How to Write an A+ Research Paper. This Chapter outlines the logical steps to writing a good research paper. To achieve supreme excellence or perfection in anything you do, you need more than just the knowledge. Like the Olympic athlete aiming for the gold medal, you must have a positive attitude and the belief that you have the ability to achieve it. That is the real start to writing an A+ research paper. Choose a topic which interests and challenges you. Your attitude towards the topic may well determine the amount of effort and enthusiasm you put into your research.
Focus on a limited aspect, e.g. narrow it down from "Religion" to "World Religion" to "Buddhism". Select a subject you can manage. Surf the Net. For general or background information, check out useful URLs, general information online, almanacs or encyclopedias online such as Britannica. Pay attention to domain name extensions, e.g., .edu (educational institution), .gov (government), or .org (non-profit organization).
Read and evaluate. Example of an outline: Developing an Outline. Summary: This resource describes why outlines are useful, what types of outlines exist, suggestions for developing effective outlines, and how outlines can be used as an invention strategy for writing. Contributors:Elyssa Tardiff, Allen BrizeeLast Edited: 2013-03-01 09:20:56 Ideally, you should follow the four suggestions presented here to create an effective outline. When creating a topic outline, follow these two rules for capitalization: For first-level heads, present the information using all upper-case letters; and for secondary and tertiary items, use upper and lower-case letters. Parallelism—How do I accomplish this? Each heading and subheading should preserve parallel structure. ("Choose" and "Prepare" are both verbs. Coordination—How do I accomplish this?
All the information contained in Heading 1 should have the same significance as the information contained in Heading 2. (Campus and Web sites visits are equally significant. Subordination—How do I accomplish this? Applications of artificial intelligence. Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore," Nick Bostrom reports.[1] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry.
" In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. Computer science[edit] AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered a part of AI. Finance[edit] Hospitals and medicine[edit] Heavy industry[edit] Apple's Siri and the Future of Artificial Intelligence. A Voice App With Siri-ous Implications for TV Apple's iPhone 4S voice recognition app sets the stage for a revolution in user interfaces for TVs, cars and many other devices So I tested Siri, Apple's iPhone 4S voice recognition app, and here's my conclusion: It ain't a miracle app, but it raises the bar dramatically and sets the stage for a revolution in user interfaces for TVs, cars and many other devices.
My tests weren't exactly scientific — they mostly involved shoving aside other customers at my local Verizon outlet and hogging the iPhone 4S for 15 minutes. But I think I got a good idea. “Who is the president of the United States?” Gave me a chart of all Barack Obama's particulars. “What's the phone number for Apple?” “Where's the nearest Chinese food in Culver City?” When I asked “Who's your daddy?” The point is, Siri is miles ahead of any other mobile phone voice recognition program on the planet. On my current iPhone4, if I ask it to call my local bike shop, I end up calling someone I haven't talked to in 10 years.
Some Thoughts On The Future Of Siri [Opinion. We’ve seen the first rash of iPhone 4 reviews coming in, and they all agree on one thing: Siri is very impressive. It works because it does several things all at once. It understands what you’re saying, irrespective of your accent, and without a lot of initial training. And it understands what you mean, because it has the built-in smarts to know that if you say “Tell my wife I’m running late,” you mean “Send a text message to this particular contact with text that says I’m running late.” But this is just the start for Siri (which Apple’s acknowledged by calling it a beta). First, let’s think about new features it might support on the iPhone 4S. So the first, most immediate, improvements we can expect for Siri are a broadening of the databases it knows about and can query. Then there’s the possibility of opening Siri up to other applications on the device. How about in games? Finally, expansion beyond the iPhone 4S. I suspect yes, but for different purposes.
Over to you. Related. Artificial intelligence. AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based. Artificial Intelligence - Future. Artificial intelligence is a field that attempts to provide machines with human-like thinking. History Edit But despite some significant results, the grand promises failed to materialise and the public started to see AI as failing to live up to its potential [this is not impersonal, this is a opinion from someone, hence this is wrong]. This culminated in the "AI winter" of the 1990s, when the term AI itf fell out of favour, funding decreased and the interest in the field temporarily dropped.
However, computer power has increased exponentially since the 1960s and with every increase in power A.I. programs have been able to tackle new problems using old methods with great success. Approaches Historically there were two main approaches to AI: classical approach (designing the AI), based on symbolic reasoning - a mathematical approach in which ideas and concepts are represented by symbols such as words, phrases or sentences, which are then processed according to the rules of logic. 1. 2. 3.
The Future of Artificial Intelligence. Rise of the Robots--The Future of Artificial Intelligence. Editor's Note: This article was originally printed in the 2008 Scientific American Special Report on Robots. It is being published on the Web as part of ScientificAmerican.com's In-Depth Report on Robots. In recent years the mushrooming power, functionality and ubiquity of computers and the Internet have outstripped early forecasts about technology’s rate of advancement and usefulness in everyday life.
Alert pundits now foresee a world saturated with powerful computer chips, which will increasingly insinuate themselves into our gadgets, dwellings, apparel and even our bodies. Yet a closely related goal has remained stubbornly elusive. In stark contrast to the largely unanticipated explosion of computers into the mainstream, the entire endeavor of robotics has failed rather completely to live up to the predictions of the 1950s. Obviously, it hasn’t turned out that way. The single best reason for optimism is the soaring performance in recent years of mass-produced computers.
Smart Machines, and Why We Fear Them. I.B.M.’s Watson - Computers Close In on the ‘Paris Hilton’ Problem.