The GraphGrail Ai project has given an interview to Simon Coking, Editor in Chief of IrishTechNews, a leading blockchain technology news resource.

AI in finance could impact on the functioning of our financial system in a profound way. Some suggest that AI is enhancing the power of the human brain in the same way that electricity enhanced the power of the body 150 years ago. Hence, it could become a big thing in finance. Artificial intelligence and big data are currently the strongest and most vivid innovation factors in the financial sector. Using AI in finance may trigger dramatic improvements in many businesses. AI elevates the role of data as a key commodity.

We are not only living in an age where women are being under-represented in many spheres of economic life, but technology could make this even worse. Women hold just 19% of board directorships in the US and Europe. This gender gap in the boardroom persists, despite the fact that, on average, women have obtained higher educational qualifications than their male counterparts for more than two decades in many OECD countries. And the main reason is social bias.

For years schooling has been typified by its aspect of the physical grind on the part of both students and their teachers: teachers cull and prepare educational materials, manually grade students' homework, and provide feedback to the students (and the students' parents) on their learning progress. They may be burdened with an unmanageable number of students, or a wide gulf of varying student learning levels and capabilities in one classroom. Students, on the other hand, have generally been pushed through a "one-size-fits-all" gauntlet of learning, not personalized to their abilities, needs, or learning context. I'm always reminded by this quote by world-renowned education and creativity expert Sir Ken Robinson: "Why is there this assumption that we should educate children simply according to how old they are? It's almost as if the most important thing that children have in common is their date of manufacture."

Officials in Moscow, like many other city governments, are discussing how artificial intelligence technologies can help the city do its work better. But the Russian capital has perhaps the most pervasive vision for AI in government yet. "We want AI to work everywhere," Moscow's Chief Information Officer, Artem Ermolaev, tells GovInsider. His team is already trialling AI to advise doctors in treating cancer patients; help residents find new homes; and assess applications for government services and support. "The Moscow vision of the future is when artificial intelligence is equal to humans – or sometimes, artificial intelligence is higher than the human," he says.

It's a myth that Artificial Intelligence is just for large corporations. Robotics and automation are transforming the workplace for smaller businesses as the technology becomes ever cheaper and more accessible. There are many reasons to be optimistic about AI. An Accenture report suggests that it could bring an additional £814 billion to the UK economy by 2035, whist research firm Gartner reports that in 2020, AI will create 2.3 million jobs. General AI-based productivity tools are becoming more readily available and increasingly affordable – sometimes even free.

A recent survey of more than 300 marketers by Resulticks found that almost half thought artificial intelligence was an overhyped industry buzzword and 40% felt skeptical when they saw or heard the term. The survey also found that 47% of marketers believed AI was more fantasy than reality. Artificial intelligence has certainly become the buzzword of 2018 as brands and retailers increasingly explore how it will impact their business this year and beyond. Speaking at an event recently hosted by StoryStream on the hype behind AI in marketing, it became apparent that whilst AI is in its infancy today, many brands and retailers are already actively using AI for the likes of content creation, content management, customer insights, personalization and customer acquisition. Brands including Aston Martin, Shiseido and Mars are already actively embracing AI experimentation in marketing and some have been at it for some time.

According to Nathan Mondragon, finding the right employee is all about looking at the little things. Tens of thousands of little things, as it turns out. Mondragon is the head psychologist at Hirevue, a company that offers software that screens job candidates using algorithms and artificial intelligence (AI). Hirevue's flagship product, used by global giants such as Unilever and Goldman Sachs, asks candidates to answer standard interview questions in front of a camera. Meanwhile its software, like a team of hawk-eyed psychologists hiding behind a mirror, makes note of thousands of barely perceptible changes in posture, facial expression, vocal tone and word choice.

The use of back-propagation and its variants to train deep networks is often problematic for new users, with issues such as exploding gradients, vanishing gradients, and high sensitivity to weight initialization strategies often making networks difficult to train. In this paper, we present Local Representation Alignment (LRA), a training procedure that is much less sensitive to bad initializations, does not require modifications to the network architecture, and can be adapted to networks with highly nonlinear and discrete-valued activation functions. Furthermore, we show that one variation of LRA can start with a null initialization of network weights and still successfully train networks with a wide variety of nonlinearities, including tanh, ReLU-6, softplus, signum and others that are more biologically plausible. Experiments on MNIST and Fashion MNIST validate the performance of the algorithm and show that LRA can train networks robustly and effectively, succeeding even when back-propagation fails and outperforming other alternative learning algorithms, such as target propagation and feedback alignment.

Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory. This precludes the implementation of CNNs in low-power embedded systems. Recent research shows CNNs sustain extreme quantization, binarizing their weights and intermediate feature maps, thereby saving 8-32 memory and collapsing energy-intensive sum-of-products into XNOR-and-popcount operations. We present XNORBIN, an accelerator for binary CNNs with computation tightly coupled to memory for aggressive data reuse. Implemented in UMC 65nm technology XNORBIN achieves an energy efficiency of 95 TOp/s/W and an area efficiency of 2.0 TOp/s/MGE at 0.8 V. I.