While both have gained a lot of attention this year, these techniques have been around for quite some time, but no more so than now, has it felt so promising. Over the past few years, there has been a monumental shift in technology and how it's being applied to everyday life. From robots to search engines, deep learning and machine learning are being raved about as the tech fuelling our new innovations, but many are left wondering what truly differentiates these two models. Broadly speaking, both machine learning and deep learning are forms of Artificial Intelligence, the intelligence exhibited by machines using cutting-edge techniques to perform cognitive functions that we associate with intuitive learning; however, each application is unique and offers an array of benefits to the end-user, whether it's solving unique problems for a particular business case, aiding in speech/facial recognition, speeding up web applications or protecting against breaches or hacks. While the concepts of machine learning and deep learning have been around as early as the 1960s, each model has changed drastically over the years, creating a greater divide between the two.

Beijing, CHINA: A Chinese jobseeker walks pass a poster showing Microsoft founder Bill Gates (L) and Apple computers' Steve Jobs, at an employment fair in Beijing 27 February 2007. This week's milestones in the history of technology include the first patent for aerial photography leading eventually to Google Earth, how Popular Mechanics made Bill Gates drop out of Harvard, and Apple becoming the planet's most valuable company 22 years after going public. Cornele B. Adams is awarded the first US patent for aerial photography. His method of photogrammetry can produce a topographic map by means of photographing the same tract of land from different points from an unmanned stationary balloon on a tether. Keyhole, Inc. launched the Earth Viewer application in 2001 and was acquired by Google in 2004.

In a nutshell, Markov chains are mathematical systems that track the probabilities of state transitions. They're often used to model complex systems and predict behavior. They're used in a lot commercial applications, from text autocomplete to Google's PageRank algorithm. My first encounter with a Markov chain was actually in my high school software development class when a classmate built a chat bot using this concept. He took the log from our class Slack chat and fed it into a Markov chain.

Unsecured IoT devices were used to launch crippling DDOS attacks in October 2016, targeting services such as Twitter, Spotify, and GitHub. Subsequent testimony to Congress about potential attacks on office buildings, schools, and hospitals raised the possibility for the IoT to harm and even kill people. Does the government need to intervene? In this power panel at @ThingExpo New York, moderated by Conference Chair Roger Strukhoff, leading IoT and security experts will discuss this very serious topic. Roger Strukhoff is Conference Chair of Cloud Expo @ThingsExpo, and Editor of SYS-CON Media's CloudComputing BigData IoT Journals.

An artificial intelligence program has written a Christmas carol. Researchers at the University of Toronto used their "neural karaoke" program to write a Christmas carol based off of a "Christmassy photograph," The Guardian reports. The scientists simply uploaded the photograph into a computer hosting the program and then "let it do its thing." The artificial intelligence program then wrote several lines of lyrics and sang them to music it composed during the process. All things considered, the results were impressive, but the artificial intelligence-generated Christmas carol still falls flat in many ways.

This article was posted by SmileJet on Dev Battles. She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing.

We live in times, where science fiction authors are struggling to keep up with reality. In recent years, there has been an explosion of research and experiments that deal with creativity and A.I. Almost every week, there is a new bot that paints, writes stories, composes music, designs objects or builds houses: Artificial Intelligence systems performing creative tasks? Our research started by wondering about this phenomenon and playfully experimenting with it. This lead to an in-depth investigation, of what we call "CreativeAI". This document is the first chapter of our adventure into CreativeAI, aiming at establishing a backstory and language we can use to talk about this intricate subject.

Tech is about to change dramatically next year. Want to stay up to date? In each section, a designer explains all of the details. Which one is already on your radar? I want to hear your viewpoints, so drop me an email.

Palantir CEO Alex Karp Says Going Public Is'A Possibility' Two false news stories from a website registered in Macedonia. If one looks deeply at the world of "fake news" one finds that much of what makes it possible has its roots in modern journalistic practice. From the inverted pyramid style of summarizing major findings in the headline and lede, while burying the details at the bottom, to writing for one's audience and skipping complex detail, the sacrosanct practices of journalism have been weaponized by the clickbait and fake news communities. Take the standard inverted pyramid format of Western journalism. No matter whether you are writing for a fringe blog or one of the most respected news outlets in the world, the pyramid style of writing means the headline will summarize the article in a few meme-ready share-worthy words, the lead paragraph will offer a tantalizing preview of the major conclusions, and the remaining top-level paragraphs will all convey the big story.

The popularity of the ASOIAF series and Game of Thrones has resulted in a large community of fans.