NatWest is testing an artificial intelligence-powered "digital human" called Cora that will converse with customers in branches – raising fears that bank tellers could be replaced by avatars. Cora, described by the bank as "highly lifelike", is the result of a link-up with a New Zealand tech company whose co-founder was involved with creating digital characters in the blockbuster films Avatar, King Kong and Spider-Man 2. Cora is currently able to answer basic verbal questions such as "How do I login to online banking?", "How do I apply for a mortgage?" NatWest said it could help cut down on waiting times because it would be able to deal with simple problems, adding that Cora's AI skills would eventually expand to answering hundreds of different questions, even detecting human emotions and reacting verbally and physically with facial expressions.

Last week NAB kicked off its latest wave of job cuts billed as part of a restructure to "simplify" the bank. It's part of a three-year plan announced in 2017 to axe 6000 jobs while adding 2000 new technology roles. "As we simplify, we automate processes and things move to digital channels, we will need less people and as that happens we estimate that there will be 6000 less people needed in three years' time," NAB CEO Andrew Thorburn explained when announcing the cuts late last year. "Having said that, we're hiring 2000 people with different capabilities: data scientists, AI, robotics, automation, technology people, digital people, so the net [job loss] will be 4000 and that's just a reshaping that's going to happen." As machines learn how to perform tasks that would otherwise be done by humans, businesses will find themselves needing fewer staff to complete the same amount of work.

This Chapter, "ABC Samplers", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and algorithms used to sample from the ABC approximation to the posterior distribution, including methods based on rejection/importance sampling, MCMC and sequential Monte Carlo.

Sociotechnical systems are complex systems, where nonlinear interaction among different players can obscure causal relationships. The absence of mechanisms to help us understand how to create a change in the system makes it hard to manage these systems. Influencing and shaping are social operators acting on sociotechnical systems to design a change. However, the two operators are usually discussed in an ad-hoc manner, without proper guiding models and metrics which assist in adopting these models successfully. Moreover, both social operators rely on accurate understanding of the concept of trust. Without such understanding, neither of these operators can create the required level to create a change in a desirable direction. In this paper, we define these concepts in a concise manner suitable for modelling the concepts and understanding their dynamics. We then introduce a model for influencing and shaping and use Computational Red Teaming principles to design and demonstrate how this model operates. We validate the results computationally through a simulation environment to show social influencing and shaping in an artificial society.

Despite efforts to tackle corruption around the world, progress is still frustratingly slow, according to the latest report from Transparency International. Its annual Corruption Perception index reveals some alarming trends. It shows public service corruption is still a huge problem for two-thirds of the world's economies. The report uses a scale of zero to 100 to rank countries: zero is highly corrupt and 100 is very clean. New Zealand comes out on top but with a score of 89.

Android devices are growing exponentially and are connected through the internet accessing billion of online websites. The popularity of these devices encourages malware developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although, for the detection of malicious apps different approaches are discussed. However, proposed approaches are not suffice to detect the advanced malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcodes occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy ( 79.27%) is best among the investigated classifiers. However, true positives rate i.e. malware detection rate is highest ( 99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree---obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates on multiple datasets, for deep convolutional neural networks deployed with 1-bit-per-weight. Using wide residual networks as our main baseline, our approach simplifies existing methods that binarize weights by applying the sign function in training; we apply scaling factors for each layer with constant unlearned values equal to the layer-specific standard deviations used for initialization. For CIFAR-10, CIFAR-100 and ImageNet, and models with 1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively. We also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test results of 0.27%, 1.9%, and 41.3% / 19.1% respectively. For CIFAR, our error rates halve previously reported values, and are within about 1% of our error-rates for the same network with full-precision weights. For networks that overfit, we also show significant improvements in error rate by not learning batch normalization scale and offset parameters. This applies to both full precision and 1-bit-per-weight networks. Using a warm-restart learning-rate schedule, we found that training for 1-bit-per-weight is just as fast as full-precision networks, with better accuracy than standard schedules, and achieved about 98%-99% of peak performance in just 62 training epochs for CIFAR-10/100. For full training code and trained models in MATLAB, Keras and PyTorch see https://github.com/McDonnell-Lab/1-bit-per-weight/ .

The Bank of England's warning that the pace of artificial intelligence development now threatens 15m UK jobs has prompted calls for political intervention. According to scientists and legal experts, responding to the bank's warning this November, there is now an urgent need for the development of intelligent algorithms to be put on the political agenda. This is happening now and across the board and that's the difference. That's why a lot of us need to start talking about this now. The Government needs to pick up on this and put it on the political agenda and look at regulatory issues, said Chrissie Lightfoot, a patent lawyer and author, who debated fears over unemployment caused by AI at London's Science Museum last October.

Australia's Tasmanian tiger has been extinct for more than 80 years but scientists have taken a step closer to bringing the animal back to life. Stunning 3D scans were used to reconstruct the development of the animal through its early life, using persevered specimens of the species. Unlike other marsupials, like kangaroos and wallabies, Tasmanian tigers have dog-like features - despite last sharing a common ancestor around 160 million years ago. The new scans have revealed that these canine attributes appear once their young, called joeys, have left their mother's womb. The find could help to unlock the genetic mysteries of the iconic creatures, which will aid scientists who hope to clone them and bring them back from extinction.