CAHRS Partners\u27 Implementation of Artificial Intelligence

[Excerpt] The ideas and uses for Artificial Intelligence (AI) are abundant, and each business is seemingly ripe for disruption, including HR. As the hype surrounding AI continues to be championed by popular press, we began our research in order to determine whether the press’ biased view that AI was here and ready to implement was accurate. We found that in reality, AI programs were far behind the progress discussed, as the software was slower, more expensive, and there was a general lack of amalgamation throughout the industry. From there, we asked CAHRS partners to tell us where AI was used in their company, and how it helped them deliver HR differently. Our research focused on how AI technology will disrupt, change, or bolster the HR function, specifically in Talent Acquisition and Learning and Development (L&D) spaces. We found our CAHRS partners dove into AI, and represented three key points along a spectrum of AI implementation. Of the 59 participants at 32 companies, 26% are Observers, 48% are Explorers, and 26% are Implementers. Observers were companies that did not believe AI fits with their strategy, and therefore do not intend to implement AI right now. Explorers are companies that have begun to actively explore AI through industry research, vendor exploration, and piloting AI and machine learning (ML) technologies. Implementers are companies that have either built in house or worked with an external vendor to implement an AI or machine learning technology. The CAHRS partners represented such a wide range along this spectrum because there are no best practices for AI implementation. However, each of our partners that leveraged AI understood the tool, while also understanding their business needs, people, and technology, which allowed them to utilize AI technology

Basic protocols in quantum reinforcement learning with superconducting circuits

Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback-loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.Comment: Published versio

Do Chatbots Dream of Androids? Prospects for the Technological Development of Artificial Intelligence and Robotics

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. It also compares AI&R abilities and human abilities in areas such as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, we investigate in which areas AI&R’s performance is inferior to a human, and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of artificial intelligence and robotics. In such implementations, on the one hand, the ‘Turing wall’ is removed, and on the other hand, artificial intelligence gets its physical implementation

Research in advanced formal theorem-proving techniques

The results are summarised of a project aimed at the design and implementation of computer languages to aid in expressing problem solving procedures in several areas of artificial intelligence including automatic programming, theorem proving, and robot planning. The principal results of the project were the design and implementation of two complete systems, QA4 and QLISP, and their preliminary experimental use. The various applications of both QA4 and QLISP are given