The emulation task of a nonlinear autoregressive moving average model, i.e., the NARMA10 task, has been widely used as a benchmark task for recurrent neural networks, especially in reservoir computing. However, the type and quantity of computational capabilities required to emulate the NARMA10 model remain unclear, and, to date, the NARMA10 task has been utilized blindly. Therefore, in this study, we have investigated the properties of the NARMA10 model from a dynamical system perspective. We revealed its bifurcation structure and basin of attraction, as well as the system's Lyapunov spectra. Furthermore, we have analyzed the computational capabilities required to emulate the NARMA10 model by decomposing it into multiple combinations of orthogonal nonlinear polynomials using Legendre polynomials, and we directly evaluated its information processing capacity together with its dependences on some system parameters. The result demonstrates that the NARMA10 model contains an unstable region in the phase space that makes the system diverge according to the selection of the input range and initial conditions. Furthermore, the information processing capacity of the model varies according to the input range. These properties prevent safe application of this model and fair comparisons among experiments, which are unfavorable for a benchmark task. As a result, we propose a benchmark model that can clearly evaluate equivalent computational capacity using NARMA10. Compared to the original NARMA10 model, the proposed model is highly stable and robust against the input range settings.

Swarm-based optimizers like Particle Swarm Optimization or Imperialistic Competitive Algorithm that act under influences of cooperation or competition among groups, are unable to search in multiple volumes of locality or globality and do not have nested localities. As hybrid optimizers, they may not give satisfactory results as initializers in Gradient Descent approximators used in plenty of multimodal problems like nonlinear subspace learning and neural network training, which have hierarchies of convex spaces due to nonlinearity and multi-layer nature of these models. To search in various levels of scale in a homogenous way, a framework is proposed to equip PSO and ICA a multi-scale search capability. Then, the resulted optimizers are evaluated in single and GD-hybridized mode. Hybrid evaluation as GD randomizer is implemented with the help of a nonlinear subspace filtering objective function over EEG data and optimization loss and validation data accuracy is compared with other hybrids containing GD. A single evaluation is also taken place between the proposed ones, PSO, ICA, CLPSO, and CICA, which are used more in hybrid learning-based approaches. Evaluations were with respect to solution error. Before concluding the paper, it is shown and analyzed that proposed optimizers outperform algorithms of related context both in single and hybrid-GD mode.

In this study, we consider an empirical Bayes method for Boltzmann machines and propose an algorithm for it. The empirical Bayes method allows estimation of the values of the hyperparameters of the Boltzmann machine by maximizing a specific likelihood function referred to as the empirical Bayes likelihood function in this study. However, the maximization is computationally hard because the empirical Bayes likelihood function involves intractable integrations of the partition function. The proposed algorithm avoids this computational problem by using the replica method and the Plefka expansion. Our method does not require any iterative procedures and is quite simple and fast, though it introduces a bias to the estimate, which exhibits an unnatural behavior with respect to the size of the dataset. This peculiar behavior is supposed to be due to the approximate treatment by the Plefka expansion. A possible extension to overcome this behavior is also discussed.

In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space. Our NAS framework searches the structure of each layer including neural connectivities and operation types in both of the encoder and decoder. Since optimizing over a large discrete architecture space is difficult due to high-resolution 3D medical images, a novel stochastic sampling algorithm based on a continuous relaxation is also proposed for scalable gradient based optimization. On the 3D medical image segmentation tasks with a benchmark dataset, an automatically designed architecture by the proposed NAS framework outperforms the human-designed 3D U-Net, and moreover this optimized architecture is well suited to be transferred for different tasks.

According to media reports, China and the US are leading the way in adopting AI, with the former's percentage of AI patents granted growing by 190 per cent in a five-year period and the latter investing close to $10 billion in venture capital. Not to be left behind by the other two superpowers though, Russia's president has announced his intentions to make 30 per cent of its military equipment robotic by 2025. The compound annual growth rate of AI has hit 60 per cent and is still growing. The UK is itself funding over £603 million investment in AI and one industry here that stands to benefit from AI is field and customer service. Field service organisations increasingly feel the pressure to maximise the productivity and efficiency of their workforce so they get every job right on the first try, keeping customers happy and reducing costs through increased productivity and SLA compliance.

How will emerging autonomous and intelligent systems affect the international landscape of power and coercion two decades from now? Will the world see a new set of artificial intelligence (AI) hegemons just as it saw a handful of nuclear powers for most of the twentieth century? Will autonomous weapon systems make conflict more likely or will states find ways to control proliferation and build deterrence, as they have done (fitfully) with nuclear weapons? And importantly, will multilateral forums find ways to engage the technology holders, states as well as industry, in norm setting and other forms of controlling the competition? The answers to these questions lie not only in the scope and spread of military applications of AI technologies but also in how pervasive their civilian applications will be.

The Guardian is right to oppose automated facial recognition (Editorial, 10 June), but in spite of concerns from MPs and peers, parliament has hardly discussed the issue. As the person who organised the only debate on the subject in the House of Lords, I have started legal action against the police for failing to have a regulatory framework for its use. It's shocking that we are allowing the police and companies to set the rules as we abolish privacy in public spaces, because ministers are failing to act. The difference between ourselves and the likes of China and Russia is that we have a fairly open democracy, but this is no defence against state oppression and commercial exploitation if politicians fail to recognise that when our face becomes an identity card, all the rules change. Click here to upload it and we'll publish the best submissions in the letters spread of our print edition

In the following, I share the key learnings of the webinar. AI is not a secret sauce and requires lots of good data to create real value. Companies need to first separate the hype from the actual capabilities of AI, defining what AI means for them and how it might create value. Moving an entire company towards the adoption of AI is a challenging task and needs lots of educational effort. AI is not the solution to all problems. Building products do not start with thinking about AI but finding a meaningful problem that once solved adds value for the customer or user.

Every field has people working behind the scenes,

When Hima Patel was in her third year of computer science engineering at Sardar Patel University in Gujarat, a professor of hers gave her a book on artificial neural networks. "I was a good student. The book was not part of our curriculum, but the professor thought I might like it," recollects Hima. It took time to fully understand the book. But at the end of it, she loved it.