This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. "I'm extremely confident that level 5 [self-driving cars] or essentially complete autonomy will happen, and I think it will happen very quickly," Tesla CEO Elon Musk said in a video message to the World Artificial Intelligence Conference in Shanghai earlier this month. "I remain confident that we will have the basic functionality for level 5 autonomy complete this year." Musk's remarks triggered much discussion in the media about whether we are close to having full self-driving cars on our roads. Like many other software engineers, I don't think we'll be seeing driverless cars (I mean cars that don't have human drivers) any time soon, let alone the end of this year. I wrote a column about this on PCMag, and received a lot of feedback (both positive and negative). So I decided to write a more technical and detailed version of my views about the state of self-driving cars. I will explain why, in its current state, deep learning, the technology used in Tesla's Autopilot, won't be able to solve the challenges of level 5 autonomous driving.

JavaScript is the most popular cross-platform language with a mature Node Package Manager (npm) ecosystem among web developers. According to the latest TIOBE Index report, JavaScript is the 7th most preferred languages among 20 popular programming languages used by developers. Here, we list the top machine and deep learning libraries in JavaScript. Written in JavaScript, Brain.js is a GPU-accelerated library for neural networks. The library is simple to use and performs computations using GPU and fallback to pure JavaScript when GPU is unavailable.

An immense frustration ecologists encounter is prompted by the attempt to keep track of individual animals in a study. This task only becomes more difficult when trying to pinpoint small, mobile animals like songbirds. While intelligent computer algorithms can help scientists better complete this task, training these systems to recognize different species -- let alone individuals in a species -- can take thousands of data points, time, and money. However, French and Portuguese researchers recently devised a way to streamline this process. They designed a deep-learning network that can identify individual birds with up to 92 percent accuracy in three different species. This tech can not only save scientists resources but can help them collect important data about the lives of birds -- and better understand what may be leading to their decline in North America.

To implement and test a deep learning approach for the segmentation of the arterial and venous cerebral vasculature with four-dimensional (4D) CT angiography. Patients who had undergone 4D CT angiography for the suspicion of acute ischemic stroke were retrospectively identified. A total of 390 patients evaluated in 2014 (n 113) or 2018 (n 277) were included in this study, with each patient having undergone one 4D CT angiographic scan. One hundred patients from 2014 were randomly selected, and the arteries and veins on their CT scans were manually annotated by five experienced observers. The weighted temporal average and weighted temporal variance from 4D CT angiography were used as input for a three-dimensional Dense-U-Net.

Solutions to most complex tasks can be decomposed into simpler, intermediate skills, reusable across wider ranges of problems. We follow this concept and introduce Hindsight Off-policy Options (HO2), a new algorithm for efficient and robust option learning. The algorithm relies on critic-weighted maximum likelihood estimation and an efficient dynamic programming inference procedure over off-policy trajectories. We can backpropagate through the inference procedure through time and the policy components for every time-step, making it possible to train all component's parameters off-policy, independently of the data-generating behavior policy. Experimentally, we demonstrate that HO2 outperforms competitive baselines and solves demanding robot stacking and ball-in-cup tasks from raw pixel inputs in simulation. We further compare autoregressive option policies with simple mixture policies, providing insights into the relative impact of two types of abstractions common in the options framework: action abstraction and temporal abstraction. Finally, we illustrate challenges caused by stale data in off-policy options learning and provide effective solutions.

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catastrophic forgetting when learning classes incrementally, while most incremental learning approaches require a large amount of training data per class. We examine the problem of incremental learning using only a few training examples, referred to as Few-Shot Incremental Learning (FSIL). To solve this problem, we propose a novel approach inspired by the concept learning model of the hippocampus and the neocortex that represents each image class as centroids and does not suffer from catastrophic forgetting. We evaluate our approach on three class-incremental learning benchmarks: Caltech-101, CUBS-200-2011 and CIFAR-100 for incremental and few-shot incremental learning and show that our approach achieves state-of-the-art results in terms of classification accuracy over all learned classes.

Text simplification is concerned with reducing the language complexity and improving the readability of professional content so that the text is accessible to readers at different ages and educational levels. As a promising practice to improve the fairness and transparency of text information systems, the notion of text simplification has been mixed in existing literature, ranging all the way through assessing the complexity of single words to automatically generating simplified documents. We show that the general problem of text simplification can be formally decomposed into a compact pipeline of tasks to ensure the transparency and explanability of the process. In this paper, we present a systematic analysis of the first two steps in this pipeline: 1) predicting the complexity of a given piece of text, and 2) identifying complex components from the text considered to be complex. We show that these two tasks can be solved separately, using either lexical approaches or the state-of-the-art deep learning methods, or they can be solved jointly through an end-to-end, explainable machine learning predictor. We propose formal evaluation metrics for both tasks, through which we are able to compare the performance of the candidate approaches using multiple datasets from a diversity of domains.

Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language. Neural architectures have been employed to generate text excerpts to various degrees of success, in a multitude of contexts and tasks that fulfil various user needs. Notably, high capacity deep learning models trained on large scale datasets demonstrate unparalleled abilities to learn patterns in the data even in the lack of explicit supervision signals, opening up a plethora of new possibilities regarding producing realistic and coherent texts. While the field of natural language generation is evolving rapidly, there are still many open challenges to address. In this survey we formally define and categorize the problem of natural language generation. We review particular application tasks that are instantiations of these general formulations, in which generating natural language is of practical importance. Next we include a comprehensive outline of methods and neural architectures employed for generating diverse texts. Nevertheless, there is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field. To this end, we also review current approaches to evaluating natural language generation systems. We hope this survey will provide an informative overview of formulations, methods, and assessments of neural natural language generation.

This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long short-term memory recurrent neural network for a global approximation of an optimal contraction metric, the existence of which is a necessary and sufficient condition for exponential stability of nonlinear systems. The optimality stems from the fact that the contraction metrics sampled offline are the solutions of a convex optimization problem to minimize an upper bound of the steady-state Euclidean distance between perturbed and unperturbed system trajectories. We demonstrate how to exploit NCMs to design an online optimal estimator and controller for nonlinear systems with bounded disturbances utilizing their duality. The performance of our framework is illustrated through Lorenz oscillator state estimation and spacecraft optimal motion planning problems.