In a fast-moving world, customers require efficiency and promptness when talking to any company. Here is where chatbots and Intelligent Virtual Assistants (IVAs) come into play. Thanks to their ability to engage into more advanced conversations, unlike rule-based chatbots, AI-powered systems are equipped with a multitude of features to assist and even entertain the users in their day-to-day activities. In addition to their customizable features, their self-learning ability and scalability have lead virtual assistants to gain popularity across various global enterprises. According to Grand View Research, the global intelligent virtual assistant market size was valued at USD 3.7 billion in 2019, growing at a Compound Annual Growth Rate (CAGR) of 34.0% over the forecast period.

Usually, every month we write an article about the best and most promising AI research papers from that month. In addition to that, we list fifteen AI articles we have found amazing that month. This collection of articles should give you an overview of what happened that month in the AI industry both from technical, business and from an ethical perspective. Are you afraid that AI might take your job? Make sure you are the one who is building it.

Ayn serves as AI Analyst at Emerj - covering artificial intelligence use-cases and trends across industries. She previously held various roles at Accenture. Several factors have contributed to the advancement of AI in the pharmaceutical industry. These factors include the increase in the size of and the greater variety of types of biomedical datasets, as a result of the increased usage of electronic health records. This article intends to provide business leaders in the pharmacy space with an idea of what they can currently expect from Ai in their industry.

The first in a series, this white paper provides an overview of Artificial Intelligence (AI) and its common methods.

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.

The rest of the thread, Tell me about a paper that you found inspiring, from u/mitare is also quite interesting. This paper is a really comprehensive review detailing what exactly current ML techniques are unable to do that humans can do very well. It lays the groundwork that needs to be done to make human-level artificial intelligence.

The global Artificial Intelligence Courses for C-suite Executives market has been analyzed by the researchers for a definite forecast period of 2020 to …

Natural language processing (NLP) research predominantly focuses on developing methods that work well for English despite the many positive benefits of working on other languages. These benefits range from an outsized societal impact to modelling a wealth of linguistic features to avoiding overfitting as well as interesting challenges for machine learning (ML). There are around 7,000 languages spoken around the world. The map above (see the interactive version at Langscape) gives an overview of languages spoken around the world, with each green circle representing a native language. Most of the world's languages are spoken in Asia, Africa, the Pacific region and the Americas.

Robots are increasingly being deployed in retail environments. The reasons for this include: to relieve staff from the performance of repetitive and mundane tasks; to reallocate staff to more value-added, customer-facing activities; to realize operational improvements; and, to utilize real-time in-store generated data. Due to the impact of the 2020 Coronavirus outbreak, we can now add a new reason to use robots in retail: to assist with customer and employee safety. In this Research Article, the Retail Analytics Council at NWU presents information on the benefits associated with deploying robots in stores. Estimates of the size of the global retail robot market are advanced.

Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization. Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks.