AI-complete
Encyclopedia
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 solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI
. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.
AI-complete problems are hypothesised to include computer vision
, natural language understanding
, and dealing with unexpected circumstances while solving any real world problem.
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 attacks.
by analogy with NP-complete
and NP-hard
in complexity theory
, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 Ph.D. dissertation and in Eric Raymond
's 1991 Jargon File
.
, perception
, and 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.
has no other skills to fall back on.
deals with the relative computational difficulty of computable function
s. By definition it does not cover problems whose solution are unknown or have not been characterised formally. Since many AI problems have no formalisation yet, conventional complexity theory does not allow the definition of AI-completeness.
To address this problem, a complexity theory for AI has been proposed. 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 classes to be defined.
The complexity of executing an algorithm with a human-assisted Turing machine is given by a pair
, where the first element represents the complexity of the human's part and the second element is the complexity of the machine's part.
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI
Strong AI
Strong AI is artificial intelligence that matches or exceeds human intelligence — the intelligence of a machine that can successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and an important topic for science fiction writers and...
. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.
AI-complete problems are hypothesised to include computer vision
Computer vision
Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions...
, natural language understanding
Natural language understanding
Natural language understanding is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension....
, and dealing with unexpected circumstances while solving any real world problem.
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
CAPTCHA
A CAPTCHA is a type of challenge-response test used in computing as an attempt to ensure that the response is generated by a person. The process usually involves one computer asking a user to complete a simple test which the computer is able to generate and grade...
s, and for computer security
Computer security
Computer security is a branch of computer technology known as information security as applied to computers and networks. The objective of computer security includes protection of information and property from theft, corruption, or natural disaster, while allowing the information and property to...
to circumvent brute-force attacks.
History
The term was coined by Fanya MontalvoFanya Montalvo
Fanya Montalvo Studied for Ph.D. in Computer and Information Science, attending Massachusetts University Amherst in 1976.The dissertation she wrote was entitled Aftereffects, Adaptation, and Plasticity: A Neural Model for Tunable Feature Space of this she was advised by Michael Anthony Arbib...
by analogy with NP-complete
NP-complete
In computational complexity theory, the complexity class NP-complete is a class of decision problems. A decision problem L is NP-complete if it is in the set of NP problems so that any given solution to the decision problem can be verified in polynomial time, and also in the set of NP-hard...
and NP-hard
NP-hard
NP-hard , in computational complexity theory, is a class of problems that are, informally, "at least as hard as the hardest problems in NP". A problem H is NP-hard if and only if there is an NP-complete problem L that is polynomial time Turing-reducible to H...
in complexity theory
Computational complexity theory
Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other...
, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 Ph.D. dissertation and in Eric Raymond
Eric S. Raymond
Eric Steven Raymond , often referred to as ESR, is an American computer programmer, author and open source software advocate. After the 1997 publication of The Cathedral and the Bazaar, Raymond was for a number of years frequently quoted as an unofficial spokesman for the open source movement...
's 1991 Jargon File
Jargon File
The Jargon File is a glossary of computer programmer slang. The original Jargon File was a collection of terms from technical cultures such as the MIT AI Lab, the Stanford AI Lab and others of the old ARPANET AI/LISP/PDP-10 communities, including Bolt, Beranek and Newman, Carnegie Mellon...
.
AI-complete problems
AI-complete problems are hypothesised to include:- Computer visionComputer visionComputer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions...
(and subproblems such as object recognitionObject recognitionObject recognition in computer vision is the task of finding a given object in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes / scale...
) - Natural language understandingNatural language understandingNatural language understanding is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension....
(and subproblems such as text miningText miningText mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as...
, machine translationMachine translationMachine translation, sometimes referred to by the abbreviation MT is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another.On a basic...
, and word sense disambiguationWord sense disambiguationIn computational linguistics, word-sense disambiguation is an open problem of natural language processing, which governs the process of identifying which sense of a word is used in a sentence, when the word has multiple meanings...
) - Dealing with unexpected circumstances while solving any real world problem, whether it's navigationRobotic mappingRobotic mapping is related to cartography. The goal for an autonomous robot to be able to construct a map or floor plan and to localize itself in it...
or planningAutomated planning and schedulingAutomated planning and scheduling is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are...
or even the kind of reasoning done by expert systemExpert systemIn artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert, and not by following the procedure of a developer as is the case in...
s.
Machine translation
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 reason. It must have extensive 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 reason, commonsense knowledge and the intuitions that underlie motion and manipulationRobotics
Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots...
, perception
Machine perception
In computing, machine perception is the ability of computing machines to sense and interpret images, sounds, or other contents of their environments, or of the contents of stored media....
, and social intelligence. Machine translation
Machine translation
Machine translation, sometimes referred to by the abbreviation MT is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another.On a basic...
, therefore, is believed to be AI-complete: it may require strong AI
Strong AI
Strong AI is artificial intelligence that matches or exceeds human intelligence — the intelligence of a machine that can successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and an important topic for science fiction writers and...
to be done as well as humans can do it.
Software brittleness
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 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 AIStrong AI
Strong AI is artificial intelligence that matches or exceeds human intelligence — the intelligence of a machine that can successfully perform any intellectual task that a human being can. It is a primary goal of artificial intelligence research and an important topic for science fiction writers and...
has no other skills to fall back on.
Formalisation
Computational complexity theoryComputational complexity theory
Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other...
deals with the relative computational difficulty of computable function
Computable function
Computable functions are the basic objects of study in computability theory. Computable functions are the formalized analogue of the intuitive notion of algorithm. They are used to discuss computability without referring to any concrete model of computation such as Turing machines or register...
s. By definition it does not cover problems whose solution are unknown or have not been characterised formally. Since many AI problems have no formalisation yet, conventional complexity theory does not allow the definition of AI-completeness.
To address this problem, a complexity theory for AI has been proposed. It is based on a model of computation
Model of computation
In computability theory and computational complexity theory, a model of computation is the definition of the set of allowable operations used in computation and their respective costs...
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
Turing machine
A Turing machine is a theoretical device that manipulates symbols on a strip of tape according to a table of rules. Despite its simplicity, a Turing machine can be adapted to simulate the logic of any computer algorithm, and is particularly useful in explaining the functions of a CPU inside a...
. The formalisation defines algorithm complexity, problem complexity and reducibility which in turn allows equivalence classes to be defined.
The complexity of executing an algorithm with a human-assisted Turing machine is given by a pair
, where the first element represents the complexity of the human's part and the second element is the complexity of the machine's part.Results
The complexity of solving the following problems with a human-assisted Turing machine is:- Optical character recognitionOptical character recognitionOptical character recognition, usually abbreviated to OCR, is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files, to computerize a record-keeping...
for printed text:
- Turing testTuring testThe Turing test is a test of a machine's ability to exhibit intelligent behaviour. In Turing's original illustrative example, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All...
:- for an
-sentence conversation where the oracle remembers the conversation history (persistent oracle): 
- for an
-sentence conversation where the conversation history must be retransmitted: 
- for an
-sentence conversation where the conversation history must be retransmitted and the person takes linear time to read the query
- for an
- ESP gameESP GameThe ESP Game is an idea in computer science for addressing the problem of creating difficult metadata.The idea behind the game is to use the computational power of humans to perform a task that computers cannot yet do by packaging the task as a game.It was originally conceived by Luis von Ahn of...
:
- Image labelling (based on the Arthur–Merlin protocol):
- Image classificationObject categorization from image searchIn computer vision, the problem of object categorization from image search is the problem of training a classifier to recognize categories of objects, using only the images retrieved automatically with an Internet search engine. Ideally, automatic image collection would allow classifiers to be...
: human only:
, and with less reliance on the human: 
.
See also
- ASR-completeASR-completeASR-complete is, by analogy to NP-completeness in complexity theory, a term to indicate that the difficulty of a computational problem is equivalent to solving the central Automatic Speech Recognition problem, i.e. recognize and understanding spoken language...
- List of open problems in computer science
- Synthetic intelligenceSynthetic intelligenceSynthetic intelligence is an alternative term for artificial intelligence which emphasizes that the intelligence of machines need not be an imitation or any way artificial; it can be a genuine form of intelligence. John Haugeland proposes an analogy with artificial and synthetic diamonds—only the...