The Coast Guard uses the Armorway software for port security in New York, Boston and Los Angeles, drawing on data sources that includes passenger load numbers to traffic changes, and creating a schedule that makes it difficult for a terrorist to predict when there will be increased police presence. A recent National Academies study reached the conclusion that there are not yet technological alternatives to bulk collection and analysis of civilian metadata.
The issue of data mining and privacy are particularly complex, essentially because the machine-learning and data-mining technologies posting the threats are oblivious to the consequences of exploitation or trespassing on personal privacy laws.
Good policy is key in the long-term, as machine learning software evolves and data is accidentally stumbled upon or intentionally mined by analysts across industries. For example, the communications giant has implemented large-scale ML capabilities that allow them to pull data from contacts, chat and voice operations; process data to make predictions in near real-time; and provide that intelligence to managers and supervisors.
Managers and supervisors can then look and monitor to identify anomalies, asking important questions along the way — were my customers happy or not? If I put them on hold, did that make them unhappy? Did my agent solve their problem the first time? Using ML to determine customer sentiment in real time for example, why they called, will they call again, etc. If a van breaks down and a customer ends up unhappy about the delay, the company faces two losses.
The Internet of Things IoT is enabling cost-efficient implementation of condition-based maintenance for a number of complex assets, with ML playing a current driving role in the analysis of incoming data. The Transportation Security Administration TSA is pursuing a project to overhaul and reformulate airport security nationwide. Called DARMS , short for Dynamic Aviation Risk Management Solution, the smart system will integrate information across the aviation sector to tailor a personalized security profile for each person, on a per-flight basis.
Th e European Union, through its Horizon program, currently supports AI-simulated security training efforts through projects like LawTrain , where the next steps are to move from simulation to real investigations that utilize such tools in machine-human collaborations. Many other cities have already added drones for surveillance of potential criminal activity, and while the debate about ethics and rules and regulations will stay strong, it seems likely that smart drones able to detect crimes-in-the-making and alert authorities will be used in the future to reinforce security at major crossways such as ports, airports, industrial facilities, and others.
These analyses of anonymized data — whether publicly disclosed or privately held — are capable of yielding valuable results and insights on public health questions across populations. The federal HIPAA regulations limits the release of personal healthcare records, information that could be useful in many large-scale aggregate studies. The reasons for limiting this information are valid.
The 18th International Conference on Artificial Intelligence: Methodology, Systems, Applications
As suggested by a Microsoft research team , even outside of healthcare records there is room today — thanks to big data — to infer health conditions and risks from non-medical information, whether through informational or social contexts. This provides ample opportunity for reflection on directions in balancing innovation and regulation.
Perhaps the most important innovation in this arena is developing good policy i.
In , ten of the world's leading electrical engineers convened on the burgeoning topic of "Artificial Intelligence" - which was far from being recognized as a field. A decade later, the progress of early AI efforts had some researchers envisioning a relatively easy glide to human-level intelligence in machines. The inferencing process moves from the facts of the case to a goal conclusion. The strategy is thus driven by the facts available in the working memory and by the premises that can be satisfied.
The inference engine attempts to match the condition IF part of each rule in the knowledge base with the facts currently available in the working memory. If several rules match, a conflict resolution procedure is invoked; for example, the lowest-numbered rule that adds new information to the working memory is fired. The conclusion of the firing rule is added to the working memory.
Forward-chaining systems are commonly used to solve more open-ended problems of a design or planning nature, such as, for example, establishing the configuration of a complex product. If such a rule is found, its premise becomes the new subgoal. In an ES with few possible goal states, this is a good strategy to pursue. If a hypothesized goal state cannot be supported by the premises, the system will attempt to prove another goal state. Thus, possible conclusions are review until a goal state that can be supported by the premises is encountered.
Backward chaining is best suited for applications in which the possible conclusions are limited in number and well defined. Classification or diagnosis type systems, in which each of several possible conclusions can be checked to see if it is supported by the data, are typical applications. Fuzzy logic is a method of reasoning that resembles human reasoning since it allows for approximate values and inferences and incomplete or ambiguous data fuzzy data.
Fuzzy logic is a method of choice for handling uncertainty in some expert systems.
AIMSA - Artificial Intelligence: Methodology, Systems, Applications (Conference) | AcronymFinder
Expert systems with fuzzy-logic capabilities thus allow for more flexible and creative handling of problems. These systems are used, for example, to control manufacturing processes. There are several levels of ES technologies available. Two important things to keep in mind when selecting ES tools include:. The tool selected for the project has to match the capability and sophistication of the projected ES, in particular, the need to integrate it with other subsystems such as databases and other components of a larger information system.
The tool also has to match the qualifications of the project team. A shell is an expert system without a knowledge base. A shell furnishes the ES developer with the inference engine, user interface, and the explanation and knowledge acquisition facilities. Domain-specific shells are actually incomplete specific expert systems, which require much less effort in order to field an actual system. Expert system development environments. They run on engineering workstations, minicomputers, or mainframes; offer tight integration with large databases; and support the building of large expert systems.
High-level programming languages.
ESs are now rarely developed in a programming language. Three fundamental roles in building expert systems are:. Expert - Successful ES systems depend on the experience and application of knowledge that the people can bring to it during its development.
Large systems generally require multiple experts. Knowledge engineer - The knowledge engineer has a dual task. This person should be able to elicit knowledge from the expert, gradually gaining an understanding of an area of expertise. Intelligence, tact, empathy, and proficiency in specific techniques of knowledge acquisition are all required of a knowledge engineer. Knowledge-acquisition techniques include conducting interviews with varying degrees of structure, protocol analysis, observation of experts at work, and analysis of cases.
On the other hand, the knowledge engineer must also select a tool appropriate for the project and use it to represent the knowledge with the application of the knowledge acquisition facility. User - A system developed by an end user with a simple shell, is built rather quickly an inexpensively. Larger systems are built in an organized development effort.
A prototype-oriented iterative development strategy is commonly used. ESs lends themselves particularly well to prototyping. Steps in the methodology for the iterative process of ES development and maintenance include:. Problem Identification and Feasibility Analysis:. The needed degree of integration with other subsystems and databases is established. Testing and Refinement of Prototype:. End users test the prototypes of the ES. Expert systems offer both tangible and important intangible benefits to owner companies.
These benefits should be weighted against the development and exploitation costs of an ES, which are high for large, organizationally important ESs. An ES is no substitute for a knowledge worker's overall performance of the problem-solving task. But these systems can dramatically reduce the amount of work the individual must do to solve a problem, and they do leave people with the creative and innovative aspects of problem solving. Knowledge-based systems: languages and systems for representing knowledge, object-based knowledge representation systems, knowledge acquisition, validation of knowledge-based systems.
Natural language processing: syntax, semantics, discourse, speech recognition and understanding, natural language generation, natural language front ends. Perception and action: computer vision, object recognition, stereo vision, active vision, robotics systems, robot programming, robot motion planning, mobile robotics, behavior-based reactive robots.