With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to billions of mobile and smart sensing devices connected to the Internet, generating zettabytes of data at the network edge. The opportunity to combine these two domains of technologies to power interconnected devices with intelligence is likely to pave the way for a new wave of technology revolutions. Embracing this technology revolution, in this article, we present a novel computing vision named Deep Edge Intelligence (DEI). DEI employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc. aiming to provision reliable and secure intelligence services to every person and organisation at any place with better user experience. The vision, system architecture, key layers and features of DEI are also detailed. Finally, we reveal the key enabling technologies and research challenges associated with it.

We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user. CABINET supports law students in their learning as well as professionals in their work. The results of our experiments focused on the feasibility of the proposed framework are promising. We show that the system is capable of identifying a potential error in the analysis with very low false positives rate (2.0-3.5%), as well as of predicting the key argument element type (e.g., an issue or a holding) with a reasonably high F1-score (0.74).

Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s). The advancements in different technologies like augmented reality, virtual reality, extended reality (XR), artificial intelligence (AI), and 5G/6G communication will be the key enablers behind the realization of AI-XR metaverse applications. While AI itself has many potential applications in the aforementioned technologies (e.g., avatar generation, network optimization, etc.), ensuring the security of AI in critical applications like AI-XR metaverse applications is profoundly crucial to avoid undesirable actions that could undermine users' privacy and safety, consequently putting their lives in danger. To this end, we attempt to analyze the security, privacy, and trustworthiness aspects associated with the use of various AI techniques in AI-XR metaverse applications. Specifically, we discuss numerous such challenges and present a taxonomy of potential solutions that could be leveraged to develop secure, private, robust, and trustworthy AI-XR applications. To highlight the real implications of AI-associated adversarial threats, we designed a metaverse-specific case study and analyzed it through the adversarial lens. Finally, we elaborate upon various open issues that require further research interest from the community.

In the era of billion-parameter-sized Language Models (LMs), start-ups have to follow trends and adapt their technology accordingly. Nonetheless, there are open challenges since the development and deployment of large models comes with a need for high computational resources and has economical consequences. In this work, we follow the steps of the R&D group of a modern legal-tech start-up and present important insights on model development and deployment. We start from ground zero by pre-training multiple domain-specific multi-lingual LMs which are a better fit to contractual and regulatory text compared to the available alternatives (XLM-R). We present benchmark results of such models in a half-public half-private legal benchmark comprising 5 downstream tasks showing the impact of larger model size. Lastly, we examine the impact of a full-scale pipeline for model compression which includes: a) Parameter Pruning, b) Knowledge Distillation, and c) Quantization: The resulting models are much more efficient without sacrificing performance at large.

How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.

AI and the law is a vital upcoming profitable opportunity for lawyers, law firms, and law students. The gauntlet had been thrown. You see, I was the invited keynote speaker at a major legal industry conference and my heralded topic was squarely in my wheelhouse, namely Artificial Intelligence (AI) and the law (typically coined as AI & Law). Rather than being entirely heralded, maybe the more apt phrasing is to say that the topic was met with a mixture of excitement by some and outright eyebrow-raising skepticism by others. The assembled collection of several hundred law firm partners and associates murmured and questioned subtly whether anything about AI and the law especially needed to be known by them. AI was generally perceived as a pie-in-the-sky topic. On top of that contention, AI when combined with the law was equally or even further at the outreaches of what daily hard-working nose-to-the-grind lawyers would seem to be thinking about. I'm pleased to say that my remarks were well-taken and the response was quite positive, including that this was the first time many of them had ever heard a no-nonsense compelling and comprehensive case made for why lawyers ought to know about AI and the law. The discussion got those top-notch legal-minded gears going and the attendees had plenty to ruminate on. Let's see if the same can be said for those of you that might be interested or at least intrigued by the AI & Law topic. First, a vital facet to know is that AI & Law consists of two intertwined conceptions. I want to emphatically make clear-cut that these are both bona fide and rapidly expanding ways in which AI and the law are being combined. Many attorneys are only familiar with one or the other of the two perspectives, or oftentimes not familiar with either of the two. Depending upon your lawyering preferences, it is perfectly fine to concentrate on one of the two and not particularly focus on the other. By and large, lawyers that seem less inclined toward having an interest in technology are bound to keep their eye on the law as applied to AI, wherein you don't necessarily need to get your hands into the tech per se. Those lawyers that seem to relish the high-tech infusion into the legal realm are more apt to gravitate toward the realm of AI as applied to the law. You are welcome to embrace both aspects and do so with your head held high. I'll first herein do some meaty unpacking on the law as applied to AI. When referring to the law as applied to AI, you should immediately be thinking about the emerging litany of new laws seeking to govern the advent of AI systems. Laws are springing up like wildfire. International laws are coming forth about AI & Law, federal laws too, state laws also, and local laws aplenty, see my ongoing coverage at the link here and the link here, just to name a few.

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It encompasses many points of view and can withstand just as many or more definitions. As a term, it's ever-evolving, and the boundaries for what can be deemed art continue to get pushed. Artificial intelligence is not generally associated with art, and yet, AI has made its mark on the art industry. The question is, would that endure, or is AI art a fluke? Will AI carve itself a space in art, or will it be quickly forgotten as a failed experiment?

Predictive coding has emerged as a prominent model of how the brain learns through predictions, anticipating the importance accorded to predictive learning in recent AI architectures such as transformers. Here we propose a new framework for predictive coding called active predictive coding which can learn hierarchical world models and solve two radically different open problems in AI: (1) how do we learn compositional representations, e.g., part-whole hierarchies, for equivariant vision? and (2) how do we solve large-scale planning problems, which are hard for traditional reinforcement learning, by composing complex action sequences from primitive policies? Our approach exploits hypernetworks, self-supervised learning and reinforcement learning to learn hierarchical world models that combine task-invariant state transition networks and task-dependent policy networks at multiple abstraction levels. We demonstrate the viability of our approach on a variety of vision datasets (MNIST, FashionMNIST, Omniglot) as well as on a scalable hierarchical planning problem. Our results represent, to our knowledge, the first demonstration of a unified solution to the part-whole learning problem posed by Hinton, the nested reference frames problem posed by Hawkins, and the integrated state-action hierarchy learning problem in reinforcement learning.

Recent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets. However, a considerable amount of work has raised concerns due to the potential risks of adversaries participating in the framework to poison the global model for an adversarial purpose. This paper investigates the feasibility of model poisoning for backdoor attacks through rare word embeddings of NLP models. In text classification, less than 1% of adversary clients suffices to manipulate the model output without any drop in the performance on clean sentences. For a less complex dataset, a mere 0.1% of adversary clients is enough to poison the global model effectively. We also propose a technique specialized in the federated learning scheme called Gradient Ensemble, which enhances the backdoor performance in all our experimental settings.