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"Artificial intelligence (AI) would be the possession of intelligence, or the exercise of thought, by machines such as computers."

"While we don't know what thought or intelligence is, essentially, and while we're very far from agreed on what things do and don't have it, almost everyone agrees that humans think, and agrees with Descartes that our intelligence is amply manifest in our speech. Along these lines, Alan Turing suggested that if computers showed human level conversational abilities we should, by that, be amply assured of their intelligence. Turing proposed a specific conversational test for human-level intelligence, the "Turing test" it has come to be called. Turing himself characterizes this test in terms of an "imitation game"" This seems to be a wonderful source, and I feel that it is reliable because it is an official academic resource, reviewed by many. I plan on using this fantastic source for general information, and specifically the bit I quoted for determining exactly what qualifies as an artificial intelligence. Many computers are considered "thoughtful" but what truly defines AI I play to learn from Turing's Test; an experiment developed by Alan Turing (another facet I would consider researching)

"e intellectual roots of AI, and the concept of intelligent machines, may be found in Greek mythology. Intelligent artifacts appear in literature since then, with real (and fraudulent) mechanical devices actually demonstrated to behave with some degree of intelligence. Some of these conceptual achievements are listed below under "Ancient History.""

"Earnings calls are usually meant to do little more than reassure investors - but in Facebook's most recent call, the day before Halloween, Mark Zuckerberg unveiled something unexpected. Facebook had formed an Artificial Intelligence group, Zuckerberg announced, and the company was acquiring a machine-translation company and hiring the best academic minds in the field. The goal, he told investors, was "to do world-class artificial intelligence research using all of the knowledge that people have shared on Facebook." He teased new products that would be more natural to interact with, and could solve problems beyond the reach of current technology. For anyone on the call, the point was clear: the future of Facebook would be powered by AI."

"We're going to see artificial intelligence do more and more, and as this happens costs will go down, outcomes will improve, and our lives will get better. Soon countless pieces of AI will be working on our behalf, often in the background. They'll help us in areas ranging from trivial to substantive to life changing. Trivial uses of AI include recognizing our friends' faces in photos and recommending products. More substantive ones include automatically driving cars on the road, guiding robots in warehouses, and better matching jobs and job seekers. But these remarkable advances pale against the life-changing potential of artificial intelligence."

The history of AI as written by Think Quest

"Evidence of Artificial Intelligence folklore can be traced back to ancient Egypt, but with the development of the electronic computer in 1941, the technology finally became available to create machine intelligence. "

"The growing power of computer vision is a crucial first step for the next generation of computing, robotic and artificial intelligence systems."

"Artificial intelligence (AI) is the intelligence exhibited by machines or software, and the branch of computer science that develops machines and software with intelligence."

AI

AI current progression "Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. "We can only see a short distance ahead," admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. "But," he added, "we can see much that must be done."[3]"

"The growing power of computer vision is a crucial first step for the next generation of computing, robotic and artificial intelligence systems. Once machines can identify objects and understand their environments, they can be freed to move around in the world. And once robots become mobile they will be increasingly capable of extending the reach of humans or replacing them." This source is useful because it give some good background information on artificial intelligence and also an idea of what it will be like in the near future. The information from this article will be used in our project when we present what AI is like today and how it is quickly expanding. The facts and knowledge from this article are credible because it is from an official news source, the New York Times. This article is also reliable because it contains real quotes from real scientists studying AI.

In any realistic learning application, the entire instance space will be so large that any learning algorithm can expect to see only a small fraction of it during training. From this small fraction, a hypothesis must be formed that classifies all the unseen instances. If the learning algorithm performs well then most of these unseen instances should be classified correctly. However, if no restric- tions are placed on the hypothesis space and no "preference criterion" 1124] is supplied for comparing competing hypotheses, then all possible classifications of the unseen instances are equally possible and no inductive method can do better on average than random guessing [261. Hence all learning algorithms employ some mechanism whereby the space of hypotheses is restricted or whereby some hypotheses are preferred a priori over others. This is known as inductive bias I hope to use this source to learn more about how artificial intelligence learns, as I have read in other places that the kind that learns from the "bottom up" learns by making mistakes and learning from them. AI, if to be truly intelligent, is probably going to have to learn the way we did; by experience and example. In Kaku's book, he mentions the differences between two artificially intelligent robots that he "met". One, STAIR, had a limited database and was programmed to do what it did. Another, LAGR, piloted itself through a park, bumping into miscellaneous objects and learning their locations so that on the next pass, it would not hit them. I hope to learn more about that kind of logic by reading this article, as I think it is important to have a better understanding of exactly how artificial intelligence learns.

"Michio Kaku on Artificial Intelligence" This site is reliable because it is and interview with Michio Kaku. This site is very useful because it states some of Michio's ideas about AI and Computers of the future.

If you have not already, I recommend attempting to intelligently interact with an artificial intelligence such as this. It helps understand where we are so far in making a computer as intelligent as we are (which is not very impressive, in my opinion).

"Top-down and bottom-up are both strategies of information processing and knowledge ordering, used in a variety of fields including software, humanistic and scientific theories (see systemics), and management and organization. In practice, they can be seen as a style of thinking and teaching." This article gives information about the two ways to approach artificial intelligence: top-down, which is teaching the robot all of the rules of intelligence from the beginning, and bottom-up, which is making the computer learn like a human instead of teaching it everything. This source is reliable because it cites where it got its information.

"The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain. "

i don't believe this will work in the long run "The Google driverless car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur.[2] Lettering on the side of each car identifies it as a "self-driving car." The project is currently being led by Google engineer Sebastian Thrun, director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense.[3] The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.[4]"

The Google driverless car is a project by Google that involves developing technology for autonomous cars

ASIMO The robot "Honda began developing humanoid robots in the 1980s, including several prototypes that preceded ASIMO. It was the company's goal to create a walking robot which could not only adapt and interact in human situations, but also improve the quality of life. The E0 was the first bipedal (two-legged) model produced as part of the Honda E series, which was an early experimental line of humanoid robots created between 1986 and 1993. This was followed by the Honda P series of robots produced from 1993 through 1997, which included the first self-regulating, humanoid walking robot with wireless movements.[6][7]"

a video of 2045ru

"Many scientists believe the exponential growth in computing power leads inevitably to a future moment when computers will attain human-level intelligence: an event known as the "singularity"." I find this site very useful because it gives insight from many different perspectives about different possibilities for the future of the computer. It talks about how computers will possibly be capable of out thinking humans. On the contrary, the source talks about how computers may not be capable of outperforming humans. I believe this source is reliable because it contains quotes from many respected scientists, including Michio Kaku.

"Rather than being programmed to anticipate every possible answer or action needed to perform a function or set of tasks, cognitive computing systems are trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, infer and, in some ways, think." This source provides a method for the bottom-up approach to AI, which is letting the computer learn like an infant instead of teaching it how to respond to certain cases. Cognitive computing will most likely be mentioned in our project when we talk about the technological singularity. This article is trustworthy because it is written by IBM, one of the world innovators in technology and computing.

"Our basic computational element (model neuron) is often called a node or unit. It receives input from some other units, or perhaps from an external source. Each input has an associated weight w, which can be modified so as to model synaptic learning." This is a simple and brief article, but I think that a better understanding of neuroscience and how neurons work will help us to better grasp what research is being done regarding AI. This is an important research category, because if we wish to recreate ourselves on a scale of intelligence, we will need to better analyze our neural relays and systems. Apparently, according to Kaku's book, it is a very complicated endeavor that has been met so far with limited success. Hopefully, we can better understand the basics of neuron structure and function with this article.