This enables a'human-like chat experience' where machine learning assists in responding to queries and helps lower the number of actual human representatives needed to run the platform, meaning resources can be allocated elsewhere. The software also analyses the information coming in and uses it to improve its ability to offer a more targeted, streamlined and specialised offering to the customer in the future - something that would require unfeasibly large amounts of staff for a traditional bank.

Machine learning has largely been enabled by the coming together of large datasets, algorithms capable of making sense of the data, and affordable computing to underpin everything. It's interesting to see, therefore, that supercomputing giant Cray Inc. have recently undertaken a deep learning collaboration with Microsoft and the Swiss National Supercomputing Centre. The project aimed to improve the ability of companies to run deep learning algorithms at scale. The partnership worked to leverage their collective computing expertise to scale up the Microsoft Cognitive Toolkit onto a Cray XC50 supercomputer. The aim is to speed up the training process, and thus obtain results in hours that would typically take weeks, or even months.

Already, as of 2010, a quarter of Americans (24 percent) had posted product reviews or comments online, and 78 percent of internet users had gone online for product research. But those are ancient stats. More recently, BrightLocal found in 2016 that 91 percent of consumers regularly or occasionally read online reviews, with 47 percent taking sentiment of local-business reviews -- the tonality of a review's text -- into account in purchasing decisions. Breaking out the figures, 74 percent of consumers say that positive reviews make them trust a local business more, and 60 percent say that negative reviews make them not want to use a business, according to BrightLocal. So reviews are important, and the feelings expressed are key.

Ten days ago, in a Davos still shellshocked by Trump's victory, one of the former masters of the universe sat down with an interviewer to talk about artificial intelligence (AI) and the future. His name is Sergey Brin and he is one of the co-founders of Google. The tone of the conversation was thoughtful but subdued, possibly because Brin is not a master in Trump's universe, but mainly because he's a smart and thoughtful guy. When asked about the future of AI he replied – sensibly – that it was "impossible to forecast accurately" and followed up with a story from his own experience. Poker requires reasoning and intelligence that have up until now eluded machines.

Rage Frameworks published a report called "Can Artificial Intelligence Deliver for Today's Enterprise?" based on a survey of business executives. The primary reason given is to improve "Reasoning and Traceability". This includes having the ability to comprehend the logic behind why the AI solution reached its conclusion, which is now regarded as essential for widespread adoption in many enterprise-level applications. This will be problematic as most AI is based on computational statistics, which is essentially a "black box," and therefore the rationality is not traceable. Some of these firms do claim that the rationality is traceable, but often these are at such a high level of abstraction they fall short in terms of decision science. Alternative AI solutions such as Chatbot Author aimed at Policies and Procedures, uses a "white box" where "Reasoning and Traceability" is provided by user generated algorithms and generation of dialogue data for compliance, audit, measurements and pattern analysis.

Attributes of Participatory Culture @TransformSoc (Henry Jenkins) • Affiliations: online communities • Expressions: new creative forms • Collaborations: Problem-solving in teams • Circulations: Shaping media flow Source: Confronting The Challenges Of Participatory Culture, by Henry Jenkins, MIT Press, 2009 31.

Artificial intelligence (AI) is a branch of computer science that is developing machines capable of intelligent behaviour. This involves building machines that can learn from example and complete tasks that would normally require human intelligence, such as speech recognition, language translation, decision-making and visual perception. Artificial intelligence uses machine learning, including deep learning (i.e. Artificial intelligence is the science, whilst machine learning is the enabler for AI. Companies like Google and Nvidia are at the forefront of AI development, conducting research and applying the science to work in areas such as visual processing (e.g.

As it goes every year, one hot feature sets a trend in technology, and suddenly every company boasts some variation of which that is uniquely theirs. This year, that feature is AI. Hot on the heels of Alexa's and Google Assistant's holiday successes, Artificial Intelligence on phones has become the de facto must-have feature – whether consumers know it or not. In any case, manufacturers seem not to realize that AI doesn't mean "Anything Intuitive" – that's just how operating systems are supposed to be. Yet it seems that OEM's are eager to label nearly any vaguely intuitive feature as AI.

In 1872 a British general named Alexander Cunningham, excavating an area in what was then British-controlled northern India, came across something peculiar. Buried in some ruins, he uncovered a small, one inch by one inch square piece of what he described as smooth, black, unpolished stone engraved with strange symbols -- lines, interlocking ovals, something resembling a fish -- and what looked like a bull etched underneath. The general, not recognizing the symbols and finding the bull to be unlike other Indian animals, assumed the artifact wasn't Indian at all but some misplaced foreign token. The stone, along with similar ones found over the next few years, ended up in the British Museum. In the 1920s many more of these artifacts, by then known as seals, were found and identified as evidence of a 4,000-year-old culture now known as the Indus Valley Civilization, the oldest known Indian civilization to date. Since then, thousands more of these tiny seals have been uncovered.

Many common applications of predictive analytics, from customer segmentation to medical diagnosis, arise from complex relationships between features (also called variables or characteristics). Feature selection is the process of finding the most relevant variables for a predictive model. These techniques can be used to identify and remove unneeded, irrelevant and redundant features that do not contribute or decrease the accuracy of the predictive model. Mathematically, feature selection is formulated as a combinatorial optimization problem. Here the function to optimize is the generalization performance of the predictive model, represented by the error on a selection data set.