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Interesting Research Papers Presented By Meta AI At NeurIPS 2021

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Meta AI researchers will be presenting a total of 83 papers at NeurIPS 2021. NeurIPS 2021 is the Thirty-fifth Conference on Neural Information Processing Systems held from December 6 to 14. The virtual conference boasts 2334 papers, 60 workshops, eight keynote speakers, and 15k attendees. In 2020, Meta AI (previously Facebook) presented 48 research papers at Neurips 2020. This article highlights the top 10 featured research publications by Meta at NeurIPS 2021.


Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021

arXiv.org Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.


Multi-Task Learning on Networks

arXiv.org Artificial Intelligence

The multi-task learning (MTL) paradigm can be traced back to an early paper of Caruana (1997) in which it was argued that data from multiple tasks can be used with the aim to obtain a better performance over learning each task independently. A solution of MTL with conflicting objectives requires modelling the trade-off among them which is generally beyond what a straight linear combination can achieve. A theoretically principled and computationally effective strategy is finding solutions which are not dominated by others as it is addressed in the Pareto analysis. Multi-objective optimization problems arising in the multi-task learning context have specific features and require adhoc methods. The analysis of these features and the proposal of a new computational approach represent the focus of this work. Multi-objective evolutionary algorithms (MOEAs) can easily include the concept of dominance and therefore the Pareto analysis. The major drawback of MOEAs is a low sample efficiency with respect to function evaluations. The key reason for this drawback is that most of the evolutionary approaches do not use models for approximating the objective function. Bayesian Optimization takes a radically different approach based on a surrogate model, such as a Gaussian Process. In this thesis the solutions in the Input Space are represented as probability distributions encapsulating the knowledge contained in the function evaluations. In this space of probability distributions, endowed with the metric given by the Wasserstein distance, a new algorithm MOEA/WST can be designed in which the model is not directly on the objective function but in an intermediate Information Space where the objects from the input space are mapped into histograms. Computational results show that the sample efficiency and the quality of the Pareto set provided by MOEA/WST are significantly better than in the standard MOEA.


Work Without the Worker, book review: Microtasking, automation and the future of work

ZDNet

"Will we be retired -- or unemployed?" the leader of a futurist conference asked in 2007 while envisioning a world filled with AIs possessed of superhuman intelligence. More recent -- and more restrained -- researchers such as Kate Darling have argued that our best option lies in human-machine partnerships, although with the caveat suggested by Madeleine Claire Elish in her paper Moral Crumple Zones that the human partner will be the one that gets the blame when things go wrong. However, in the vast majority of the human-machine partnerships already in existence, the human partner is one or more invisible microtask workers being paid tiny amounts to label images, remotely take over a faltering delivery drone, or transcribe bits of text. We have seen these workers' lives documented before -- for example, in Mary L. Gray and Siddharth Suri's 2019 book Ghost Workers, Sarah T. Roberts' 2019 book Behind the Screen, and Kate Crawford's recent book on the extractive nature of the AI industry, Atlas of AI. SEE: Managers aren't worried about keeping their IT workers happy.


Artificial Intelligence in Drug Design

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This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.


5 Best Machine Learning & AI Books of All Time

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The world of AI can be intimidating due to the terminology and different machine learning algorithms that are available. After having read over 50 of the most highly recommended books on machine learning, I have compiled my personal list of must read books. The books that were chosen are based on the types of ideas that are introduced, and how well different concepts such as deep learning, reinforcement learning, and genetic algorithms are presented. Most importantly the list is based on the books that best pave the path forward for futurists and researchers towards building provably responsible, and explainable AI. "Life 3.0" has an ambitious goal and that is to explore the possibilities of of how we will co-exist with AI in the future. Artificial General Intelligence (AGI) is the eventual and inevitable consequence of the intelligence explosion argument made by British mathematician Irving Good back in 1965.


Book Review: I, ROBOT by Isaac Asimov

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This book is a collection of short stories published between 1940 and 1950, connected by the common theme of how intelligent, benevolent robots enter human society and gradually gain more influence. . In these early robot stories, Asimov introduced the Three Robot Laws - a programming which (presumably) prevents robots from becoming a menace. The Three Laws have since become a common trope in the SF genre. . You can read I, ROBOT mainly as a series of detective stories where the prime suspects are robots, and the human investigators must figure out the logical explanation of a robot "malfunction." These robot mysteries are entertaining, but seem rather simplistic today.


Book Review- 2084: Artificial Intelligence and the Future of Humanity by John C. Lennox

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Where did we come from and where will we go are some of the most pertinent questions of the day? The effects that the increased incorporation of AI has in our lives, the security of our homes, our political and personal freedoms, and the future of our entire species is still very uncertain. In 2084, the scientist and philosopher John Lennox introduces the readers to an ocean of ideas that revolve around the key developments in technological advancements, bioengineering, and specifically artificial intelligence. In this book, the readers will explore the current possibilities through AI, its advantages and disadvantages, the facts and the fiction, as well as the potential future implications. John Lennox is a talented professor of mathematics at Oxford University who often ventures into the intersection of religion, humanity, and technology.



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