The SP theory of intelligence: benefits and applications

This article describes existing and expected benefits of the "SP theory of intelligence", and some potential applications. The theory aims to simplify and integrate ideas across artificial intelligence, mainstream computing, and human perception and cognition, with information compression as a unifying theme. It combines conceptual simplicity with descriptive and explanatory power across several areas of computing and cognition. In the "SP machine" -- an expression of the SP theory which is currently realized in the form of a computer model -- there is potential for an overall simplification of computing systems, including software. The SP theory promises deeper insights and better solutions in several areas of application including, most notably, unsupervised learning, natural language processing, autonomous robots, computer vision, intelligent databases, software engineering, information compression, medical diagnosis and big data. There is also potential in areas such as the semantic web, bioinformatics, structuring of documents, the detection of computer viruses, data fusion, new kinds of computer, and the development of scientific theories. The theory promises seamless integration of structures and functions within and between different areas of application. The potential value, worldwide, of these benefits and applications is at least $190 billion each year. Further development would be facilitated by the creation of a high-parallel, open-source version of the SP machine, available to researchers everywhere.Comment: arXiv admin note: substantial text overlap with arXiv:1212.022

Professional expectations and patient expectations concerning the development of Artificial Intelligence (AI) for the early diagnosis of Pulmonary Hypertension (PH)

The expectations of professionals working on the development of healthcare Artificial Intelligence (AI) technologies and the patients who will be affected by them have received limited attention. This paper reports on a Foresight Workshop with professionals involved with pulmonary hypertension (PH) and a Focus Group with members of a PH patient group, to discuss expectations of AI development and implementation. We show that while professionals and patients had similar expectations of AI, with respect to the priority of early diagnosis; data risks of privacy and reuse; and responsibility, other expectations differed. One important point of difference was in the attitude toward using AI to point up other potential health problems (in addition to PH). A second difference was in the expectations regarding how much clinical professionals should know about the role of AI in diagnosis. These findings allow us to better prepare for the future by providing a frank appraisal of the complexities of AI development with foresight, and the anxieties of key stakeholders

A Study Evaluating if Targeted Training for Startle Effect can Improve Pilot Reactions in Handling Unexpected Situations in a Flight Simulator

Recent airline accidents point to a crew’s failure to make correct and timely decisions following a sudden and unusual event that startled the crew. This study sought to determine if targeted training could augment decision making during a startle event. Following a startle event cognitive function is impaired for a short duration of time (30-90 seconds). In aviation, critical decisions are often required to be made during this brief, but critical, time frame. A total of 40 volunteer crews (80 individual pilots) were solicited from a global U.S. passenger airline. Crews were briefed that they would fly a profile in the simulator but were not made aware of what the profile would entail. The study participants were asked to complete a survey on their background and flying preferences. Every other crew received training on how to handle a startle event. The training consisted of a briefing and simulator practice. Crew members (subjects) were either presented a low altitude or high altitude scenario to fly in a full-flight simulator. The maneuver scenarios were analyzed using a series of one-way ANOVAs, ttests and regression for the main effect of training on crew performance. The data indicated that the trained crews flew the maneuver profiles significantly better than the untrained crews and significantly better than the Federal Aviation Administration (FAA) Airline Transport Pilot (ATP) standards. Each scenario’s sub factors were analyzed using regression to examine for specific predictors of performance. The results indicate that in the case of the high altitude profile, problem diagnosis was a significant factor, in the low altitude profile, time management was also a significant factor. These predicators can be useful in further targeting training. The study’s findings suggest that targeted training can help crews manage a startle event, leading to a potential reduction of inflight loss of control accidents. The training was broad and intended to cover an overall aircraft handling approach rather than being aircraft specific. Inclusion of this type of training by airlines has the potential to better aid crews in handling sudden and unusual events

Innovation as a community-spanning process: strategies to handle path dependency.

In this paper, we further develop and apply the notions of path creation and path dependency during technological innovation processes. The process of technological innovation is portrayed as an activity of spanning boundaries between and across communities of practitioners. Communities of practice are characterised by shared beliefs, evaluation routines and artefacts. These beliefs, routines and artefacts create powerful path-dependencies that inhibit path-breaking innovations. Based on exploratory empirical research, a model on handling path-dependency during the creation of technological innovations is proposed.Processes; Strategy; Evaluation; Innovations; Model;

The role of artificial intelligence in skilled work and consequences for vocational training

Artificial intelligence (AI) has long been a present-day topic and is having an impact on the economy, society, skilled work and the work environment. However, there are often very different assessments of the effects: On the one hand the loss of jobs and even professions has been predicted, on the other hand new support and shaping options for work are emerging. In addition, AI is treated as a powerful buzzword without considering the real technologies and requirements behind it. Nevertheless, consequences for the world of work and its employees can only be derived and vocational training concepts designed if the handling of AI in skilled work has been concretized beforehand. The impact of AI on vocational education and training and on the skilled worker has so far been discussed in a rather abstract way and only very rarely focused on research. At the same time, technological developments in certain areas (including expert systems, machine learning approaches, digital twins) have already proceeded to such an extent that the effects on skilled work are noticeable and are thus evident. Much will depend on the design of the human-machine interface. In order to evaluate how skilled labour and AI can successfully “cooperate” in manufacturing, a model is presented here that can support the evaluation process

Report of the workshop on Aviation Safety/Automation Program

As part of NASA's responsibility to encourage and facilitate active exchange of information and ideas among members of the aviation community, an Aviation Safety/Automation workshop was organized and sponsored by the Flight Management Division of NASA Langley Research Center. The one-day workshop was held on October 10, 1989, at the Sheraton Beach Inn and Conference Center in Virginia Beach, Virginia. Participants were invited from industry, government, and universities to discuss critical questions and issues concerning the rapid introduction and utilization of advanced computer-based technology into the flight deck and air traffic controller workstation environments. The workshop was attended by approximately 30 discipline experts, automation and human factors researchers, and research and development managers. The goal of the workshop was to address major issues identified by the NASA Aviation Safety/Automation Program. Here, the results of the workshop are documented. The ideas, thoughts, and concepts were developed by the workshop participants. The findings, however, have been synthesized into a final report primarily by the NASA researchers

Human-Machine-Interaction in Innovative Work Environment 4.0 – A Human-Centered Approach

The working environment is constantly changing and companies face the challenge of adapting to new and constantly changing customer requirements. Employees are faced with the challenge of identifying and learning new, helpful technologies and using them in order to achieve efficiency gains and increase productivity. This article addresses the three technologies Artificial Intelligence, Robotic Process Automation and Virtual Reality, which will play an important role in the future of work and will influence the Work Environment 4.0. Artificial Intelligence and Robotic Process Automation relieve employees of repetitive and manual tasks which thus accelerate and simplify business processes. Virtual Reality offers employees new opportunities to collaborate in virtual environments. Instead of performing routine tasks, employees will increasingly promote the use of such technologies in future and orchestrate their application. In addition, it is important for employees to continuously look for new use cases within their own organization and to collaborate with external partners. The article aims to describe the opportunities that arise from the application of the technologies and to explain their effects on the Work Environment 4.0 and the employee