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.
indeed artificial intelligence is used for recycling tasks. In some recycling factories mechanical sorting is carried out by a robot to facilitate the work of operators. Where does the recyclable waste that we put in the recycling bins go? For some communities such as the ones using dumpster rental Panama City services, they begin by being dumped by collection trucks in the huge hangar of the waste management factory where a sweetish smell floats. Teams of eleven people take turns each day to select and separate from this mass twelve raw materials (aluminum, steel, cardboard, paper, clear PET, dark PET, plastic film, etc.). These are then packaged in large homogeneous bales to be recycled in specific channels. The sorting center has been modernized several times. Before all sorting operations were carried out by operators. Since then, an initial sorting by sequence has been carried out by machines, trommels and blowers, which separate the major families of waste. Optical sorting was also introduced a few years ago. The objective was first to improve working conditions and, in particular, to reduce contact with hazardous waste.
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.
New recently developed technology could allow the recycling of polymeric hydrocarbons, which constitute around a quarter of plastic waste around the world. In the war against plastic pollution, all options must be considered. A new technique has recently been developed by a team of chemists from the University of Purdue in the United States, thanks to which it would be possible to transform polypropylene into a hydrocarbon. A solution which, in the long term, would make it possible to re-treat a quarter of plastic waste.
Turn plastic into liquid
This technique is based on a strange characteristic of water: when it is compressed to a pressure of 23 megapascals (about 225 terrestrial atmospheres) and heated to a temperature around 500 °C for a few hours, it then starts to behave both as a liquid and a gas. We speak of a supercritical fluid, and it is in this phase that it can act as a catalyst intended to reshape the molecular structure of the polymer. Polypropylene is then transformed from its plastic form into a fluid made up of hydrocarbon chains better known under the name of naphtha, thanks to the transfer of hydrogen atoms. This process, called hydrothermal liquefaction, is already used among other things in decontaminating petroleum. For the first time, researchers have also succeeded in applying it to this polymer, which constitutes 25% of our plastic waste, with a higher yield than that of its combustion or recycling.
In total more than 90% of the mass of the polypropylene could be converted into fuel. This conversion technology has the potential to boost profits in the recycling and waste reduction industries and reduce the world's plastic waste inventory according to the co-author of the study published in the journal ACS Sustainable Chemistry and Engineering. Even if the prospect of creating more oil using this technology does not delight everyone, it nevertheless has the crucial advantage of creating a real impetus for the recycling of these polymers, with an important financial stake for the actors involved. It would also imply a partial transition to oil production rather than its extraction, an approach which promises in theory to be less risky for the environment.
The law on waste disposal and waste recovery sets the essential principles on which the system is legally based, which regulates waste disposal. The polluter pays principle is respected, which therefore entails the responsibility of the waste producer. In addition, it defines the collection and treatment of waste as being under the responsibility of the municipalities which must ensure the elimination of waste household and similar, of commercial or artisanal origin, likely to be absorbed without risk to people and the environment, with Long Beach dumpsters or not. This does not imply responsibility for a total elimination of the harmful effects that can cause waste. But with due respect for this law, it is more concretely to avoid the harmful effects on the natural environment (soil, fauna and flora), the degradation of sites or landscapes that cause aesthetic and tourist damage, air pollution and water, which varies with the grain size of the soil used, noises and odors and generally any harm to man and the environment.
In which cases does recycling seem to be reaching its limits today
So-called mechanical recycling is a proven technology that has made it possible to achieve a recycling rate of 58% of PET and HDPE bottles and flasks. From post-consumer plastic packaging waste, mechanical recycling consists of sorting, crushing, washing and manufacturing granules, without modifying the structure of the material. This technology is reliable, mastered, but it can only process waste of a certain quality and recycled plastic still loses some of its properties. In addition, the additives, dyes, mineral fillers that make it up and which provide different functionalities to the packaging, are not eliminated during the process. Therefore, other methods must be found. Scientists need to be looking for complementary paths on the side of new recycling technologies and waste management solutions resulting from R&D on plastics. They make it possible to return to the original molecules, the monomers, and to obtain a purified material, ready to be reassembled (this is the polymerization process) to form plastic materials.
Among these waste management technologies, let us mention depolymerization, which consists of breaking the bonds of polymers (thanks to the use of alcohol, such as methanol or glycol, or even enzymes) to return to the monomer state. Another solution, dissolution: it consists of dissolving a plastic material in a specific solvent then filtering the mixture, cleaning it in a way, to obtain a purified material. Finally, thermal solutions such as pyrolysis and gasification aim to treat plastics with heat to transform them into basic chemical compounds used in plastics processing. Are these technologies really new? In reality, specialists know that they have existed in the laboratory for several decades, without having really broken through yet. But they have progressed and we are looking at them in a new light: that of environmental urgency and more ambitious recycling targets. Twenty years ago, the European Commission set the target of a 22.5% plastic recycling rate. Today, we want to aim for 100%, in the United States. The challenge is to get these recycling technologies out of laboratories and validate their feasibility on a large scale to reduce the need for dumpster rentals to keep on filling up local landfills.