Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.

Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and their confidence intervals. Scaling up the posterior inference for massive-scale matrices is challenging and requires distributing both data and computation over many workers, making communication the main computational bottleneck. Embarrassingly parallel inference would remove the communication needed, by using completely independent computations on different data subsets, but suffers from the inherent unidentifiability of BMF solutions. We introduce a hierarchical decomposition of the joint posterior distribution, which couples the subset inferences, allowing for embarrassingly parallel computations in a sequence of at most three stages. Using an efficient approximate implementation, we show empirically on both real and simulated data that our distributed approach is able to achieve a speed-up of almost an order of magnitude, with a negligible effect on predictive accuracy.

Automation, cognitive technologies, and artificial intelligence are becoming increasingly important in business. Over the past decade technology has changed the way we live and work. Technology has already made us more productive, transforming our communication, entertainment and even shopping. And this is just the beginning -- achievements in artificial intelligence are opening new opportunities to improve our education, healthcare, and achieve a more sustainable future. We need to redefine the working roles today, not limited to the means of "human" and "machine" work, but also introduce new, hybrid labor models in which the use of new technologies drastically increase human productivity.

Amsterdam, Netherlands – February 8, 2018 – Etiya, the leading independent software vendor providing AI and catalog driven BSS/OSS, will showcase its artificial intelligence (AI)-driven customer experience management and self-care solution at Mobile World Congress in Barcelona. As announced in December 2017, Etiya and Openet are jointly providing a Telco as a Service (TaaS) solution, which has been built on the AI-driven end-to-end Digital Business Platform. The two companies now plan to demonstrate TaaS together at Mobile World Congress. TaaS is a cloud-based, modular solution that helps MVNOs and'digital first' sub-brand operators develop their businesses and manage costs predictably. Its subscription model also reduces the risk of an upfront investment.

Think that human augmentation is still decades away? Think again. This week, government leaders met with experts and innovators ahead of the World Government Summit in Dubai. Their goal? To determine the future of artificial intelligence. It was an event that attracted some of the biggest names in...

Today, top individuals from around the world convened at the World Government Summit to discuss the agenda that should govern the next generation of governments. Yesterday, a select few of these leaders gathered in a secretive meeting to discuss the guidelines that nations should use as they help their people come to terms with no longer being the only sentient species on the planet. Of course, this was just one of the many topics discussed. Officials also deliberated on the most immediate ways they can implement AI technologies to make our lives better, who should govern AI, and how to best navigate the perilous roads ahead. The event was organized by the AI Initiative from the Future Society at the Harvard Kennedy School of Government and H.E. Omar bin Sultan Al Olama, the UAE's Minister of State for Artificial Intelligence.

The number of companies using artificial intelligence in healthcare has increased from less than 20 in 2012 to 100 last year, according to GlobalData Healthcare estimates. Growth is expected to accelerate with the AI healthcare market set to reach $6.6bn by 2021, a 40 percent growth from its current size, research from Accenture shows. The three most cost-saving uses of AI in healthcare are robot assisted surgery, virtual nursing assistants, and administrative workflow assistance, Accenture has found. Although healthcare AI is widely used in the US, take up has been slower in the UK though healthcare apps are gaining traction. Babylon is an AI app which uses speech recognition to check symptoms and connect patients with doctors while MedyMatch helps A&E departments make better decisions under extreme pressure.

This is a group for anyone interested in Artificial Intelligence, how it can be used in the enterprise and what benefits it can bring. This group was started to meet others with an interest in AI technology and to share different experiences. The group is aligned with Neurons.AI - The Online Professional Network for AI, but you don't have to be a member of Neurons to join this meetup ... everyone is welcome to these meetings. Don't forget to follow us on Twitter @Neurons_AI and sign up to Neurons.AI Would you like to run a Neurons.AI Meetup Chapter in another City, if so please let us know at meetups@neurons.ai. For more information on this and other Neurons.AI meetups please visit http://Chapters.Neurons.AI

Artificial intelligence is changing the world but is it for the better? Tamara McCleary, CEO at Thulium, argues that Europe's power sector has a crucial role to play in leading the responsible application of AI. It will come as no surprise to hear that artificial intelligence is used in some of the most exciting technologies coming to the fore in the 21st century. After all, there's nothing new about dreams of technological utopias, populated by machines that anticipate and cater to our every need. Machines that empower us to do more of what we really value, better.

It depends who you ask. AI might be a hot topic but you'll still need to justify those projects. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to recognise what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI.