In the last few years, you cannot have a discussion around technology without those terms entering the conversation. They have been major technology disruptors impacting all aspects of the business. Change seems to occur at breakneck speeds and shows no sign of slowing. Today, it appears the one constant in technology is change. Constant change requires constant innovation which thereby introduces more new technologies.

Stories about racist Twitter accounts and crashing self-driving cars can make us think that artificial intelligence (AI) is a work in progress. But while these headline-grabbing mistakes reveal the frontiers of AI, versions of this technology are already invisibly embedded in many systems that we use everyday. These everyday uses include everything from fraud detection systems that monitor credit card transactions to email filters that learn not to swamp your inbox with spam. You've probably already interacted with an AI system today without even knowing it and probably enjoyed the experience. One increasingly common form of AI can be found in chatbots, a type of software that lets you interact with it by having a conversation.

When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.

When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.

Some of society's brightest minds have warned that artificial intelligence (AI) may lead to dangerous unintended consequences, yet leaders of the U.S. intelligence community--with its vast budgets and profound capabilities--have yet to decide who within these organizations is responsible for the ethics of their AI creations. When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. If scenario-based exercises were conducted, the intelligence community provides no guidelines for deciding when a risk is too great and a system should not be built and assigns no authority to make such decisions. Intelligence agencies use advanced algorithms to interpret the meaning of intercepted communications, identify persons of interest and anticipate major events within troves of data too large for humans to analyze. If artificial intelligence is the ability of computers to create intelligence that humans alone could not have achieved, then the U.S. intelligence community invests in machines with such capabilities.

Robots are going to play an integral role in hospital care soon. At least that's what Julie Shah -- an MIT professor who leads the Interactive Robotics Group at the university's Computer Science and Artificial Intelligence Laboratory -- is working on.

Macy's is testing a mobile tool using artificial intelligence that lets shoppers get answers customized to the store they're in -- like where a particular brand is located or what's in stock -- that they would normally ask a sales associate face-to-face. The tool, which the nation's largest department store chain calls a "mobile companion," can be accessed for now through a browser and will accept questions in 10 U.S. locations about products, services and facilities. It uses natural language and offers feedback in seconds. It's developed by IBM Watson -- the Jeopardy-winning "cognitive computing" service and is designed to keep learning more about the store's customers. That's a key element as Macy's seeks to spur sluggish sales, make being at the store more enjoyable and distinguish itself from online portals and specialty retailers.

Google's acquisition of artificial intelligence startup DeepMind in 2014 has resulted in the search giant making a 15 percent improvement in power usage efficiency, DeepMind's co-founder reportedly said. Google reportedly paid 650 million for the London-based artificial intelligence firm founded by neuroscientist Demis Hassabis, Shane Legg and Mustafa Suleyman. The AI built by the company mastered playing the Atari video games but most of its projects are yet to translate into revenue. However, it is helping the tech giant tackle the massive energy bills it deals with because of its data centers. Google said it used 4,402,836 MWh of electricity in 2014, equivalent to the average yearly consumption of about 366,903 U.S. family homes, according to Bloomberg.

Facebook unveiled a new laser-based concept for high-speed internet that will bypass the need for dedicated wavelength spectra and government licenses that come with them. Published Tuesday in the journal Optica, the technique was developed by researchers at Facebook's Connectivity Lab. The team is exploring a variety of technologies, including high-altitude long-endurance planes, satellites and lasers." It calls its new concept, which also uses drone technology, free-space optical communication. Explaining their rationale behind turning to laser for data transmission, the researchers say in the paper: "While optical communications have become the de facto standard for high-throughput wired communication channels, microwave and millimeter wave carrier frequencies are still the standard for wireless links. However, the limited availability of spectrum restricts the data rates that can be achieved through these channels."

Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. The problem we are going to look at in this post is the international airline passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. Below is a sample of the first few lines of the file.