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| In the field of [[artificial intelligence]], the most difficult problems are informally known as '''AI-complete''' or '''AI-hard''', implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or [[strong AI]].<ref name="Shapiro92">Shapiro, Stuart C. (1992). [http://www.cse.buffalo.edu/~shapiro/Papers/ai.pdf Artificial Intelligence] In Stuart C. Shapiro (Ed.), ''Encyclopedia of Artificial Intelligence'' (Second Edition, pp. 54–57). New York: John Wiley. (Section 4 is on "AI-Complete Tasks".)</ref> To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.
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| AI-complete problems are hypothesised to include [[computer vision]], [[natural language understanding]], and dealing with unexpected circumstances while solving any real world problem.<ref>Roman V. Yampolskiy. Turing Test as a Defining Feature of AI-Completeness . In Artificial Intelligence, Evolutionary Computation and Metaheuristics (AIECM) --In the footsteps of Alan Turing. Xin-She Yang (Ed.). pp. 3-17. (Chapter 1). Springer, London. 2013. http://cecs.louisville.edu/ry/TuringTestasaDefiningFeature04270003.pdf</ref>
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| With current technology, AI-complete problems cannot be solved by computer alone, but also require [[human computation]]. This property can be useful, for instance to test for the presence of humans as with [[CAPTCHA]]s, and for [[computer security]] to circumvent [[brute-force attack]]s.<ref>Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford. [http://www.captcha.net/captcha_crypt.pdf CAPTCHA: Using Hard AI Problems for Security]. In Proceedings of Eurocrypt, Vol. 2656 (2003), pp. 294-311.</ref><ref>{{cite paper | first = Richard | last = Bergmair | title = Natural Language Steganography and an "AI-complete" Security Primitive | id = {{citeseerx|10.1.1.105.129}} | date = January 7, 2006 }} (unpublished?)</ref>
| | <br><br>When dad started to receive hand tremors we suspected there was a problem. Then when my aunt suggested possess him tested for Parkinson's' Disease, our fears were confirmed.<br><br>Use tape over trim if ensure paint however. Paint can run even when you are super careful when you paint. Protect the trim in your home by taping it off before protecting. If you do get paint in regards to the trim, those surfaces need to be repainted.<br><br>Learn the way to use the available spaces of your home in your own landscape design and have a few things in travel. For example, could be wondering have noisy cars passing by, and by be solved by placing some hedges around the house to cut down on many. Make a play area for kids if you some or plan on having a. You can possess a gathering using your land also.<br><br>Cold and damp air can also cause other problems such as damp and mould. Preference visit your holiday home, open the windows and let some fresh air in. However for security reasons it's very important in order to ensure you shut and lock the windows when you depart. If your bathroom possesses damp, you'll find that mould has grown on the grouting from the bathroom wall tiles. Giving the grout a good clean with bleach, or renewing get your bath room looking fresh again.<br><br>Pork Belly Futures will be most illiquid, volatile, and hard to switch. However, you can make a good fortune if you're right. This is not a niche for the badly informed. It's very common to see Pork Belly futures go limit up and down in the same ceremony. Account margin is $1600 to control a $30,000 futures decision. This is similar to hogs and cattle commodity.<br><br>You think about a plant's full size, not just its current size, when deciding which plants opt for your landscape. You may be discover that half involving your plants perish from deficient numbers of sunlight and water through overcrowding. Proper spacing of plants is exceedingly important, so be sure you exactly how large the plants you choose will increase.<br><br>This is definitely an inexpensive 224 piece Lego with 12 Lego microfigures and building instructions. The mighty Minotaur, a mythical creature, protects a secret temple hidden deep inside a labyrinth. Clear and simple rules get this game fun for all the family.<br><br>Trees the particular most planning to drought conditions and would not spot problems until its too late so these a few regular buckets of bath water 1 week. And if the council has planted a tree in your street during the last year then get out and supply it with an extra boost with a bucket as well!<br><br>When you adored this informative article and also you wish to get more details with regards to [http://www.hedgingplants.com/ www.hedgingplants.com] generously go to our webpage. |
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| ==History==
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| The term was coined by [[Fanya Montalvo]]<ref> John C. Mallery. "Thinking About Foreign Policy: Finding an Appropriate Role for Artificially Intelligent Computers" Master's thesis, M.I.T. Political Science Department. 1988</ref> by analogy with [[NP-complete]] and [[NP-hard]] in [[Computational complexity theory|complexity theory]], which formally describes the most famous class of difficult problems.<ref>{{Citation| last=Mallery | first=John C. |year=1988 | url=http://citeseer.ist.psu.edu/mallery88thinking.html | contribution=Thinking About Foreign Policy: Finding an Appropriate Role for Artificially Intelligent Computers | title=The 1988 Annual Meeting of the International Studies Association. | location=St. Louis, MO }}.</ref> Early uses of the term are in Erik Mueller's 1987 Ph.D. dissertation<ref>Mueller, Erik T. (1987, March). [ftp://ftp.cs.ucla.edu/tech-report/198_-reports/870017.pdf ''Daydreaming and Computation'' (Technical Report CSD-870017)] Ph.D. dissertation, University of California, Los Angeles. ("Daydreaming is but one more ''AI-complete'' problem: if we could solve any one artificial intelligence problem, we could solve all the others", p. 302)</ref> and in [[Eric S. Raymond|Eric Raymond]]'s 1991 [[Jargon File]].<ref>Raymond, Eric S. (1991, March 22). [http://catb.org/esr/jargon/oldversions/jarg282.txt Jargon File Version 2.8.1] (Definition of "AI-complete" first added to jargon file.)</ref>
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| ==AI-complete problems==
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| AI-complete problems are hypothesised to include:
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| *[[Computer vision]] (and subproblems such as [[object recognition]])
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| *[[Natural language understanding]] (and subproblems such as [[text mining]], [[machine translation]], and [[word sense disambiguation]])
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| *Dealing with unexpected circumstances while solving any real world problem, whether it's [[robotic mapping|navigation]] or [[automated planning and scheduling|planning]] or even the kind of [[reasoning]] done by [[expert system]]s.
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| ===Machine translation===
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| {{main|Machine translation}}
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| To translate accurately, a machine must be able to understand the text. It must be able to follow the author's argument, so it must have some ability to [[artificial intelligence#Deduction, reasoning, problem solving|reason]]. It must have extensive [[commonsense knowledge|world knowledge]] so that it knows what is being discussed — it must at least be familiar with all the same commonsense facts that the average human translator knows. Some of this knowledge is in the form of facts that can be explicitly represented, but some knowledge is unconscious and closely tied to the human body: for example, the machine may need to understand how an ocean makes one ''feel'' to accurately translate a specific metaphor in the text. It must also model the authors' goals, intentions, and emotional states to accurately reproduce them in a new language. In short, the machine is required to have wide variety of human intellectual skills, including [[artificial intelligence#Deduction, reasoning, problem solving|reason]], [[commonsense knowledge]] and the intuitions that underlie [[robotics|motion and manipulation]], [[machine perception|perception]], and [[artificial intelligence#Social intelligence|social intelligence]]. [[Machine translation]], therefore, is believed to be AI-complete: it may require [[strong AI]] to be done as well as humans can do it.
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| ==Software brittleness==
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| {{main|Software brittleness}}
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| Current AI systems can solve very simple restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempt to "scale up" their systems to handle more complicated, real world situations, the programs tend to become excessively [[brittle (software)|brittle]] without [[commonsense knowledge]] or a rudimentary understanding of the situation: they fail as unexpected circumstances outside of its original problem context begin to appear. When human beings are dealing with new situations in the world, they are helped immensely by the fact that they know what to expect: they know what all things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. A machine without [[strong AI]] has no other skills to fall back on.<ref>{{Citation | last=Lenat | first=Douglas | last2=Guha | first2=R. V.| year = 1989 | title = Building Large Knowledge-Based Systems | publisher = Addison-Wesley| author-link=Douglas Lenat|pages=1–5}}</ref>
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| ==Formalization==
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| [[Computational complexity theory]] deals with the relative computational difficulty of [[computable function]]s. By definition it does not cover problems whose solution is unknown or has not been characterised formally. Since many AI problems have no formalisation yet, conventional complexity theory does not allow the definition of AI-completeness.
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| To address this problem, a complexity theory for AI has been proposed.<ref name="ucl.ac.uk">Dafna Shahaf and Eyal Amir (2007) [http://www.cs.uiuc.edu/~eyal/papers/ai-complete-commonsense07.pdf Towards a theory of AI completeness]. [http://www.ucl.ac.uk/commonsense07 Commonsense 2007, 8th International Symposium on Logical Formalizations of Commonsense Reasoning].</ref> It is based on a [[model of computation]] that splits the computational burden between a computer and a human: one part is solved by computer and the other part solved by human. This is formalised by a '''human-assisted [[Turing machine]]'''. The formalisation defines algorithm complexity, problem complexity and reducibility which in turn allows [[equivalence class]]es to be defined.
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| The complexity of executing an algorithm with a human-assisted Turing machine is given by a pair <math>\langle\Phi_{H},\Phi_{M}\rangle</math>, where the first element represents the complexity of the human's part and the second element is the complexity of the machine's part.
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| ===Results===
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| The complexity of solving the following problems with a human-assisted Turing machine is:<ref name="ucl.ac.uk"/>
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| * [[Optical character recognition]] for printed text: <math>\langle O(1), poly(n) \rangle </math>
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| * [[Turing test]]:
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| ** for an <math>n</math>-sentence conversation where the oracle remembers the conversation history (persistent oracle): <math>\langle O(n), O(n) \rangle </math>
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| ** for an <math>n</math>-sentence conversation where the conversation history must be retransmitted: <math>\langle O(n), O(n^2) \rangle </math>
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| ** for an <math>n</math>-sentence conversation where the conversation history must be retransmitted and the person takes linear time to read the query: <math>\langle O(n^2), O(n^2) \rangle </math>
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| * [[ESP game]]: <math>\langle O(n), O(n) \rangle </math>
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| * Image labelling (based on the [[Arthur–Merlin protocol]]): <math>\langle O(n), O(n) \rangle </math>
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| * [[Object categorization from image search|Image classification]]: human only: <math>\langle O(n), O(n) \rangle </math>, and with less reliance on the human: <math>\langle O(\log n), O(n \log n) \rangle </math>.
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| ==See also==
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| *[[ASR-complete]]
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| *[[List of open problems in computer science]]
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| *[[Synthetic intelligence]]
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| ==References==
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| <references/>
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| {{DEFAULTSORT:Ai-Complete}}
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| [[Category:Artificial intelligence]]
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| [[Category:Computational problems]]
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When dad started to receive hand tremors we suspected there was a problem. Then when my aunt suggested possess him tested for Parkinson's' Disease, our fears were confirmed.
Use tape over trim if ensure paint however. Paint can run even when you are super careful when you paint. Protect the trim in your home by taping it off before protecting. If you do get paint in regards to the trim, those surfaces need to be repainted.
Learn the way to use the available spaces of your home in your own landscape design and have a few things in travel. For example, could be wondering have noisy cars passing by, and by be solved by placing some hedges around the house to cut down on many. Make a play area for kids if you some or plan on having a. You can possess a gathering using your land also.
Cold and damp air can also cause other problems such as damp and mould. Preference visit your holiday home, open the windows and let some fresh air in. However for security reasons it's very important in order to ensure you shut and lock the windows when you depart. If your bathroom possesses damp, you'll find that mould has grown on the grouting from the bathroom wall tiles. Giving the grout a good clean with bleach, or renewing get your bath room looking fresh again.
Pork Belly Futures will be most illiquid, volatile, and hard to switch. However, you can make a good fortune if you're right. This is not a niche for the badly informed. It's very common to see Pork Belly futures go limit up and down in the same ceremony. Account margin is $1600 to control a $30,000 futures decision. This is similar to hogs and cattle commodity.
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This is definitely an inexpensive 224 piece Lego with 12 Lego microfigures and building instructions. The mighty Minotaur, a mythical creature, protects a secret temple hidden deep inside a labyrinth. Clear and simple rules get this game fun for all the family.
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