Influencer marketing is arguably the most sought after form of marketing that exists today. If you're a brand marketer who is looking to quickly spread a message about your brand and start a viral conversation, there is no better way to do it than through influencer marketing. Though this form of social marketing is relatively new, the way that it's been done, until recently, has been highly inefficient. AI, or cognitive computing, has a crucial role to play here. When influencer marketing burst onto the scene a few years back, it caught the attention of talent agencies, as well as upstart influencer agencies, that looked to wrangle up the most impressive stable of influencer talent that they could.

This article is the first in a series of posts that will focus on an exploratory journey of reinforcement based Deep Learning utilizing the VizDoom platform. In terms of goals, my destination is the creation of a Doom AI capable of thriving in a Deathmatch environment (woohoo killer AI). In this particular post I will outline the initial setup process of both Theano, an open source deep learning framework, as well as the VizDoom environmental setup. VizDoom is not the only platform available for reinforcement learning, but it does provide us with an iconic and complicated world for our AI to navigate. Once our environment is properly set up, we will dip our toes into reinforcement learning by running some basic examples and then utilizing some optimization techniques to increase the performance of our AI agent.

Nvidia continued to see demand for its graphics processors in the emerging world of artificial intelligence in its fourth quarter earnings reported Thursday. In its fourth quarter earnings release, the Santa Clara, Calif.-based company reported revenue of $2.17 billion, up 55% year over year, on earnings per share of $1.13, up 117% a year ago. Wall Street analysts estimated $2.11 billion in revenue on EPS of 83 cents. Traditionally, the company's processors have been mostly used to power the latest gaming graphics, but the chips have become popular to run AI software in the data center and autonomous vehicles. A specific branch of AI, called deep learning, is where Nvidia's processors particularly shine.

I'm going to start this piece with a confession; I am a'trend' skeptic. Over the last few years I've developed something akin to list fatigue; growing weary of the constant stream of'six things you must know' clickbait headlines that pop up in my feed. If I was being kind I would say that as an industry we are sometimes guilty of exaggerating our predictions on the potential impact of emerging technologies (if I wasn't being kind I'd probably say that in truth a lot of our industry habitually over hypes everything'new' so that anything actually meaningful gets buried in the noise). Artificial Intelligence (AI) is one of those topics that you'll regularly have seen on lists throughout 2016. And editorially, it's fair to say that it's been covered across something of a broad spectrum. A quick Google search will either lead you to an article where AI will bring an end to the food going off in your fridge, or where AI will bring an end to humanity.

In the battle for the 21st century workplace, computers are winning. A January 2017 report from the McKinsey Global Institute estimated that roughly half of today's work activities could be automated by 2055, give or take 20 years. Bottom line is robots want our jobs. And no one is going to build a wall around them or tariff them out of existence. In a way this is nothing new.

When technology and human ingenuity gets together, everybody in society profits. If you look at graphs of GDP / capita over long time periods (850 years), the trend is always upwards. The only things that drag this progress down are severe periods of sickness (like the black death) and widespread war (such as WW2). Overall though, technology makes everyone healthier and richer. At this point in our society's history – technology has never been more important to the global economy.

Artificial intelligence is slowly creeping into our daily lives, and soon, it will become necessary to understand how different agents behave in social dilemmas. To see what would happen in such a scenario, Google's DeepMind researchers developed two games known as'Gathering' and'Wolfpack,' which build off the Prisoner's Dilemma from game theory. Over time, the AI agents learned how to behave rationally – and while they showed the researchers that they would sometimes cooperate, the games revealed the AI would turn on others when necessary. In the first game, known as Gathering, the AI agents (Red and Blue) are tasked with collecting apples. They can also'tag' the other player by shooting a beam at them, which would remove temporarily remove the tagged agent from the game.

Most robot arms we've seen in the past few years have been either attached to our favorite rolling dog/mech robots or been innovative prosthetics on our favorite humans. But why not a little worker bee robolimb plopped on your desk and ready to perform menial and/or delicate tasks? UFactory launched an Indiegogo campaign for its newest domestic helper a few weeks ago, the open source uArm Swift, which has leaped past its $10,000 goal to raise $280,000 at the time of this post. The Swift is an upgrade of UFactory's protoype Metal arm, which came out last May, but keeps its articulated desk lamp appearance that looks like its about to help you build the next Iron Man suit. The new version has expanded functionality, with a port for Grove modular electronics and OpenMV cameras.

An emerging trend promises to bring the power of machine learning to mobile devices, opening the door to a plethora of valuable new applications. Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But recent developments may enable machine learning to be embedded into mobile devices, thus greatly expanding applications for its use and providing new opportunities for marketers. Neural networks--computer models designed to mimic aspects of the human brain's structure and function, with elements representing neurons and their interconnections--are an increasingly popular way of implementing machine learning. They are particularly well suited for performing perceptual tasks such as computer vision and speech recognition.