Maybe you'd love to dig deeper and get an image recognition program running in TensorFlow or Theano? Perhaps you're a kick-ass developer or systems architect and you know computers incredibly well but there's just one little problem: I share your dirty little secret and I have some books and websites that will really help you get rolling fast. Like many folks, my love of intelligent machines didn't come from calculus class. It sprang from science fiction. I remember reading "I, Robot" one beautiful summer evening and imagining ways to trip up Asimov's Three Rules of Robotics.

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.

It was actually about when technology goes wrong: In many ways, 2016 was the year of the exploding batteries. A little over a year ago, hoverboards topped many holiday wish lists. By December 2015, they were being recalled by the thousands. According to Popular Science, "…cheaply made hoverboards have exploded and caught fire, forcing Amazon to stop selling specific models and Overstock to discontinue all sales." Rolling into 2016, major computer companies recalled batteries for fire hazards, baby monitors were pulled from shelves and major airlines diverted flights for emergency landings when tablets caught fire onboard.

Rippleshot is a next generation fraud analytics firm. They use a cloud-based technology solution that takes a big data approach by leveraging machine learning/artificial intelligence to distinguish fraudulent activity more quickly and efficiently (and pro-actively). Rippleshot's technology processes millions of payment card transactions to proactively pinpoint when and where a data breach occurred. Following detection, Rippleshot provides banks with the tools they need to update fraud detection rules in order to lower their fraud losses while avoiding unnecessary card re-issuance. Rippleshot protects consumer credit information and the integrity of the merchant payment network by proactively detecting data breaches through a cloud-based solution.

NVIDIA (NASDAQ: NVDA) is primarily known as the company that revolutionized computer gaming. The debut of the Graphics Processing Unit (GPU) in 1999 provided gamers with faster, clearer, and more lifelike images. The GPU was designed to quickly perform complex mathematical calculations that were necessary to accelerate the creation of realistic graphics. It achieved this feat by performing many functions at the same time, known as parallel computing. This resulted in faster, smoother motion in game graphics and a revolution in modern gaming.

IBM (NYSE:IBM) jointly announced with the German conglomerate Siemens (OTCPK:SIEGY) that they are planning to include IBM's Watson in Siemens's industry analytics platform MindSphere. Siemens is Europe's largest manufacturing and electronics company with a worldwide presence. Siemens operates in the industrial sector with lots of on-premises software suites, which the company is willing to send to cloud. As a result, IBM's Watson will get a significant boost. This article investigates how IBM's Watson platform will benefit from the development.

Japanese auto major Nissan is working on autonomous driving cars and to that effect, it will use the Seamless Autonomous Mobility (SAM) system – derived from NASA-sourced technology – that utilises artificial intelligence to determine if human intervention is required in unusual and unexpected situations. Nissan believes NASA's technology built for the Mars Rover will help control fleets of autonomous vehicles. SAM is adapted from NASA's Visual Environment for Remote Virtual Exploration (VERVE), designed to supervise interplanetary robots like the Mars rovers. NASA scientists may use the system to chart out safe driving paths on other planets. Nissan uses the example of a police officer using hand signals at a traffic junction.

It's up for debate whether AI can master the subtleties of language, thought, and reason all within the next 14 years. One of the greatest hurdles for machine learning is grasping social interactions. Many AI systems today are still less capable (cognitively speaking) than a 6-year-old. Frey trusts 14 years isn't too generous a timeline for the technology to ramp up, given how quickly technology innovation builds on itself. The internet was just beginning to enter a lot of people's homes 14 years ago, in 2002.

Recently the 10 year old daughter of my friend Ed asked him if Artificial Intelligence means we can stop thinking. We both were perplexed and were not able to give an answer to this curious question. This extremely smart question cannot be simply answered with a yes or no. It triggered my own thinking and curiosity, after we had just mentioned the word Innovation. How will Artificial Intelligence contribute to Innovation in a Corporate Environment?

Coway makes Airmega air filtration systems that suck allergens out of the air in your home. And now it's making the devices easier to control, with the integration of Amazon Alexa voice commands, starting in mid-January. On top of that, the company will ship a graphite version of its filtration system in mid-February. The graphite unit will come in the Airmega 400 ($750) and 400s ($850) models. The Alexa integration is one more example of how much easier it can be to control devices when they become part of the Internet of Things, which is making everyday objects smart and connected. With Alexa, you can now ask Airmega for updates on indoor air quality, filter lifetime, fan speed changes, timer setup, and more.