Throughout time, humans have been fascinated by the idea of machines "thinking" like humans. In Eagle Eye we became acquainted with ARIIA (an Autonomous Reconnaissance Intelligence Integration Analyst), in Her, we were introduced to Samantha (a highly capable computer program that doubled as a personal assistant), in Ex Machina we fell for Ava (a humanoid robot with genuine human traits) and in I, Robot, we befriended Sonny (a friendly robot). Concepts like machine learning, robotics, deep learning and artificial intelligence, have been thrown around, here and there, spiking our interest and commercializing these once scientific concepts. But what exactly is artificial intelligence, and what are its implications? John McCarthy, an American computer scientist and cognitive scientist coined the term "artificial intelligence" in 1955 describing it as "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to stimulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves".

LESLIE FAUGHNAN finds that while automation, augmented intelligence and artificial intelligence are developing at a furious pace in today's enterprise, we are a long way from replacing people and the things they are good at One of the many dictionary definitions of the word'smart' includes the phrase "quick-witted intelligence", which seems as good a way as any of describing what we currently mean by a smart enterprise. Definition is a challenge yet to be met because there is no tech spec for smart. But both the IT industry and its corporate clients share a broad vision of what we are aiming for -- an organisation that is fully joined up digitally and capable of realising the benefits of that synergy. That in turn means corporate agility based on the ability to utilise all of its collected data, complemented by relevant external data, in real time. A useful term in the ether now is'Augmented Intelligence', which will help us somewhat limited humans to make better decisions.

When it comes to understanding intelligence, the greatest challenge out there is not a Rubik's Cube, or chess, or even Go. These games are difficult in the sense that there are often many options, but they are still transparent: nothing is hidden; every bit of information is in front of you. The main obstacle is converting this perfect information into a strategy. There is a fixed set of rules out there, and if a computer can find them, it will achieve the optimal result in every game. When Garry Kasparov lost to IBM's Deep Blue chess computer in 1997, he lamented this approach.

You punch the destination into your phone and a driverless car soon swings to a stop next to you. You jump in and it whisks you north-west towards the I-80 on-ramp. But as you merge with the highway traffic, the car pipes up: "This car runs on the Uber network, which does not cover Detroit. You will be dropped at an appropriate interchange point." The way things are going, this could be the short-term prospect for driverless cars.

As the BigDataNYC 2016 event kicked off at the Mercantile Annex in New York, NY, Dave Vellante (@dvellante), Jeff Frick (@JeffFrick), Peter Burris (@plburris) and George Gilbert (@ggilbert41), cohosts of theCUBE, from the SiliconANGLE Media team, sat down to discuss what could be expected from this year's event coverage. In particular, the analysts discussed the recent trends in Big Data toward Artificial Intelligence (AI) and machine learning, becoming increasingly attractive to many companies in the enterprise. AI and machine learning with Hadoop has always been a focus area of Big Data, but recently it appears that more companies are moving away from Hadoop in order to bring more productivity and value to the datasets, according to theCUBE analyst team. The main reason for this was because developers wanted more flexibility, which in turn brought unexpected complexity and then a new wave of innovation. "There is this new wave of innovation, a new wave of opportunity," explained Frick.

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Although cyber attacks against businesses like the recent one against Yahoo are increasing, hackers are using the same techniques they always have. Stuart McClure, the CEO of security startup Cylance, told Fortune's Robert Hackett that "there is nothing new" in how hackers are breaching computer defenses. He compared cyber attacks to thieves breaking into homes. There are only so many ways thieves can sneak inside, and no one has created new methods like a "teleportation device" for criminals to more easily get through the front door, he explained. What's different now is that organizations can now use an artificial intelligence technique called machine learning to better defend themselves against these attacks, McClure said.

There are multiple approaches to tackling self-driving; one is to program algorithms or rules that will tell a car how to behave in specific situations.

With more than 250 automotive companies in the state, from Lear, Kemet, Koyo, to Michelin, BMW, and Bridgestone (to name a few), it is little wonder that South Carolina is ranked #3 in automotive manufacturing strength for many reasons. South Carolina is one of leading locations for vision guided vehicles (VGV), driven in part to the increasing North American fork truck free (FTF) initiatives. Encouraging public-private partnerships, which serve as the foundation for research in South Carolina, world-class brands like BMW and Michelin are partnering with universities to bring collaboration to the next level. At facilities across the state, researchers driven by the needs of the automotive industry work with students, multi-disciplinary faculty members, and industry partners to determine the next generation automation technologies. The automotive sector often requires a proof-of-concept success story in one location before the technology solution is implemented enterprise-wide.

When robots take over the world, they may end up spouting the kind of quick-paced dialogue that makes Romance novels and thrillers the most popular genres among readers. The Guardian reports that Google has "swallowed" thousands of books to create artificial intelligence, including thrillers, romance novels, and other genres. Forster's thriller is just one of 11,000 novels that researchers including Oriol Vinyals and Andrew M Dai at Google Brain have been using to improve the technology giant's conversational style. After feeding these books into a neural network, the system was able to generate fluent, natural-sounding sentences. According to a Google spokesman – who didn't want to be named – products such as the Google app will be "much more useful if they can capture the nuance of language better".