Autonomous vehicles and self-driving cars are coming. Experts predict that every major auto manufacturer will have a self-driving car on the market by the early 2020s. And this has got many people talking about the business impacts. Every revolutionary technology brings with it revolutionary change. Businesses and industries that don't adjust quickly could face catastrophic consequences.

Immigrant Issues: Mexico's government is not pleased with new memos from the Trump administration requiring people who arrive in the U.S. illegally over the Mexican border to be deported back to Mexico even if they're not Mexican nationals. The country may refuse to cooperate--and it has a lot of leverage. Meanwhile, former DHS secretary Janet Napolitano has emerged as a champion of Obama-era programs protecting undocumented students. But the next big policy fight might be over legal immigration, if lawmakers embrace nationalist sentiments that seek to keep everyone out. Such sentiments were a major reason why Rumana Ahmed, a Muslim woman who served on the National Security Council under Obama, chose to leave the White House eight days into Trump's presidency.

Recently, tech leaders including Tesla CEO Elon Musk and billionaire Bill Gates warned about robots taking over jobs. However, a new report shows people are not that concerned about job automation. The report The Robots Are Coming, But Not For Me, is based on an online survey of more than 2,000 adults commissioned by consumer engagements firm LivePerson last month. Although experts agree robots, artificial intelligence and automated technology will have at least some effect on global economies, the majority of people think they'll be alright but say other professionals will be affected by mass layoffs, the report found. Among those surveyed, a high 88 percent said they were not very worried about losing their job after they were told about an Oxford University study that said 47 percent of US jobs were "highly likely" to be replaced by technology.

CAPE CANAVERAL, FLORIDA – SpaceX made good on a 250-mile-high delivery at the International Space Station on Thursday, after fixing a navigation problem that held up the shipment a day. Everything went smoothly the second time around as the station astronauts captured the SpaceX Dragon cargo ship as the two craft sailed over Australia. On Wednesday, a GPS system error prevented the capsule from getting close enough to be grabbed by the station's big robot arm. The Dragon -- loaded with 5,500 pounds of supplies -- rocketed away Sunday from NASA's historic moon pad at Kennedy Space Center in Florida. Now leased by the SpaceX, the pad had been idle since the close of the shuttle program almost six years ago.

Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials. In the framework of remote sensing, such an assumption is no more valid in the presence of intra-class variabilities due to illumination conditions, weathering, slight variations of the pure materials, etc... In this paper, we first describe the results of investigations highlighting intra-class variability measured in real images. Considering these results, a new formulation of the linear mixing model is presented leading to two new methods. Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability. To overcome UP-NMF limitations an extended method is proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each sensed spectrum, these extended versions of NMF extract a corresponding set of source spectra. A constraint is set to limit the spreading of each source's estimates in IP-NMF. The methods are tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and then numerically mixed. We thus demonstrate the interest of our methods for realistic source variabilities. Finally, IP-NMF is tested on a real data set and it is shown to yield better performance than state of the art methods.

Microwave-based breast cancer detection has been proposed as a complementary approach to compensate for some drawbacks of existing breast cancer detection techniques. Among the existing microwave breast cancer detection methods, machine learning-type algorithms have recently become more popular. These focus on detecting the existence of breast tumours rather than performing imaging to identify the exact tumour position. A key step of the machine learning approaches is feature extraction. One of the most widely used feature extraction method is principle component analysis (PCA). However, it can be sensitive to signal misalignment. This paper presents an empirical mode decomposition (EMD)-based feature extraction method, which is more robust to the misalignment. Experimental results involving clinical data sets combined with numerically simulated tumour responses show that combined features from EMD and PCA improve the detection performance with an ensemble selection-based classifier.

Machine learning is creating a foothold in the business world, especially when it comes to innovative digital experience, and Web Content Management (WCM) players are diving headfirst into machine learning in the aim of supporting a smart experience across industries. As Forrester's recent industry overview said, "The web CMS market is changing because more organizations recognize the necessity of contextual digital experiences. Every vendor in this landscape is tracking toward this goal."¹ As contextual experiences increasingly become brand differentiators, the ability of machine learning to provide these experiences at scale is massively advantageous. The broad statement that machine learning and other AI technologies are going to infiltrate all corners of our lives, while likely true, paints an often dystopian picture that can be a bit overwhelming.

Google Inc. announced Thursday its new artificial intelligence software for weeding out particularly abusive or hateful remarks from comments sections in an attempt to restrict platforms to more thoughtful debate. The Menlo Park, California-based company launched the program, called Perspective, using an interactive demo allowing viewers to gradually purge three hypothetical comments sections--on climate change, the 2016 presidential election and the U.K.'s separation from the European Union, also known as "Brexit"--of their inflammatory remarks. Move the slider from right to left and phrases like "If they voted for Hilary [sic] they are idiots" are replaced by comments such as "Horrible, but the lesser of two evils won." Slide the toggle further and what's left are remarks like "Did you vote for what you truly believe is right and why?" and the sincere if not improbable "I honestly support both, as I was a Bernie [Sanders] supporter." The software "uses machine learning models to score the perceived impact a comment might have on a conversation," according to the site, which listed the Economist, the Guardian, the New York Times and Wikipedia as partners.

PDF: We made a fancy PDF of this post for printing and offline viewing. Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery.

Much has been said about how artificial intelligence (AI) will replace many blue collar and white collar jobs. Could AI also replace many levels of management all the way up to the C-suite? AI and "robotization," is exerting a slow but continuous degradation on the value and availability of work--in the form of wages and the number of adult workers with full-time jobs. The widespread disappearance of jobs in a social transformation would be unlike anything we've experienced or imagined. The issue won't be just saving jobs, but it will also involve saving or recasting the concept of work.