As a species, we humans are awfully obsessed with the future. We love to speculate about where our evolution is taking us. We try to imagine what our technology will be like decades or centuries from now. And we fantasise about encountering intelligent aliens – generally, ones who are far more advanced than we are. Lately those strands have begun to merge. From the evolution side, a number of futurists are predicting the singularity: a time when computers will soon become powerful enough to simulate human consciousness, or absorb it entirely. In parallel, some visionaries propose that any intelligent life we encounter in the rest of the Universe is more likely to be machine-based, rather than humanoid meat-bags such as ourselves.

It was nearly 30 years ago that I first got infatuated with Artificial Intelligence (AI) and I ended up focusing both my undergraduate and graduate engineering research on applications of Artificial Neural Networks (ANNs). My first two jobs after graduate school stayed in the same groove; over 6 years I developed AI and machine learning techniques to address real world problems that ranged from recognizing human speech and natural language, to converting handwriting to searchable digitized text, and to streamlining maintenance procedures in nuclear reactor cores. So it is with a mix of amazement and amusement that I am soaking up the resurgence of AI and machine learning as the buzzword-du-jour: "Deep Learning". Deep Learning is very visible in the high hopes we hold for driverless cars and in the triumph of machines over chess champions. It is less conspicuously and more frequently used in the form of Apple's Siri, Amazon's Echo, playlists generated on Spotify, that auto-tag feature on Facebook Photos, the voice assistant that answers the phone when you call your bank, or when your fingerprint is recognized by a machine.

As far as locales go, the bottom of the ocean is a particularly exasperating place to explore. Anyone or anything you send down there has to contend with the dark, with thousands of pounds of pressure on every square inch, with the inability to replenish fuel supplies without returning to the mother ship. In recent years, unmanned undersea vehicles (UUVs) have improved the situation, eliminating the need to send a human down below, or to attach an unmanned vessel to a surface ship with a long umbilical cord. Those include Boeing's Echo Ranger and Echo Seeker underwater robots, which can spend a few days at at time below the surface, with ranges measured in the tens or hundreds of miles. Those UUV's are "nothing more than an extension, or an application of the surface ship," says Lance Towers, who carries the impressively potent title of director of sea and land at Phantom Works, Boeing's R&D arm.

Japan is spending more than 1 billion to resurrect the area around the wrecked Fukushima No. 1 nuclear plant as the country's "Innovation Coast." The region is trying to capitalize on technology developed in the five years spent cleaning up the worst nuclear disaster since Chernobyl, including Hitachi Ltd. and Toshiba Corp. robots that slither like snakes or cruise through radioactive water like speed boats to investigate the flooded reactors. Fukushima Prefecture -- like Beirut or post-bankruptcy Detroit -- is ripe to develop a strong tech community, according to Samhir Vasdev, an innovation consultant at the World Bank. "To lead the future from Fukushima, we must overcome our failures," Fukushima Gov. Masao Uchibori said at the Foreign Press Center in Tokyo last month. "Creating new industries will attract new people, which will be vital to revitalizing the region."

Machine learning has taken a significant role in many data initiatives today. Facebook, for instance, is using machine learning to offer personalized ads, whilst Google uses it to learn about its users, and other technology companies are now able to crunch data in a fraction of the time. Organizations have been looking at machine learning as something that has the most use in looking at optimizing its current markets, but this may not be the case for too much longer. Several companies are now using machine learning combined with predictive analytics to help expand into new markets and exploit opportunities as soon as they come up. We heard about this from Wolf Rendall, Data Scientist at Auction.com, at last year's Social Media & Web Analytics Innovation Summit.

In this series of exploratory blog posts, we explore the relationship between recurrent neural networks (RNNs) and IoT data. The article is written by Ajit Jaokar, Dr Paul Katsande and Dr Vinay Mehendiratta as part of the Data Science for Internet of Things practitioners course. RNNs are already used for Time series analysis. Because IoT problems can often be modelled as a Time series, RNNs could apply to IoT data. In this multi-part blog, we first discuss Time series applications and then discuss how RNNs could apply to Time series applications.

Toshiba Corp. said its profitability will improve in the next fiscal year as it scales back on products such as personal computers and home appliances. Operating profit will be 120 billion ( 1.1 billion) and revenue 4.9 trillion in fiscal 2016, the electronics maker said on Friday, in a presentation titled "A road map to a new Toshiba." Analysts were projecting, on average, a profit of 145.5 billion on sales of 5.77 trillion, according to data compiled by Bloomberg. Toshiba, which makes everything from computers to nuclear power equipment, is seeking to revive profits by narrowing the scope of its business lines. An accounting scandal has left the Japanese conglomerate in tatters, facing record losses, job cuts and potential spinoffs.

For decades, futurists have predicted that commercial miners would one day tap the unimaginable mineral wealth of the world's ocean floor. Soon, that subsea gold rush could finally begin: The world's first deep-sea mining robots are poised to rip into rich deposits of copper, gold, and silver 1,600 meters down at the bottom of the Bismarck Sea, near Papua New Guinea. The massive machines, which are to be tested sometime in 2016, are part of a high-stakes gamble for the Toronto-based mining company Nautilus Minerals. Nautilus's machines have been ready to go since 2012, when a dispute between the firm and the Papua New Guinean government stalled the project. What broke the impasse was the company's offer, in 2014, to provide Papua New Guinea with certain intellectual property from the mining project.

There's still a huge amount of radioactive waste cleanup to do at the Fukushima nuclear power plant in Japan. Some of that cleanup can be done by careful humans. And there's some that's too dangerous for humans, but not quite dangerous enough to dissuade robots. Clearing the fuel rods out of the pool in reactor 3 is one of those tasks, and Toshiba has built a robot to tackle it. If you had to pick somewhere to eat a picnic lunch in the Fukushima Daiichi nuclear power plant, inside the containment building of reactor 3 probably wouldn't be at the top of your list, but it also wouldn't be at the very bottom.

In the 2015 film "Creed," aged boxing legend Rocky Balboa stares up at the sky in confusion after his young protege tells him a smartphone picture has been saved in the cloud. Rocky might feel even more befuddled if he heard about Microsoft's experiment in putting the cloud's computer servers under the sea. As crzay as it sounds, the underwater data center initiative, called Project Natick, could revolutionize the way companies Internet services such as streaming video, music, or games. Microsoft's first underwater test involved a car-sized capsule that weighs more than 17,236 kilograms and has a computing power equivalent to 300 desktop computers. That's tiny compared with existing data centers.