This article concerns the approximation and statistical estimation of high-dimensional, discontinuous functions by neural networks. More precisely, we study a certain class of target functions for classification problems, such as those encountered when automatically labeling images. For such problems, deep learning methods--based on the training of deep neural networks with gradient-based methods--achieve state of the art performance [32, 34]. The underlying functional relationship of such an (image) classification task is typically extremely high-dimensional. For example, the most widely used image databases used to benchmark classification algorithms are MNIST [35] with 28 28 pixels per image, CIFAR-10/CIFAR-100 [31] with 32 32 pixels per image and ImageNet [14, 32] which contains high-resolution images that are typically down-sampled to 256 256 pixels. Compared to practical applications, these benchmark datasets are relatively low-dimensional. Yet, already for MNIST, the simplest of those databases, the input dimension for the classification function is d 784. It is well known in classical approximation theory that high-dimensional approximation problems typically suffer from the so-called curse of dimensionality [11,40].

Deep neural networks generalize well despite being exceedingly overparametrized, but understanding the statistical principles behind this so called benign-overfitting phenomenon is not yet well understood. Recently there has been remarkable progress towards understanding benign-overfitting in simpler models, such as linear regression and, even more recently, linear classification. This paper studies benign-overfitting for data generated from a popular binary Gaussian mixtures model (GMM) and classifiers trained by support-vector machines (SVM). Our approach has two steps. First, we leverage an idea introduced in (Muthukumar et al. 2020) to relate the SVM solution to the least-squares (LS) solution. Second, we derive novel non-asymptotic bounds on the classification error of LS solution. Combining the two gives sufficient conditions on the overparameterization ratio and the signal-to-noise ratio that lead to benign overfitting. We corroborate our theoretical findings with numerical simulations.

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DarwinAI, the explainable artificial intelligence (XAI) company, and Red Hat, the world's leading provider of open source solutions, announced a collaboration to accelerate the deployment of COVID-Net--a suite of deep neural networks for COVID-19 detection and risk stratification via chest radiography--to hospitals and other healthcare facilities. DarwinAI and Red Hat are also leveraging the expertise of a computation research group, the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children's Hospital to better focus the software for real world clinical and research use. "The COVID-Net system is a promising tool, but needs to be coupled with a compelling GUI to be effective -- Boston Children's ChRIS framework and the Red Hat OpenShift platform provides an effective way to get COVID-Net into the hands of health care professionals on the front lines." Since the launch of COVID-Net by DarwinAI and the University of Waterloo's Vision and Imaging Processing (VIP) Lab, the project has continued to evolve with assistance, participation and collaboration from researchers and clinicians around the world. The initiative eventually led to a collaboration between DarwinAI and Red Hat, using underlying technology from Boston Children's, the number one pediatric hospital in the nation.

AI has blasted its way into the public consciousness and our everyday lives. It is powering advances in medicine, weather prediction, factory automation, and self-driving cars. Even golf club manufacturers report that AI is now designing their clubs. Google Translate helps us understand foreign language webpages and talk to Uber drivers in foreign countries. Vendors have built speech recognition into many apps.

KDnuggets recently conducted a survey to find out which data science skills our readers reported currently being in possession of, and which skills they had hoped to add or improve upon. This article will present some no-cost options for getting started learning about each of these skills. Instead of throwing a bunch of resources at the wall for each skill, I will instead point out one or two which I believe have proven their merit, some of which in turn point to additional vetted resources. Keep in mind that these are approaches to learning the basics of a given topic, and in many cases gaining the expertise necessary for utilizing any of these particular skills on a daily basis or having an expert-level in-depth understanding of them could take many, many hours or even years of practice and study. But don't let that discourage you; get started learning now. Reinforcement learning topped our list of wanted skills, with 51.9% of respondents stating that they hoped to add it to their skills portfolio.

Below the great illustration from the paper "Towards deep learning models resistant to adversarial attacks" is shown. Here red stars are such pixel vicinities of training samples that after applying the initial separating hyperplane (here -- just black line) they occurred to lie in the region of the neighbor (so, wrong) class.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves, i know that sounds a little bit confuse but will be clear at the end. At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video. The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.

BEGIN ARTICLE PREVIEW: Some visual recognition datasets have set benchmarks for supervised learning (Caltech101, Caltech256, CaltechBirds, CIFAR-10 andCIFAR-100) and unsupervised or self-taught learning algorithms(STL10) using deep learning across different object categories for various researches and developments. Under visual recognition mainly comes image classification, image segmentation and localization, object detection and various other use case problems. Many of these datasets have APIs present across some deep learning frameworks. I’ll be mentioning some of them in this article which can be directly imported and used to train models. Cifar(Canadian Institute of Advanced Research) is a subset of 80 million tiny images dataset which has been collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Dataset can be found on the official website of the Computer Science department of the University of Toronto.  California Institute of Technology

Cutting-edge AI tech has been described as'conscious' by a leading philosophy of mind expert. New York University's Professor David Chalmers made the bombshell claim while discussing the highly-controversial Generative Pre-trained Transformer 3 (GPT-3) - OpenAI's powerful new language generator able to create content better than anything else ever made.