In this observation, we ranked nearly 1,750 articles posted in August 2016 about machine learning, deep learning and AI. Mybridge AI evaluates the quality of content and ranks the best articles for professionals. This list is competitive and carefully includes quality content for you to read. You may find this condensed list useful in learning and working more productively in the field of machine learning.

Reuters – The new Apple iPhone has something missing: the small socket millions of us have used for years to plug in headphones. While some fans opposed the widely anticipated move – one online petition urging Apple to keep the headphone jack drew more than 300,000 signatures – equipment suppliers and experts heralded a change in how users will interact with their devices. Axing the jack, they say, paves the way for discreet, bean-sized earbuds that can simultaneously translate, filter out unwanted noise or let us control other devices by voice – and drive up the value of the so-called'hearables' market to 16 billion within five years. It's the vision of the futuristic 2013 movie "Her", where a human has a love affair with a disembodied voice in his ear. But some who follow the industry say it's closer than many think, noting improvements in wireless technologies, materials, artificial intelligence and battery life.

The first international beauty contest judged by "machines" was supposed to use objective factors such as facial symmetry and wrinkles to identify the most attractive contestants. After Beauty.AI launched this year, roughly 6,000 people from more than 100 countries submitted photos in the hopes that artificial intelligence, supported by complex algorithms, would determine that their faces most closely resembled "human beauty". But when the results came in, the creators were dismayed to see that there was a glaring factor linking the winners: the robots did not like people with dark skin. Out of 44 winners, nearly all were white, a handful were Asian, and only one had dark skin. That's despite the fact that, although the majority of contestants were white, many people of color submitted photos, including large groups from India and Africa.

Israel has deployed autonomous military vehicles to patrol the border of the Palestinian Authority. These vehicles are apparently unarmed, so far, and groups of them are controlled by a remotely placed soldier, so they are not quite up to the level of a Robocop, which I suppose is some kind of relief. There are no fully robotic warriors out there, yet. Something along the lines of, that which does not kill us now, might still kill us later if we aren't ready. The short version of all this is that as AI research engages more and more with the real world, things may take some dangerous turns.

The year 2015 might be seen as the year that "artificial intelligence risk" or "artificial intelligence danger" went mainstream (or close to it). With the founding of Elon Musk's Open AI and The Leverhulme Centre for the Future of Intelligence, the increased attention on the Future of Life Institute and Oxford's Future of Humanity Institute, and a flurry of attention around celebrity comments around AI dangers (including the now well-known statements of Bill Gates and Elon Musk), it's safe to say that AI risk has embedded itself as a topic of pop-culture discourse – even if it's not a very serious one at present. Recently, we interviewed and reached out to a total of over 30 artificial intelligence researchers (all except one hold a PhD) and asked them about the AI risks that they believe to be the most pressing in the next 20 years, as well as the next 100 years. Below you can see a list of all of our respondents; clicking on a respondent will bring up their answer to the 20-year risk question. Interestingly enough, automation and economic impact topped the list, coinciding with the massive amount of media attention on autonomous vehicles and improved robotic manufacturing, among other industries.

In the age of machine learning, what should managers know -- and what must non-tech companies do to stay ahead? This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. For data scientists and machine-learning experts, March 2016 was a momentous month. AlphaGo, a computer program developed by Google, beat world champion Lee Sedol at the ancient Chinese board game Go by a score of 4 to 1. In contrast to chess, where players might make about 40 moves per game, games of Go may have 200 moves.1 Whereas IBM's Deep Blue was used to defeat chess grand master Garry Kasparov in 1997, computer scientists can't calculate all the moves required to win at Go. Instead, Google had to create another kind of machine algorithm that could approximate humanlike qualities, playing the game by intuition and feel.

As the artificial intelligence market is projected to grow by 53% in by 2020, advertisers are looking for ways to use the technology to their advantage. Vernon Vasu, CMO at ReFUEL4 states that researchers are looking into using AI for creative development in the future, but for now advertisers can use AI's incredible data mining and organizing capabilities to understand audiences like never before Artificial intelligence is one of the most buzzed-about terms in technology. The AI market is estimated to reach 5.05 billion USD by 2020, up from 419.7 million USD in 2014 – a 53% increase. With the launch of Facebook's chatbots, Amazon's Echo, and IBM's Watson, companies in many fields are considering how they can use new AI tools to their advantage. Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes.

Three years ago, Jeff Bezos announced that drones are eventually going to deliver Amazon orders. In the past year, he brought out Amazon's Alexa artificial intelligence service, which understands speech well enough that you can say, "Alexa, I really need a waffle cone maker," and she'll put one in your Amazon online shopping cart, even though nobody needs a waffle cone maker. Both of these technologies--drones and cloud AI--are exciting today, yet still wobbly works in progress. But in coming years, Amazon or some other company is going to put them together. And that, finally, will evolve into a technology that could become as significant to humans as domesticated dogs.

Self-driving vehicles promised to revolutionize the transportation industry. Automated cars could reshape every industry that relies on the automobile, from car manufacturing, to taxis, limousines and trucking. By taking human beings--who aren't the drivers they think they are--out from behind the wheel, self-driving cars could also save millions of lives. Self-driving cars will also have a profound effect on the environment--but whether it's for better or for worse will depend on technological and policy choices that have yet to be made. Automated vehicles could reduce energy consumption in transportation by as much as 90%, or increase it by more than 200%, according to research from the Department of Energy (DOE).

"Don't you know who I am?" Yes, your Uber driver or AirBnB host may see your star rating and past reviews, but how they combine that data with the context of their current experience with you may vary. According to the 2015 Gartner Hype Cycle, machine learning, internet of things, natural language processing and hybrid cloud computing should have all matured past the "peak of inflated expectations" at roughly the same time. The 2016 Gartner Hype Cycle shows this trend continuing (plus drones). This confluence seems to be driving the emergence of cognitive systems, which thrive on context and help businesses recognize customers and solve for their unique needs at scale. Without the aid of machine learning, the process of providing context needed for humans to leverage data in a personalized way does not scale.