Too much data and too few people: Firstly, this is a no surprise that machine learning algorithms will work at the pace not matching their counter scientist friends. If trained properly, machine could easily pacify majority of data preparation and analysis demand in data analytics world. Another cool thing about machine learning is that once code is prepped and machine is programmed, you could use it multiple times and multiple places and see the magic happen. The trick is to not overkill first but to use it for overhead tasks first and keep making it more and more sophisticated, so that it will start doing all the heavy lifting and pacifying the resource demand as a result. Hence, machine learning single handedly can reduce big-data resource crunch and make the resource distribution relevant and appropriately.

Although Google is selling Boston Dynamics to distance itself from "terrifying" humanoid robots, there's still plenty of robot projects underway. SCHAFT, a Tokyo-based robotics company run by Google's parent company Alphabet, presented the bipedal robot at the New Economic Summit in Japan. SCHAFT is best know as the winner of the 2013 DARPA Robotics Challenge that put it on the map. There aren't too many details on the robot yet, except that it can carry up to 132 pounds and can tackle uneven terrain. But it's nice to be in the snow once in a while too.

Although 11.5 million is a large number, most readers probably had no idea what went into drawing meaningful conclusions from that huge cache of documents. In fact, it took some 400 journalists at more than 100 news organizations an entire year to peruse the 2.6 terabytes of data in those documents and piece together the story of a company that helped the world's wealthiest people set up offshore bank accounts. In a lecture hosted by the University of Delaware Cybersecurity Initiative on Wednesday, April 6, computer scientist James Nolan used the Panama Papers as an example of the need for new machine learning techniques to address the problems associated with living in a data-rich, information-poor world. "Why can't we put that 2.6 terabytes through an algorithm and spit out relationships in a few hours?" he asked. Nolan emphasized the distinction between raw data which is collected from cameras, phones, sensors, satellites, written documents, cyber-logs, and other sources and information, which is the knowledge gained from studying data and teasing out relationships, resolving ambiguities, understanding scenes, and labeling events.

California Rep. Duncan Hunter is reportedly being questioned by the Federal Election Commission over his use of campaign funds to buy video games. The San Diego Union-Tribune reported Hunter listed 1,302 worth of Steam Games on his campaign financial disclosure for the end of 2015, with a note saying "personal expense – to be paid back." The Republican lawmaker has said that the purchases were a mistake by his teenage son, who also made several other unauthorized purchases. The Union-Tribune reported that the purchases run from Oct. 13 to Dec. 16, and no payback was listed during that time period. Joe Kasper, a spokesman for Hunter, said the congressman's son used his father's credit card for one game and then several more charges were made after Hunter tried to close access to Steam.

Aman's background is in the intersection of Business Applications and Artificial Intelligence, using both to drive the next generation of business applications Aman also founded and worked in various startups in search, social, trading systems, and enterprise software. His last startup was TopCorner, a political platform for micro-lobbying. Aman was the architect for IBM SuperSell Enterprise and Oracle CRM. He was previously the Director of Special Projects for the CEO's office at Oracle. Aman earned a MS in Computer Science with research focused on natural language processing (NLP) from Stanford.

Too much data and too few people: Firstly, this is a no surprise that machine learning algorithms will work at the pace not matching their counter scientist friends. If trained properly, machine could easily pacify majority of data preparation and analysis demand in data analytics world. Another cool thing about machine learning is that once code is prepped and machine is programmed, you could use it multiple times and multiple places and see the magic happen. The trick is to not overkill first but to use it for overhead tasks first and keep making it more and more sophisticated, so that it will start doing all the heavy lifting and pacifying the resource demand as a result. Hence, machine learning single handedly can reduce big-data resource crunch and make the resource distribution relevant and appropriately.

Workers looking for jobs in 2035 might consider retraining as remote-controlled vehicle operators or online chaperones. Those are two of the jobs of the future suggested in a report by the CSIRO that charts 20-year trends in increasingly digitally focused and automated Australian workplaces. Related: Automation may mean a post-work society but we shouldn't be afraid The employment minister, Michaelia Cash, released the report on Friday at the Australian Computer Society's conference. Cash said the report showed "some jobs will inevitably become automated over the coming years but technological change will improve others and also create new jobs and opportunities". "The future won't be about people competing with machines, it will be about people using machines and doing work that is more interesting and fulfilling," she said.

A Falcon 9 rocket took off from Cape Canaveral, carrying more supplies to the International Space Station. SpaceX was able to land its rocket on a barge April 8, 2016, about 200 miles off the shore of Cape Canaveral, Fla. (Photo: SpaceX) CAPE CANAVERAL -- SpaceX successfully returned its Dragon spacecraft to flight Friday and landed the first stage of its Falcon 9 rocket almost on target on a barge about 200 miles offshore. Flying for the first time since a launch failure June, the Dragon and its nearly 7,000 pounds of cargo are headed for a Sunday morning rendezvous with the International Space Station. The booster landing -- not exactly in the center of the platform but enough to keep the equipment from getting wet in the Atlantic Ocean -- bolsters SpaceX's confidence that it can regularly recover rockets that could be flown again, lowering launch costs. The experiment was SpaceX's first successful ocean landing.

The computer screen in an autonomous prototype Continental Chrysler 300C sedan, seen during an event featuring numerous self-driving cars on Capitol Hill, March 15, 2016. WASHINGTON -- Self-driving cars are more likely to hurt than help public safety because of unsolved technical issues, engineers and safety advocates told the government Friday, countering a push by innovators to speed government approval. Even a trade association for automakers cautioned the National Highway Traffic Safety Administration at a public meeting that a slower, more deliberative approach may be needed than the agency's plan to provide its guidance for deploying the vehicles in just six months. There are risks to deviating from the government's traditional process of issuing regulations and standards, Paul Scullion, safety manager at the Association of Global Automakers, told a public meeting on self-driving cars hosted by NHTSA. Issuing new regulations takes an average of eight years, NHTSA has said.

A Falcon 9 rocket took off from Cape Canaveral, carrying more supplies to the International Space Station. SpaceX was able to land its rocket on a barge April 8, 2016, about 200 miles off the shore of Cape Canaveral, Fla. (Photo: SpaceX) CAPE CANAVERAL -- The first stage of a SpaceX Falcon 9 rocket almost hit the bull's eye Friday, landing on a barge about 200 miles offshore. Though not exactly in the center of the platform, the maneuver was enough to keep the equipment from getting wet in the Atlantic Ocean. The experiment was the first successful landing. The booster possibly could have returned to land, like one did in December, SpaceX said.