The scope of Artificial Intelligence is much broader, including technologies like Virtual Agents, Natural Language Processing, Machine Learning Platforms and many other. The main focus in GE is on making machines smarter, leveraging machine learning to create "digital twins" – a digital replica, or data-based representation of an industrial machine. Unfortunately, SalesForce's Connected Small Business Report notes that only 21% of small businesses are currently using business intelligence and analytics. World's top technology leaders Stephen Hawking and Elon Musk are on the sceptical side of this debate, while Microsoft, Apple, Google and many others are already eagerly taking advantage of the AI technology.

"The most significant arousal-raising properties for aesthetics are novelty, surprisingness, complexity, ambiguity, and puzzlingness," say Elgammal and co. "Novelty refers to the degree a stimulus differs from what an observer has seen/experienced before. "Too little arousal potential is considered boring, and too much activates the aversion system, which results in negative response," say Elgammal and co. That has important implications for the way their generative adversarial network, or agent, is set up. "The agent's goal is to generate art with increased levels of arousal potential in a constrained way without activating the aversion system," they say. Some of the machine-generated images were produced by the creative adversarial network, but others were produced by the generative adversarial network that simply reproduces artistic styles it has learned.

For many in the enterprise, artificial intelligence (AI) versus intelligence augmentation (IA) is a distinction without a difference. The "intelligence" provided by AI technology entails tapping into increasingly cheap computer processing power to evaluate alternate options more quickly than humans could. Evaluating many options and learning from past experience -- using a technology called machine learning -- is how artificial intelligence is able to pick the best outcome available. With all the related information presented in a coherent context, the human can then make intelligent decisions about what to do next.

Customers are getting accustomed to algorithms recommending them products, and as predictive analytics evolves, greater levels of personalization will produce great results. As the attention span of mobile devices moves content to video, how retailers reach their customers online is changing too. Chatbots can help with customer service 24/7, help retailers with marketing and help augment small businesses with artificial intelligence. Retailers have to leverage this trend in new ways to boost customer retention and create offers that improve customer loyalty that leads to higher customer lifetime value from the right customers.

Inside a simple computer simulation, a group of self-driving cars are performing a crazy-looking maneuver on a four-lane virtual highway. Half are trying to move from the right-hand lanes just as the other half try to merge from the left. It seems like just the sort of tricky thing that might flummox a robot vehicle, but they manage it with precision. I'm watching the driving simulation at the biggest artificial-intelligence conference of the year, held in Barcelona this past December. What's most amazing is that the software governing the cars' behavior wasn't programmed in the conventional sense at all.

How can Artificial Intelligence (AI) help companies operate in the 21st century? And, when Ardire talks about Machine Intelligence, he means intelligent computers "that process data for pattern discovery, discern context, make inferences, reasons, learns, and improves over time" without supervision by humans. According to the study, for 80 percent of enterprise executives artificial intelligence makes workers more productive and creates new jobs. "Powerful Artificial Intelligence can help make sense of the conversations people have on their networks."

Frey is a co-founder of Deep Genomics, a professor at the University of Toronto and a co-founder of its Machine Learning Group, a senior fellow of the Neural Computation program at the Canadian Institute for Advanced Research, and a fellow of the Royal Society of Canada. My team studied learning and inference in deep architectures, using algorithms based on variational methods, message passing, and Markov chain Monte Carlo (MCMC) simulation. My group's approach was inspired by Beer and Tavazoie's work, but differed in three ways: we examined mammalian cells, we used more advanced machine learning techniques, and we focused on splicing instead of transcription. We built a framework for extracting biological features from genomic sequences, pre-processing the noisy experimental data, and training machine learning techniques to predict splicing patterns from DNA.

Initiatives such as language translation and image, facial, activity and emotion recognition - are based on predictive analytics that get more accurate as the data behind them gets richer. In particular, the emergence of GPU-based computing can greatly accelerate neural network processing capabilities - and if more processing power is needed there are the vast cloud computing resources of Amazon, Microsoft, Google. "Taken together, deep learning software and parallel processing hardware now provide a powerful [machine intelligence] platform," the report said. Cloud business models: The emergence of machine learning business models based on the use of the cloud is the single biggest reason that the field is so energized today, the report said: "We are essentially seeing the merger of machine intelligence with cloud economics."

While the Turing test is a well-known method for evaluating machine intelligence, it has a number of drawbacks that make it problematic as a rigorous and practical test for assessing progress in general-purpose AI. For example, the Turing test is deception based, subjectively evaluated, and narrowly focused on language use. We suggest that a test would benefit from including the following requirements: focus on rational behavior, test several dimensions of intelligence, automate as much as possible, score as objectively as possible, and allow incremental progress to be measured. The approach, which we call the I-athlon, is intended to ultimately enable the community to evaluate progress towards machine intelligence in a practical and repeatable way.

We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine's ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.