Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) (Minh et al. 2020) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to provide a generalization of distributed learning that goes beyond existing mechanisms such as federated learning. Inspired from this philosophy, a novel distributed learning approach is proposed in this paper. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, a hierarchical generalized learning problem in a recursive form is formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical generalized averaging mechanism. To that end, a distributed learning algorithm, namely DemLearn and its variant, DemLearn-P is proposed. Extensive experiments on benchmark MNIST and Fashion-MNIST datasets show that proposed algorithms demonstrate better results in the generalization performance of learning model at agents compared to the conventional FL algorithms. Detailed analysis provides useful configurations to further tune up both the generalization and specialization performance of the learning models in Dem-AI systems.

Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such property labels are generally obtained by Density Functional Theory (DFT) calculation which is extremely computational costly. An effective solution is to incorporate the unlabeled molecules in a semi-supervised fashion. However, learning semi-supervised representation for large amounts of molecules is challenging, including the joint representation issue of both molecular essence and structure, the conflict between representation and property leaning. Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules. Specifically, ASGN adopts a teacher-student framework. In the teacher model, we propose a novel semi-supervised learning method to learn general representation that jointly exploits information from molecular structure and molecular distribution. Then in the student model, we target at property prediction task to deal with the learning loss conflict. At last, we proposed a novel active learning strategy in terms of molecular diversities to select informative data during the whole framework learning. We conduct extensive experiments on several public datasets. Experimental results show the remarkable performance of our ASGN framework.

Online policy optimization (OPO) views policy optimization for sequential decision making as an online learning problem. In this framework, the algorithm designer defines a sequence of online loss functions such that the regret rate in online learning implies the policy convergence rate and the minimal loss witnessed by the policy class determines the policy performance bias. This reduction technique has been successfully applied to solving various policy optimization problems, including imitation learning, structured prediction, and system identification. Interestingly, the policy improvement speed observed in practice is usually much faster than existing theory suggests. In this work, we provide an explanation of this fast policy improvement phenomenon. Let $\epsilon$ denote the policy class bias and assume the online loss functions are convex, smooth, and non-negative. We prove that, after $N$ rounds of OPO with stochastic feedback, the policy converges in $\tilde{O}(1/N + \sqrt{\epsilon/N})$ in both expectation and high probability. In other words, we show that adopting a sufficiently expressive policy class in OPO has two benefits: both the convergence rate increases and the performance bias decreases, as the policy class becomes reasonably rich. This new theoretical insight is further verified in an online imitation learning experiment.

Python is and will be the leading language for data science and machine learning. The Python Data Science Handbook is the perfect book for boosting our Python skills. This is a perfect reference to keep close by for those frequent data manipulation tasks using Pandas. This book covers IPython, Numpy for computations, Data manipulation with Pandas, Data visualizations with Matplotlib, Machine learning with Scikit-Learn. It provides easy to understand explanations of concepts and coding examples with R. The book covers K-fold cross-validation, Regularization, Feature selection, Polynomial regression, Decision Trees, Support vector machines, Unsupervised learning i.e.

Click here to read the full article. THE WORLD first took notice of Beijing's prowess in artificial intelligence (AI) in late 2017, when BBC reporter John Sudworth, hiding in a remote southwestern city, was located by China's CCTV system in just seven minutes. At the time, it was a shocking demonstration of power. Today, companies like YITU Technology and Megvii, leaders in facial recognition technology, have compressed those seven minutes into mere seconds. What makes those companies so advanced, and what powers not only China's surveillance state but also its broader economic development, is not simply its AI capability, but rather the math power underlying it.

It has become clear, in these last decades of decadence, decline, towering institutional violence, and rampant bad taste, that American life is stuck somewhere inside the Paul Verhoeven cinematic universe. In the bloody, satirical sci-fi films that made his name with American audiences, Verhoeven dealt in a singularly unappealing vision of the future, one both luridly inventive and careful about where not to be imaginative. "RoboCop," from 1987, set in a futuristic Detroit, is a gleeful exaggeration of the anxieties of Reagan-era urban life: the office towers are even more isolated, and their boardrooms more brazenly sociopathic; the popular culture is a tick or two more savage and leering; the police are more overmatched and the streets more ungovernable. "Total Recall," released in 1990 and adapted from a short story by Philip K. Dick, does feature humans living on Mars, a private company that implants bespoke memories in its clients, and a brassy three-breasted space prostitute, but its vision of 2084 is in other respects familiar. Mars is dirty, violent, and unequal, and the colony is overseen by the private security force of a capitalist who has staked out a monopoly on oxygen itself.

When you hear Artificial Intelligence (AI) the first thing that comes to mind are robots; in particular, the Steven Spielberg movie titled A.I. where a robot child is built that can love and behave just like a real human. This idea appears to be closer to a dream than reality. Truth is, AI is more ubiquitous than we might think. It ranges from self-driving cars, movie recommendations on Netflix, e-mail spam detection to voice-controlled assistants such as Apple's SIRI. The fact is that AI is already present across many businesses and various industries, as is shown in the figure below.

As annoying as this sounds, it is very essential in this field. I think it is safe to assume that anyone reading this has some basic to intermediate knowledge in mathematics from high school. You would need to dive deep a little further and learn some concepts in statistics, algebra and other topics. I would compile a list of topics and resources to help you study math for data science but it has already been perfectly done in this article by Ibrahim Sharaf ElDen. As a beginner, do not jump straight into learning to write code for machine learning but rather, learn the core concepts of programming in general.

This magic is performed using powerful AI software that has only recently become powerful enough to master this formidable challenge. But two French engineering students, Virgile Garnier and Juliette Breurec, decided to take it on and have created an simple, elegant approach to transforming hand-drawn visual diagrams into computer-based maps that can be modified and improved – and save their creators the hours that would be required to do this task manually. I recently interviewed them to learn more about doUmind – how they envisioned this remarkable tool, how it accomplishes this remarkable task and what's next for it. Chuck Frey: Where did you and Juliette come up with the idea for doUmind? Garnier and Breurec: We were both engineering students specializing in mechatronics at the end of our studies at our university, IMT Mines Alès.

A study led by University of Toronto alumna Kimberly Ren is among the first to quantify predictors that could lead women towards, or away from, pursuing careers in machine learning and artificial intelligence, or AI. Women currently make up 22 per cent of global AI professionals, with that proportion oscillating between 21 per cent and 23 per cent over a four-year trend, according to a 2018 report by the World Economic Forum. "The talent gap isn't closing," says Ren, who recently graduated from the Faculty of Applied Science & Engineering and was awarded the Best Paper Award at the American Society for Engineering Education Conference for her fourth-year thesis project. She led the study under the supervision of Alison Olechowski, an assistant professor in the department of mechanical and industrial engineering. "What I hope this research does is find some reasoning behind this gap, so that we can increase the persistence of women in the field going forward."