we hop into driverless cars, plan trips to Mars, and chat with robots as if they're our best friends. Which got us thinking: With such massive changes expected to come over the next eight years, how has tech altered the world since Barack Obama's election eight years ago? When he was elected, most of us were still on desktop or laptop computers much of the workday. The iPhone was just one year old, and our new president fought long and hard with White House security to keep using his beloved BlackBerry.

In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the limitation of LDA to incorporate arbitrary topic correlations, by assuming that the hidden topic proportions are drawn from a flexible class of Normalized Infinitely Divisible (NID) distributions. NID distributions are generated through the process of normalizing a family of independent Infinitely Divisible (ID) random variables. The Dirichlet distribution is a special case obtained by normalizing a set of Gamma random variables. We prove that this flexible topic model class can be learned via spectral methods using only moments up to the third order, with (low order) polynomial sample and computational complexity. The proof is based on a key new technique derived here that allows us to diagonalize the moments of the NID distribution through an efficient procedure that requires evaluating only univariate integrals, despite the fact that we are handling high dimensional multivariate moments. In order to assess the performance of our proposed Latent NID topic model, we use two real datasets of articles collected from New York Times and Pubmed. Our experiments yield improved perplexity on both datasets compared with the baseline.

A photograph shows a lone automobile among dozens of horse-drawn buggies on Manhattan's famed Fifth Avenue in 1900. Thirteen years later, another photo depicts the same thoroughfare clogged with gas-guzzling vehicles. The images illustrate how quickly transportation innovations can take hold in society, a mobility-minded futurist recently told Southern Nevada leaders. His name is Paul Godsmark, and he's co-founder of the Canadian Automated Vehicles Centre of Excellence. Godsmark's message: Autonomous vehicles could be the biggest thing to disrupt society since the Internet.

Artificial intelligence (AI), or the process by which computers or robots perform tasks that need human intelligence, was one of the key themes of this week's Web Summit in the Portuguese capital, Lisbon. The poll, conducted among 224 venture capitalists attending the conference showed 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The survey also found that 83 percent of the investors canvassed expected Britain's exit from the European Union to damage Europe's economy and 77 percent believed it would damage British startups. London is widely seen as the main tech startup hub in Europe, thanks to its large pool of talent and a much bigger pool of funding than in rival centers. Cities like Berlin, Amsterdam and Lisbon are eager to attract more tech startups.

Let's inject a virus into the attacking alien spacecraft and save Earth! Let's hack into the enemy mainframe with six keystrokes and abort the torpedo launch! Cybersecurity has long been a staple of science fiction, whether it's in movies like "Independence Day" or television shows like "Star Trek." Yet in our real 21st Century world, artificial intelligence is the new face of cybersecurity, even if it doesn't sound like Hal from "2001: A Space Odyssey." The most obvious place for added intelligence is to detect whether some pattern of network traffic is benign or hostile.

The results of the 2016 US Presidential Election were, to put it mildly, a surprise. Pre-election polls and forecasts based on these polls pointed to a Clinton victory, a prediction shared by betting markets and pundits. In the aftermath of the vote, the main question asked is "why?" with answers ranging from the political to the economic to the social/cultural. In this article we attempt to provide a preliminary answer to a fundamental question: who voted for Trump, who voted for Clinton, and who voted for a third party or did not vote? By combining data from the United States census with the election results and recently proposed machine learning methods for ecological inference using regressions based on samples from a distribution, we provide local demographic estimates of voting and nonvoting. Unlike with exit polls, we are able to draw conclusions across the entire US and at a local level, about voters and non-voters, for interesting and novel combinations of predictor variables. It is our hope that this analysis will help inform the typical election post mortems, which are usually informed by incomplete information, due the following factors: - Vote counts will not be finalized in many precincts until days or in rare cases weeks after the election. Very close popular vote totals yield winner-take-all results, a fact of the US's electoral system but one that can lead to winner-take-all explanations.

Investors believe it is'inevitable' that artificial intelligence will destroy millions of jobs and that governments are unprepared for such an impact, according to a new survey. Artificial intelligence (AI), or the process by which computers or robots take on tasks that need human intelligence, is one of the key themes of this week's Web Summit in Lisbon. The poll among 224 venture capitalists attending the conference showed 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The poll among 224 venture capitalists attending the Web summit in Lisbon found 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The survey also found that 83 percent of the investors canvassed expect Britain's exit from the European Union to damage Europe's economy and 77 percent believe it will damage British startups.

The results of Tuesday's US election shocked many--including pollsters and campaign insiders. As a result, many have begun questioning the data and methods behind predictions, wondering what went wrong. But not everyone got it wrong: An AI tool created by an Indian startup in Mumbai in 2004 has correctly predicted the last three US elections, including this one. By collecting and analyzing 20 million social media data points, MogIA, developed by Sanjiv Rai, has used sentiment to determine political outcomes. And social media has proven to have a powerful impact on candidates' popularity.