Wednesday, August 26, 2020

Habits and Traits of Gall Wasps

Propensities and Traits of Gall Wasps Have you at any point seen those deformed bumps on the twigs of oak trees? Those curious developments are called nerves, and theyre quite often brought about by nerve wasps. In spite of the fact that theyre very normal, nerve wasps (family Cynipidae) regularly go unnoticed in light of their little size. How Are Gall Wasps Classified? Realm: AnimaliaPhylum: ArthropodaClass: InsectaOrder: HymenopteraFamily: Cynipidae What Do Gall Wasps Resemble? Cynipid wasps are very little, with scarcely any species estimating more than 5 millimeters long, and typically dreary in shading, which makes them rather unnoticeable. Its regularly simpler to distinguish nerve wasps from the nerves themselves. Tracks and Sign of Insects and Other Invertebrates is an amazing reference for recognizing North American nerve producers from the nerves they abandon. Cynipids overrun plants in the rose, willow, aster, and oak families. Cynipid nerves fluctuate significantly in size, shape, and appearance, contingent upon the host plant and the nerve wasp species included. Nerve wasps arent the main living beings that trigger nerve advancement in plants, yet they are likely the most productive nerve creators, particularly in oak trees. About 80% of nerve wasps target oaks explicitly. In North America, well more than 700 nerve wasp species make bothers in oaks. Nerve wasps look like minuscule hunchbacks. When seen from over, the midsection may seem to have only two fragments, however the rest are basically compacted underneath, in extending style. Nerve wasps have negligible wing venation and filiform radio wires (as a rule comprising of 13 portions in females, and 14-15 fragments in guys). Youre improbable to see nerve wasp hatchlings except if youre prone to analyze nerves. Each minuscule, white hatchling lives inside its own chamber, taking care of continually. They need legs and have biting mouthparts. What Do Gall Wasps Eat? Nerve wasp hatchlings get nourishment from the nerves in which they live. Grown-up nerve wasps are brief and don't take care of. Shockingly for a creepy crawly that eats so a lot, the hatchlings dont crap. Nerve wasp hatchlings dont have butts, so there is basically no chance to get for them to oust their waste. They hold up until the pupal stage to free their assemblages of fecal issue. The Life Cycle of Gall Wasps The cynipid life cycle can be very perplexing. In certain species, male and female nerve wasps mate and the female oviposits in the host plant. Some nerve wasps are parthenogenetic, and produce guys once in a while, if at any time. Still others exchange sexual and abiogenetic ages, and these unmistakable ages may utilize diverse host plants. By and large terms, the nerve wasp life cycle includes total transformation, with four life stages: egg, hatchling, pupa, and grown-up. The female stores an egg into the meristematic tissue of the host plant. At the point when the egg hatches and the hatchling starts to take care of, it triggers a response in the host plant, causing the development of the nerve. The hatchling takes care of inside the nerve and inevitably pupates. The grown-up nerve wasp for the most part bites a leave opening to get away from the nerve. Unique Behaviors of Gall Wasps Some nerve wasps dont produce irritates in their host plants however are rather inquilines of different species nerves. The female wasp oviposits into a current nerve, and her posterity bring forth and feed on it. The inquiline hatchlings may in a roundabout way murder the hatchlings that prompted the nerve to frame, just by outcompeting them for food. Where Do Gall Wasps Live? Researchers have depicted 1,400 types of nerve wasps around the world, however many gauge that the family Cynipidae may really incorporate upwards of 6,000 species. More than 750 species occupy North America. Assets and Further Readingâ Capinera, John L., editor. Encyclopedia of Entomology. second ed., Springer, 2008.Frogge, Mary Jane. â€Å"Most Leaf Galls Dont Hurt Trees (Galls).†Ã‚ Institute of Agriculture and Natural Resources: The Nebline, University of Nebraska-Lincoln in Lancaster County, May 2012.Johnson, Norman F., and Charles A. Triplehorn. Borror and DeLongs Introduction to the Study of Insects. seventh ed., Cengage Learning, 2004.Leung, Richard, et al. â€Å"Family Cynipidae - Gall Wasps.†Ã‚ BugGuide.Net, Iowa State University, 13 Apr. 2005.

Saturday, August 22, 2020

Where Have They Gone essays

Where Have They Gone expositions For some reasons mankind could be known as a gift. Incredible progressed in innovation, medication and even the reality we are the most complex species on earth. Is it true that we are a blessing to planet Earth, or a long way from it? With cast measures of contamination and annihilation of the planet, also incomprehensible demonstrations of brutality and despise that has been going on since the very beginning. Is it true that we are truly as advanced and significant as we have persuaded? It is safe to say that we are any better than some other animal since we are all the more mechanically progressed? Is humankind a gift? People have decimated and imperiled a greater number of animal categories on our planet than some other species or gathering, with our nonstop contamination and absence of regard for out own condition. One zone of the world influenced by our imprudent propensities is our coastlines and the marine territories that tremendous measures of species depend on. These specific zones of the world are being obliterated in light of the fact that people dont appear to mind as long as they make two or three dollars simultaneously. Oil slicks like the one in the Prince William Sound on the shore of Alaska and Hawaiian ocean turtles and their numerous issues with people are only a few instances of human lack of regard and the results that the earth, especially marine untamed life bring about, which frequently are deadly. I picked this specific subject since I discover the sea and its exceptional and uncommon occupants to be intriguing. Each coastline has its one of a kind animal types and no two regions are the equivalent. I needed to become familiar with how people are decimating the environments of these novel animals. I found that all species are in someway being compromised by human strength and lack of regard. From the regular fumble or ocean star you can discover when you stroll over the sea shore to an uncommon fish like the coelacanth (ancient fish that was accepted to be terminated until one was gotten off the layer of Madagascar by a nearby business angler until in ... <!

Friday, August 21, 2020

Artificial Intelligence A Complete Guide

Artificial Intelligence A Complete Guide Over the course of the last fifty years, the artificial intelligence research field spurred immense features that are not conceived as AI by the general public. Most of our online endeavors include forms of AI (virtual agents, pattern recognition, targeted advertising). However, all that has been done so far is a mere grain of sand in reference to the predicaments for the sandy future. In order to position ourselves according to these advancements, we need to acquire knowledge on the process.Business enterprises have become increasingly aware that artificial intelligence can be (and in the future â€" will be) a definitive factor for success. Currently, these properties are implemented in data analysis algorithms which have the capability to properly store, process and analyze Big Data (another growing sphere of business management) but will soon include product optimization algorithms and complex customer engagement techniques. © Shutterstock.com | Tatiana ShepelevaIn this article, we are presenting a complete guide to artificial intelligence through sections 1) Origins of AI; 2) Goals of AI; 3) Approaches and Tools; 4) Issues of AI; 5) Application in Entrepreneurship, and 6) Examples of AI implementation in Business.ORIGINS OF ARTIFICIAL INTELLIGENCEThe Idea and Philosophical BackgroundFoundations of ideas revolving around the creation of artificial intelligence can be traced back to automatons built by Egyptian and Chinese civilizations as well as to ancient Greek mythology. Implementing human properties to objects and abstract ideas is one of the ways people have been reasoning with their existence from the moment they acquired consciousness.With the development of logic and emergence of the symbolic reasoning field of philosophy, the creation of machines that could emulate human intelligence became possible to achieve in practice. The symbolic reasoning states that symbols (numbers, graphs, calculation s, statistics, etc.) can be used as synonymous substitutes for longer expressions in order to solve problems. The idea was proposed in the 16th century by Thomas Hobbes, who is considered to be the ‘Grandfather of AI’.Further on, as engineering advanced over the centuries, the two fields begun to correlate. The first computer â€" Analytical Engine, was designed in the 19th century by Charles Babbage (but it was not built until 1991). With the ongoing progress of technology from the early 20th century onward as well as the increasing necessity of better understanding of processes of computing, various models, and theoretical discourses were created.The Turing TestAlan Turing published a fundamental work on the issue in 1950 the Computing Machinery and Intelligence paper. In the paper, he proposed the Turing machine model through which he discussed the theoretical possibilities of what can be computed. In order to deduct whether the computing possibilities extended to the spheres of human intelligence, he created the Turing test. The objective of the test was to identify whether a machine can convince a suspicious interrogator that it was indeed a human being. The test seemed to be quite simple â€" no complex assignments (such as creating original art, for example) were involved; in order to pass, the computer was to be able to make small talk with a human being and show understanding of the given context. As simple as it sounds from the perspective of a human, realization of such results proved to be extremely difficult and, up to this date, unachievable. Primary problems were those related to hardware technology of the mid 20th century â€" storage room issues camouflaged the future issues regarding software realization.Researchers are still trying to create software that would pass the Turing test and present them on the annual Turing Competition. The Leobner prize of $100,000 in cash is still waiting for the first software to prove to be sentient.AI â€" Field of StudyBased on philosophical, logical, mathematical, cybernetic, neuroscience and information technology advancements, artificial intelligence field of study was born in 1956 at a conference at Dartmouth College. Experts John McCarthy and Marvin Minsky became prominent names in the wide-spanning effort to create intelligent machines for the next fifty years.Naturally, in order to create intelligence one must know what intelligence is. However, the abstract definitions of intelligence as a property of human beings (and some animals) which is manifested in logic, reasoning, learning through experience, appliance of knowledge, creativity and a myriad of other, cannot simply be translated into symbols and produce sentient machinery.Computer-Chess and Expert SystemsScientists implemented different approaches and methods so as to build up artificial intelligence. One of the approaches was the evolution of the chess-playing software. Due to the fact that it was much easier to achie ve high efficiency through brute force techniques â€" meaning that the computer computes solution algorithms on the principle of minimal cost for the maximum damage possible for a certain amount of future moves â€" the chess-playing software did not focus much on building sentient but rather on advanced search techniques and sustainable hardware for large databases.On the other hand, expert systems were developed so as to provide expert assistance in different industries. By creating a proficient knowledge database and incorporating machine learning software â€" which enables machines to make predictions and provide consultation regarding given data; as well as interaction software (based on natural language development) â€" scientists broadened the properties of their ‘intelligent machines’. These achievements are now used in navigation systems, medicine as well as a business.Winters of AIAfter the initial exhilaration with the AI field of research, it soon became clear that so lid results are going to take more time than what was anticipated and announced. After ALPAC and Lighthill reports, which showed unsatisfactory advancement in the AI projects (problems with natural language software, slow advancements), the flux of investment was terminated â€" the first AI Winter begun in the 1974 and lasted until early 1980s when the British government instigated AI projects as a response to Japanese endeavours regarding logic programming. However, in 1987, due to the collapse of the general-purpose computers market and the decrease in funding, the second AI Winter emerged and lasted for five years.In the ‘winter’ periods, AI research continued under different names which will become sub-categories of the field in the future â€" evolutionary programming, machine learning, speech recognition, data mining, industrial robotics, search engines and many other.Where is AI now?The artificial intelligence research field enabled much progresses which are regarded as †˜common’ nowadays â€" specified and personalized search engine results, intelligent personal assistant software â€" Siri, Google Translate, vehicle navigation systems, diverse robotics enhancements and countless other.Some notable achievements include:IBM’s Deep Blue became the first computer to win a chess game against a chess champion â€" Garry Kasparov, in 1997.IBM’s question answering system Watson won the Jeopardy quiz against proficient opponents in 2011.Eugene Goostman, a chatbot persuaded a member of the Turing test jury that it was a 13-year-old boy from Ukraine in 2014. However, Eugene passed the bare minimum of conviction with 33%. Such a result is not considered to be a pass of the Turing test in essence because it relies mostly on the external condition (a child from a non-English speaking country can be forgiven for insufficiencies in small talk, while an adult native speaker would not have been). In the course of the 2015, the developers of Eugene are expected t o defend their victory and prove that they invented sentient software (which they most probably did not).As can be noted from all that is stated above, it is clear that hard issues of artificial intelligence have not seen immense progress much in the last fifty years. Consequently, experts predict at least fifty more years of trial and error in order to emulate human intelligence. It is simply too broad and complex of a subject to be resolved in a short period of time. However, the advances that were made during the quest so far have influenced and shaped the world we live in greatly.GOALS OF ARTIFICIAL INTELLIGENCEThe ‘final’ goal of artificial intelligence endeavors is to create an intelligent machine which is capable of reasoning, planning, solving problems, thinking abstractly, comprehending complex ideas, learning quickly and learning from experience (which is an agreed definition of human intelligence). In practice, this artificially emulated intelligence is to reflect a b road and deep ability to comprehend its surroundings so as to figure out what to do in infinite possible situations. In order to adequately position itself in environment, the AI needs to be socially intelligent (meaning that it has to be able to perceive and properly react to a broad specter of abstract features and properties of intelligible universe for example, emotion). In order to manage problems optimally, it needs to be able to implement creativity in its functioning. All of the stated properties are attributed to the long-term goal of AI studies â€" general intelligence.However, in order to achieve such a goal, scientists have to focus on a wide variety of complex concepts that are its building blocks, both individually and in correlation. The builders of the future intelligent machine need to implement in their work the empirical studies of existing intelligent systems (mainly of human beings) as well as results of theoretical exploration and analysis of possible systems of intelligence (and their mechanisms and representations). These factors are essential for resolution of issues related to existing intelligent systems as well as designing new intelligent or semi-intelligent machines. Essentially, this means that a full view of the complexity of the task must be acquired because by restricting endeavors solely to one field (for example, engineering), the efforts will not provide satisfactory results. It would have been impossible to construct airplanes without examination of birds.Deduction, reasoning, problem-solvingIn the beginnings of AI research, the reasoning process was induced through step by step imitation of human processes in solving puzzles or logical deductions. However, this approach depended greatly on computational resources and computer memory that was at the time rather confined. These issues pointed out the necessity of imitation of immediate judgment processes in human beings rather than those of deliberate reasoning. Immediate judgment can be seen as the intuitive, subconscious knowledge which governs the direction of deliberate actions.AI makes attempts at reaching the goal of immediate judgment through combination of:Embodied Agents (autonomous entities that can interact with environment and are presented as a three-dimensional virtual-simulation/real-robot body);Sensorimotor Skills (combination of perceiving environment through sensors and reacting with motor skills â€" for example, a robot perceives that a person is approaching and offering a hand as a greeting â€" the robot reacts by shaking its hand with the person);Neural Networks (simulation of structures and processes in the neural systems, most notably, human brain: computing values from inputs; machine learning; pattern recognition; adaptive nature);Statistical Approaches (mathematical approaches to specific problem resolutions).Knowledge representationIn order to emulate a human being, AI needs to incorporate immense amounts of knowledge regar ding objects, their properties, categories and relations among each other. Moreover, it has to implement situations and states, causes, effects and abstracts ideas. The AI field uses ontological approach to knowledge representation â€" that is, knowledge is postulated in sets of concepts whose relationship is defined within a domain.IssuesImpossibility of true/false statements â€" everything has exceptions;The width of human knowledge makes creating comprehensive ontology almost impossible;The sub-conscious and sub-symbolic forms of knowledge must be incorporated.SolutionsStatistical AI â€" mathematical resolution of certain problems;Situated AI â€" systems as autonomous entities through interaction with environment develop elementary behaviors;Computational Intelligence â€" computer that understood enough concepts, so it is able to provide further ontology by itself (via Internet, for example).Automated planningAI must be able to construct complex and optimized solutions in multidi mensional space and perform realization of these strategies/sequences of action. In other words, intelligent agents need to be able to visualize potential future (predictive analysis), set goals of action (decision making) and perform in a manner which will maximize efficiency (value) of the process.These goals are to be handled both offline (for the known environment) and online (for unexpected environments). Scientists still have to deal with the issues of unpredicted scenarios â€" when the machine is expected to react intelligently.Machine learningMachine learning is the construction and study of algorithms which allow AI systems to make predictions and decision based on data input and knowledge acquired through it.It can be focused on:unsupervised pattern recognition in streams of input (for example, defining spam mail from non-spam mail in electronic mail systems);supervised (programmed) classification and relation formation in the input data (for example guiding spam and non-s pam mail into different categories in the system).Machine learning is used in various spheres of information technology such as spam filtering (mentioned as an example above), optical character recognition, search engines personalization, computer vision and data mining (predictive analysis).Further enhancement of machine learning algorithms should attribute to the overall computational intelligence of machines.Natural language processingNatural language processing and generation are one of the central issues which the artificial intelligence field of study deals with. It is no wonder that Turing test revolves around the ability of machines to converse (at least seemingly) conscientiously â€" a machine that will be able to understand spoken or written words within their context and be able to respond accordingly is something which can be characterized as an intelligent entity (because it involves abstract properties â€" social intelligence, knowledge, perception, problem-solving, et c.).Machine PerceptionMachine perception represents the capability of input interpretation that resembles processes of human perception through senses. The important issues which are trying to be addresses are those of comprehensive perception, transmission to an intelligent core of the entity and systems of response (that is, machine perception meets difficulties in both engineering and computing features).Vision collecting information based on the image of the high-dimensional outside world and transforming them to algorithms/solutions for given problems (currently, machines can exercise facial recognition and esthetic judgment but there is a long road of development ahead);Hearing â€" ability to process audio data such as music or speech (currently: voice recognition, voice translators);Touch â€" ability to process surface properties and dexterity in order to effectively and intelligently interact with environment.RoboticsGoals in robotics combine engineering with artificial int elligence studies and revolve around questions of:object manipulation;navigation;localization;mapping;motion planning.APPROACHES AND TOOLS OF AIApproachesFrom the emergence of the AI research in the 1950s, numerous approaches have been undertaken through the implementation of knowledge in diverse industries and academic circles. These approaches evolved as a response to shortcomings that each of them showed regarding the realization of the goal â€" general intelligence. When the AI research lost funding during the winters of AI, the disintegration of approaches was the only way to acquire investments for continuous studies. What can be concluded from today’s point of view is that all of these approaches are essential to the vast complexities of artificial intelligence and that all of them contributed immensely to the process (no matter how slow or lacking in exhilarating advancements the process itself might be).ConnectivityCombining techniques and knowledge of neurology, informat ion technology, and cybernetics, scientists achieved a simulation of basic intelligence in the 1950s. The approach was abandoned in the following decade only to re-emerge in the 1980s.Achievementssensory processing;behavior of neural networks;knowledge on regulatory systems.SymbolismThe approach states that human intelligence can be simulated exclusively through manipulation of symbols. It is also called the ‘good old-fashioned artificial intelligence’ â€" GOFAI and had success in high intelligence simulation in the 1960s â€" restricted to confined demonstration programs.Achievementsexpert systemsCognitive SimulationCognitive simulation approach is embodied in psychological tests that were conducted in order to acquire knowledge on human problemâ€"solving skills. The results were to be formalized so as to develop programs that would simulate these properties of human intelligence.Achievementsfoundations for artificial intelligence research â€" machine learning, natural language processing, etc.LogicRepresentatives of the logical approach held that human intelligence in its essence spurs from abstract reasoning and problem-solving and can thus be treated with logic’s techniques.Achievementsknowledge representation;automated planning;machine learning;logic programming.Anti-LogicOpponents of the logic approach stated that no general principle can capture the complexity of intelligent behavior.Achievementspointed out the lack of efficiency of the logic approach in matters of machine vision and natural language processingKnowledgeKnowledge-based approach began to be highly implemented in the artificial intelligence research studies since the emergence of expert systems and the increase of storage capacities of operational systems.Achievementsimplementation into expert systems;one of the crucial elements of general intelligence.AbstractThe abstract approach emerged from the necessity of addressing sub-symbolic and intuitive specters of human intelligence in or der to provide optimal solutions for problems of artificial intelligence.Achievementscomputer perception;robotics;machine learning;pattern recognition.SituatedSituated or novel artificial intelligence approach focuses on basic engineering problems and rejects the exclusivity of the symbolic approach. The goal is to construct a realistic machine that can exist in the real environment.Achievementsmotor skills;sensory skills;computer perception.StatisticalStatistical approach uses measurable and verifiable mathematical tools and combines them with economics in order to solve specified problems. The approach is criticized in the matter of disregard towards the goal of general intelligence.Achievementssuccessful addressing of particular problemsToolsArtificial intelligence field of study has encountered infinite problems in its quest for realization. However, it implemented diverse methods through which problems can be successfully addressed.Search and Optimization MethodSearching for ma ny possible solutions, eliminating those which are unlikely to lead to the particular (or overall) goal and choosing an optimal pathway can be an efficient way of resolving issues. Reasoning, planning and robotics algorithms are created with the assistance of search techniques based on optimization.Mathematical optimization theory is formed by beginning the search for solution with an intelligent guess and advancing towards its refinement (also referred to as ‘hill climbing’: choosing a random point in the landscape and advancing in random moves towards the hill top).The evolutionary computation follows the ‘survival of the fittest’ principle â€" a series of guesses is postulated, through refinement some of the guesses fall of, and thus the optimal solution presents itself.Logic as a Solution MethodLogic is used for solving problems regarding automated planning and machine learning, as well as those of logic programming. It is used for determining validity through true/false attribution, expressing facts about objects, their properties and relations which is essential for ontology in knowledge representation.Other MethodsProbability algorithms for filtering and predictive analysis of streams of data;Classifiers and statistical learning methods;Artificial neural networks;Programming languages (differ according to specific needs of a sub-category of AI).ISSUES OF ARTIFICIAL INTELLIGENCEMost researchers in the artificial intelligence field state that general intelligence in machines will be achieved in the course of the following fifty years. Although we cannot confirm such statements, it seems plausible that the advancements will happen, and will change the world entirely. Consequently, various issues are bound to arise.Primarily, AI systems have capabilities of data processing and predictive analysis which surpass those of humans greatly. In order to achieve optimal performance, they are somewhat autonomous, governed by a carefully chosen set of rules i n order to reach a goal of a sort. However, due to their autonomy they can perform in a misbalance regarding their users â€" if a potential problem was not addressed in the programming, the system would undertake it if it serves the goal (and it is impossible for humans to predict all possible situations and adequate algorithms for them). The issue must be addressed by providing clear safety criteria in order to minimize damage if an error occurs. Moreover, proper attribution of responsibility is a question that needs to be addressed regarding artificial intelligence endeavors.Further on, as the general intelligence emerges, humans must define moral systems according to which they will structure the AI systems but also the moral rules according to which they will position themselves in relation to AI systems. The questions of ethics in artificial intelligence are impossibly complex â€" how to define whether a system is programmed to behave and claim sentient or sentient?Additionall y, who is going to be in charge of decision-making regarding general AI? While we are all introduced to the positive and advanced opportunities that AI technology will bring â€" termination of disease, space travel, reduction of work, etc., we seem to forget that humans are capable of massive destruction for power and money acquisition. Obviously, some regulations on the usage of AI systems will have to be made.AI APPLICATION IN ENTREPRENEURSHIPBig Data and Specialized AnalyticsOver the past few years, the exponential growth in technology capabilities (those of storage and computing primarily), the influx of data has increased enormously. Today, companies can collect and process Big Data in structured and unstructured (pictures, videos) forms and analyze it so as to attain valuable insights regarding business strategy. One of the issues of Big Data management is the lack of experts that could make sense out of it and put it into practice. Various software solutions have been present ed to simplify the process â€" such as expert systems and predictive analysis. Obviously, these are products of artificial intelligence studies.However, as the algorithms evolve, so will their influence on data managements. Machine learning is a data-based predictive and decision-making algorithm that can, when combined with natural language processing, present usable (and valuable) information and solutions regarding business strategies (advertising, customer relations, coaching employees) with the overall goal of increasing productivity and customer engagement (satisfaction), competitiveness on the market and growth.Optimization of Products and ServicesArtificial intelligence algorithms will be implemented not only in the business management spheres but also in the product efficiency and desirability. For example, lawn mowers will be able to lawn mown without human participation. Moreover, they will be able to perform specialized and personalized constructive tasks such as not pul ling out flowers. All this will contribute to customer satisfaction because it represents a continuous exponential decrease of time and effort requirements from the customer for maximized efficiency and value.EXAMPLES OF AI IMPLEMENTATION IN BUSINESSIn addition to significant efforts of IBM in artificial intelligence since its beginnings, big companies such as Google and Facebook had to attend to AI possibilities as well because of massive amounts of data and complex management and strategy defining processes. Here we will take a look at these three companies and their entanglement in AI.IBMIn addition to a significant success which IBM received publicly with their endeavours in AI technologies such as Deep Blue chess-playing algorithm and the complex Watson system, the actual benefits lie in the properties which their technologies mastered and their implementation in business. Deep Blue algorithm managed to process an enormous amount of predictive analysis based on maximizing effic iency according to the rules of chess and showed that by clear formulation of goals, there is no need (as it would be impossible) to cover possible solutions manually â€" the computer did it autonomously and, restricted to the objective that it was programmed for, optimized in such a manner that even a chess champion could not override the process.The Watson system was developed as a real-time question and answer algorithm that managed to perceive and process natural language as well as reason correct answers and generate them in the natural language â€" won the Jeopardy quiz while operating offline. It was created on machine learning basis because it would be a time-consuming and possibly non-effective approach to implement ontology of vast knowledge into it manually.These advancements are extremely significant for business strategies as they optimize broad processing of relevant content and enable constructive communication in order to present insights and perform decisions based on these analytical processes.Currently, IBM is focused on implementing their algorithms in a cloud-based environment and creating databases for health-care, business and education.GoogleGoogle has been using artificial intelligence features for personalization and specification of their search engines, developed Google Translate which is a sufficient natural language processing and generation tool (aside from its lacking in matters of context and sub-symbolic meanings) as well as implemented a neural network strategy in management of their immense databases. These neural strategies are designed to recognize patterns and make decisions upon them extremely fast. Also, the machine learning algorithms are included which means that systems learn through experience and as such perform more effectively.FacebookFacebook profiles are a melting pot for structured and unstructured data: friends lists, pages liked, groups joined. In order to optimize customer experience, Facebook implements ar tificial intelligence to recognize behavioral patterns of individual users (on the Facebook domain, as well as online in general, ) and offers according to particular inclinations and interests. Their efforts are heading towards creating an intelligent agent who will be able to interact with users and provide valuable information instantaneously.Considering Moore’s theory of exponential growth of technology and knowledge, we can predict that the science fiction depictions of future are actually right around the corner, especially if we are taking the complexity of the objectives into consideration. Although there are numerous issues regarding AI realization and ethical conundrums regarding diverse specters of AI, the progress is happening and will bring a lot of positive features with it. In business, it will enable strategies designed for individual users â€" increasing their satisfaction and profit generation for the enterprise. It will have even more far-reaching consequences i n medicine, sustainable economies, poverty reduction and education. We should only hope that the progress will always serve its altruistic purposes.

Artificial Intelligence A Complete Guide

Artificial Intelligence A Complete Guide Over the course of the last fifty years, the artificial intelligence research field spurred immense features that are not conceived as AI by the general public. Most of our online endeavors include forms of AI (virtual agents, pattern recognition, targeted advertising). However, all that has been done so far is a mere grain of sand in reference to the predicaments for the sandy future. In order to position ourselves according to these advancements, we need to acquire knowledge on the process.Business enterprises have become increasingly aware that artificial intelligence can be (and in the future â€" will be) a definitive factor for success. Currently, these properties are implemented in data analysis algorithms which have the capability to properly store, process and analyze Big Data (another growing sphere of business management) but will soon include product optimization algorithms and complex customer engagement techniques. © Shutterstock.com | Tatiana ShepelevaIn this article, we are presenting a complete guide to artificial intelligence through sections 1) Origins of AI; 2) Goals of AI; 3) Approaches and Tools; 4) Issues of AI; 5) Application in Entrepreneurship, and 6) Examples of AI implementation in Business.ORIGINS OF ARTIFICIAL INTELLIGENCEThe Idea and Philosophical BackgroundFoundations of ideas revolving around the creation of artificial intelligence can be traced back to automatons built by Egyptian and Chinese civilizations as well as to ancient Greek mythology. Implementing human properties to objects and abstract ideas is one of the ways people have been reasoning with their existence from the moment they acquired consciousness.With the development of logic and emergence of the symbolic reasoning field of philosophy, the creation of machines that could emulate human intelligence became possible to achieve in practice. The symbolic reasoning states that symbols (numbers, graphs, calculation s, statistics, etc.) can be used as synonymous substitutes for longer expressions in order to solve problems. The idea was proposed in the 16th century by Thomas Hobbes, who is considered to be the ‘Grandfather of AI’.Further on, as engineering advanced over the centuries, the two fields begun to correlate. The first computer â€" Analytical Engine, was designed in the 19th century by Charles Babbage (but it was not built until 1991). With the ongoing progress of technology from the early 20th century onward as well as the increasing necessity of better understanding of processes of computing, various models, and theoretical discourses were created.The Turing TestAlan Turing published a fundamental work on the issue in 1950 the Computing Machinery and Intelligence paper. In the paper, he proposed the Turing machine model through which he discussed the theoretical possibilities of what can be computed. In order to deduct whether the computing possibilities extended to the spheres of human intelligence, he created the Turing test. The objective of the test was to identify whether a machine can convince a suspicious interrogator that it was indeed a human being. The test seemed to be quite simple â€" no complex assignments (such as creating original art, for example) were involved; in order to pass, the computer was to be able to make small talk with a human being and show understanding of the given context. As simple as it sounds from the perspective of a human, realization of such results proved to be extremely difficult and, up to this date, unachievable. Primary problems were those related to hardware technology of the mid 20th century â€" storage room issues camouflaged the future issues regarding software realization.Researchers are still trying to create software that would pass the Turing test and present them on the annual Turing Competition. The Leobner prize of $100,000 in cash is still waiting for the first software to prove to be sentient.AI â€" Field of StudyBased on philosophical, logical, mathematical, cybernetic, neuroscience and information technology advancements, artificial intelligence field of study was born in 1956 at a conference at Dartmouth College. Experts John McCarthy and Marvin Minsky became prominent names in the wide-spanning effort to create intelligent machines for the next fifty years.Naturally, in order to create intelligence one must know what intelligence is. However, the abstract definitions of intelligence as a property of human beings (and some animals) which is manifested in logic, reasoning, learning through experience, appliance of knowledge, creativity and a myriad of other, cannot simply be translated into symbols and produce sentient machinery.Computer-Chess and Expert SystemsScientists implemented different approaches and methods so as to build up artificial intelligence. One of the approaches was the evolution of the chess-playing software. Due to the fact that it was much easier to achie ve high efficiency through brute force techniques â€" meaning that the computer computes solution algorithms on the principle of minimal cost for the maximum damage possible for a certain amount of future moves â€" the chess-playing software did not focus much on building sentient but rather on advanced search techniques and sustainable hardware for large databases.On the other hand, expert systems were developed so as to provide expert assistance in different industries. By creating a proficient knowledge database and incorporating machine learning software â€" which enables machines to make predictions and provide consultation regarding given data; as well as interaction software (based on natural language development) â€" scientists broadened the properties of their ‘intelligent machines’. These achievements are now used in navigation systems, medicine as well as a business.Winters of AIAfter the initial exhilaration with the AI field of research, it soon became clear that so lid results are going to take more time than what was anticipated and announced. After ALPAC and Lighthill reports, which showed unsatisfactory advancement in the AI projects (problems with natural language software, slow advancements), the flux of investment was terminated â€" the first AI Winter begun in the 1974 and lasted until early 1980s when the British government instigated AI projects as a response to Japanese endeavours regarding logic programming. However, in 1987, due to the collapse of the general-purpose computers market and the decrease in funding, the second AI Winter emerged and lasted for five years.In the ‘winter’ periods, AI research continued under different names which will become sub-categories of the field in the future â€" evolutionary programming, machine learning, speech recognition, data mining, industrial robotics, search engines and many other.Where is AI now?The artificial intelligence research field enabled much progresses which are regarded as †˜common’ nowadays â€" specified and personalized search engine results, intelligent personal assistant software â€" Siri, Google Translate, vehicle navigation systems, diverse robotics enhancements and countless other.Some notable achievements include:IBM’s Deep Blue became the first computer to win a chess game against a chess champion â€" Garry Kasparov, in 1997.IBM’s question answering system Watson won the Jeopardy quiz against proficient opponents in 2011.Eugene Goostman, a chatbot persuaded a member of the Turing test jury that it was a 13-year-old boy from Ukraine in 2014. However, Eugene passed the bare minimum of conviction with 33%. Such a result is not considered to be a pass of the Turing test in essence because it relies mostly on the external condition (a child from a non-English speaking country can be forgiven for insufficiencies in small talk, while an adult native speaker would not have been). In the course of the 2015, the developers of Eugene are expected t o defend their victory and prove that they invented sentient software (which they most probably did not).As can be noted from all that is stated above, it is clear that hard issues of artificial intelligence have not seen immense progress much in the last fifty years. Consequently, experts predict at least fifty more years of trial and error in order to emulate human intelligence. It is simply too broad and complex of a subject to be resolved in a short period of time. However, the advances that were made during the quest so far have influenced and shaped the world we live in greatly.GOALS OF ARTIFICIAL INTELLIGENCEThe ‘final’ goal of artificial intelligence endeavors is to create an intelligent machine which is capable of reasoning, planning, solving problems, thinking abstractly, comprehending complex ideas, learning quickly and learning from experience (which is an agreed definition of human intelligence). In practice, this artificially emulated intelligence is to reflect a b road and deep ability to comprehend its surroundings so as to figure out what to do in infinite possible situations. In order to adequately position itself in environment, the AI needs to be socially intelligent (meaning that it has to be able to perceive and properly react to a broad specter of abstract features and properties of intelligible universe for example, emotion). In order to manage problems optimally, it needs to be able to implement creativity in its functioning. All of the stated properties are attributed to the long-term goal of AI studies â€" general intelligence.However, in order to achieve such a goal, scientists have to focus on a wide variety of complex concepts that are its building blocks, both individually and in correlation. The builders of the future intelligent machine need to implement in their work the empirical studies of existing intelligent systems (mainly of human beings) as well as results of theoretical exploration and analysis of possible systems of intelligence (and their mechanisms and representations). These factors are essential for resolution of issues related to existing intelligent systems as well as designing new intelligent or semi-intelligent machines. Essentially, this means that a full view of the complexity of the task must be acquired because by restricting endeavors solely to one field (for example, engineering), the efforts will not provide satisfactory results. It would have been impossible to construct airplanes without examination of birds.Deduction, reasoning, problem-solvingIn the beginnings of AI research, the reasoning process was induced through step by step imitation of human processes in solving puzzles or logical deductions. However, this approach depended greatly on computational resources and computer memory that was at the time rather confined. These issues pointed out the necessity of imitation of immediate judgment processes in human beings rather than those of deliberate reasoning. Immediate judgment can be seen as the intuitive, subconscious knowledge which governs the direction of deliberate actions.AI makes attempts at reaching the goal of immediate judgment through combination of:Embodied Agents (autonomous entities that can interact with environment and are presented as a three-dimensional virtual-simulation/real-robot body);Sensorimotor Skills (combination of perceiving environment through sensors and reacting with motor skills â€" for example, a robot perceives that a person is approaching and offering a hand as a greeting â€" the robot reacts by shaking its hand with the person);Neural Networks (simulation of structures and processes in the neural systems, most notably, human brain: computing values from inputs; machine learning; pattern recognition; adaptive nature);Statistical Approaches (mathematical approaches to specific problem resolutions).Knowledge representationIn order to emulate a human being, AI needs to incorporate immense amounts of knowledge regar ding objects, their properties, categories and relations among each other. Moreover, it has to implement situations and states, causes, effects and abstracts ideas. The AI field uses ontological approach to knowledge representation â€" that is, knowledge is postulated in sets of concepts whose relationship is defined within a domain.IssuesImpossibility of true/false statements â€" everything has exceptions;The width of human knowledge makes creating comprehensive ontology almost impossible;The sub-conscious and sub-symbolic forms of knowledge must be incorporated.SolutionsStatistical AI â€" mathematical resolution of certain problems;Situated AI â€" systems as autonomous entities through interaction with environment develop elementary behaviors;Computational Intelligence â€" computer that understood enough concepts, so it is able to provide further ontology by itself (via Internet, for example).Automated planningAI must be able to construct complex and optimized solutions in multidi mensional space and perform realization of these strategies/sequences of action. In other words, intelligent agents need to be able to visualize potential future (predictive analysis), set goals of action (decision making) and perform in a manner which will maximize efficiency (value) of the process.These goals are to be handled both offline (for the known environment) and online (for unexpected environments). Scientists still have to deal with the issues of unpredicted scenarios â€" when the machine is expected to react intelligently.Machine learningMachine learning is the construction and study of algorithms which allow AI systems to make predictions and decision based on data input and knowledge acquired through it.It can be focused on:unsupervised pattern recognition in streams of input (for example, defining spam mail from non-spam mail in electronic mail systems);supervised (programmed) classification and relation formation in the input data (for example guiding spam and non-s pam mail into different categories in the system).Machine learning is used in various spheres of information technology such as spam filtering (mentioned as an example above), optical character recognition, search engines personalization, computer vision and data mining (predictive analysis).Further enhancement of machine learning algorithms should attribute to the overall computational intelligence of machines.Natural language processingNatural language processing and generation are one of the central issues which the artificial intelligence field of study deals with. It is no wonder that Turing test revolves around the ability of machines to converse (at least seemingly) conscientiously â€" a machine that will be able to understand spoken or written words within their context and be able to respond accordingly is something which can be characterized as an intelligent entity (because it involves abstract properties â€" social intelligence, knowledge, perception, problem-solving, et c.).Machine PerceptionMachine perception represents the capability of input interpretation that resembles processes of human perception through senses. The important issues which are trying to be addresses are those of comprehensive perception, transmission to an intelligent core of the entity and systems of response (that is, machine perception meets difficulties in both engineering and computing features).Vision collecting information based on the image of the high-dimensional outside world and transforming them to algorithms/solutions for given problems (currently, machines can exercise facial recognition and esthetic judgment but there is a long road of development ahead);Hearing â€" ability to process audio data such as music or speech (currently: voice recognition, voice translators);Touch â€" ability to process surface properties and dexterity in order to effectively and intelligently interact with environment.RoboticsGoals in robotics combine engineering with artificial int elligence studies and revolve around questions of:object manipulation;navigation;localization;mapping;motion planning.APPROACHES AND TOOLS OF AIApproachesFrom the emergence of the AI research in the 1950s, numerous approaches have been undertaken through the implementation of knowledge in diverse industries and academic circles. These approaches evolved as a response to shortcomings that each of them showed regarding the realization of the goal â€" general intelligence. When the AI research lost funding during the winters of AI, the disintegration of approaches was the only way to acquire investments for continuous studies. What can be concluded from today’s point of view is that all of these approaches are essential to the vast complexities of artificial intelligence and that all of them contributed immensely to the process (no matter how slow or lacking in exhilarating advancements the process itself might be).ConnectivityCombining techniques and knowledge of neurology, informat ion technology, and cybernetics, scientists achieved a simulation of basic intelligence in the 1950s. The approach was abandoned in the following decade only to re-emerge in the 1980s.Achievementssensory processing;behavior of neural networks;knowledge on regulatory systems.SymbolismThe approach states that human intelligence can be simulated exclusively through manipulation of symbols. It is also called the ‘good old-fashioned artificial intelligence’ â€" GOFAI and had success in high intelligence simulation in the 1960s â€" restricted to confined demonstration programs.Achievementsexpert systemsCognitive SimulationCognitive simulation approach is embodied in psychological tests that were conducted in order to acquire knowledge on human problemâ€"solving skills. The results were to be formalized so as to develop programs that would simulate these properties of human intelligence.Achievementsfoundations for artificial intelligence research â€" machine learning, natural language processing, etc.LogicRepresentatives of the logical approach held that human intelligence in its essence spurs from abstract reasoning and problem-solving and can thus be treated with logic’s techniques.Achievementsknowledge representation;automated planning;machine learning;logic programming.Anti-LogicOpponents of the logic approach stated that no general principle can capture the complexity of intelligent behavior.Achievementspointed out the lack of efficiency of the logic approach in matters of machine vision and natural language processingKnowledgeKnowledge-based approach began to be highly implemented in the artificial intelligence research studies since the emergence of expert systems and the increase of storage capacities of operational systems.Achievementsimplementation into expert systems;one of the crucial elements of general intelligence.AbstractThe abstract approach emerged from the necessity of addressing sub-symbolic and intuitive specters of human intelligence in or der to provide optimal solutions for problems of artificial intelligence.Achievementscomputer perception;robotics;machine learning;pattern recognition.SituatedSituated or novel artificial intelligence approach focuses on basic engineering problems and rejects the exclusivity of the symbolic approach. The goal is to construct a realistic machine that can exist in the real environment.Achievementsmotor skills;sensory skills;computer perception.StatisticalStatistical approach uses measurable and verifiable mathematical tools and combines them with economics in order to solve specified problems. The approach is criticized in the matter of disregard towards the goal of general intelligence.Achievementssuccessful addressing of particular problemsToolsArtificial intelligence field of study has encountered infinite problems in its quest for realization. However, it implemented diverse methods through which problems can be successfully addressed.Search and Optimization MethodSearching for ma ny possible solutions, eliminating those which are unlikely to lead to the particular (or overall) goal and choosing an optimal pathway can be an efficient way of resolving issues. Reasoning, planning and robotics algorithms are created with the assistance of search techniques based on optimization.Mathematical optimization theory is formed by beginning the search for solution with an intelligent guess and advancing towards its refinement (also referred to as ‘hill climbing’: choosing a random point in the landscape and advancing in random moves towards the hill top).The evolutionary computation follows the ‘survival of the fittest’ principle â€" a series of guesses is postulated, through refinement some of the guesses fall of, and thus the optimal solution presents itself.Logic as a Solution MethodLogic is used for solving problems regarding automated planning and machine learning, as well as those of logic programming. It is used for determining validity through true/false attribution, expressing facts about objects, their properties and relations which is essential for ontology in knowledge representation.Other MethodsProbability algorithms for filtering and predictive analysis of streams of data;Classifiers and statistical learning methods;Artificial neural networks;Programming languages (differ according to specific needs of a sub-category of AI).ISSUES OF ARTIFICIAL INTELLIGENCEMost researchers in the artificial intelligence field state that general intelligence in machines will be achieved in the course of the following fifty years. Although we cannot confirm such statements, it seems plausible that the advancements will happen, and will change the world entirely. Consequently, various issues are bound to arise.Primarily, AI systems have capabilities of data processing and predictive analysis which surpass those of humans greatly. In order to achieve optimal performance, they are somewhat autonomous, governed by a carefully chosen set of rules i n order to reach a goal of a sort. However, due to their autonomy they can perform in a misbalance regarding their users â€" if a potential problem was not addressed in the programming, the system would undertake it if it serves the goal (and it is impossible for humans to predict all possible situations and adequate algorithms for them). The issue must be addressed by providing clear safety criteria in order to minimize damage if an error occurs. Moreover, proper attribution of responsibility is a question that needs to be addressed regarding artificial intelligence endeavors.Further on, as the general intelligence emerges, humans must define moral systems according to which they will structure the AI systems but also the moral rules according to which they will position themselves in relation to AI systems. The questions of ethics in artificial intelligence are impossibly complex â€" how to define whether a system is programmed to behave and claim sentient or sentient?Additionall y, who is going to be in charge of decision-making regarding general AI? While we are all introduced to the positive and advanced opportunities that AI technology will bring â€" termination of disease, space travel, reduction of work, etc., we seem to forget that humans are capable of massive destruction for power and money acquisition. Obviously, some regulations on the usage of AI systems will have to be made.AI APPLICATION IN ENTREPRENEURSHIPBig Data and Specialized AnalyticsOver the past few years, the exponential growth in technology capabilities (those of storage and computing primarily), the influx of data has increased enormously. Today, companies can collect and process Big Data in structured and unstructured (pictures, videos) forms and analyze it so as to attain valuable insights regarding business strategy. One of the issues of Big Data management is the lack of experts that could make sense out of it and put it into practice. Various software solutions have been present ed to simplify the process â€" such as expert systems and predictive analysis. Obviously, these are products of artificial intelligence studies.However, as the algorithms evolve, so will their influence on data managements. Machine learning is a data-based predictive and decision-making algorithm that can, when combined with natural language processing, present usable (and valuable) information and solutions regarding business strategies (advertising, customer relations, coaching employees) with the overall goal of increasing productivity and customer engagement (satisfaction), competitiveness on the market and growth.Optimization of Products and ServicesArtificial intelligence algorithms will be implemented not only in the business management spheres but also in the product efficiency and desirability. For example, lawn mowers will be able to lawn mown without human participation. Moreover, they will be able to perform specialized and personalized constructive tasks such as not pul ling out flowers. All this will contribute to customer satisfaction because it represents a continuous exponential decrease of time and effort requirements from the customer for maximized efficiency and value.EXAMPLES OF AI IMPLEMENTATION IN BUSINESSIn addition to significant efforts of IBM in artificial intelligence since its beginnings, big companies such as Google and Facebook had to attend to AI possibilities as well because of massive amounts of data and complex management and strategy defining processes. Here we will take a look at these three companies and their entanglement in AI.IBMIn addition to a significant success which IBM received publicly with their endeavours in AI technologies such as Deep Blue chess-playing algorithm and the complex Watson system, the actual benefits lie in the properties which their technologies mastered and their implementation in business. Deep Blue algorithm managed to process an enormous amount of predictive analysis based on maximizing effic iency according to the rules of chess and showed that by clear formulation of goals, there is no need (as it would be impossible) to cover possible solutions manually â€" the computer did it autonomously and, restricted to the objective that it was programmed for, optimized in such a manner that even a chess champion could not override the process.The Watson system was developed as a real-time question and answer algorithm that managed to perceive and process natural language as well as reason correct answers and generate them in the natural language â€" won the Jeopardy quiz while operating offline. It was created on machine learning basis because it would be a time-consuming and possibly non-effective approach to implement ontology of vast knowledge into it manually.These advancements are extremely significant for business strategies as they optimize broad processing of relevant content and enable constructive communication in order to present insights and perform decisions based on these analytical processes.Currently, IBM is focused on implementing their algorithms in a cloud-based environment and creating databases for health-care, business and education.GoogleGoogle has been using artificial intelligence features for personalization and specification of their search engines, developed Google Translate which is a sufficient natural language processing and generation tool (aside from its lacking in matters of context and sub-symbolic meanings) as well as implemented a neural network strategy in management of their immense databases. These neural strategies are designed to recognize patterns and make decisions upon them extremely fast. Also, the machine learning algorithms are included which means that systems learn through experience and as such perform more effectively.FacebookFacebook profiles are a melting pot for structured and unstructured data: friends lists, pages liked, groups joined. In order to optimize customer experience, Facebook implements ar tificial intelligence to recognize behavioral patterns of individual users (on the Facebook domain, as well as online in general, ) and offers according to particular inclinations and interests. Their efforts are heading towards creating an intelligent agent who will be able to interact with users and provide valuable information instantaneously.Considering Moore’s theory of exponential growth of technology and knowledge, we can predict that the science fiction depictions of future are actually right around the corner, especially if we are taking the complexity of the objectives into consideration. Although there are numerous issues regarding AI realization and ethical conundrums regarding diverse specters of AI, the progress is happening and will bring a lot of positive features with it. In business, it will enable strategies designed for individual users â€" increasing their satisfaction and profit generation for the enterprise. It will have even more far-reaching consequences i n medicine, sustainable economies, poverty reduction and education. We should only hope that the progress will always serve its altruistic purposes.

Sunday, May 24, 2020

Horizontal Violence Against Nurses During Workplace

Horizontal Violence against Nurses in Workplace Cyron Christian Viado Ryerson University Family and Violence CSOC 502 Dr. Maria Wallis June 9, 2015 Horizontal Violence against Nurses in Workplace The primary purpose of health care is to serve the patients’ physical, emotional, and spiritual needs. However in a recent news headline in Vancouver Sun newspaper in February 24, 2015 tackle the stories entitled: B.C. nurses to begin filing charges against violent and aggressive patients. It stated that nurses are tired of being kicked, punched and slapped. Nurses are tired for the government and health authorities to take action that leads them the need to protect themselves to get more harm. Such incidents is one of the many example of†¦show more content†¦Literature Review Horizontal violence is a hidden pattern of individual behavior in controlling other individual that risk health and safety (Hinchberger, 2009). According to Roche, Duffield and Catling-Paull, violence can be describe as emotional abuse, threat, or actual violence in any health care setting. Although the definition varies according to situations and practice settings, there is agreement that workplace violence has a negative impact on the health and wellbeing of nurses and the delivery of quality nursing care (Hinchberger, 2009). Violence mostly occur in any health care setting, However, it mostly occur in emergency department, waiting room, psychiatric ward and geriatric unit on which people involved psychological situations. Workplace violence commonly occurs between nurses, between nurses and patient, between nurses and families, or even between nurses and physician. Violence from relatives and friends of patients may occur as a result of frustration with a perceived lack of care or communication (Roche et.al. 2010). According to Woefle and McCaffrey there two consequences of violence (physical and psychological) for nurses and organization. Physical by the mean nurses can possibly experience weight loss, cardiac palpitation, stress, hypertension and irritable bowel syndrome. Psychological by the means of being mentally dr ain of nurses that can cause danger in giving a quality care. According to the three articles it is

Wednesday, May 13, 2020

Hiv Is A Human Immunodeficiency Virus - 1721 Words

Picture this: a young child who is very skinny, ribs and all other bones are showing through the skin, they are born with HIV. it then leads to AIDS, due to their parents. HIV is a Human Immunodeficiency Virus. If HIV is left untreated, it can lead to AIDS, which is an acquired Immunodeficiency Syndrome. In Nigeria, Africa millions of people have the disease of AIDS and HIV. There is not many treatment options or solutions for this serious issue that takes place all over the country. There are a few aspects one must understand about this movement to fully understand its importance, whether those are what exactly HIV/AIDS is and how deadly it actually is, how potently it is actually affecting Nigeria and other countries, and what can be†¦show more content†¦HIV is not only spread through sexual contact. Sex workers are another common way for people to carry the disease. The majority of the people do not know their HIV or AIDS status. There are many solutions that may have wor ked in the past, but with a twist to this solution of people living with AIDS and HIV. For the people who do not know their HIV and AIDS status, at home tests would allow them to test themselves. This would give individuals the knowledge of what their own status is if they were HIV positive or HIV negative. If the people were more educated about their health and disease, these problems would not be so bad in Africa. Another way to treat these people with the disease is by medications and antibiotics. In the United States, they are using many medications to fight HIV infection. The medication they are using is called Antiretroviral therapy, also known as ART, this medication is not a cure. Although it does control the virus so they can live and reduce the risk of transmitting HIV to others, from the â€Å"Overview of HIV Treatments† (2015), they state that, These HIV medicines prevent HIV from multiplying (making copies of itself), which reduces the amount of HIV in your bod y. Having less HIV in your body gives your immune system a chance to recover and fight off infections and cancers. Even though there is still some HIV in the body, the immune system is strong enough to fight off infections and cancers (para.Show MoreRelatedHuman Immunodeficiency Virus ( Hiv )1359 Words   |  6 PagesThis paper explores the human immunodeficiency virus (HIV) as well as the simian immunodeficiency virus (SIV). The virus has infected two million adults and children by the year 2005 already. The virus continues to race around the world, and new HIV infections are at 50,000 per year (Martine Peeters, Matthieu Jung, Ahidjo Ayouba) (2013). 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The virus immediately beginsRead MoreThe Human Immunodeficiency Virus ( Hiv )948 Words   |  4 PagesThe Uses of Blood by the HIV Virus Blood-borne diseases have contributed greatly to poor health outcomes among individuals and communities. Though blood fulfills various functions to ensure our survival, it can also act as the mechanism through which we become diseased. Understanding the characteristics of such infectious diseases is essential to preventing further cases. In this paper I will discuss how the human immunodeficiency virus (HIV) uses blood to cause illness within the infected individualRead MoreHuman Immunodeficiency Virus ( Hiv )1261 Words   |  6 PagesHuman Immunodeficiency Virus Human immunodeficiency virus (HIV) has become more commonly seen in the world. It is important to show compassion rather than judging that patient based on a virus. The hygienist plays an important role in making the patient feel comfortable and in a judgment free environment. 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(Healthy Living, pg. 79) If left untreated this virus can turn into AIDS (Acquired Immunodeficiency Syndrome). AIDS is the final stage of the Human Immunodeficiency Virus (HIV). (aids.gov) AIDS is an incurable progressive disease that causes gradual destruction of CD4 T cells by the human immunodeficiency virus (HIV). (Diseases, pg. 431) A healthyRead MoreHiv, Or Human Immunodeficiency Virus998 Words   |  4 PagesQuestion 1 HIV, or Human Immunodeficiency Virus, attacks the human immune system and greatly weakens the body’s ability to fight foreign invaders and infection. HIV first demanded notice in the early 1980s in the United States in homosexual men displaying illnesses like Pneumocystis carinii pneumonia and Kaposi’s sarcoma. The disease was soon observed in IV drug users, hemophiliacs, and blood transfusion recipients, but became publicized as a â€Å"gay disease,† nicknamed by the media as GRID, or Gay-RelatedRead MoreHuman Immunodeficiency Virus ( Hiv )1349 Words   |  6 Pages Human Immunodeficiency Virus (HIV) is a retro virus that causes AIDs by infecting the T Helper cells of the body’s immune system. The AIDS virus is the final stages of the HIV virus. HIV is a lentivirus genus, which is a subgroup of the retrovirus that causes the AIDS virus. Even with proper treatment, an infected person has a life expectancy of less than ten years.As the virus weakens t he human immune systems, this effectleaves the patient compromised and at risk to opportunistic infections

Wednesday, May 6, 2020

Prospects for student Entrepreneurship Free Essays

Entrepreneurship is acquiring impetus as a influential instrument for exhilarating economic growth in the country. This is a commanding educational means of training business professionals regardless of whether they started a new venture or not. Entrepreneurs see business in a more holistic way than do managers, who often see issues in terms of functional domains. We will write a custom essay sample on Prospects for student Entrepreneurship or any similar topic only for you Order Now We live in an increasingly busy world in which the divide between the haves and the have-nots is growing. This requires job creation at rates that are not easily possible for most businesses and Governments. This results in high rate of unemployment among educated youths. This is a major challenge and necessitates Entrepreneurship development programmes. Numbers of countries are pursuing the opportunities to become new hubs of global business activity through innovation. Even though management education emphasizes profit maximization and shareholder value creation, entrepreneurship envisages maximizing common good and minimizing social injustice and environmental impact. Entrepreneurship leadership has three principles. They include Cognitive ambidexterity, social, environment and economic responsibility and sustainability and self and social awareness. Developing cognitive ambidexterity has two logics. They are prediction logic as a traditional approach and creation logic which is a decision approach. An uncertain future can be predicted in the prediction logic whereas creation logic is based on action, discovery and creation. An entrepreneurial leader can use both prediction and creation logics to create new ideas or innovations. Globally entrepreneurial leaders are concerned about social, environment and economic responsibility and sustainability. Individuals and organizations are increasingly being held accountable for the social, environment economic outcomes of their actions. They must understand the inherent tensions and potential synergies that exist among social, environmental and economic value creation. Self and social awareness focuses on developing self and social awareness among the entrepreneurial leaders. It involves critical understanding of themselves and the societal context of business opportunities. Recently as Government of India is giving more thrust to food security and food safety issues, entrepreneurship and innovation management in agriculture/ food processing sectors are emerging as some of the specialization areas in entrepreneurship. Government of India is giving more thrust to food security and food safety issues. Kerala Veterinary and Animal Sciences University had contributed substantially in this regard with special focus on farmer entrepreneurship for food security; the farmers being the primary producers are the key to local economic growth and sustainable agri-food systems, and food security for all. Promoting students with entrepreneurial capacities to create opportunities for muti-stakeholder action would contribute to availability of qualitatively good food and nutrition and market efficiency with sustainable food chains. The University student-focused activities include programmes like ‘earn while you learn’ projects, NSS activities, farm and field visits, educating students in cultivating a startup business idea, teaching them basic strategies like estimating costs and writing a project proposal/ business plan etc.Student Entrepreneurship is acquiring momentum across the globe. Government of Kerala have announced a unique Student Entrepreneurship Scheme to encourage entrepreneurship among the Collegiate level Students of the state. Recently taking in to account the importance of the programme Government of Kerala issued separate guidelines for issuing grace marks and attendance to students during ideation stage, teaming and company formation, technology formation and development of business models. As part of the same Technology Business incubator (TBI) can be established at the campuses so that student entrepreneur can associate with the TBI. University should establish Directorate of Entrepreneurship to strengthen this sector. Entrepreneurship division give major emphasis to entrepreneurship development, extension, knowledge dissemination, distance learning and awareness programmes. Rationale of the project: Livestock sector plays an important role in the National economy and in the socio economic development of the country. Livestock production performance has been more impressive than that of food grain production. Milk, egg, meat and fish showed impressive growth rates i.e. 5-10 percent. Livestock represents the only way in which natural vegetation can be converted in to products that can be used by man. This sector has been well knit with the socio-economic fabric of rural economy and plays an important role in the employment and income generation. The major component through which livestock contributes to the agricultural income are milk and milk products, meat and eggs. These products contribute about one-sixth of the calories and one-third of the proteins in the per capita food supplies of the world; the balance comes from vegetable products. Per capita consumption of livestock products is however, four to five times higher in the developed countries than in the developing countries. Presuming that one family member is employed in looking after the livestock, 25 million people are estimated to be employed with livestock rearing activity. Dairying and poultry sector is emerging as one of the important livelihood options. Although India has huge livestock population, in terms of trade it is having only a low impact in the world trade of livestock products. However, it is to be noted that India has tremendous potential to produce and export various livestock products. The high potential of the domestic market coupled with marketing opportunities abroad under WTO regime now render India to an enviable position to cater to the huge global market of livestock products, particularly dairy products. The emergence of India as a net exporter Nation of livestock products is a new trend in exporting group of nations in these products. It is also widely believed that with the ushering in of the agricultural policy reforms in major industrial countries, the demand for livestock products from developing countries, like India, will get a significant boost. India needs innovation in livestock sector to reach the productivity frontier and to use the best sustainable practices for production. The desire for innovation in India has been driven by search for low cost solutions to food security and food safety issues. Innovation is the application of new ideas to solve problems with resultant benefits to different stakeholders. India needs to create 1.5 Crore jobs per year for the next ten years to provide employment to youngsters. In this juncture accelerating entrepreneurship and creating business are crucial for massive employment generation. Government of India is promoting startups to scale up startup ecosystem in the country. Opportunity for startups was unparalled especially in the food processing sector in the country. NASSCOM aims to nurture 10,000 startups in the next 10 years and give a major boost to software product development in India. The Startup Warehouse will provide a well-connected, infrastructure for technology startups and will support entrepreneurs in their early stage of operations as well as act as a hub for innovation, collaboration, and entrepreneurship. Innovation in startup ecosystem depends on ideas, validation process and good partner relationship. Major steps in this process are laying the foundation, creating a challenge book, building participation, experimentation with cost, speedy implementation, building innovation sand box and putting a margin of safety. ? How to cite Prospects for student Entrepreneurship, Papers