The Montreal 2007 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2007) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, to report on up-to-the-minute innovations and developments, to summarize the state of-the-art, and to exchange ideas and advances in all aspects of systems engineering, human machine interface, and emerging cybernetics.
Machine learning is an area of particular interest as it permeates most of the subjects in the above list. Applications have a wide range encompassing topics from information filtering methodologies that discover user intent and preferences in online systems to data-mining multi-user gaming software large historical data sets. Sessions include learning sets of rules dynamically, analytical learning and evolutionary and population-based methods applied to the game of blackjack. Social signal processing is a related session where methodologies dealing with interpretation of social interralations are put forward. This cutting-edge research addresses the rising concensus that modern computers will need some form of social intelligence in order to be more efficient, with applications in gaming, politics and psychology.
In fact it is believed that the human brain itself follows machine learning principles in order to learn about life. Likewise animals sophisticated like higher mammals or simple ones like viruses thinner than an hair all follow a machine learning methodology to test, decipher and learn about their surroundings. It is not by solving the complex equations of mechanics that a child learns how to perform efficient locomotion of his own body, but but a trial and error process akin to machine learning. In this sense machine learning is a more potent method of learning than any other known computerized technique.
Machine Learning is a computer-based learning method on which most artificial intelligence (AI) applications are based. In machine learning, systems or algorithms progress as they mix with the data, without relying on explicit programming. The algorithms used for machine learning are very varied tools capable of forecasting while acquiring knowledge from trillions of observations. Machine learning is considered a modern extension of predictive analytics. Effective model recognition and self-learning are the pillars of machine learning schemes, which automatically adapt to changing models to ensure appropriate action. Today, many organizations rely on machine learning algorithms to better understand their customers and potential revenue opportunities. Hundreds of existing and new machine learning algorithms are applied to obtain accurate predictions that direct decisions in real time, thus less dependent on human intervention.
Machine learning can be used to measure employee satisfaction in real time. This is common and simple application of this technology, but which nevertheless bears fruit. Chicago real estate broker firm Kale Realty has gained higher employee retention since they started using such technology to measure satisfaction at theur work place. Machine learning applications can be highly complex. But it is also easy for the company to use a machine learning algorithm that compares the level of employee satisfaction with their salary. Instead of plotting a predictive satisfaction curve based on the wages of various employees, as predictive analysis suggests, the algorithm treats gigantic amounts of random training data at the time of data entry and whenever data is collected. The results of the forecasts vary in order to give accurate and more useful forecasts in real time. This machine learning algorithm uses self-learning and automated recalibration in response to changing patterns in training data, making machine learning more reliable than other AI concepts for training to make real time forecast. Increasing or repeatedly updating the drive data block ensures better predictions. Machine learning can also be used in the areas of image classification and facial recognition through advanced neural network and learning techniques.
Professional application of predictive analytics: optimization of marketing campaigns
In the past, the valuable resources of marketing campaigns were squandered by companies, who simply followed their instincts to try to capture commercial niches. Nowadays, many predictive analytics strategies help companies identify and build markets for the services and products they offer, making marketing campaigns more effective. A well-known application is to use the visitor search history and usage patterns on e-commerce websites to generate product recommendations. Sites like Amazon are increasing their sales potential by recommending products based on the consumer's personal interests. Predictive analytics now plays a crucial role in marketing activities in almost all sectors: real estate, insurance, retail, and so on. This is even true in industries that do not sound highly technological, such as waste management, recycling and sustainable energy. Companies such as Orlando junk removal services or dumpster rentals in Florida are at the leading front of leveraging this novel approach.
Linkages between machine learning and predictive analytics
Just as it is important for organizations to understand the differences between machine learning and predictive analytics, they also need to understand the connections between them. Basically, machine learning is a branch of predictive analytics. Although these two disciplines may have similar objectives and processes, there are two major differences between them.
Machine learning generates forecasts and recalibrates models in real time automatically after they are designed. Predictive analytics, on the other hand, rely strictly on cause data and must be updated with change data. Unlike machine learning, predictive analytics still relies on expert intervention to develop and test cause-result associations. That is why machine learning is gaining in popularity, as it does not require the input of a human.