Computer Science Capstone Project Examples

COMPUTER SCIENCE CAPSTONE EXAMPLE

ARTIFICIAL INTELLIGENCE TODAY INTRODUCTION Efforts to build artificial intelligence have experienced a striking rise in recent years with numerous investments in the application and development of intelligent systems in areas such as communications, commerce, health services, internet search, referral systems, etc. While numerous scientific fantasy works entertain themselves by creating some form of consciousness artificial intelligence, it seems that the first signs of intelligent machines are already present in computers that learn, recognize objects, and use the language of communicating to people better. The paper deals with artificial intelligence today, i.e. the ability of computers to behave in an intelligent way. The main aim of the paper is to describe the technology of artificial intelligence that is present today and to give examples in which it is used. The reason for choosing a theme is the fact that in the last few years the development of machine learning and the application of intelligent systems have been growing steadily, but the wider public knows very little about it. Today we can safely say that artificial intelligence will mark the next decade, launch a new technological revolution and change our lives in the way that in this the moment we cannot fully imagine.

Artificial Intelligence (UI) is the ability of a digital computer or a computercontrolled robot to perform tasks commonly associated with intelligent creatures. The field of artificial intelligence, shortened UI, seeks to understand intelligent entities. So one of the reasons for studying this area is to better understand ourselves. However, as opposed to philosophy or psychology, which also studies intelligence, the field of artificial intelligence seeks the construction of artificial entities as well as their understanding. Artificial intelligence asks puzzles. How can a slow or small brain, biologically or electronically, be able to perceive, understand and anticipate the world, and manipulate the world much larger and more complex than it is? How do we build something with such properties? These questions are tough, but unlike traveling at speeds greater than the speed of light or antigravity, the researcher in the field of artificial intelligence has strong evidence that the task can be accomplished. All we have to do is look into the mirror to see an example of an intelligent system. There are two main goals sought by most artificial intelligence researches. The first, most important goal is to build intelligent machines. Another goal is to understand the nature of intelligence or what psychologists call the g-factor in intelligence tests (an attempt to measure general intelligence spreading through different human

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COMPUTER SCIENCE CAPSTONE EXAMPLE

ARTIFICIAL INTELLIGENCE TODAY domains). Both goals have in their essence the need to define the notion of intelligence.

General Artificial Intelligence GUI is a term that describes research aimed at creating machines capable of generally intelligent operations. It is crucial that general artificial intelligence must be able to master different areas and learn new areas that they have not faced before. It is not necessary to have equal capabilities in all domains. When in that context we say human mind or GUI have a general purpose, we do not think that any problem can be solved in every domain, but that there is a potential for solving any problem in any domain if a proper experience is obtained. As long as it does not prove that GUI is not possible, it remains a legitimate topic of research. And considering the complexity of the problem, there is no reason to expect the GUI to achieve the goal in a short period, so all popular theoretical controversies will probably even exist after the GUI is realized as planned. The fact that research has little consensus needs to make us more cautious when we think of a new idea as completely wrong. As history has already happened more than once, the real penetration could come from something that opposes the intuition.

In recent years, technology companies and academic researchers are trying to create a so-called neuromorphic computer architecture - that is, chips that emulate the ability of the human brain to be both analytical and intuitive to enable the creation of context and meaning from a large amount of data. Scientists who conduct such research are called neuromorphic engineers. Instead of thinking about the brain as a computer, they are trying to create computers that resemble the brain. In this way, humanity will gain not only a better understanding of brain work, but better, more intelligent accounts. While the human brain has 100 trillion of synapses and consumes only 20W, today's superfine in the effort to simulate brain work consumes the force of the order of magnitude MW. Leading efforts to achieve a system whose properties are similar to the brain have reached a new milestone by producing a transistor chip containing more than 4000 neurosinaptic nuclei. Each core consists of computational components corresponding to their biological duality - the core memory function similar to are synapses among neurons, processors represent core neurons, and communication is accomplished by conductors similar to neural axons. The goal of non-neurological scientists is to build a computer that has some or all of the features that a brain has, and today's computers do not have that low energy consumption; fault tolerance (failure of a transistor creates serious problems in the microprocessor, but the brain permanently loses some neurons, which does not cause problems in the functioning

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COMPUTER SCIENCE CAPSTONE EXAMPLE

ARTIFICIAL INTELLIGENCE TODAY of the nervous system) and the need for programming (brains learn and change spontaneously through their interaction with the world instead of following the default paths and branches of the predetermined algorithm). To achieve this goal, scientists should know brain work, but it is still a big unknown. There is no way they could, for now, be able to study the brain at a fundamental level. On the other hand, suitable computer simulations could answer some questions about basic brain functions and vice versa. For right, achieving a good brain imaging would have turned into a computer literacy that could ultimately serve better brain function, artificial intelligence development, and possibly conscientious computer systems. It might happen that the models are first and then help brain mapping.

Neuromorphic engineering could, in other words, reveal the underlying principles of thinking before neuroscientist do. The large gap in brain incomprehension is in the middle scale, or in the middle degree of brain anatomy. Science is known for the work of individual neurons. It is also relatively well known how individual brain neurons and ganglia (neuronal clusters constructing a peripheral nervous system) work, where speech or vision centers are located in the brain. However, it is unclear how neurons are organized in the ganglia and the brain trays, and that is precisely the level of organization in which the thinking is realized - and it is assumed that consciousness is inhabited.

What is increasingly mentioned is IoT (Internet of Things). It is accomplished when we take objects from everyday life and add to them the ability to collect information (about our custom habits), communicate with each other and with us. That term does not include personal computers, smartphones, and tablets, so it is only about everyday things like home appliances. The predictions indicate that the growth of the IoT will exceed the other connecting devices. Â Connecting things, the flood of information gathered from the sensor, which will be cheaper and smaller, will require intelligent systems that will manage the devices inexperienced users in some future smart cities. Personalization is a solution to the world's overwhelming information. Personalization is carried out by applying intelligent systems to data collected by users. Data is collected over the internet on social networks or through what the user enters in the search engine or from a variety of sensors, tracking systems or credit cards. Search results tailored first to the user's personal interests, tailored to the user's habits and preferences, customized user news, filtering "relevant" topics, and customizing advertiser bids for consumer habits. All this is based on the ability of smart systems to process and interpret the collected data. The ad customization carried out by theÂ

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COMPUTER SCIENCE CAPSTONE EXAMPLE

ARTIFICIAL INTELLIGENCE TODAY advertiser's bid is based on conclusion smart software about our intentions through sampling patterns from sites we buy or based on our social media releases.

REFERENCES Bell, J. (2014). Machine Learning: Hands-On for Developers and Technical Professionals, New York: John Wiley & Sons Inc. Bostrom, N. (2014). Superintelligence, Oxford: Oxford University Press.

Kurzweil, R. (2012). How to Create a Mind: The Secret of Human Thought Revealed, New York: Viking Penguin.

Pesaran, A., Gonder, J., & Keyser, M. (2009). Ultracapacitor Applications and Evaluation for Hybrid Electric Vehicles, In: 7th Annual Advanced Capacitor World Summit Conference, National Renewable Energy Laboratory (NREL): Hotel Torrey Pines La Jolla, CA; 2009.

Tie, S. F., & Tan, C. W. (2013). A Review of Energy Sources and Energy Management System in Electric Vehicles. Renewable and Sustainable Energy Reviews, 20, 82-102.

Emadi, A., Young, J. L., & Rajashekara, K. (2008). Power Electronics and Motor Drives in Electric, Hybrid Electric, and Plug-In Hybrid Electric Vehicles. Industrial Electronics, IEEE Transactions on 2008, 55(6): 2237–45.

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