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.

Artificial Intelligence and Machine Learning are two of some of the most commonly uttered buzzwords in tech circles; while the former is familiar to a layman, thanks to the deluge of science fiction movies and books that have become a part of our pop-culture, the latter is something of a jargon. Although, both Artificial Intelligence (AI) and Machine Learning (ML) are closely associated terms and are often used interchangeably, there exist distinct differences. For a quick overview of its history, the idea of a machine doing human's work has existed for millennia, beginning from mythologies (Talos of Crete). The earliest European computers were modelled as logical machines that mimicked a human brain. Since then, technology and computers have come a long way.

Decoding of the human genome still poses puzzles that might be solved with the help of artificial intelligence.