Includes profile, demo downloads, and job openings. Developer of software systems that solve resource optimization, planning, scheduling, and deployment problems for the air transportation, gaming, healthcare, hospitality, and security industries. Source for neural network based data modeling, prediction, forecasting and optimization solutions. Areas of focus includes: Banking and Finance, Manufacturing, Marketing, Medical. Uses artificial-intelligence technologies to prevent fraud in transaction environments such as finance, e-commerce, telecommunications, and insurance.

This paper presents a knowledge-based approach to the task of learning and identifying galaxies from their images. To this effect, we propose a crowd-sourced pipeline approach that employs two systems - case based and rule based systems. First, the approach extracts morphological features i.e. features describing the structure of the galaxy such as its shape, central characteristics e.g., has a bar or bulge at its center)etc., using computer vision techniques. Then it employs a case based reasoning system and a rule based system to perform the classification task. Our initial results show that this pipeline is effective in learning reasonably accurate models on this complex task.

Every cloud platform has a large number of software components, making it difficult to manage the security of the entire system. This paper discusses the requirement for an intelligent cloud security auditing solution, and an expert system architecture is presented. The solution can identify data confidentiality threats in the OpenStack cloud platform, as well as propose solutions to remove vulnerabilities before an attack occurs. Data confidentiality threats cover a wide range of security risks where attackers usually try to steal/corrupt personal data and are a major concern of users. For this reason, cloud infrastructures need frequent security auditing. The key features of the proposed expert system architecture include: acquisition of information detailing the latest cloud security threats and solutions, the conversion of acquired raw data into usable format, the application of a forward chaining inference algorithm, and the ability for the user to add/modify knowledge, which is then utilised to provide feasible solutions in ranked order. These components provide an automated mechanism to generate human-readable audit reports, improving the overall security status without the need for expert knowledge.

Traditionally, the needs of vulnerable populations have been addressed by a plethora of public and private agencies that rely on donations of money, goods and services which they distribute based on their perception of what is needed and where. This approach, however, lacks a comprehensive understanding of the demand side as well as the ability to coordinate between various suppliers of goods and services, identify latent supply and predict future demand. To help address these issues, we have developed a knowledge-based platform that harnesses advances in several AI fields for efficient and effective provisioning of goods and services.

I write a lot about data-driven algorithms, in particular those informed by Machine Learning. I thought it would be nice to give the low-down on machine learning for the uninitiated. Below, I discuss four essential questions. The answers are based, in part, from a recent discussion with Pedro Domingos, author of The Master Algorithm. Machine learning and AI touch your life every minute of every day, from applications you use at work to how you choose products to buy (Amazon recommendations).

The Leibniz Center for Law has its roots in the former department of Computer Science & Law of the Law Faculty of the University of Amsterdam, and currently houses about 15 researchers. The Leibniz Center conducts research and provides education in the field of Artificial Intelligence and law. In the tradition of Leibniz, we focus on the development and application of techniques from Artificial Intelligence to the field of Law for the purpose of supporting legal practice, and bringing new insights to legal theory. The Leibniz Center for Law has longstanding experience on legal ontologies, automatic legal reasoning and legal knowledge-based systems, (standard) languages for representing legal knowledge and information, user-friendly disclosure of legal data, and the application of ICT in education and legal practice (e.g. It plays an important role in the development of eGovernment on both national and international level. The center provides advice on change-management issues of knowledge-intensive legal processes and the improvement of knowledge-productivity in legal organisations.

The main point of this discussion is that Prolog, and logic in general, is much more powerful than the basic sort of expert system using IF..THEN rules and backward chaining. If you want to get involved in reasoning with facts then you really do have to go to the extra trouble of learning Prolog and developing a program. It is worth saying that while Prolog isn't an impossibly difficult language, people vary in their ability to absorb it and use it naturally. That is you might spend a lot of time learning Prolog never to get very far. If you are planning an expert system project it is worth considering a simple expert system shell because most of the really cost effective applications are simple enough not to need first-order predicate calculus.

As stated in my previous article, I want to show next where AI is taking us in the future. However, I need to describe first how AI has evolved during its short life. I have written three articles that develop this theme. In this first article, I briefly outline the background context with what has been achieved up until the start of the millennium. In the next article, I describe the impact that machine learning paradigms such as genetic algorithms and neural networks have made in the last 20 years. Finally, in the third article, I outline our future in a world dominated by AI.

A survey of expert system (ES) business application papers published between 1977 and 1993 indicates that an increasing amount of ES research is being conducted for a diverse range of business activities. The classification of literature by (1) year of publication, (2) application area, (3) generic problem area addressed, (4) problem domain, (5) level of management, (6) level of task interdependence, (7) means of development, (8) corporate/academic interaction in development, and (9) technology integration provides some insights in the trend. Implications to ES developers are discussed.