Consciousness continues to be of one of the most important, interesting and complex question to focus upon. While the study of consciousness has a long and rich history in the field of philosophy, the scientific study of consciousness has become less taboo recently, and made tremendous progress in the field over the last couple of decades, due to significant contributions from disciplines like neuroscience, cognitive science and computer science. Though research interests have continued to grow, fueled by the recent artificial intelligence/machine learning (AI/ML) revolution (reigniting questions around artificial consciousness), the topic of consciousness itself has generally been ignored or dismissed by a majority of those who work in mainstream AI as either an unimportant factor for their research goals or accusing work in (artificial) consciousness as distracting flights of fantasy. It seems as this trend might change in the near future as leaders in the field of AI recognize the importance of mechanisms of higher level cognition for making progress in AI, their relationship to the'easy problems' of consciousness and the important work that has been conducted in the field of cognitive science to understand these better (Yoshua Bengio's keynote address at NEURIPS 2019 being an important example of this [1]). While this might not satisfy those who are interested in the phenomenal aspects of our conscious experience, it represents a step forward in the right direction by the larger AI community. In keeping with the (beginnings of a) trend, the author will look to make the case for a non-equilibrium thermodynamic framework of consciousness, it's relationship to the field of AI and the crucial role that computer hardware engineers might have to play in the scientific study of consciousness. The author would like to take a brief moment (to digress) and explain the journey towards these ideas, hoping that it would elucidate their motivations as an engineer to study and understand the field of consciousness from a more physics based approach. The author's primary research interests lie in the field of artificial intelligence and was lucky

Systems based on artificial intelligence and machine learning models should be transparent, in the sense of being capable of explaining their decisions to gain humans' approval and trust. While there are a number of explainability techniques that can be used to this end, many of them are only capable of outputting a single one-size-fits-all explanation that simply cannot address all of the explainees' diverse needs. In this work we introduce a model-agnostic and post-hoc local explainability technique for black-box predictions called LIMEtree, which employs surrogate multi-output regression trees. We validate our algorithm on a deep neural network trained for object detection in images and compare it against Local Interpretable Model-agnostic Explanations (LIME). Our method comes with local fidelity guarantees and can produce a range of diverse explanation types, including contrastive and counterfactual explanations praised in the literature. Some of these explanations can be interactively personalised to create bespoke, meaningful and actionable insights into the model's behaviour. While other methods may give an illusion of customisability by wrapping, otherwise static, explanations in an interactive interface, our explanations are truly interactive, in the sense of allowing the user to "interrogate" a black-box model. LIMEtree can therefore produce consistent explanations on which an interactive exploratory process can be built.

Although models using contextual word embeddings have achieved state-of-the-art results on a host of NLP tasks, little is known about exactly what information these embeddings encode about the context words that they are understood to reflect. To address this question, we introduce a suite of probing tasks that enable fine-grained testing of contextual embeddings for encoding of information about surrounding words. We apply these tasks to examine the popular BERT, ELMo and GPT contextual encoders, and find that each of our tested information types is indeed encoded as contextual information across tokens, often with near-perfect recoverability-but the encoders vary in which features they distribute to which tokens, how nuanced their distributions are, and how robust the encoding of each feature is to distance. We discuss implications of these results for how different types of models breakdown and prioritize word-level context information when constructing token embeddings.

In this tutorial article, we aim to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms: reinforcement learning algorithms that utilize previously collected data, without additional online data collection. Offline reinforcement learning algorithms hold tremendous promise for making it possible to turn large datasets into powerful decision making engines. Effective offline reinforcement learning methods would be able to extract policies with the maximum possible utility out of the available data, thereby allowing automation of a wide range of decision-making domains, from healthcare and education to robotics. However, the limitations of current algorithms make this difficult. We will aim to provide the reader with an understanding of these challenges, particularly in the context of modern deep reinforcement learning methods, and describe some potential solutions that have been explored in recent work to mitigate these challenges, along with recent applications, and a discussion of perspectives on open problems in the field.

A company called Landing Artificial Intelligence, can offer analysis of CCTV video footage in real time to highlight those people not keeping two metres apart from each other, demonstrated in the picture demonstrated in the picture. Whilst this analysis undoubtedly could help with ensuring we keep social distance from each other, it will also be seen by many as an invasion of privacy. This analysis uses Deep Learning techniques and specially convolutional neural networks.

The term AI/ML has become exceptionally popular but what exactly to the experts mean they refer to AI/ML? "AI/ML" is a field in computer science which tries to use computers to solve problems that were previously only "solvable" by humans. For years, computers excelled at number crunching but certain tasks, such as voice recognition (hearing), object recognition in photos (vision) and predicting behaviour of agents in unconstrained or uncertain environments, remained a challenge for programmers to overcome. These days, computer algorithms can perform as well or better than humans on a few narrow tasks that typically take a human between 0 and 3 seconds (rule of thumb) to perform. These tasks are usually perception tasks that don't require deep, abstract thought by the human brain. Of course, there are contradictions to this rule of thumb.

Loss Functions are one of the most important parts of Neural Network design. A loss function helps us to interact with the model and tell the model what we want -- the reason why it is related to an "objective function". Let us look at the precise definition of a loss function. In mathematical optimization and decision theory, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function.

Julia is a high-level, dynamic programming language which is fast, flexible, easy-to-use, scalable, and supports high-speed mathematical computation. The programming language also supports all hardware, including GPUs and TPUs on every cloud. Julia uses multiple dispatches as a paradigm, making it easy to express many object-oriented and functional programming patterns. In one of our articles, we discussed how this language is making AI and machine learning better. In this article, we list down top 9 machine learning frameworks in Julia, one must know.

While longitudinal studies themselves don't have a direct correlation to machine learning, their data does. Machine learning algorithms can use longitudinal data to understand and infer trends, changes over time, and possibilities of specific occurrences. The incorporation of deep learning technology has led to improved predictions of cardiovascular disease and an enriched understanding of the importance of genetic markers in understanding health risks.

For the past 15 years, astrophysicists at the University of Bern in Switzerland have used specialized techniques to predict planet masses. Unfortunately, the feat requires solving sets of complicated and time-consuming differential equations. As a solution, the Swiss scientists adopted artificial intelligence into their work to help improve and speed up their computing process. 'There is a big hype also in astronomy. Machine learning has already been used to analyze observations, but to my knowledge, we are the first to use deep learning for such a purpose." Planets usually form in stellar disks that accumulate solid materials and gasses.