Between tuition, fees, supplies and a social life, student pockets pretty much empty themselves.
In one interpretation it is that the past has predetermined the sequence which is about to unfold—and so I believe that how we have gotten to where we are in Artificial Intelligence will determine the directions we take next—so it is worth studying that past.
Another interpretation is that really the past was not much and the majority of necessary work lies ahead—that too, I believe.
We have hardly even gotten started on Artificial Intelligence and there is lots of hard work ahead. The nineteen page proposal has a title page and an introductory six pages 1 through 5afollowed by individually authored sections on proposed research by the four authors.
It is presumed that Intelligence essay contest wrote those first six pages which include a budget to be provided by the Rockefeller Foundation to cover 10 researchers. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
And then the first sentence of the second paragraph starts out: The following are some aspects of the artificial intelligence problem: No description of what human intelligence is, no argument about whether or not machines can do it i.
In the linked file above there are an additional four pages dated March 6th,by Allen Newell and Herb Simon, at that time at the RAND Corporation and Carnegie Institute of Technology respectively later both were giants at Carnegie Mellon Universityon their proposed research contribution.
The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have.
These topics are expanded upon in the individual work proposals by Shannon, Minsky, Rochester, and McCarthy. The addendum from Newell and Simon adds to the mix getting machines to play chess including through learningand prove mathematical theorems, along with developing theories on how machines might learn, and how they might solve problems similar to problems that humans can solve.
No lack of ambition! And recall that at this time there were only a handful of digital computers in the world, and none of them had more than at most a few tens of kilobytes of memory for running programs and data, and only punched cards or paper tape for long term storage.
His aim however is clear. He believes that it will be possible to make a machine that can think as well as a human, and by the year He even estimates how many programmers will be needed sixty is his answer, working for fifty years, so only 3, programmer years—a tiny number by the standards of many software systems today.
He then turns to making a a machine that fully imitates a person, even as he reasons, the brain part might be too big to be contained within the locomoting sensing part of the machine, and instead must operate it remotely.
He points out that the sensors and motor systems of the day might not be up to it, so concludes that to begin with the parts of intelligence that may be best to investigate are games and cryptography, and to a less extent translation of languages and mathematics.
Again, no lack of ambition, but a bowing to the technological realities of the day. When AI got started the clear inspiration was human level performance and human level intelligence.
I think that goal has been what attracted most researchers into the field for the first sixty years. The fact that we do not have anything close to succeeding at those aspirations says not that researchers have not worked hard or have not been brilliant.
It says that it is a very hard goal. My current blog posts are trying to fill in details and to provide an update for a new generation to understand just what a long term project this is.
To many it all seems so shiny and exciting and new. Of those, it is exciting only. By the late seventies, with twenty or thirty pounds of equipment, costing tens of thousands of dollars, a researcher could get a digital image directly from a camera into a computer.
Things did not become simple-ish until the eighties and they have gotten progressively simply and cheaper over time. Similar stories hold for every other sensor modality, and also for output—turning results of computer programs into physical actions in the world. Thus, as Turing had reasoned, early work in Artificial Intelligence turned towards domains where there was little need for sensing or action.
There was work on games, where human moves could easily be input and output to and from a computer via a keyboard and a printer, mathematical exercises such as calculus applied to symbolic algebra, or theorem proving in logic, and to understanding typed English sentences that were arithmetic word problems.
Playing games early on also provided opportunities to explore Machine Learning and to invent a particular variant of it, Reinforcement Learning, which was at the heart of the recent success of the AlphaGo program.
Before too long a domain known as blocks world was invented where all sorts of problems in intelligence could be explored. Perhaps the first PhD thesis on computer vision, by Larry Roberts at MIT inhad shown that with a carefully lighted scene, all the edges of wooden block with planar surfaces could be recovered.
That validated the idea that it was OK to work on complex problems with blocks where the description of their location or their edges was the input to the program, as in principle the perception part of the problem could be solved. This then was a simulated world of perception and action, and it was the principal test bed for AI for decades.
Some people worked on problem solving in a two dimensional blocks world with an imagined robot that could pick up and put down blocks from the top of a stack, or on a simulated one dimensional table.The JROTC Essay Contest is an annual competition designed to give Cadets an opportunity to assimilate lessons learned from JROTC curriculum and communicate their knowledge in writing.
The Bachelor of Science and Master of Science in Information Security and Intelligence (ISI) programs prepare you for a variety of careers in corporate, governmental, law enforcement and defense settings.
As part of the initiative we are holding five essay contests, based on the five Open Future themes (Borders, Ideas, Markets, Society and Progress). Each contest is open to people between 16 and Turnitin provides instructors with the tools to prevent plagiarism, engage students in the writing process, and provide personalized feedback.
CollegeXpress Scholarship Profile: The United States Naval Institute (USNI) Naval Intelligence Essay Contest. Search For More Scholarships And Colleges. Join CollegeXpress. The Mensa Foundation Scholarship Program awards more than $, every year, completely based on essays written by the applicants, who need not be Mensa members.