Having recently announced the launch of the new UK Cyber Security Council, the UK government has followed up by announcing its plans to publish a new National Artificial Intelligence Strategy (the AI Strategy) later this year. The aim of the AI Strategy is to build on the United Kingdom's position as a global center for the development, commercialization, and adoption of responsible AI. The AI Strategy will focus on the following: Growth of the economy through the widespread use of AI technologies. The intention is for the AI Strategy to align with the UK government's overall plans to support jobs and economic growth through increased investment in infrastructure, skills, and innovation. Through this strategy, they will nurture their AI pioneers to accelerate bringing new technologies to market.

The US military needs to scale up its use of AI or be left behind by adversaries, Lt. Gen. Michael Groen, chief of the Pentagon's Joint AI Center (JAIC), told a recent conference of the National Defense Industrial Association, according to a report from UPI. While current military use of AI "is a step in the right direction, we need to start building on it," stated Groen, who was appointed head of the JAIC in October. He is the second director of JAIC, or "the jake" in Pentagon parlance, which was set up by Congress in 2018. The first director was Air Force Lt. Gen. John N.T. "Jack" Shanahan, who retired last year. Noting that China has said it intends "to be dominant in AI by 2030," the Pentagon has focused on a five-year program culminating in 2027.

WASHINGTON (Reuters) - U.S. banking regulators announced on Monday they were soliciting public input on the growing use of artificial intelligence by financial institutions. In a joint statement, the regulators said they wanted feedback on the use of the technology by banks to police fraud, underwrite loans and for other purposes, and what perks and challenges it presents. The query was not connected to any specific regulatory project, but rather regulators said they were soliciting public comment to identify any areas where it may be helpful for agencies to clarify existing rules to address the use of AI. "The agencies support responsible innovation by financial institutions," the regulators said in the solicitation. "With appropriate governance, risk management, and compliance management, financial institutions' use of innovative technologies and techniques, such as those involving AI, has the potential to augment business decision-making, and enhance services available to consumers and businesses." The query from the Federal Reserve, Consumer Financial Protection Bureau, and other federal financial regulators underscores the growing prevalence of AI in the financial sector, what it could mean for lenders and borrowers alike.

Think of artificial intelligence (AI), deep learning (DL) and machine learning (ML) as the layers of an onion. Think about machine learning as a part of AI, but AI does not always utilize machine learning methods. Machine learning refers to an algorithm that can create abstractions (models) by training on a dataset and is a method of training an algorithm to accomplish a task. Rapid advances in big data, data analytics, and machine learning are used to convert millions of scattered data points into databases for use in various cybersecurity arenas, such as threat intelligence analysis. Therefore, businesses seeking to leverage machine learning enabled technology need to threat model and perform risk assessments when creating machine learning systems for cybersecurity purposes. Therefore, businesses seeking to leverage machine learning enabled technology need to threat model and perform risk assessments when creating machine learning systems for cybersecurity purposes.

Between 2015 and 2020 people applying for visas to enter the United Kingdom to work, study or visit loved ones would fill in the paperwork in the usual way, and that data would then be handed over to an algorithm to assess. It would give them a rating: red, amber or green. Of those being assessed as green 96.3 per cent were waved through. Those marked as red – the'riskiest' category – weren't automatically rejected, but were subject to further checks, with senior staff being brought in to check the data and make a final decision. This partially automated process, run by the Home Office, ultimately approved 48 per cent of red applications. Those using it trusted its decisions.

Automatic modulation classification can be a core component for intelligent spectrally efficient wireless communication networks, and deep learning techniques have recently been shown to deliver superior performance to conventional model-based strategies, particularly when distinguishing between a large number of modulation types. However, such deep learning techniques have also been recently shown to be vulnerable to gradient-based adversarial attacks that rely on subtle input perturbations, which would be particularly feasible in a wireless setting via jamming. One such potent attack is the one known as the Carlini-Wagner attack, which we consider in this work. We further consider a data-driven subsampling setting, where several recently introduced deep-learning-based algorithms are employed to select a subset of samples that lead to reducing the final classifier's training time with minimal loss in accuracy. In this setting, the attacker has to make an assumption about the employed subsampling strategy, in order to calculate the loss gradient. Based on state of the art techniques available to both the attacker and defender, we evaluate best strategies under various assumptions on the knowledge of the other party's strategy. Interestingly, in presence of knowledgeable attackers, we identify computational cost reduction opportunities for the defender with no or minimal loss in performance.

In this paper, we propose a framework for predicting frame errors in the collaborative spectrally congested wireless environments of the DARPA Spectrum Collaboration Challenge (SC2) via a recently collected dataset. We employ distributed deep edge learning that is shared among edge nodes and a central cloud. Using this close-to-practice dataset, we find that widely used federated learning approaches, specially those that are privacy preserving, are worse than local training for a wide range of settings. We hence utilize the synthetic minority oversampling technique to maintain privacy via avoiding the transfer of local data to the cloud, and utilize knowledge distillation with an aim to benefit from high cloud computing and storage capabilities. The proposed framework achieves overall better performance than both local and federated training approaches, while being robust against catastrophic failures as well as challenging channel conditions that result in high frame error rates.

In a world abundant with diverse data arising from complex acquisition techniques, there is a growing need for new data analysis methods. In this paper we focus on high-dimensional data that are organized into several hierarchical datasets. We assume that each dataset consists of complex samples, and every sample has a distinct irregular structure modeled by a graph. The main novelty in this work lies in the combination of two complementing powerful data-analytic approaches: topological data analysis (TDA) and geometric manifold learning. Geometry primarily contains local information, while topology inherently provides global descriptors. Based on this combination, we present a method for building an informative representation of hierarchical datasets. At the finer (sample) level, we devise a new metric between samples based on manifold learning that facilitates quantitative structural analysis. At the coarser (dataset) level, we employ TDA to extract qualitative structural information from the datasets. We showcase the applicability and advantages of our method on simulated data and on a corpus of hyper-spectral images. We show that an ensemble of hyper-spectral images exhibits a hierarchical structure that fits well the considered setting. In addition, we show that our new method gives rise to superior classification results compared to state-of-the-art methods.

Gradient-based adversarial attacks on deep neural networks pose a serious threat, since they can be deployed by adding imperceptible perturbations to the test data of any network, and the risk they introduce cannot be assessed through the network's original training performance. Denoising and dimensionality reduction are two distinct methods that have been independently investigated to combat such attacks. While denoising offers the ability to tailor the defense to the specific nature of the attack, dimensionality reduction offers the advantage of potentially removing previously unseen perturbations, along with reducing the training time of the network being defended. We propose strategies to combine the advantages of these two defense mechanisms. First, we propose the cascaded defense, which involves denoising followed by dimensionality reduction. To reduce the training time of the defense for a small trade-off in performance, we propose the hidden layer defense, which involves feeding the output of the encoder of a denoising autoencoder into the network. Further, we discuss how adaptive attacks against these defenses could become significantly weak when an alternative defense is used, or when no defense is used. In this light, we propose a new metric to evaluate a defense which measures the sensitivity of the adaptive attack to modifications in the defense. Finally, we present a guideline for building an ordered repertoire of defenses, a.k.a. a defense infrastructure, that adjusts to limited computational resources in presence of uncertainty about the attack strategy.

Neural networks are known for their ability to detect general patterns in noisy data. This makes them a popular tool for perception components in complex AI systems. Paradoxically, they are also known for being vulnerable to adversarial attacks. In response, various methods such as adversarial training, data-augmentation and Lipschitz robustness training have been proposed as means of improving their robustness. However, as this paper explores, these training methods each optimise for a different definition of robustness. We perform an in-depth comparison of these different definitions, including their relationship, assumptions, interpretability and verifiability after training. We also look at constraint-driven training, a general approach designed to encode arbitrary constraints, and show that not all of these definitions are directly encodable. Finally we perform experiments to compare the applicability and efficacy of the training methods at ensuring the network obeys these different definitions. These results highlight that even the encoding of such a simple piece of knowledge such as robustness in neural network training is fraught with difficult choices and pitfalls.