Compressing neural network architectures is important to allow the deployment of models to embedded or mobile devices, and pruning and quantization are the major approaches to compress neural networks nowadays. Both methods benefit when compression parameters are selected specifically for each layer. Finding good combinations of compression parameters, so-called compression policies, is hard as the problem spans an exponentially large search space. Effective compression policies consider the influence of the specific hardware architecture on the used compression methods. We propose an algorithmic framework called Galen to search such policies using reinforcement learning utilizing pruning and quantization, thus providing automatic compression for neural networks. Contrary to other approaches we use inference latency measured on the target hardware device as an optimization goal. With that, the framework supports the compression of models specific to a given hardware target. We validate our approach using three different reinforcement learning agents for pruning, quantization and joint pruning and quantization. Besides proving the functionality of our approach we were able to compress a ResNet18 for CIFAR-10, on an embedded ARM processor, to 20% of the original inference latency without significant loss of accuracy. Moreover, we can demonstrate that a joint search and compression using pruning and quantization is superior to an individual search for policies using a single compression method.

The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the realm of affective neuroscience including complex decision making processes and the eternal free-will dilemma and facilitates diagnostics of a range of neurological deceases. So far this was possible only using bulky, expensive and immobile fMRI equipment. Here we present an interpretable domain grounded solution to recover the activity of several subcortical regions from the multichannel EEG data and demonstrate up to 60 % correlation between the actual subcortical blood oxygenation level dependent (sBOLD) signal and its EEG-derived twin. Then, using the novel and theoretically justified weight interpretation methodology we recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei. The described results not only pave the road towards wearable subcortical activity scanners but also showcase an automatic knowledge discovery process facilitated by deep learning technology in combination with an interpretable domain constrained architecture and the appropriate downstream task.

A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.

We propose an agent-based opinion formation model characterised by a two-fold novelty. First, we realistically assume that each agent cannot measure the opinion of its neighbours with infinite resolution and accuracy, and hence it can only classify the opinion of others as agreeing much more, or more, or comparably, or less, or much less (than itself) with a given statement. This leads to a classification-based rule for opinion update. Second, we consider three complementary agent traits suggested by significant sociological and psychological research: conformism, radicalism and stubbornness. We rely on World Values Survey data to show that the proposed model has the potential to predict the evolution of opinions in real life: the classification-based approach and complementary agent traits produce rich collective behaviours, such as polarisation, consensus, and clustering, which can yield predicted opinions similar to survey results.

We introduce and analyze Structured Stochastic Zeroth order Descent (S-SZD), a finite difference approach which approximates a stochastic gradient on a set of $l\leq d$ orthogonal directions, where $d$ is the dimension of the ambient space. These directions are randomly chosen, and may change at each step. For smooth convex functions we prove almost sure convergence of the iterates and a convergence rate on the function values of the form $O(d/l k^{-c})$ for every $c<1/2$, which is arbitrarily close to the one of Stochastic Gradient Descent (SGD) in terms of number of iterations. Our bound also shows the benefits of using $l$ multiple directions instead of one. For non-convex functions satisfying the Polyak-{\L}ojasiewicz condition, we establish the first convergence rates for stochastic zeroth order algorithms under such an assumption. We corroborate our theoretical findings in numerical simulations where assumptions are satisfied and on the real-world problem of hyper-parameter optimization, observing that S-SZD has very good practical performances.

The Kremlin has said Patriot missile defence systems would be a legitimate target if sent to Ukraine to intercept the barrage of incoming Russian missiles that have crippled the war-torn country's power infrastructure. Former Russian President Dmitry Medvedev on Wednesday warned NATO against equipping Kyiv with Patriot missile batteries. It is likely the Kremlin will view the move as an escalation. The comments come as Moscow said no "Christmas ceasefire" was on the cards after nearly 10 months of the war in Ukraine, even as the release of dozens more prisoners, including a United States national, showed some contact between the two sides remained. Russia and Ukraine are not currently engaged in talks to end the fighting, which is raging in Ukraine's east and south while Moscow has carried out missile and drone strikes on power and water facilities across the country, including the capital city Kyiv.

MIT Sloan Management Review and BCG have assembled an international panel of AI experts that includes academics and practitioners to help us gain insights into how responsible artificial intelligence (RAI) is being implemented in organizations worldwide. This month's question for our panelists: Should RAI be a top management agenda item at organizations across industries and geographies?1 Eighty-six percent of them (18 out of 21) agree or strongly agree that it should be. In aggregate, their replies offer a compelling rationale for top management to oversee RAI efforts. We distill and explain this rationale below. We also conducted a global survey of more than 1,000 executives that generated similar findings: Eighty-two percent of managers in companies with at least $100 million in annual revenues agree or strongly agree that RAI should be part of their company's top management agenda.

The world is making rapid progress in the areas of Big Data, Artificial Intelligence and Machine Learning. These are the core drivers of what many analysts have come to refer to as the Fourth Industrial Revolution, epitomized by the increased whittling away of the boundaries that hitherto existed between the physical, digital and biological worlds. There is a clear imperative for pharmacists, pharmaceutical scientists and medical professionals in the field of research and development in developing countries like Nigeria, to increasingly tap into this world of big data, artificial intelligence and machine learning and partake of the revolution that is happening before our very eyes. And the reason is simple. Artificial intelligence is helping to make pharmaceutical research and new drug discovery less expensive and definitely more productive.

The second day of the World Cup 2022 semifinals will pit two-time champions and holders France against Morocco. Kashef, our artificial intelligence (AI) robot, has analysed more than 200 metrics, including the number of wins, goals scored and FIFA rankings, from matches played over the past century to see who is most likely to win on Wednesday. Morocco are currently unbeaten in this tournament and have stunned the footballing world – not once or twice but three times – by beating Belgium, Spain and Portugal. On every occasion, Kashef was left stunned. The only remaining Arab and African team are now just two wins away from lifting the World Cup trophy.

Methods of pattern recognition and machine learning are applied extensively in science, technology, and society. Hence, any advances in related theory may translate into large-scale impact. Here we explore how algorithmic information theory, especially algorithmic probability, may aid in a machine learning task. We study a multiclass supervised classification problem, namely learning the RNA molecule sequence-to-shape map, where the different possible shapes are taken to be the classes. The primary motivation for this work is a proof of concept example, where a concrete, well-motivated machine learning task can be aided by approximations to algorithmic probability. Our approach is based on directly estimating the class (i.e., shape) probabilities from shape complexities, and using the estimated probabilities as a prior in a Gaussian process learning problem. Naturally, with a large amount of training data, the prior has no significant influence on classification accuracy, but in the very small training data regime, we show that using the prior can substantially improve classification accuracy. To our knowledge, this work is one of the first to demonstrate how algorithmic probability can aid in a concrete, real-world, machine learning problem.