The bar for what counts as artificial intelligence is continually rising. Technologies should lose their AI status when they become routine (optical character recognition is no longer recognized as an example of AI for this reason). However, for marketers it's tempting to go in the opposite direction and rebrand all kinds of routine technologies as AI in order to make them sound more exciting and newsworthy. AI was a key theme at this year's Mobile World Congress. One AI-themed announcement was "aia", from telecoms IT giant Amdocs, which claims to enable the "self-driving telco".

Research into artificial intelligence is going gangbusters, and the frenetic pace won't let up for about five years -- after which the industry will concentrate around a handful of core technologies and leaders, the head of Intel's new AI division predicts. Intel is keen to be among them. In March, it formed an Artificial Intelligence Products Group headed by Naveen Rao. He previously was CEO of Nervana Systems, a deep-learning startup Intel acquired in 2016. Rao sees the industry moving at breakneck speed. "You go three weeks without reading a paper and you're behind.

AI is a core focus for BBVA and for many banks around the world. The reason for this is simple: automating tasks, adding insight, processing information and managing data can increasingly be done faster and more efficiently by machines. This is not to denigrate the impact of human interaction, rather that we can empower people – whether customers or colleagues – to do more if we use the power of computing to help them make decisions or understand information. BBVA already has examples of AI in usage today – for example our MIA chatbot service our Garanti customers in Turkey enjoy – and we'll be rolling out more in the coming months and years. But how do we design future AI products and services – often years in advance – and what are the scenarios we are looking at.

How does this lead to innovation? What does this have to do with Deep Learning? Intuition like consciousness is something that we are all aware of its existence but likely have not investigated in enough detail to have a grounded understanding of its nature. In fact, I would say that there's more research on the nature of consciousness than research on intuition. I've written earlier about a few research groups that have explored consciousness with respect to an artificial general intelligence, however I don't think has been equivalently the same effort with the study of intuition.

If there is one innovation that defines 2017 it is intelligent automation (IA) – and all it entails. New intelligent automation technology, which accesses data, processes it and then initiates actions – all the while "learning while doing" – is supporting enterprise decision-makers by providing a significantly improved quality of support services. Apart from improving overall decision-making speed and supporting smarter decision-making, the artificial intelligence underpinning IA also accelerates solutions for large-scale enterprise challenges by complementing human performance. IA's impact reaches from the private sphere of our personal decision-making … across corporate processing … to the far reaches of our solar system. While robotic operations have been used on the factory floor for decades, the adoption of similar capabilities in the "back office" processing environment is a trend that has only recently emerged.

The concept of the smart city is to integrate information and technology developments to improve the quality of life of its citizens. Smart city initiatives have been around since the dawn of the digital revolution. While there are several smart city models implemented across the world, Singapore has been one of the most ambitious in adapting this program to push the city into the digital age. The country's "smart nation" program was launched in November 2014, turning the island into a "living laboratory" testing smart solutions to address urban issues involving entities in the private and public sectors in Singapore. Vast amount of data is generated and collected in a highly digitalised economy and over time, the term big data is becoming increasingly irrelevant, with all data now considered as big data.

Both projects will be presented at the 2017 IEEE International Conference on Robotics and Automation (ICRA) May 29 -- June 3 in Singapore. In the first, five swarm quadcopters zip back and forth in formation, then change their behaviors based on user commands. The trick is to maneuver without smacking into each other or flying underneath another machine. If a robot cuts into the airstream of a higher flying quadcopter, the lower machine must quickly recover from the turbulent air or risk falling out of the sky. "Ground robots have had built-in safety'bubbles' around them for a long time to avoid crashing," said Magnus Egerstedt, the Georgia Tech School of Electrical and Computer Engineering professor who oversees the project.

AI is one of the buzzwords of the moment in the world of tech, with startups coming at the concept from all angles -- computer vision, machine learning, unstructured data inference and natural language processing being just a handful -- in a wider effort to create more intelligent machines. Now comes a new organization that hopes to find and foster the next wave of AI businesses and products, co-founded by the ex-CEO of Evernote, Phil Libin (pictured above), who has left his role as a managing director at General Catalyst to build it (but he tells me he'll stay on as an advisor). All Turtles, as the new company is called, is not your traditional startup incubator. In an interview with TechCrunch earlier, Libin (whose other co-founders are Jessica Collier (Product Design) and Jon Cifuentes (Research and Operations) described it as "startup studio", more akin to Netflix's push to develop original content than to 500 Startups. It will start out with locations in San Francisco, Tokyo and Paris.

It is a key to construct a similarity graph in graph-oriented subspace learning and clustering. In a similarity graph, each vertex denotes a data point and the edge weight represents the similarity between two points. There are two popular schemes to construct a similarity graph, i.e., pairwise distance based scheme and linear representation based scheme. Most existing works have only involved one of the above schemes and suffered from some limitations. Specifically, pairwise distance based methods are sensitive to the noises and outliers compared with linear representation based methods. On the other hand, there is the possibility that linear representation based algorithms wrongly select inter-subspaces points to represent a point, which will degrade the performance. In this paper, we propose an algorithm, called Locally Linear Representation (LLR), which integrates pairwise distance with linear representation together to address the problems. The proposed algorithm can automatically encode each data point over a set of points that not only could denote the objective point with less residual error, but also are close to the point in Euclidean space. The experimental results show that our approach is promising in subspace learning and subspace clustering.

The Pentagon has revealed a new AI system designed to lead the hunt for Islamic State militants in Iraq and Syria. Codenamed Project Maven, it will analyse aerial surveillance video to look for patterns that can help operators. It comes as military bosses say the thousands of military and civilian intelligence analysts are'overwhelmed' by the amount of video being recorded over the battlefield by drones with high resolution cameras. A US Air Force MQ-9 Reaper unmanned aerial vehicle: Military bosses say intelligence analysts are'overwhelmed' by the amount of video being recorded over the battlefield by drones with high resolution cameras. 'We have to tackle the problem a different way,' Air Force Lt. Gen. John N.T.'Jack' Shanahan, director for defense intelligence for warfighter support, and the man tasked with finding the new technology, told Defense One.