Basic filters are soooo last year, and Google knows it. It's all about turning your mundane pet photos into works of art now, spearheaded by popular mobile app Prisma. Since it launched earlier this year, Prisma's added an offline mode and video support (albeit after a me-too competitor), but just a few days ago Facebook revealed it's also working on style transfer tech for live video -- though Prisma says it's going to beat the social network to the punch in a matter of days. Now, Google has revealed it's going one better, detailing a system that can mix and match multiple art styles to create photo and video filters that are altogether unique. Google is using more or less the same neural network approach as, say, Prisma does. Simply put, algorithms break pictures down into easily understandable parts, "learn" the artistic style of a painting (like the color palette and brush stroke technique), and combine them into a new image.

For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. Complicating things further is the fact that much of machine learning, at least in terms of its enterprise value, looks somewhat like existing analytics and business intelligence tools. To set the course for this three-part series that puts the scope of machine learning into enterprise context, we define machine learning as software that extracts high-value knowledge from data with little or no human supervision. Academics who work in formal machine learning theory may object to a definition that limits machine learning to software. In the enterprise, however, machine learning is software.

The hype over A.I. and bots shows no signs of simmering down, thanks to accelerating investments, a developer "gold rush," and the ease of building bots. According to Pandorabots, there are more than 225,000 bot developers and upwards of 300,000 bots in existence today. However, a rush like the one to create bots is usually backed up by consumer demand. In this case, there's little to no demand in the form of customers asking brands to adopt bots. In fact, based on BJ Fogg's behavior model, for consumer behavior to shift, the bot needs to be easier to use than the activity it's replacing.

Several of the tech sector's biggest names have come together to form an Artificial Intelligence organisation that will explore the ethics and applications of technology that could transform the entire industry. Amazon, Facebook, Google (DeepMind), IBM and Microsoft are founding members of the'Partnership on Artificial Intelligence to Benefit People and Society', or'Partnership on AI' for short, and will invite academics, non-profit organisations, specialists in policy and ethics to join the board. Partnership on AI itself is a non-profit organisation and will conduct and publish research on an open licence in areas such as ethics, transparency, privacy, interoperability, reliability and interaction between humans and AI systems. The ultimate stated aim of the alliance is to increase public awareness of AI, maximise the benefits to society, and address various challenges. It says it is not a lobbying organisation and all members will contribute financial and research resources.

Speaking at the DATAVERSITY Smart Data Online 2016 Conference, Adrian Bowles, industry analyst, recovering academic, and founder of STORM Insights, and Steve Ardire, Merchant of Light, both talked about the future of Artificial Intelligence (AI). They addressed the question: "Will robots take my job?" The short answer could be summarized as: "Maybe." Lower level jobs with easily improved productivity are likely candidates for automated replacements, says Bowles, but higher level positions with decision making capacity are more likely to have assistance from AI, rather than being replaced by it. Bowles talked about recently going through the stack of books he used early in his AI studies and discovering that "nowhere in these books is the key phrase'Machine Learning,' and yet today, that's sort of at the center of everything we're doing."

Act now to benefit from artificial intelligence – before it's too late

With a AI becoming such a useful tool in ERP technology, the relationship between you and your solution may start to resemble that of Theodore and Samantha in "Her". Some of us may even count Alexa, Watson, and Siri among our best friends, although I really hope that's not true. Anyways, it looks like artificial intelligence will be extending far beyond domestic smartphone use and into the realm of enterprise resource planning technology. With its powerful capabilities, AI tech is already looking to optimize operating models and business processes for companies around the world. While it may initially seem like some kind of futuristic ideal, it actually may not be too long until your business begins utilizing its functionalities to satisfy your own ERP needs.

In today's digital world, work can arrive at your organisation in a multitude of ways – email, digital forms, web chat, phone calls, paper, face to face etc. It's increasingly difficult to meet customer expectations, reduce operating costs and simply keep control of your operation – The Omniflow Digital Workspace solves all of these issues and more Analytics and Artificial Intelligence ensure that work is completed in the optimum way Operating performance is tracked in detail in real time. Omniflow does not replace your existing systems. Omniflow is developed using the latest technologies and design principles to maximise interoperability. Automatically distributes work for completion according to parameters set by you – Like an aeroplane's autopilot navigation along a fight path Identifies patterns in workload, operational costs and user behaviour to highlight optimum performance, improvement opportunities and areas for review Automatically map your business processes as they have been performed One of the biggest challenges facing organisations today is the hype around "Straight Through Processing" (STP) Typically, many business processes do not provide a strong ROI when converting to STP Omniflow's Artificial Intelligence will identify processes and tasks where STP will provide maximum ROI and then automate only the steps where ROI is evident In partnership with Process Automation Group, P&N Bank recently implemented the Omnifow Digital Workspace which has transformed the approach and outputs of our Operations Team.

Human beings have an uneasy relationship with robots. We're fascinated by the prospect of intelligent machines. At the same time, we're wary of the existential threat they pose, one emboldened by decades of Hollywood tropes. In the near-term, robots are supposed to pose a threat to our livelihood, with automation promising to replace human workers while the steady march of artificial intelligence puts a machine behind every fast food counter, toll booth, and steering wheel. The palm-sized robot, from San Francisco-based company Anki, is both a harmless toy and a bold refutation of that uneasy relationship so loved by film and television.

Machine learning is a branch of AI. Other tools for reaching AI include rule-based engines, evolutionary algorithms, and Bayesian statistics. While many early AI programs, like IBM's Deep Blue, which defeated Garry Kasparov in chess in 1997, were rule-based and dependent on human programming, machine learning is a tool through which computers have the ability to teach themselves, and set their own rules. In 2016, Google's DeepMind, beat the world champion in Go by using machine learning--training itself on a large data set of expert moves. In supervised learning, the "trainer" will present the computer with certain rules that connect an input (an object's feature, like "smooth," for example) with an output (the object itself, like a marble). In unsupervised learning, the computer is given inputs and is left alone to discover patterns. In reinforcement learning, a computer system receives input continuously (in the case of a driverless car receiving input about the road, for example) and constantly is improving. A massive amount of data is required to train algorithms for machine learning. First, the "training data" must be labeled (for instance: a GPS location attached to a photo).