Google is teaching machines to play Atari games like Space Invaders, Video Pinball, and Breakout. At DeepMind, a Google subsidiary based in Cambridge, England, researchers have built artificial intelligence software that's so adept at these classic games, it can sometimes beat a human player--and a professional, at that. This may seem like a frivolous, if intriguing, pursuit. If a machine can learn to navigate the digital world of a video game, Google says, it eventually could learn to navigate the real world, too. Today, this AI can play Space Invaders.

Major technology firms are racing to infuse smartphones and other internet-linked devices with software smarts that help them think like people. The effort is seen as an evolution in computing that allows users to interact with machines in natural conversation style, telling devices to tend to tasks such as ordering goods, checking traffic, making restaurant reservations or searching for information. The artificial intelligence (AI) component in these programs aims to make create a world in which everyone can have a virtual aide that gets to know them better with each interaction. Google Google is making a high-profile push into AI, with the internet titan's chief referring to it as a force for change as powerful as powerful as smartphones. Google Assistant software is being built into new Pixel handsets -- aiming to outdo Apple's Siri -- enabling users to organise and use information on the devices and in the cloud -- to check emails, stay up to date on calendar appointments, news or ask for traffic and weather data.

Singapore-based Nugit promises analysts 50-80% less time spent "extracting, cleaning and combining" data when using its artificial intelligence-enhanced reports The Singapore-based Nugit (a play on "nuggets," of data) announced on Thursday that it raised 5.2 million from Sequoia India, with the goal of doubling its staff and expanding its client base in the near future for a market that will be worth over 185 billion in 2017. This is their first public round of financing, though they raised an undisclosed seed round in March 2015. Nugit's core services center around its work in "natural language generation" (NLG), an artificial intelligence process first developed in the 1990s to automate weather forecasts. NLG works differently from other types of machine learning linguistics because, "NLG systems start with a set of ideas locked in data and turn them into into language that, in turn, communicates them." The goal of this interactive information access is to draw out trends and patterns for reports quickly and clearly, using preprogrammed conditional prompts to review large data sets. Marketing and advertising companies, for instance, want to collect data that can compare data on campaign spending, sales, and reach.

He's a lightning-fast learner, he speaks eight languages and he's considered an expert in multiple fields. He's got an exemplary work ethic, is a speed reader and finds insights no one else can. On a personal note, he's a mean chef and even offers good dating advice. Named after IBM's first CEO, Watson was born back in 2007 as part of an effort by IBM Research to develop a question-answering system that could compete on the American quiz show "Jeopardy." Since trouncing its human opponents on the show in 2011, it has expanded considerably.

With improvements in cloud technology spreading like wildfire, small businesses are in a position where they can take advantage of these advancements. They can leverage tools that will help them run their business more effectively from anywhere at anytime, gain real-time insights into their performance and streamline their selling and customer service initiatives. Considering almost half of small businesses don't have a website, maybe not all businesses are utilizing the wealth of smart technologies at their disposal. Here are three to get you started. For small business owners, being awake used to be the prerequisite for making money.

Major technology firms are racing to infuse smartphones and other internet-linked devices with software smarts that help them think like people. The effort is seen as an evolution in computing that allows users to interact with machines in natural conversation style, telling devices to tend to tasks such as ordering goods, checking traffic, making restaurant reservations or searching for information. The artificial intelligence (AI) component in these programs aims to make create a world in which everyone can have a virtual aide that gets to know them better with each interaction. Google is making a high-profile push into AI, with the internet titan's chief referring to it as a force for change as powerful as powerful as smartphones. Google Assistant software is being built into new Pixel handsets -- aiming to outdo Apple's Siri -- enabling users to organize and use information on the devices and in the cloud -- to check emails, stay up to date on calendar appointments, news or ask for traffic and weather data.

Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce, are competing in the race to acquire private AI companies, with Samsung emerging as a new entrant this month with its acquisition of startup Viv Labs, which is developing a Siri-like AI assistant. In 2014 Google acquired British company DeepMind Technologies for some 600M (Google DeepMind's program recently beat a human world champion in the board game "Go"). This year, it acquired visual search startup Moodstock, and bot platform Api.ai. Salesforce, which joined the race last year with the acquisition of Tempo AI, has already made two major acquisitions this year: Khosla Ventures-backed MetaMind and open-source machine-learning server PredictionIO.

Internet of Things (IoT) is creating a tectonic shift in the way brands react to consumers and how buyers interact with each other and seek out and find sources of information that assist their buying decisions. A spate of recent events are re-shaping the way consumers communicate with each others, with the brand, conduct research and shop for goods and services. New devices, systems, microprocessors, data hubs, networks, analytics and software demonstrating artificial intelligence are increasingly making their presence felt in the retail markets. "IoT data harvested from smartphones, wearables, sensors and other devices would soon provide significant insights and opportunities for both the customer as well as the brand. The use of predictive selling powered by artificial intelligence would enable retailers to deliver superior buying experience to customers, resulting in lifelong brand loyalty, says Suhale Kapoor, Co-Founder and Executive Vice-President of Absolutedata Analytics.

Google just announced that they're making a big bet on artificial intelligence to move the company's services forward. During Google's Q3 earnings call CEO Sundar Pichai said the company was "re-thinking" all of its products to include more artificial intelligence and an approach called machine learning. Artificial intelligence (AI) has always played a huge part in Google's success. We talked to Geoffrey Hinton, AI researcher at Google and the University of Toronto, over email about the future of the field. He predicted that AI will likely loom ever larger in our lives, without our even knowing it.

Speaking to attendees at a deep learning conference in London last month, there was one particularly noteworthy recurring theme: humility, or at least, the need for it. While companies like Google are confidently pronouncing that we live in an "AI-first age," with machine learning breaking new ground in areas like speech and image recognition, those at the front lines of AI research are keen to point out that there's still a lot of work to be done. Just because we have digital assistants that sound like the talking computers in movies doesn't mean we're much closer to creating true artificial intelligence. Problems include the need for vast amounts of data to power deep learning systems; our inability to create AI that is good at more than one task; and the lack of insight we have into how these systems work in the first place. Machine learning in 2016 is creating brilliant tools, but they can be hard to explain, costly to train, and often mysterious even to their creators.