Former chess champion Garry Kasparov sounded the alarm about Vladimir Putin's regime in his 2015 book Winter Is Coming. "As you can guess, I'm a big fan," Kasparov says in Episode 321 of the Geek's Guide to the Galaxy podcast. "I recently had a great experience touring some of the spots when they were shooting in Belfast, taking my wife and my daughter, who are also big fans of the show." Kasparov sees many parallels between life in Westeros and real-world politics. He says that Americans--like our fictional counterparts--are so consumed with petty squabbles that we've ignored a gathering threat.

New research from ESET reveals that three in four IT decision makers (75%) believe that AI and ML are the silver bullet to solving their cybersecurity challenges. In the past year, the amount of content published in marketing materials, media and social media on the role of AI in cybersecurity has grown enormously. ESET surveyed 900 IT decision makers across the US, UK and Germany on their opinions and attitudes to AI and ML in response to this growing hype. The findings showed that US IT decision makers are most likely to consider the technologies as a panacea to solve their cybersecurity challenges, compared to their European counterparts – 82% compared to 67% in the UK and 66% in Germany. The majority of respondents said that AI and ML would help their organization detect and respond to threats faster (79%) and help solve a skills shortage (77%). "If the past decade has taught us anything, it's that some things do not have an easy solution – especially in cyberspace where the playing field can shift in a matter of minutes.

Our survey mission has reached the outer perimeter of Galaxy W2246-0526. We rode our bubble of space through 12 and a half billion light years in 7.2 seconds. Took a bit longer than calculated. Traces of minor chemical adjustments in the organic passengers have occurred which are being monitored. Unease is apparent within the arrival pod as we await inflation of Habitat One. Some, like Judge Bulbous, have been pulled away from political turmoil back home only to be riding in close quarters with political rivals who are on this journey. "Watch Csaynik take credit for anything we find out here…" chuckled Senator Pretyman moments after we got here. She's one of the more vocal organics who can't accept that President Csaynik is the first "artificially" intelligent president of the U.S. ever elected. I would caution that more trouble is likely to come from the ego-driven nature of our organic crew members than from a weakness in their physiology. And in so cautioning, I will restate that which I said before we left the Milky Way; intergalactic voyages should be conducted by self-repairing artintels* like myself and our mission Alpha. Hard to believe this galaxy was once the most luminous galaxy known, putting out 10,000 times the energy of the Milky Way. The perpetual energy generation that we've come to observe seems completely self-contained near the core of the galaxy. One might say "disturbingly" dark. My examination of Alpha reveals tampering from outside. Concerned, being the only other artintel aboard Resolution, I run the same diagnostic on myself. I, too, have been compromised. Playback reveals the most likely perpetrator, who infected me and Alpha by touching our skin and releasing microlegions to drill into us, interfering with cognitive and motor functions. Senator Pretyman is arrested and an autoguard activated at Detainment. What made that organic conservative think it was okay to inject updates into artintels? Human power over sentient life was abolished with the 47th Amendment. By the time the home world's finished with her, Ms. Pretyman won't even be allowed to vote! I've given Ms. Pretyman a drink from her own subversive swamp in the form of an injected immune-weakening microbe that will leave her susceptible to ailments of all variety. Tragically, it might even cause her demise. Unlike organic physicians, I've not taken an oath to do no harm. The organics say that an oath taken by one of us is no better than an oath taken by a parrot. I can do all the harm I please. I can't find a way to save Mrs. Ichnida. She's gone into a sort of coma. However Alpha and I should be good for several days beyond their deaths.

This article was written in collaboration with Deloitte. While chatting with some non-technology colleagues recently, I learned many believe Brexit may drive a level of technology change within organizations comparable to the Y2K bug in the late 1990s. The high level of technology replacement for Y2K (or the Millennium Bug, as it was also called) was driven by the risk that many systems would simply quit operating at midnight on 1 January 2000. There was a significant degree of nervousness, grounded partially in commercial reality but short of some of the wilder predictions, like that planes might fall out of the sky. Organizations had to make significant preparations for the "worst case scenario" to protect themselves from the most disruptive potential outcomes.

While NASA's rovers have looked for signs of life outside of our world, they haven't searched for life directly. But Melissa Floyd, a scientist at NASA's Goddard Space Flight Center, is working on a device that might change that. She wants to build an instrument that could look through soil and rock samples for evidence of bacteria or another type of single-celled microorganism called archaea. These organisms are thought to have been the first to appear on Earth and Floyd began to wonder if maybe life on nearby planets evolved like it did on our own. "I had this idea, actually a major assumption on my part: what if life evolved on Mars the same way it did here on Earth? Certainly, Mars was bombarded with the same soup of chemistry as Earth," Floyd said in a statement.

Bear with me while I explain. A quick observation: Donald Trump is not like normal people. In particular, he doesn't have any principles to speak of, that might guide him. That doesn't mean he doesn't have a method. He does, but it's local rather than global.

In the 1970 science fiction film "Colossus: The Forbin Project," the United States decides to turn over control of its strategic arsenal to Colossus, a massive supercomputer. Almost immediately it becomes clear that, as its creator Dr. Charles Forbin says, "Colossus is built even better than we thought." In fact, it's a self-aware artificial intelligence -- quickly discovering that the Soviets have also activated an almost identical system and joining up with it to take over the planet. Along the way, Colossus nukes a Russian oil complex and a U.S missile base to enforce its control. Now, instead of two human superpowers threatening nuclear Armageddon, humanity's continued survival is at the mercy (or mercy's AI equivalent) of a supercomputer.

Thousands of leading scientists have urged their colleagues against helping governments create killer robots, making the movie The Terminator a reality. In other words, killer robots, that could eventually develop a mind of their own and take over the world. Scientists have vowed they will not support robots "that can identify and attack people without human oversight." Two leading experts backing the commitment, Demis Hassabis at Google DeepMind and Elon Musk at SpaceX, are among the more than 2,400 signatories whom have pledged to deter military firms and nations from building lethal autonomous weapon systems, referred to as Laws. The move is the latest from concerned scientists and organizations about giving a machine the power to choose someone's fate of life or death.

In a previous blog post, we addressed how machine learning is becoming ever more useful in cybersecurity and introduced some basic terms, techniques, and workflows that are essential for those who work in machine learning. Although traditional machine learning methods are already successful for many problems, their success often depends on choosing and extracting the right features from a dataset, which can be hard for complex data. For instance, what kinds of features might be useful, or possible to extract, in all the photographs on Google Images, all the tweets on Twitter, all the sounds of a spoken language, or all the positions in the board game Go? This post introduces deep learning, a popular and quickly-growing subfield of machine learning that has had great success on problems about these datasets, and on many other problems where picking the right features for the job is hard or impossible. A mathematical description of a dataset, with certain properties that can be adjusted to make the description more accurately represent the data, is called a model.

While many companies are turning to machine learning tools to fight hackers, they may not be as helpful as they seem thanks to a talent shortage and a lack of transparency. By the end of 2017, some 61% of businesses had implemented artificial intelligence (AI) into their organizations--a 23% jump from the previous year, according to Narrative Science. And the incorporation of AI into business will only rise: The number of medium and large enterprises using machine learning is predicted to double by the end of 2018, said Deloitte. Machine learning is a form of AI that interprets massive amounts of data, applying algorithms to the material, and making predictions off its observations. Common technologies that employ machine learning include facial recognition, speech recognition, translation services, and object recognition.