Abstract: A surprising property of word vectors is that word analogies can often be solved with vector arithmetic. However, it is unclear why arithmetic operators correspond to non-linear embedding models such as skip-gram with negative sampling (SGNS). We provide a formal explanation of this phenomenon without making the strong assumptions that past theories have made about the vector space and word distribution. Our theory has several implications. Past work has conjectured that linear substructures exist in vector spaces because relations can be represented as ratios; we prove that this holds for SGNS.

While populist politicians lower the standards of public discourse and cynically ignore science and shun data-driven debate, Azeem reminds us each week in Exponential View what the curious mind can learn.

Hey guys, i am writing a thesis and the topic is about using AI / ML for data analysis. I was wondering if you guys maybe know of any scientific paper that puts those terms in place? How are AI / ML / DM connected and the differences among them? The source has to be kind of scientific.

I, Robot was first released to theaters back in 2004. In it, the movie's filmmakers paint a fictional future (2035) where humanoid robots serve humanities needs. Our beloved Fresh Prince of Bel Air superstar is cast as a Chicago police detective named Del Spooner. Del hates robots with a deep skepticism after his experience with one that was unable to navigate a moral conundrum. Throughout the film, he condescends to these mechanical stewards for being unable to empathize and emote the way he believes only humans can. In one memorable scene, Del proclaims defiantly that robots are incapable of writing a symphony, or turning a canvas into a beautiful masterpiece.

North Korea is reforming education at universities to place greater emphasis on artificial intelligence, according to state media. Pyongyang's Workers' Party newspaper Rodong Sinmun reported Sunday Pyongyang University of Computer Science is changing its computer-programming department into a department for the study of AI. The goal is to improve the quality of school courses so classes on AI are more readily available in the department, according to the report. PyongyangUniversity of Computer Science has decided to improve artificial intelligence education because AI is a "key technology in the information industry," the Rodong article said. The university is developing the new program following directives from Kim Jong Un, issued at the fourth plenum of the seventh party central committee meeting in April, state media said.

Deepfakes have only been around for a few years; the first known videos, posted to Reddit in 2017, featured celebrities' faces swapped with those of porn stars. Shales got interested in making them himself early this year, and in February released his first deepfake: in it, he plastered the face of actor Nicolas Cage onto Elon Musk's body to make it appear as if Cage, rather than Musk, was smoking marijuana during a podcast interview with comedian Joe Rogan. Shales admits it isn't a great video; the resulting face is more of a morph than a swap, he said. And the voice is still unmistakably Musk's. But Shales kept going and quickly got better.

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