In all likelihood androids won't just one day spontaneously realize that we suck and rise up against us in a civil rights slave revolt. Instead, robots and animals will gradually follow the paths more naturally suited to them by moving along different, most likely conflicting, trajectories into the future. It's scary, we actually have no idea what's going on in the mind of a machine learning algorithm. We set them in motion and what they really do as a result is a black box mystery to us. Not only do our computers pick up on our racist and sexist biases, they see how much porn we look at and how many cat videos we post and they think that those things represent the human condition.

In this talk we will present some techniques that we use on a day to day basis in our research, where we combine our internet-wide data scanning and acquisition platform with ML/Data science techniques which allows us to find things faster or extract results in a more automated way. We will focus on practical cases and examples that even our audience at home will be able to use if they want. A couple of examples we will look at is how to classify images such as VNC screenshots, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs. We will also talk a bit about a data analysis and classification pipeline architecture, we will look at the different technologies and what they do and how they can be used. We will start by giving a very brief entry to the data science world and talk about: Technologies Techniques How these relate to infosec Algorithms and how they can be used How people can come into the world of data and machine learning Data visualization techniques and what are the best choices for different types of data A couple of examples we will look at is how to classify images such as VNC or x11 screenshots, OCR, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs.

Symantec has launched Endpoint Protection 14, a new security solution which harnesses artificial intelligence to protect clients. Announced on November 1, the new security offering is powered by AI and machine learning on the endpoint and in the cloud. Symantec says that by harnessing machine learning to collate data and detect patterns and anomalies which may indicate a cyberattack, AI provides "a multi-layered solution able to stop advanced threats and respond at the endpoint regardless of how the attack is launched." Symantec Endpoint Protection combines machine learning, memory exploit mitigation, and threat intelligence provided by Symantec and Blue Coat, which combined their research and security operations in October after Symantec completed the acquisition of Blue Coat for $4.6 billion. The company also says that the solution is capable of 99.9 percent efficacy, low false positives, and a 70 percent carbon footprint reduction in comparison to past endpoint software.

Let's inject a virus into the attacking alien spacecraft and save Earth! Let's hack into the enemy mainframe with six keystrokes and abort the torpedo launch! Cybersecurity has long been a staple of science fiction, whether it's in movies like "Independence Day" or television shows like "Star Trek." Yet in our real 21st Century world, artificial intelligence is the new face of cybersecurity, even if it doesn't sound like Hal from "2001: A Space Odyssey." The most obvious place for added intelligence is to detect whether some pattern of network traffic is benign or hostile.

The new science-fiction film, it says, has created word-of-mouth, wowed audiences and earned a 100% Fresh rating on the movie review website Rotten Tomatoes. Denis Villeneuve's latest work, starring Amy Adams as a linguist chosen to communicate with alien visitors, may well be that. But it isn't easy to market a masterpiece -- especially a sci-fi masterpiece with spaceships that don't engage in dogfights, aliens who don't fire lasers and protagonists who don't throw punches. When "Arrival" touches down at 2,200 theaters this weekend, it will do so not only as one of the most well-regarded science-fiction movies in some time but as one of the greatest marketing puzzles in recent memory. The Paramount release is quiet, subtle and patient -- an artisanal offering in a time of studio fast food.

But that is not to say it is perfect by any stretch of the imagination. "Deep learning has led to some big advances in computer vision, natural language processing, and other areas," Tommi Jaakkola, a Massachusetts Institute of Technology professor of electrical engineering and computer science, told Digital Trends. "It's tremendously flexible in terms of learning input/output mappings, but the flexibility and power comes at a cost. That is it that it's very difficult to work out why it is performing a certain prediction in a particular context." This black-boxed lack of transparency would be one thing if deep learning systems were still confined to being lab experiments, but they are not.

Google has built machine learning systems that can create their own cryptographic algorithms -- the latest success for AI's use in cybersecurity. But what are the implications of our digital security increasingly being handed over to intelligent machines? Google Brain, the company's California-based AI unit, managed the recent feat by pitting neural networks against each other. Two systems, called Bob and Alice, were tasked with keeping their messages secret from a third, called Eve. None were told how to encrypt messages, but Bob and Alice were given a shared security key that Eve didn't have access too.

More chatbots will begin to solve real-world problems. In many instances, early chatbots seemed more like technologies in search of problems than customer-centric solutions. As the chatbot hype subsides, technologies mature, and companies get feedback from customers, the problems that chatbots tackle will become more obvious, and, in turn, more valuable. An example is our own ReplyYes' The Edit, which endeavors to solve the problem of product discovery for music lovers. Through a use of progressive disclosure, short keyword interactions, and machine-based curation of vinyl albums, we give customers a personalized and serendipitous experience to help them find music they love.

Donald Trump's election victory is seen as a blow to Silicon Valley, putting the presidency in the hands of a vocal critic of several big technology companies and an advocate of policies tech executives have said could hurt the industry's development. During his campaign, Mr. Trump didn't offer a specific plan for how he would tackle technology policy -- unlike his Democratic opponent Hillary Clinton, who in June issued a detailed tech platform that executives broadly applauded. Meanwhile, Mr. Trump's advocacy of tighter limits on immigration and trade alarmed an industry that prizes high-skilled immigrants and gets most of its revenue from overseas. The electorate's endorsement of Mr. Trump's populist message, which broadly blamed elites for the problems of many disaffected Americans, could also spell trouble for Silicon Valley, which has spawned companies that delivered far more in profits and stock-market valuations than they have jobs for middle-class workers. Mr. Trump took aim at several big names in technology during the campaign.

Efforts to understand suicide risks can be roughly clustered into traits or states. Trait analyses focus on stable characteristics rooted in and measured using biological processes (Costanza et al., 2014; Le-Niculescu et al., 2013), whereas state analyses measure dynamic characteristics like verbal and nonverbal communication, termed "thought markers" (Pestian et al., 2015). Machine learning and natural language processing have successfully identified differences in retrospective suicide notes, newsgroups, and social media (Gomez, 2014; Huang, Goh, & Liew, 2007; Matykiewicz, Duch, & Pestian, 2009). Jashinsky et al. (2015) used multiple annotators to identify the risk of suicide from the keywords and phrases (interrater reliability .79) in geographically based tweets. Thompson, Poulin, and Bryan (2014) and Desmet (2014) used text-based signals to identify suicide risk that ranged from 60% to 90%.