Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that's more efficient and powerful than the best human-designed systems. An AutoML system recently broke a record for categorizing images by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system's 39 percent.

There's growing excitement – admittedly, at times, borderline hype – about what artificial intelligence can, and will, do for businesses. While speculation abounds among pundits, journalists, and'thought leaders' surrounding the impact that AI will have on jobs (CBInsights predicts 10 million jobs are at risk in the next 5-10 years), there's relatively little analysis of the tangible effect AI will have on marketer's day-to-day work, and the opportunity to'upskill' us all. Writing exclusively for ExchangeWire, Gareth Davies (pictured below), founder and CEO, Adbrain, explains why and how artificial intelligence can realise tangible benefits for marketers. Today's marketers will benefit by navigating an increasingly AI-centric (and AI-literate) world where bots, intelligent software and machine learning play an increased role in the marketing function. To help you cut through the noise, here are some tangible examples of where AI is likely to become a relevant part of the modern marketers' workflow, as well as ideas on how to better understand and qualify the impact that AI can have on your business.

This morning at the WSJ's D.Live event, Intel formally unveiled its Nervana Neural Network Processor (NNP) family of chips designed for machine learning use cases. Intel has previously alluded to these chips using the pre-launch name Lake Crest. The technology underlying the chips is heavily tied to Nervana Systems, a deep learning hardware startup Intel purchased last August for $350 million. Intel's NNP chips nix standard cache hierarchy and use software to manage on-chip memory to achieve faster training times for deep learning models. Intel has been scrambling in recent months to avoid being completely leveled by Nvidia.

The rise of robots could lead to'unprecedented' change and wipe out over a third of jobs in some areas by the 2030's a new report warns. A'heat map' of Britain shows the areas most at risk of automation, with workers in the ex industrial heartlands of the North and Midlands most likely to lose their jobs. The upheaval tossed up by'supercharged' technological change over the next 15 years could make the industrial revolution pale in comparison, the study says. The report, The impact of AI in UK constituencies, by think-tank Future Advocacy, slams the government for failing to prepare for the rapid change looming. Researchers said the results are'startling' and told ministers to urgently look at new education and training to help the country adapt to the challenge. It shows that the UK's former industrial heartlands of the Midlands and the north are most at risk from the march of the machines Meanwhile, a YouGov poll carried out for the report found that just seven per cent of Brits are worried about losing their jobs to automation.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

We are looking for a Machine Learning Researcher with a specialised focus on Reinforcement and Active Learning. The candidate will have a sound understanding of modern machine learning, deep learning, probabilistic modelling techniques and expertise in Reinforcement and Active Learning and their applications in real-world problems. You will have the opportunity to contribute to this high performing team who seek to apply their knowledge in the high impact field of improving human's capability in drug discovery. If this challenge and opportunity excites you, please email your CV and a covering letter to careers@benevolent.ai

Yesterday I had the chance to meet Andreas Liebl, partner at UnternehmerTUM, who launched a programme Applied.AI, wherein people from across the world are invited to apply. If you are an engineer, or a professional, and wish to launch an application or gain expertise in the field of AI, this is the programme for you. The institute was founded by entrepreneur Susanne Klatten in 2002 and is supported by several corporations such as Nvidia, Mercedes Benz and SAP, to name a few. What I found interesting about the institute is that not only do they train you, but can also support you, should you wish to start up. The institute has a venture capital arm that can facilitate investments towards startups with promising ideas.

Blocks, a Theano framework for training neural networks Caffe, a deep learning framework made with expression, speed, and modularity in mind. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm. ConvNet, a Matlab based convolutional neural network toolbox - a type of deep learning, can learn useful features from raw data by itself.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

What if you could create an accurate summary of a lengthy article at the touch of a button? What if you could quickly scroll through a bibliography, filtered to show only the citations relevant to your needs? What if you could get your research out into the world faster, and have that knowledge built upon sooner? Science and technology are generating more data than ever faster than ever, so it's getter harder and harder to keep up and manage this information. Therefore, it's crucial to find ways to automate the discovery and interpretation of the information we need – and only that information.