Since 1956, when computer science researchers gathered in the small town of Hanover, N.H. at Dartmouth College to talk about the field's nascent investigations into artificial intelligence, both government and industry in the U.S. have grappled with how to structure a systematic approach to research and development in the newly important field. From the government's perspective, this is increasingly important. With both federal research institutions and private companies pursuing artificial intelligence breakthroughs at breakneck speed, the federal government is frankly having a bit of an existential crisis about its role in research efforts and the priorities it has for what AI research should look like. To wit, in 2015 government spending on unclassified research and development in AI-related technologies was around 1.1 billion, according to one of the twin reports released today. But in the last five years alone, mergers and acquisitions among private companies vying for dominance in the AI market have far outstripped that figure, according to data from CB Insights.

Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to. When we use a voice command to call our spouses, we reach them now. We aren't connected to Amtrak or an angry ex. In fact, we are increasingly interacting with our computers by just talking to them, whether it's Amazon's Alexa, Apple's Siri, Microsoft's Cortana, or the many voice-responsive features of Google.

Recent news about Google, Microsoft, Facebook and other tech giants teaming up to advance the capabilities of AI only confirms that the next generation of technology is going to be focused on machine learning. With artificial intelligence being at the core of all the new and upcoming programs and technologies, you can only stay relevant if you start learning about it now. Stay ahead of the pack with The Complete Machine Learning Bundle, which is now available for a special price of 25 only. The offer is valid for 24 hours, so grab the deal from WCCFtech Deals while it lasts.

Vox tech writer Timothy B. Lee used to be one of the most ardent techno-optimists. But he's had a bit of a conversion, of late, and is now on the side of those who think tech progress is slowing. Maybe it was the economist Robert Gordon who convinced him, or maybe years of observing the tech world changed his mind. In any case, Lee now broadly suggests that the inventions of tomorrow won't be as world-changing as those of yesteryear. There are a number of industries -- with health care and education being the most important -- where there's an inherent limit on how much value information technology can add.

Oracle, like so many other enterprise IT specialists, is talking up plans to integrate machine learning into its software. What the Redwood City giant hopes will set it apart from competitors is the way it plans to use those features. Where rivals have been touting AI and machine learning as a way to overhaul and redefine platforms, Oracle hopes that its Adaptive Intelligence features will instead complement its other cloud compute offerings. Introduced last month at the annual OpenWorld conference in San Francisco, Adaptive Intelligence will look to tie machine learning into Oracle's cloud to make features more personalized and better suited for individual companies. Part of Oracle's approach is based on the availability of data it has to feed into the machine learning system.

Start developing applications with advanced AI and computer vision today using NVIDIA's deep learning tools, including TensorRT and DIGITS. Webinar access will be emailed to you. Sign up anyway to get the recording! A renowned GPGPU developer and systems architect, Dustin Franklin works to deploy CUDA-accelerated embedded applications and solve challenging sensor processing problems on Jetson, NVIDIA's low-power, high-performance embedded platform. Dustin and his team utilize the parallel compute horsepower of NVIDIA GPUs for real-time computer vision, autonomous navigation, and other toolsets, to enable new product categories and industries.

Leading UK MPs are calling on the government to set up a commission on artificial intelligence (AI) to examine the potential problems the technology could create for future generations. A report into the issue by the House of Commons science and technology committee, which was chaired by MP Dr Tania Mathias warned that the government is failing to prepare for how AI will "fundamentally" change people's lives. The research concluded that there is no strategy for developing the new skills workers will need to succeed as AI becomes more popular, and cautioned that more must be done to tackle the ethical and social problems posed by the advent of robots. "Concerns about machines'taking jobs' and eliminating the need for human labour have persisted for centuries. Nevertheless it is conceivable that we will see AI technology creating new jobs over the coming decades while at the same time displacing others," said Mathias. Dr Cotton-Barratt from the Future of Humanity Institute, who is quoted in the report, said that the UK is "well-positioned" to respond to this challenge.

IT'S HARD TO think of a single technology that will shape our world more in the next 50 years than artificial intelligence. As machine learning enables our computers to teach themselves, a wealth of breakthroughs emerge, ranging from medical diagnostics to cars that drive themselves. A whole lot of worry emerges as well. Will it take over our jobs? President Obama was eager to address these concerns.

From shipping to shopping, nearly every industry across the globe has undergone a data revolution. Companies now have the ability to collect massive amounts of information from every facet of their operations--information that's unparalleled not only in its sheer quantity but also in its frequency and immediacy. This data deluge is transforming not only the way executives run their companies, but also the way those companies are audited. The potential benefits of this shift are broad and far-reaching. At the same time, customer demands are evolving, says Felice Persico, EY Global Vice Chair for Assurance.

Car manufacturers will have difficulty demonstrating just how safe self-driving vehicles are because of what's at the core of their smarts: machine learning. "You can't just assume this stuff is going to work," says Phillip Koopman, a computer scientist at Carnegie Mellon University who works in the automotive industry. In 2014, a market research firm projected that the self-driving car market will be worth 87 billion by 2030. Several companies, including Google, Tesla, and Uber, are experimenting with computer-assisted or fully autonomous driving projects--with varying success because of the myriad technical obstacles that must be overcome. Koopman is one of several researchers who believe that the nature of machine learning makes verifying that these autonomous vehicles will operate safely very challenging.