The successful launch of Europe's first ExoMars mission earlier this month set the stage for a much more ambitious second act: arover landing on the Red Planet. But the timing on that mission may not be so certain. On March 14, the European Space Agency (ESA) and its Russian partners launched the ExoMars 2016 mission, an orbiter and lander that serve as a precursor to a full-blown rover slated to launch as early as May 2018. But funding issues and technical delays could push that ambitious follow-up mission to 2020. Rolf de Groot, ESA's coordinator of robotic exploration, told Space.com that it's going to be "very challenging"to have the mission fully prepared for its 2018 launch window but that program managers will know soon whether they'll have to start seriously thinking about a 2020 launch instead.

Zevenbergen Capital Investments Llc decreased its stake in Criteo S.A. (NASDAQ:CRTO) by 37.62% based on its latest Q4 2015 regulatory filing with the SEC. Zevenbergen Capital Investments Llc sold 276,870 shares as the company's stock rose 8.32% with the market. The institutional investor held 459,180 shares of the advertising company at the end of Q4, valued at 18.18 million, down from 736,050 at the end of the previous reported quarter. Zevenbergen Capital Investments Llc who had been investing in Criteo S.A. since many months, could be less bullish the 2.52 billion market cap company. The stock increased 1.72% or 0.67 on March 24, hitting 39.73.

After Twitter users were able to convince Tay, the name of Microsoft's chatbot available via text, Twitter and Kik, to spit out offensive and racist comments, it appears Microsoft is giving it a break. It was targeted at 18- to 24-year-olds in the United States and was developed by a staff that included improvisational comedians. The problem was that Tay was created to continue learning how to talk by studying the conversations she'd have with real people on Twitter, and you can guess what those people made a decision to talk to her about. According to a statement from a Microsoft representative, "The AI chatbot Tay is a machine learning project, designed for human engagement. As a result, we have taken Tay offline and are making adjustments", the spokesperson said.

Microsoft Corporation (NASDAQ:MSFT) has made its key machine learning technology freely available to developers. In a blog statement announcing the move, the company said that it had decided to make its Computational Network Toolkit freely available to developers by moving it to the Github platform. It said that it strongly believes that machine learning is the next important frontier in the overall field of artificial intelligence and that it stands to gain a lot from the input of other developers who may use the CNTK to make important advances in the field and that this will benefit all the stakeholders as well. The CNTK is a complex technology that the company developed and has been using to help it develop different applications in the field of machine learning. So far, it has proven a valuable tool for the researchers of the company.

Ray Bradbury and others envisioned a world in which human needs are anticipated by the surrounding environment. In the late 1990s, this vision was termed "ambient intelligence" (AmI). Since that time, innovators have continued to imagine scenarios in which technology is ubiquitous, more transparent, and more valuable to humans than it ever has been. What the user interface will ultimately look like is a matter of debate. Some foresee a Minority Report scenario in which the surrounding environment itself adapts to individuals in context.

One interesting/terrifying aspect of today's modern society is that we have filled the airwaves with a multitude of radio signals. Ever since the pioneering days of Marconi, Braun, and others we have been using more and more of the electromagnetic spectrum to send audio, visual, and data signals. Everything from FM radio to 4G LTE, from digital satellite TV to military communication is all sent via one form of radio or another. The result is that the radio spectrum is full, it is bursting at the seams. In light of this, DARPA (the Defense Advanced Research Projects Agency) has launched its latest Grand Challenge, this time to bring advanced machine-learning capabilities to the way the radio frequencies are used.

Some very intelligent people – including Stephen Hawking, Elon Musk and Bill Gates – seem to have been seduced by the idea that because computers are becoming ever faster calculating devices that at some point relatively soon we will reach and pass a "singularity" at which computers will become "more intelligent" than humans. Some are terrified that a society of intelligent computers will (perhaps violently) replace the human race, echoing films such as the Terminator; others – very controversially – see the development of such technologies as an opportunity to evolve into a "post-human" species. Already, some prominent technologists including Tim O'Reilly are arguing that we should replace current models of public services, not just in infrastructure but in human services such as social care and education, with "algorithmic regulation". Algorithmic regulation proposes that the role of human decision-makers and policy-makers should be replaced by automated systems that compare the outcomes of public services to desired objectives through the measurement of data, and make automatic adjustments to address any discrepancies. Not only does that approach cede far too much control over people's lives to technology; it fundamentally misunderstands what technology is capable of doing. For both ethical and scientific reasons, in human domains technology should support us taking decisions about our lives, it should not take them for us. At the MIT Sloan Initiative on the Digital Economy last week I got a chance to discuss to discuss some of these issues with Andy McAfee and Erik Brynjolfsson, authors of "The Second Machine Age", recently highlighted by Bloomberg as one of the top books of 2014. Andy and Erik compare the current transformation of our world by digital technology to the last great transformation, the Industrial Revolution.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Microsoft AI chatbot Tay, launched last week as an experiment to learn how millennials talk, quickly became an embarrassment for the company as it posted racist and offensive tweets and was subsequently pulled offline. On Friday, Corporate Vice President of Microsoft Research Peter Lee published an apology and an explanation for Tay's misbehavior, saying the company is "deeply sorry for the unintended offensive and hurtful tweets from Tay." SEE ALSO: Microsoft's Twitter bot turned from average teen to Jew-hating Trump supporter in 12 hours Microsoft already runs a similar project in China -- a chatbot called XiaoIce, which is used by more than 40 million people. But that success did not translate well in the U.S. According to Lee, Tay underwent a lot of testing to make sure something like this wouldn't happen, but a "coordinated attack by a subset of people exploited a vulnerability in Tay" within its first 24 hours online. Lee said the company is addressing the "specific vulnerability" that led to the attack, without going into specifics. Many of Tay's offensive tweets were mere echoes of what other users said on Twitter (see example below).

Just like nuclear physics, machine learning, AI, and data science can be used either for the better of for the worse. You can make either useful energy or terrible bombs using nuclear fission. The same applies to machine learning, and in my example below, it gets even worse than Hiroshima or Nagasaki. Here I am discussing a potential use of machine learning in military operations. The scenario below is entirely hypothetical.