Discovering the neural mechanisms underpinning cognition is one of the grand challenges of neuroscience. However, previous approaches for building models of RNN dynamics that explain behaviour required iterative refinement of architectures and/or optimisation objectives, resulting in a piecemeal, and mostly heuristic, human-in-the-loop process. Here, we offer an alternative approach that automates the discovery of viable RNN mechanisms by explicitly training RNNs to reproduce behaviour, including the same characteristic errors and suboptimalities, that humans and animals produce in a cognitive task. Achieving this required two main innovations. First, as the amount of behavioural data that can be collected in experiments is often too limited to train RNNs, we use a non-parametric generative model of behavioural responses to produce surrogate data for training RNNs. Second, to capture all relevant statistical aspects of the data, we developed a novel diffusion model-based approach for training RNNs. To showcase the potential of our approach, we chose a visual working memory task as our test-bed, as behaviour in this task is well known to produce response distributions that are patently multimodal (due to swap errors). The resulting network dynamics correctly qualitative features of macaque neural data. Importantly, these results were not possible to obtain with more traditional approaches, i.e., when only a limited set of behavioural signatures (rather than the full richness of behavioural response distributions) were fitted, or when RNNs were trained for task optimality (instead of reproducing behaviour). Our approach also yields novel predictions about the mechanism of swap errors, which can be readily tested in experiments. These results suggest that fitting RNNs to rich patterns of behaviour provides a powerful way to automatically discover mechanisms of important cognitive functions.

Normally when comparing different models/algorithms, effort should be taken to find the best performing parameters (or more generally most suitable formalisations) for each model.

Continuous behavioural authentication methods add a unique layer of security by allowing individuals to verify their unique identity when accessing a device. Maintaining session authenticity is now feasible by monitoring users' behaviour while interacting with a mobile or Internet of Things (IoT) device, making credential theft and session hijacking ineffective. Such a technique is made possible by integrating the power of artificial intelligence and Machine Learning (ML). Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats. In this paper, we propose a novel Federated Learning (FL) approach that protects the anonymity of user data and maintains the security of his data. We present a warmup approach that provides a significant accuracy increase. In addition, we leverage the transfer learning technique based on feature extraction to boost the models' performance. Our extensive experiments based on four datasets: MNIST, FEMNIST, CIFAR-10 and UMDAA-02-FD, show a significant increase in user authentication accuracy while maintaining user privacy and data security.

Whether or not your organisation suffers a cyber attack has long been considered a case of'when, not if', with cyber attacks having a huge impact on organisations. In 2018, 2.8 billion consumer data records were exposed in 342 breaches, ranging from credential stuffing to ransomware, at an estimated cost of more than $654bn. In 2019, this had increased to an exposure of 4.1 billion records. While the use of artificial intelligence (AI) and machine learning as a primary offensive tool in cyber attacks is not yet mainstream, its use and capabilities are growing and becoming more sophisticated. In time, cyber criminals will, inevitably, take advantage of AI, and such a move will increase threats to digital security and increase the volume and sophistication of cyber attacks.

Accenture Labs Dublin is looking for a Post-Doctoral researcher in the domain of Graph Representation Learning and Explainable AI. You will be in charge of designing interpretable machine learning models to infer knowledge from a graph of clinical, genomic, and behavioural data. Explanations will use a wide range of techniques, such as rules derived from the deep learning models, gradient-based attribution methods, or graph-based explanations based on network analysis. The length of the PostDoc is 3 years. You will join a multi-partner project whose goal is identifying factors that can cause development of new medical conditions, and worsen the quality of life of cancer survivors.

Segmentation, Targeting and Positioning (STP) is a common strategic model in today's marketing approach. It reflects the increasing popularity of customer centric marketing strategies over product differentiation strategies. The audience focused approach in marketing e.g. STP therefore goes hand in hand with marketing personas. The popularity of segmentation in the strategy derives on the one hand from past limitations of CRM and ad-tech systems as well as a dependency on human decision making in the STP process on the other hand.

And the problem with living through a revolution is that it's impossible to take the long view of what's happening. Hindsight is the only exact science in this business, and in that long run we're all dead. Printing shaped and transformed societies over the next four centuries, but nobody in Mainz (Gutenberg's home town) in, say, 1495 could have known that his technology would (among other things): fuel the Reformation and undermine the authority of the mighty Catholic church; enable the rise of what we now recognise as modern science; create unheard-of professions and industries; change the shape of our brains; and even recalibrate our conceptions of childhood. And yet printing did all this and more. Because we're about the same distance into our revolution, the one kicked off by digital technology and networking. And although it's now gradually dawning on us that this really is a big deal and that epochal social and economic changes are under way, we're as clueless about where it's heading and what's driving it as the citizens of Mainz were in 1495. That's not for want of trying, mind. Library shelves groan under the weight of books about what digital technology is doing to us and our world.

And the problem with living through a revolution is that it's impossible to take the long view of what's happening. Hindsight is the only exact science in this business, and in that long run we're all dead. Printing shaped and transformed societies over the next four centuries, but nobody in Mainz (Gutenberg's home town) in, say, 1495 could have known that his technology would (among other things): fuel the Reformation and undermine the authority of the mighty Catholic church; enable the rise of what we now recognise as modern science; create unheard-of professions and industries; change the shape of our brains; and even recalibrate our conceptions of childhood. And yet printing did all this and more. Because we're about the same distance into our revolution, the one kicked off by digital technology and networking. And although it's now gradually dawning on us that this really is a big deal and that epochal social and economic changes are under way, we're as clueless about where it's heading and what's driving it as the citizens of Mainz were in 1495. That's not for want of trying, mind. Library shelves groan under the weight of books about what digital technology is doing to us and our world.

In an increasingly fluid retail marketplace, where purchasing continues to shift from the physical to the digital, companies both big and small must look for new ways to differentiate their brands, build loyalty and keep their customers coming back for more. Whether through personalisation, same-day delivery, or digital payment solutions such as Apple Pay, retailers are turning to new technologies as the source of this differentiation. At the forefront of this trend is a focus on Customer Experience, with brands increasingly realising that it can be just as beneficial to change the way consumers experience a brand as it can be to change a product itself. Unfortunately for retailers, however, many of the experiential factors that they must use to differentiate their brands are extremely subtle. As a result, it can be difficult to identify those areas of the customer's experience that are points of frustration or cause for cart abandonment.

Artificial Intelligence (AI) is becoming one of the most important business disruptors of our times. As we move from digitalisation to the digital era, it is critical to change mindsets and move away from a focus on threats to a focus on opportunities offered by cognitive technologies. HR teams need to play an active role in embracing this new technology and turn it to their advantage. At the heart of artificial intelligence is the use of data to analyse patterns, and in particular patterns of behaviours. At Walking the Talk, we define culture as "the patterns of behaviour that are encouraged, discouraged, and tolerated by people and systems, over time."