On 18 September the European Commission published a report on the Ethics of Connected and Automated Vehicles (CAVs). Written by an independent group of experts, the report includes twenty recommendations on road safety, privacy, fairness, AI explainability and responsibility, for the development and deployment of connected and automated vehicles. The recommendations have been made actionable for three stakeholder groups: 1. The aim of the report is to "promote a safe and responsible transition to connected and automated vehicles (CAVs) by supporting stakeholders in the systematic inclusion of ethical considerations in the development and regulation of CAVs". The report recognises the potential of CAV technology to deliver benefits, such as reduced fatalities and emissions, but also recognises that technological progress alone is not sufficient to realise this potential.

Vision is the biggest gift given to humans. As we continue to struggle towards making technology more and more like us, this is one thing we need to put the most effort into. Machines are now easily able to capture images, but recognizing the surrounding environment and objects cannot be done if they don't let how to interpret the information that lies in them. That's why Computer Vision is important if we want to make humans truly intelligent. Let's see what it is and how it is making different fields better.

It has now been over three decades since the Morris Worm infected an estimated 10% of the 60,000 computers that were online in 1988. It was the personal malware project of a Harvard graduate named Robert Tappan Morris, and is now widely deemed to be the world's first cyber-attack. Fast forward to today, and cyber attacks now stand among natural disasters and climate change in the World Economic Forum's annual list of global society's gravest threats. As businesses, schools, hospitals, and pretty much every other thread in the fabric of society have embraced the internet, cyber crime has transformed from an academic research project into a global marketplace of professional hacking services, and on the geopolitical stage, governments have turned to hyper-advanced cyber attack tools as a means of causing physical damage and disruption to their adversaries' critical infrastructure. The National Cyber Security Centre (NCSC) has detected a rise in cyber attacks targeting academic institutions, including schools and universities.

While the world marvels at the massive technological advancements at play in a variety of fields, there's a particular niche that is growing rather concerned. Despite all the promises and brighter outlooks promised by technology, there is an increased threat to cybersecurity that experts are trying to wrap their heads around to plan a solution for. There was a need to introduce a new player into the world of Artificial Intelligence(AI) in cybersecurity as it showcased the understanding of cyber-attacks. Organizations through advanced tools will be able to not only track but also provide effective responses to security incidents using the latest technologies. A massive improvement might be the introduction of next-generation firewalls with learning technology that can find patterns from network packets and block flagged threats.

Disinformation is when someone knows the truth but wants us to believe otherwise. Better known as "lying," disinformation is rife in election campaigns. However, under the guise of "fake news," it's rarely been as pervasive and toxic as it's become in this year's US presidential campaign. Sadly, artificial intelligence has been accelerating the spread of deception to a shocking degree in our political culture. AI-generated deepfake media are the least of it.

We present a new method for score-based adversarial attack, where the attacker queries the loss-oracle of the target model. Our method employs a parameterized search space with a structure that captures the relationship of the gradient of the loss function. We show that searching over the structured space can be approximated by a time-varying contextual bandits problem, where the attacker takes feature of the associated arm to make modifications of the input, and receives an immediate reward as the reduction of the loss function. The time-varying contextual bandits problem can then be solved by a Bayesian optimization procedure, which can take advantage of the features of the structured action space. The experiments on ImageNet and the Google Cloud Vision API demonstrate that the proposed method achieves the state of the art success rates and query efficiencies for both undefended and defended models. Although deep learning has many applications, it is known that neural networks are vulnerable to adversarial examples, which are small perturbations of inputs that can fool neural networks into making wrong predictions (Szegedy et al., 2014). While adversarial noise can easily be found when the neural models are known (referred to as white-box attack) (Kurakin et al., 2016). However, in real world scenarios models are often unknown, this situation is referred to as black-box attack.

The decision boundary of a learning algorithm applied to a given task can be viewed as the outcome of a random process: (i) generate a training set and, (ii) apply to it the, potentially randomized, learning algorithm. Recall, see Definitions 4 and 5, that a query-bounded adversary does not know the sample on which the model was trained nor the randomness used by the learner. This means that if the decision boundary has high entropy then the adversary needs to ask many questions to recover the boundary to a high degree of precision. This suggest that high-entropy decision boundaries are robust against query-bounded adversaries since intuitively it is clear that an approximate knowledge of the decision boundary is a prerequisite for a successful attack. Following this reasoning, we present two instances where high entropy of the decision boundary leads to security.

Loss-function metalearning can be used to discover novel, customized loss functions for deep neural networks, resulting in improved performance, faster training, and improved data utilization. A likely explanation is that such functions discourage overfitting, leading to effective regularization. This paper theoretically demonstrates that this is indeed the case: decomposition of learning rules makes it possible to characterize the training dynamics and show that loss functions evolved through TaylorGLO regularize both in the beginning and end of learning, and maintain an invariant in between. The invariant can be utilized to make the metalearning process more efficient in practice, and the regularization can train networks that are robust against adversarial attacks. Loss-function optimization can thus be seen as a well-founded new aspect of metalearning in neural networks.

An expanding field of substantive interest for the theory of the law and the practice-of-law entails Legal Sentiment Analysis and Opinion Mining (LSAOM), consisting of two often intertwined phenomena and actions underlying legal discussions and narratives: (1) Sentiment Analysis (SA) for the detection of expressed or implied sentiment about a legal matter within the context of a legal milieu, and (2) Opinion Mining (OM) for the identification and illumination of explicit or implicit opinion accompaniments immersed within legal discourse. Efforts to undertake LSAOM have historically been performed by human hand and cognition, and only thinly aided in more recent times by the use of computer-based approaches. Advances in Artificial Intelligence (AI) involving especially Natural Language Processing (NLP) and Machine Learning (ML) are increasingly bolstering how automation can systematically perform either or both of Sentiment Analysis and Opinion Mining, all of which is being inexorably carried over into engagement within a legal context for improving LSAOM capabilities. This research paper examines the evolving infusion of AI into Legal Sentiment Analysis and Opinion Mining and proposes an alignment with the Levels of Autonomy (LoA) of AI Legal Reasoning (AILR), plus provides additional insights regarding AI LSAOM in its mechanizations and potential impact to the study of law and the practicing of law.

Over the last fifty years, a large body of research in microeconomics has introduced many different mechanisms for resource allocation. Despite the wide variety of available options, "simple" mechanisms such as posted price and serial dictatorship are often preferred for practical applications, including housing allocation [Abdulkadiroğlu and Sönmez, 1998], online procurement [Badanidiyuru et al., 2012], or allocation of medical appointments [Klaus and Nichifor, 2019]. There has been considerable interest in formalizing different notions of simplicity. Li [2017] identifies mechanisms that are particularly simple from a strategic perspective, introducing the concept of obviously strategyproof mechanisms; under obviously strategyproof mechanisms, it is obvious that an agent cannot profit by trying to game the system, as even the worst possible final outcome from behaving truthfully is at least as good as the best possible outcome from any other strategy. Pycia and Troyan [2019] introduce the still stronger concept of strongly obviously strategyproof (SOSP) mechanisms, and show that this class can essentially be identified with sequential price mechanisms, where agents are visited in turn and offered a choice from a menu of options (which may or may not include transfers). SOSP mechanisms are ones in which an agent is not even required to consider her future (truthful) actions to understand that the mechanism is obviously strategyproof.