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 .

Service 6.1.2.1 Managed 6.1.2.2 Professional 6.2 By Deployment 6.2.1 Cloud 6.2.2 On-Premise 6.3 By Application 6.3.1 Supply Chain & Logistics 6.3.2

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.

As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.

Who We Are: ShopperTrak is on a mission: we are revolutionizing the brick and mortar retail world with innovative products that fuel their success. Our path forward includes active exploration of IoT, Computer Vision, Machine (Deep) Learning and other technologies. We believe this is a thrilling journey and providing clarity on our future and empowering people to do their best work is the key to our success. We are inspired by people who are passionate, curious and want to push the envelope on innovation. Around here, our motto is "build great stuff" that helps solve problems for our customers and great things will follow.

PLEASE NOTE THE NEW ADDRESS OF MORSE BARNES-BROWN & PENDLETON at 480 Totten Pond Road. Artificial intelligence technologies are threatening to take over many decision-making tasks humans perform at work and in personal life. AI systems are already making critical decisions in areas previously thought to be the exclusive domain of humans: driving cars, reviewing job applications, underwriting loans, and even endeavoring to create patentable innovation and recommending sentencing in the criminal justice system. What does this rapid and seemingly unstoppable development in artificial intelligence mean for the legal profession? In his talk, Joe Barkai will provide an overview of key AI technologies.

Matthew is one of the founders of the World Futures Forum (WFF) and will be one of our highly anticipated speakers at WFF on 24th September 2019. The inaugural 2019 World Futures Forum Summit, being held in London this September is your opportunity to discover and discuss the exponential technologies and megatrends that are shaping our world, and driving the new global economy. It's where business, culture, and technology converge, to give you access to some of the most innovative companies and people in the world.