Daily Archives: June 20, 2022

What is AI? Artificial intelligence (AI) is the ability of a machine or computer to imitate the capabilities of the human mind. AI taps into multiple technologies to equip machines in planning, acting, comprehending, learning, and sensing with human-like intelligence. AI systems may perceive environments, recognize objects, make decisions, solve problems, learn from experience, and imitate examples. These abilities are combined to accomplish actions that would otherwise require humans to do, such as driving a car or greeting a guest.

Artificial intelligence may have entered everyday conversation over the last decade or so but it has been around much longer (see the History of AI section below). The relatively recent rise in its prominence is not by accident.

AI technology, and especially machine learning, relies on the availability of vast volumes of information. The proliferation of the Internet, the expansion of cloud computing, the rise of smartphones, and the growth of the Internet of Things has created enormous quantities of data that grows every day. This treasure trove of information combined with the huge gains made in computing power have made the rapid and accurate processing of enormous data possible.

Today, AI is completing our chat conversations, suggesting email responses, providing driving directions, recommending the next movie we should stream, vacuuming our floors, and performing complex medical image analyses.

The history of artificial intelligence goes as far back as ancient Greece. However, its the rise of electronic computing that made AI a real possibility. Note that what is considered AI has changed as the technology evolves. For example, a few decades ago, machines that could perform optimal character recognition (OCR) or simple arithmetic were categorized as AI. Today, OCR and basic calculations are not considered AI but rather an elementary function of a computer system.

Artificial intelligence asserts that there are principles governing the actions of intelligent systems. It is based on reverse-engineering human capabilities and traits onto a machine. The system uses computational power to exceed what the average human is capable of doing. The machine must learn to respond to certain actions. It relies on historical data and algorithms to create a propensity model. Machines learn from experience to perform cognitive tasks that are ordinarily the preserve of the human brain. The system automatically learns from features or patterns in the data.

AI is founded on two pillars engineering and cognitive science. The engineering involves building the tools that rely on human-comparable intelligence. Large volumes of data are combined with series of instructions (algorithms) and rapid iterative processing. Cognitive science involves emulating how the human brain works, and brings to AI multiple fields including machine learning, deep learning, neural networks, cognitive computing, computer vision, natural language processing, and knowledge reasoning.

Artificial intelligence isnt one type of system. Its a diverse domain. Theres the simple, low-level AI systems focused on performing a specific task such as weather apps, business data analysis apps, taxi hailing apps, and digital assistants. This is the type of AI, called "Narrow AI", that the average person is most likely to interact with. Their main purpose is driving efficiency.

On the other end of the spectrum are advanced systems that emulate human intelligence at a more general level and can tackle complex tasks. These include thinking creatively, abstractly, and strategically. Strictly speaking, this kind of truly sentient machine, called "Artificial General Intelligence" or AGI, only exists on the silver screen for now, though the race toward its realization is accelerating.

Humans have pursued artificial intelligence in recognition of how invaluable it can be for business innovation and digital transformation. AI can cut costs and introduce levels of speed, scalability, and consistency that is otherwise out of reach. You probably interact with some form of AI multiple times each day. The applications of AI are too numerous to exhaustively cover here. Heres a high level look at some of the most significant ones.

As cyberattacks grow in scale, sophistication, and frequency, human-dependent cyber defenses are no longer adequate. Traditionally, anti-malware applications were built with specific threats in mind. Virus signatures would be updated as new malware was identified.

But keeping up with the sheer number and diversity of threats eventually becomes a near impossible task. This approach was reactive and depended on the identification of a specific malware for it to be added to the next update.

AI-based anti-spam, firewall, intrusion detection/prevention, and other cybersecurity systems go beyond the archaic rule-based strategy. Real-time threat identification, analysis, mitigation, and prevention is the name of the game. They deploy AI systems that detect malware traits and take remedial action even without the formal identification of the threat.

AI cybersecurity systems rely on the continuous feed of data to recognize patterns and backtrack attacks. By feeding algorithms large volumes of information, these systems learn how to detect anomalies, monitor behavior, respond to threats, adapt to attack, and issue alerts.

Also referred to as speech-to-text (STT), speech recognition is technology that recognizes speech and converts it into digital text. Its at the heart of computer dictation apps, as well as voice-enabled GPS and voice-driven call answering menus.

Natural language processing (NLP) relies on a software application to decipher, interpret, and generate human-readable text. NLP is the technology behind Alexa, Siri, chatbots, and other forms of text-based assistants. Some NLP systems use sentiment analysis to make out the attitude, mood, and subjective qualities in a language.

Also known as machine vision or computer vision, image recognition is artificial intelligence that allows one to classify and identify people, objects, text, actions, and writing occurring within moving or still images. Usually powered by deep neural networks, image recognition has found application in self-driving cars, medical image/video analysis, fingerprint identification systems, check deposit apps, and more.

E-commerce and entertainment websites/apps leverage neural networks to recommend products and media that will appeal to the customer based on their past activity, the activity of similar customers, the season, the weather, the time of day, and more. These real-time recommendations are customized to each user. For e-commerce sites, recommendations not only grow sales but also help optimize inventory, logistics, and store layout.

The stock market can be extremely volatile in times of crisis. Billions of dollars in market value may be wiped out in seconds. An investor who was in a highly profitable position one minute could find themselves deep in the red shortly thereafter. Yet, its near impossible for a human to react quick enough to market-influencing events. High-frequency trading (HFT) systems are AI-driven platforms that make thousands or millions of automated trades per day to maintain stock portfolio optimization for large institutions.

Lyft, Uber, and other ride-share apps use AI to connect requesting riders to available drivers. AI technology minimizes detours and wait times, provides realistic ETAs, and deploys surge-pricing during spikes in demand.

Self-driving cars are not yet standard in most of the world but theres already been a concerted push to embed AI-based safety functions to detect dangerous scenarios and prevent accidents.

Unlike land-based vehicles, the margin for error in aircraft is extremely narrow. Given the altitude, a small miscalculation may lead to hundreds of fatalities. Aircraft manufacturers had to push safety systems and become one of the earliest adopters of artificial intelligence.

To minimize the likelihood and impact of human error, autopilot systems have been flying military and commercial aircraft for decades. They use a combination of GPS technology, sensors, robotics, image recognition, and collision avoidance to navigate planes safely through the sky while keeping pilots and ground crew updated as needed.

Artificial Intelligence accelerates and simplifies test creation, execution, and maintenance through AI-powered intelligent test automation. AI-based machine learning and advanced optical character recognition (OCR) provide for advanced object recognition, and when combined with AI-based mockup identification, AI-based recording, AI-based text matching, and image-based automation, teams can reduce test creation time and test maintenance efforts,and boost test coverage and resilience of testing assets.

Artificial intelligence allows you to test earlier and faster with functional testing solutions. Combine extensive technology support with AI-driven capabilities. Deliver the speed and resiliency that supports rapid application changes within a continuous delivery pipeline.

Both IT and business face the challenges of too many manual, error-prone workflows, an ever-increasing volume of requests, employees dissatisfied with the level and quality of service, and more. Artificial Intelligence and machine learning technology can take service management to the next level:

Read How AI Is Enabling Enterprise Service Management from the resource list below for more thoughts and information on the role of artificial intelligence (AI) in the adoption and expansion of enterprise service management (ESM).

What is true of IT support, is also true for ESM; AI makes operations and outcomes better. To find out more read Ten Tips for Empowering Your IT Support with AI.

Robotic process automation (RPA) uses software robots that mimic screen-based human actions to perform repetitive tasks and extend automation to interfaces with difficult or no application programming interfaces (APIs). Thats why RPA is perfect for automating processes typically completed by humans or that require human intervention. Resilient robots adapt to screen changes and keep processes flowing when change happens. When powered by AI-based machine learning, RPA robots identify screen objects even ones they havent seen before and emulate human intuition to determine their functions. They use OCR to read text (for example, text boxes and links) and computer vision to read visual elements (for example, shopping cart icons and login buttons). When a screen object changes, robots adapt. Machine learning drives them to continuously improve how they see and interact with screen objects just like a human would.

There are plenty of ways you could leverage artificial intelligence for your business to stay competitive, drive growth, and unlock value. Nevertheless, your organization doesnt possess infinite resources. You must prioritize. Begin by defining what your organizations values and strategic objectives are. From that point, assess the possible applications of AI against these values and objectives. Choose the AI technology that is bound to deliver the biggest impact for the business.

The world is only going to grow more AI-dependent. Its no longer about whether to adopt AI but when. Organizations that tap into AI ahead of their peers could gain a significant competitive advantage. Developing and pursuing a well-defined AI strategy is where it all begins. It may take a bit of experimenting before you know what will work for you.

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What Is Artificial Intelligence (AI)? | Micro Focus

Artificial Intelligence (AI) tries to enable computers to do the things that minds can do. These things include seeing pathways, picking things up, learning categories from experience, and using emotions to schedule one's actionswhich many animals can do, too. Thus, human intelligence is not the sole focus of AI. Even terrestrial psychology is not the sole focus, because some people use AI to explore the range of all possible minds.

There are four major AI methodologies: symbolic AI, connectionism, situated robotics, and evolutionary programming (Russell and Norvig 2003). AI artifacts are correspondingly varied. They include both programs (including neural networks) and robots, each of which may be either designed in detail or largely evolved. The field is closely related to artificial life (A-Life), which aims to throw light on biology much as some AI aims to throw light on psychology.

AI researchers are inspired by two different intellectual motivations, and while some people have both, most favor one over the other. On the one hand, many AI researchers seek solutions to technological problems, not caring whether these resemble human (or animal) psychology. They often make use of ideas about how people do things. Programs designed to aid/replace human experts, for example, have been hugely influenced by knowledge engineering, in which programmers try to discover what, and how, human experts are thinking when they do the tasks being modeled. But if these technological AI workers can find a nonhuman method, or even a mere trick (a kludge) to increase the power of their program, they will gladly use it.

Technological AI has been hugely successful. It has entered administrative, financial, medical, and manufacturing practice at countless different points. It is largely invisible to the ordinary person, lying behind some deceptively simple human-computer interface or being hidden away inside a car or refrigerator. Many procedures taken for granted within current computer science were originated within AI (pattern-recognition and image-processing, for example).

On the other hand, AI researchers may have a scientific aim. They may want their programs or robots to help people understand how human (or animal) minds work. They may even ask how intelligence in general is possible, exploring the space of possible minds. The scientific approachpsychological AIis the more relevant for philosophers (Boden 1990, Copeland 1993, Sloman 2002). It is also central to cognitive science, and to computationalism.

Considered as a whole, psychological AI has been less obviously successful than technological AI. This is partly because the tasks it tries to achieve are often more difficult. In addition, it is less clearfor philosophical as well as empirical reasonswhat should be counted as success.

Symbolic AI is also known as classical AI and as GOFAIshort for John Haugeland's label "Good Old-Fashioned AI" (1985). It models mental processes as the step-by-step information processing of digital computers. Thinking is seen as symbol-manipulation, as (formal) computation over (formal) representations. Some GOFAI programs are explicitly hierarchical, consisting of procedures and subroutines specified at different levels. These define a hierarchically structured search-space, which may be astronomical in size. Rules of thumb, or heuristics, are typically provided to guide the searchby excluding certain areas of possibility, and leading the program to focus on others. The earliest AI programs were like this, but the later methodology of object-oriented programming is similar.

Certain symbolic programs, namely production systems, are implicitly hierarchical. These consist of sets of logically separate if-then (condition-action) rules, or productions, defining what actions should be taken in response to specific conditions. An action or condition may be unitary or complex, in the latter case being defined by a conjunction of several mini-actions or mini-conditions. And a production may function wholly within computer memory (to set a goal, for instance, or to record a partial parsing) or outside it (via input/output devices such as cameras or keyboards).

Another symbolic technique, widely used in natural language processing (NLP) programs, involves augmented transition networks, or ATNs. These avoid explicit backtracking by using guidance at each decision-point to decide which question to ask and/or which path to take.

GOFAI methodology is used for developing a wide variety of language-using programs and problem-solvers. The more precisely and explicitly a problem-domain can be defined, the more likely it is that a symbolic program can be used to good effect. Often, folk-psychological categories and/or specific propositions are explicitly represented in the system. This type of AI, and the forms of computational psychology based on it, is defended by the philosopher Jerry Fodor (1988).

GOFAI models (whether technological or scientific) include robots, planning programs, theorem-provers, learning programs, question-answerers, data-mining systems, machine translators, expert systems of many different kinds, chess players, semantic networks, and analogy machines. In addition, a host of software agentsspecialist mini-programs that can aid a human being to solve a problemare implemented in this way. And an increasingly important area of research is distributed AI, in which cooperation occurs between many relatively simple individualswhich may be GOFAI agents (or neural-network units, or situated robots).

The symbolic approach is used also in modeling creativity in various domains (Boden 2004, Holland et al. 1986). These include musical composition and expressive performance, analogical thinking, line-drawing, painting, architectural design, storytelling (rhetoric as well as plot), mathematics, and scientific discovery. In general, the relevant aesthetic/theoretical style must be specified clearly, so as to define a space of possibilities that can be fruitfully explored by the computer. To what extent the exploratory procedures can plausibly be seen as similar to those used by people varies from case to case.

Connectionist systems, which became widely visible in the mid-1980s, are different. They compute not by following step-by-step programs but by using large numbers of locally connected (associative) computational units, each one of which is simple. The processing is bottom-up rather than top-down.

Connectionism is sometimes said to be opposed to AI, although it has been part of AI since its beginnings in the 1940s (McCulloch and Pitts 1943, Pitts and McCulloch 1947). What connectionism is opposed to, rather, is symbolic AI. Yet even here, opposed is not quite the right word, since hybrid systems exist that combine both methodologies. Moreover, GOFAI devotees such as Fodor see connectionism as compatible with GOFAI, claiming that it concerns how symbolic computation can be implemented (Fodor and Pylyshyn 1988).

Two largely separate AI communities began to emerge in the late 1950s (Boden forthcoming). The symbolic school focused on logic and Turing-computation, whereas the connectionist school focused on associative, and often probabilistic, neural networks. (Most connectionist systems are connectionist virtual machines, implemented in von Neumann computers; only a few are built in dedicated connectionist hardware.) Many people remained sympathetic to both schools. But the two methodologies are so different in practice that most hands-on AI researchers use either one or the other.

There are different types of connectionist systems. Most philosophical interest, however, has focused on networks that do parallel distributed processing, or PDP (Clark 1989, Rumelhart and McClelland 1986). In essence, PDP systems are pattern recognizers. Unlike brittle GOFAI programs, which often produce nonsense if provided with incomplete or part-contradictory information, they show graceful degradation. That is, the input patterns can be recognized (up to a point) even if they are imperfect.

A PDP network is made up of subsymbolic units, whose semantic significance cannot easily be expressed in terms of familiar semantic content, still less propositions. (Some GOFAI programs employ subsymbolic units, but most do not.) That is, no single unit codes for a recognizable concept, such as dog or cat. These concepts are represented, rather, by the pattern of activity distributed over the entire network.

Because the representation is not stored in a single unit but is distributed over the whole network, PDP systems can tolerate imperfect data. (Some GOFAI systems can do so too, but only if the imperfections are specifically foreseen and provided for by the programmer.) Moreover, a single subsymbolic unit may mean one thing in one input-context and another in another. What the network as a whole can represent depends on what significance the designer has decided to assign to the input-units. For instance, some input-units are sensitive to light (or to coded information about light), others to sound, others to triads of phonological categories and so on.

Most PDP systems can learn. In such cases, the weights on the links of PDP units in the hidden layer (between the input-layer and the output-layer) can be altered by experience, so that the network can learn a pattern merely by being shown many examples of it. (A GOFAI learning-program, in effect, has to be told what to look for beforehand, and how.) Broadly, the weight on an excitatory link is increased by every coactivation of the two units concerned: cells that fire together, wire together.

These two AI approaches have complementary strengths and weaknesses. For instance, symbolic AI is better at modeling hierarchy and strong constraints, whereas connectionism copes better with pattern recognition, especially if many conflictingand perhaps incompleteconstraints are relevant. Despite having fervent philosophical champions on both sides, neither methodology is adequate for all of the tasks dealt with by AI scientists. Indeed, much research in connectionism has aimed to restore the lost logical strengths of GOFAI to neural networkswith only limited success by the beginning of the twenty-first century.

Another, and more recently popular, AI methodology is situated robotics (Brooks 1991). Like connectionism, this was first explored in the 1950s. Situated robots are described by their designers as autonomous systems embedded in their environment (Heidegger is sometimes cited). Instead of planning their actions, as classical robots do, situated robots react directly to environmental cues. One might say that they are embodied production systems, whose if-then rules are engineered rather than programmed, and whose conditions lie in the external environment, not inside computer memory. Althoughunlike GOFAI robotsthey contain no objective representations of the world, some of them do construct temporary, subject-centered (deictic) representations.

The main aim of situated roboticists in the mid-1980s, such as Rodney Brooks, was to solve/avoid the frame problem that had bedeviled GOFAI (Pylyshyn 1987). GOFAI planners and robots had to anticipate all possible contingencies, including the side effects of actions taken by the system itself, if they were not to be defeated by unexpectedperhaps seemingly irrelevantevents. This was one of the reasons given by Hubert Dreyfus (1992) in arguing that GOFAI could not possibly succeed: Intelligence, he said, is unformalizable. Several ways of implementing nonmonotonic logics in GOFAI were suggested, allowing a conclusion previously drawn by faultless reasoning to be negated by new evidence. But because the general nature of that new evidence had to be foreseen, the frame problem persisted.

Brooks argued that reasoning shouldn't be employed at all: the system should simply react appropriately, in a reflex fashion, to specific environmental cues. This, he said, is what insects doand they are highly successful creatures. (Soon, situated robotics was being used, for instance, to model the six-legged movement of cockroaches.) Some people joked that AI stood for artificial insects, not artificial intelligence. But the joke carried a sting: Many argued that much human thinking needs objective representations, so the scope for situated robotics was strictly limited.

In evolutionary programming, genetic algorithms (GAs) are used by a program to make random variations in its own rules. The initial rules, before evolution begins, either do not achieve the task in question or do so only inefficiently; sometimes, they are even chosen at random.

The variations allowed are broadly modeled on biological mutations and crossovers, although more unnatural types are sometimes employed. The most successful rules are automatically selected, and then varied again. This is more easily said than done: The breakthrough in GA methodology occurred when John Holland (1992) defined an automatic procedure for recognizing which rules, out of a large and simultaneously active set, were those most responsible for whatever level of success the evolving system had just achieved.

Selection is done by some specific fitness criterion, predefined in light of the task the programmer has in mind. Unlike GOFAI systems, a GA program contains no explicit representation of what it is required to do: its task is implicit in the fitness criterion. (Similarly, living things have evolved to do what they do without knowing what that is.) After many generations, the GA system may be well-adapted to its task. For certain types of tasks, it can even find the optimal solution.

This AI method is used to develop both symbolic and connectionist AI systems. And it is applied both to abstract problem-solving (mathematical optimization, for instance, or the synthesis of new pharmaceutical molecules) and to evolutionary roboticswherein the brain and/or sensorimotor anatomy of robots evolve within a specific task-environment.

It is also used for artistic purposes, in the composition of music or the generation of new visual forms. In these cases, evolution is usually interactive. That is, the variation is done automatically but the selection is done by a human beingwho does not need to (and usually could not) define, or even name, the aesthetic fitness criteria being applied.

AI is a close cousin of A-Life (Boden 1996). This is a form of mathematical biology, which employs computer simulation and situated robotics to study the emergence of complexity in self-organizing, self-reproducing, adaptive systems. (A caveat: much as some AI is purely technological in aim, so is some A-Life; the research of most interest to philosophers is the scientifically oriented type.)

The key concepts of A-Life date back to the early 1950s. They originated in theoretical work on self-organizing systems of various kinds, including diffusion equations and cellular automata (by Alan Turing and John von Neumann respectively), and in early self-equilibrating machines and situated robots (built by W. Ross Ashby and W. Grey Walter). But A-Life did not flourish until the late 1980s, when computing power at last sufficed to explore these theoretical ideas in practice.

Much A-Life work focuses on specific biological phenomena, such as flocking, cooperation in ant colonies, or morphogenesisfrom cell-differentiation to the formation of leopard spots or tiger stripes. But A-Life also studies general principles of self-organization in biology: evolution and coevolution, reproduction, and metabolism. In addition, it explores the nature of life as suchlife as it could be, not merely life as it is.

A-Life workers do not all use the same methodology, but they do eschew the top-down methods of GOFAI. Situated and evolutionary robotics, and GA-generated neural networks, too, are prominent approaches within the field. But not all A-Life systems are evolutionary. Some demonstrate how a small number of fixed, and simple, rules can lead to self-organization of an apparently complex kind.

Many A-Lifers take pains to distance themselves from AI. But besides their close historical connections, AI and A-Life are philosophically related in virtue of the linkage between life and mind. It is known that psychological properties arise in living things, and some people argue (or assume) that they can arise only in living things. Accordingly, the whole of AI could be regarded as a subarea of A-Life. Indeed, some people argue that success in AI (even in technological AI) must await, and build on, success in A-Life.

Whichever of the two AI motivationstechnological or psychologicalis in question, the name of the field is misleading in three ways. First, the term intelligence is normally understood to cover only a subset of what AI workers are trying to do. Second, intelligence is often supposed to be distinct from emotion, so that AI is assumed to exclude work on that. And third, the name implies that a successful AI system would really be intelligenta philosophically controversial claim that AI researchers do not have to endorse (though some do).

As for the first point, people do not normally regard vision or locomotion as examples of intelligence. Many people would say that speaking one's native language is not a case of intelligence either, except in comparison with nonhuman species; and common sense is sometimes contrasted with intelligence. The term is usually reserved for special cases of human thought that show exceptional creativity and subtlety, or which require many years of formal education. Medical diagnosis, scientific or legal reasoning, playing chess, and translating from one language to another are typically regarded as difficult, thus requiring intelligence. And these tasks were the main focus of research when AI began. Vision, for example, was assumed to be relatively straightforwardnot least, because many nonhuman animals have it too. It gradually became clear, however, that everyday capacities such as vision and locomotion are vastly more complex than had been supposed. The early definition of AI as programming computers to do things that involve intelligence when done by people was recognized as misleading, and eventually dropped.

Similarly, intelligence is often opposed to emotion. Many people assume that AI could never model that. However, crude examples of such models existed in the early 1960s, and emotion was recognized by a high priest of AI, Herbert Simon, as being essential to any complex intelligence. Later, research in the computational philosophy (and modeling) of affect showed that emotions have evolved as scheduling mechanisms for systems with many different, and potentially conflicting, purposes (Minsky 1985, and Web site). When AI began, it was difficult enough to get a program to follow one goal (with its subgoals) intelligentlyany more than that was essentially impossible. For this reason, among others, AI modeling of emotion was put on the back burner for about thirty years. By the 1990s, however, it had become a popular focus of AI research, and of neuroscience and philosophy too.

The third point raises the difficult questionwhich many AI practitioners leave open, or even ignoreof whether intentionality can properly be ascribed to any conceivable program/robot (Newell 1980, Dennett 1987, Harnad 1991).

Could some NLP programs really understand the sentences they parse and the words they translate? Or can a visuo-motor circuit evolved within a robot's neural-network brain truly be said to represent the environmental feature to which it responds? If a program, in practice, could pass the Turing Test, could it truly be said to think? More generally, does it even make sense to say that AI may one day achieve artificially produced (but nonetheless genuine) intelligence?

For the many people in the field who adopt some form of functionalism, the answer in each case is: In principle, yes. This applies for those who favor the physical symbol system hypothesis or intentional systems theory. Others adopt connectionist analyses of concepts, and of their development from nonconceptual content. Functionalism is criticized by many writers expert in neuroscience, who claim that its core thesis of multiple realizability is mistaken. Others criticize it at an even deeper level: a growing minority (especially in A-Life) reject neo-Cartesian approaches in favor of philosophies of embodiment, such as phenomenology or autopoiesis.

Part of the reason why such questions are so difficult is that philosophers disagree about what intentionality is, even in the human case. Practitioners of psychological AI generally believe that semantic content, or intentionality, can be naturalized. But they differ about how this can be done.

For instance, a few practitioners of AI regard computation and intentionality as metaphysically inseparable (Smith 1996). Others ascribe meaning only to computations with certain causal consequences and provenance, or grounding. John Searle argues that AI cannot capture intentionality, becauseat baseit is concerned with the formal manipulation of formal symbols. And for those who accept some form of evolutionary semantics, only evolutionary robots could embody meaning (Searle, 1980).

See also Computationalism; Machine Intelligence.

Boden, Margaret A. The Creative Mind: Myths and Mechanisms. 2nd ed. London: Routledge, 2004.

Boden, Margaret A. Mind as Machine: A History of Cognitive Science. Oxford: Oxford University Press, forthcoming. See especially chapters 4, 7.i, 1013, and 14.

Boden, Margaret A., ed. The Philosophy of Artificial Intelligence. Oxford: Oxford University Press, 1990.

Boden, Margaret A., ed. The Philosophy of Artificial Life. Oxford: Oxford University Press, 1996.

Brooks, Rodney A. "Intelligence without Representation." Artificial Intelligence 47 (1991): 139159.

Clark, Andy J. Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. Cambridge, MA: MIT Press, 1989.

Copeland, B. Jack. Artificial Intelligence: A Philosophical Introduction. Oxford: Blackwell, 1993.

Dennett, Daniel C. The Intentional Stance. Cambridge, MA: MIT Press, 1987.

Dreyfus, Hubert L. What Computers Still Can't Do: A Critique of Artificial Reason. Cambridge, MA: MIT Press, 1992.

Fodor, Jerome A., and Zenon W. Pylyshyn. "Connectionism and Cognitive Architecture: A Critical Analysis." Cognition 28 (1988): 371.

Harnad, Stevan. "Other Bodies, Other Minds: A Machine Incarnation of an Old Philosophical Problem." Minds and Machines 1 (1991): 4354.

Haugeland, John. Artificial Intelligence: The Very Idea. Cambridge, MA: MIT Press, 1985.

Holland, John H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA: MIT Press, 1992.

Holland, John H., Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard. Induction: Processes of Inference, Learning, and Discovery. Cambridge, MA: MIT Press, 1986.

McCulloch, Warren S., and Walter H. Pitts. "A Logical Calculus of the Ideas Immanent in Nervous Activity." In The Philosoophy of Artificial Intelligence, edited by Margaret A. Boden. Oxford: Oxford University Press, 1990. First published in 1943.

Minsky, Marvin L. The Emotion Machine. Available from http://web.media.mit.edu/~minsky/E1/eb1.html. Web site only.

Minsky, Marvin L. The Society of Mind. New York: Simon & Schuster, 1985.

Newell, Allen. "Physical Symbol Systems." Cognitive Science 4 (1980): 135183.

Pitts, Walter H., and Warren S. McCulloch. "How We Know Universals: The Perception of Auditory and Visual Forms." In Embodiments of Mind, edited by Warren S. McCulloch. Cambridge, MA: MIT Press, 1965. First published in 1947.

Pylyshyn, Zenon W. The Robot's Dilemma: The Frame Problem in Artificial Intelligence. Norwood, NJ: Ablex, 1987.

Rumelhart, David E., and James L. McClelland, eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. 2 vols. Cambridge, MA: MIT Press, 1986.

Russell, Stuart J., and Peter Norvig. Artificial Intelligence: A Modern Approach. 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 2003.

Searle, John R. "Minds, Brains, and Programs," The Behavioral and Brain Sciences 3 (1980), 417424. Reprinted in M. A. Boden, ed., The Philosophy of Artificial Intelligence (Oxford: Oxford University Press 1990), pp. 6788.

Sloman, Aaron. "The Irrelevance of Turing Machines to Artificial Intelligence." In Computationalism: New Directions, edited by Matthias Scheutz. Cambridge, MA: MIT Press, 2002.

Smith, Brian C. On the Origin of Objects. Cambridge, MA: MIT Press, 1996.

Margaret A. Boden (1996, 2005)

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Artificial Intelligence | Encyclopedia.com

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