Kurt'The Cyber Guy' Knutsson breaks down how to keep Alexa from listening in. Think of all the things a microphone can pick up: voices, noises, whispers, conversations, arguments, confessions – even people alone, in a room, mumbling to themselves. Think of all the things you say in a private space, all the weird things you do. Once those sounds have been saved, that data can be stored, edited, and manipulated and shared. Now, think of all the things a camera can see, record, save, and share with who knows who.

When Ian Goodfellow dreamt up the idea of Generative Adversarial Networks (GANs) over a mug of beer back in 2014, he probably didn't expect to see the field advance so fast: In case you don't see where I'm going here, the images you just saw were utterly, undeniably, 100% … fake. Also, I don't mean these were photoshopped, CGI-ed, or (fill in the blanks with whatever Nvidia's calling their fancy new tech at the moment). I mean that these images are entirely generated through addition, multiplication, and splurging ludicrous amounts of cash on GPU computation. The algorithm that makes is stuff work is called a generative adversarial network (which is the long way of writing GAN, for those of you still stuck in machine learning acronym land), and over the last few years, there have been more innovations dedicated to making it work than there have been privacy scandals at Facebook. Summarizing every single improvement to the 2014 vanilla GANs is about as hard as watching season 8 of Game of Thrones on repeat. I'm not going to explain concepts like transposed convolutions and Wasserstein distance in detail. Instead, I'll provide links to some of the best resources you can use to quickly learn about these concepts so that you can see how they fit into the big picture. If you're still reading, I'm going to assume that you know the basics of deep learning and that you know how convolutional neural networks work.

The term "fake news" barely made a blip on most people's radars a few years ago, yet many observers are saying that this pernicious form of disinformation -- now weaponized to spread virally thanks to social media -- could potentially destabilize democracies around the world. But so-called "fake news" is nothing new: after all, the practice of spreading of false information to influence public opinion has been a relatively common one since at least ancient times. However, what's alarming today is that fake news will likely no longer be only generated by humans. While there are automated methods to detect fake news created by humans, with recent AI advancements, especially in the field of natural language generation (NLG), it will now be possible for machines to produce convincing disinformation, written in the language and tone of established news sources -- on a much larger scale and to potentially much more devastating effect -- than ever before. So how to catch this kind of machine-generated propaganda?

In the design of user-friendly robots, human communication should be understood by the system beyond mere logics and literal meaning. Robot communication-design has long ignored the importance of communication and politeness rules that are 'forgiving' and 'suspending disbelief' and cannot handle the basically metaphorical way humans design their utterances. Through analysis of the psychological causes of illogical and non-literal statements, signal detection, fundamental attribution errors, and anthropomorphism, we developed a fail-safe protocol for fallacies and tropes that makes use of Frege's distinction between reference and sense, Beth's tableau analytics, Grice's maxim of quality, and epistemic considerations to have the robot politely make sense of a user's sometimes unintelligible demands. Keywords: social robots, logical fallacies, metaphors, reference, sense, maxim of quality, tableau reasoning, epistemics of the virtual

A journalist recently asked me to comment on the feasibility of a conspiracy theory involving one of Facebook's AI algorithms. He wanted to know whether it was likely, or even possible, that Facebook was using its existing algorithm for suicide video detection to screen and censor conservative media sources. To answer the question meaningfully, I found I needed to spend an hour educating this journalist about AI in general, just to give him enough background information to understand my assessment of the conspiracy theory. In doing so I equipped him to deal with future AI-related investigations, which he and his colleagues will most certainly encounter with increasing frequency in the future as AI expands its interaction with our daily lives. From the encounter described above, I realized that journalists will soon face a growing need to explain and editorialize about AI-related social concerns of all types. I concluded that most journalists are currently ill-equipped for the task. To remedy the situation, I composed this primer to assist journalists' acquisition of sufficient knowledge about the subject to assist them at what they do best: Guiding the public discourse regarding pertinent issues of the day.

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage.

Well, as far as I can tell, the world of deep learning works very differently from regular computer science. If you've seen that popular xkcd comic about machine learning being a pile of linear algebra that we stir up and experiment with, you'll know what I mean. The idea of using the 1-Wasserstein distance instead of an approximation of the Jensen-Shanon divergence (the WGAN model) is "groundbreaking" for two reasons: It produced images that simply had a better quality overall. This was probably the most significant factor. Hypothetically, you could come up with your own weird new distance measure that has no rigorous mathematical justification, and if it beats state of the art by a non-trivial margin, it would be considered just as groundbreaking.

Even raw computing power is a bit misleading. It's the transistors per chip, which certainly should correlate to computing power, but is more likely to be inversely proportional to cost. The growth has slowed a bit.. down from his revised prediction of 2x growth closer to the original 1x, but how crazy is it that it held for around 50 years going from around 1000 transistors to 30 billion.

For an extremely prolonged stretch of time, women working in the fields of science, innovation, engineering and math are doing wonders. Take for instance the tale of Katherine Johnson and her partners, who made noteworthy commitments to the early years of NASA's space program. The world had not in any case known about her name until two years back, when the film, Hidden Figures, hit the screens. Women exceed expectations at communication, sustaining a positive aura in the group, critical thinking, problem-solving among an entire host of other things! Let's have a look at the ladies who are doing everything and motivating us to be a superior version of ourselves each and every day.

Artificial Intelligence is rewriting the rules of economics, but is also bounded by that same economics. This is a long article, even by my standards. It represents my thinking about the implications of artificial intelligence on the economy, beyond the normal cheerleading that seems to so frequently accompany discussion of topic. Feel free to respond to me at kurt.cagle@gmail.com Artificial intelligence, the use of computer processes to infer and make decisions on information about the world that is not necessarily explicitly given, has been a hallmark of much of this decade. From word processors that went from simple spell check to office suites that now have a significant hand in the production process, from cruise control to self-driving vehicles, from halting speech recognition software to fully integrated video/audio concept recognition, AI and its related technologies have quietly but perhaps irrevocably changed our relationship with computers far more than most people realize. Yet as the information revolution continues, the impacts that it is having upon our economy are now reaching an extent where most of the models that economists have formulated about how that economy works are being thrown out. We're in terra incognita at this stage, and this, in turn, is forcing politicians, policy makers, economists, business leaders and everyday people to rethink many of the fundamental assumptions on which we base our notions of work, value and utility. Almost everything that deals with intellectual property has gone digital in the last three decades, as we move from an environment where not only are the physical products that used to convey that IP - from novels to newspapers to movies to music no longer require the physical media to transport content, but increasingly IP being produced today cannot be transcribed back to physical media.