This literature review focuses on three important aspects of an autonomous car system: tracking (assessing the identity of the actors such as cars, pedestrians or obstacles in a sequence of observations), prediction (predicting the future motion of surrounding vehicles in order to navigate through various traffic scenarios) and decision making (analyzing the available actions of the ego car and their consequences to the entire driving context). For tracking and prediction, approaches based on (deep) neural networks and other, especially stochastic techniques, are reported. For decision making, deep reinforcement learning algorithms are presented, together with methods used to explore different alternative actions, such as Monte Carlo Tree Search.

In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building based on two main abstractions: data types and declarative configuration files. The data type abstraction allows for easier code and sub-model reuse, and the standardized interfaces imposed by this abstraction allow for encapsulation and make the code easy to extend. Declarative model definition configuration files enable inexperienced users to obtain effective models and increase the productivity of expert users. Alongside these two innovations, Ludwig introduces a general modularized deep learning architecture called Encoder-Combiner-Decoder that can be instantiated to perform a vast amount of machine learning tasks. These innovations make it possible for engineers, scientists from other fields and, in general, a much broader audience to adopt deep learning models for their tasks, concretely helping in its democratization.

This post was co-written with Baptiste Rocca. This old saying expresses pretty well the underlying idea that rules the very powerful "ensemble methods" in machine learning. Roughly, ensemble learning methods, that often trust the top rankings of many machine learning competitions (including Kaggle's competitions), are based on the hypothesis that combining multiple models together can often produce a much more powerful model. The purpose of this post is to introduce various notions of ensemble learning. We will give the reader some necessary keys to well understand and use related methods and be able to design adapted solutions when needed.

Artificial intelligence and the use of smart machines are shaking up law and society. Companies, governments, and universities implement AI without a full understanding of its legal and regulatory threats. This new guide provides a comprehensive overview of the legal issues surrounding artificial intelligence and smart machines. Beginning with a history of AI to exploring the special legal problems such as intellectual property development and labor replacement, this guide discusses risks imposed by artificial intelligence and how to effectively mitigate those risks. The concept of artificial intelligence influences affects broad aspects of business and society.

Although AI (Artificial Intelligence) is still in its early stages for ERP software, the demand for the technology is rising each day. During the 1990s, ERP was the emerging trend and companies were looking to utilize the new functionality and opportunities this trend brought. Many customers moved from manual systems and spreadsheets to ERP software with the main aim of improving efficiency. Today, ERP is a very common term within the industry and most companies who have adopted ERP software in their organizations are asking the question'what is next?' AI is an emerging trend but understanding terms like Big Data and the Internet of Things (IoT) which are causing digital disruption can create a misperception in the market. Today, more than before, companies need to understand how AI fits into ERP software as well as the benefits it can provide.

As intelligent systems are increasingly capable of performing their tasks without the n eed for continuous human input, direction, or supervision, new human - machine interaction concepts are needed. A promising approac h to this end is human - agent teaming, which envisions a novel interaction form where humans and machines behave as equal team partners . This paper presents an overview of the current state of the art in human - agent teaming, including the analysis of human - agent teams on five dimensions; a framework describing important teaming functionalities; a technical architecture, called SAIL, supporting social human - agent teaming through the modular implementation of the human - agent teaming functionalities; a technica l implementation of the architecture; and a proof - of - concept prototype created with the framework and architecture. We conclude this paper with a reflection on where we stand and a glance into the future showing the way forward .

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

Unless you've been hiding under a rock the past couple of years, you've heard the rumblings. Artificial intelligence is here, and here to stay. The way some people have hyped it up, though, you may have thought there's some three-eyed, two-headed AI monster -- coming to steal your job, create havoc among your tech stack, and overtake, mess up, and mutate all of your precious customer data. But this fear-based approach stems from widespread misunderstandings about what AI is, and how it can help e-commerce marketers. For teams who are ready to embrace this game-changing technology, the benefits are becoming evident.

Current state of the art methods in Domain Adaptation follow adversarial approaches, making training a challenge. Other non-adversarial methods learn mappings between source and target domains, to achieve reasonable performance. However, even these methods do not focus a key aspect of maintaining performance on the source domain, even after optimizing over the target domain. Additionally, there exist very few methods in low resource supervised domain adaptation. This work proposes a method, LRS-DAG, that aims to solve these current issues in the field. By adding a set of "encoder layers" which map the target domain to the source, and can be removed when dealing directly with the source data, the model learns to perform optimally on both domains. LRS-DAG is unique in the sense that a new algorithm for low resource domain adaptation, which maintains performance over the source, with a new metric for learning mappings has been introduced.