AI Glossary - What Is The ARTMAP-IC?

 

What Is The ARTMAP-IC Algorithm?

The fundamental fuzzy ARTMAP is enhanced by this network with distributed prediction and category instance counting.

How Is The ARTMAP-IC Used For Medical Diagnosis?

Medical diagnosis with ARTMAP-IC: Inconsistent cases and instance counting. 

The ARTMAP-IC neural network extends the fundamental fuzzy ARTMAP system with distributed prediction and category instance counting for challenging database prediction issues like medical diagnosis. 

A new version of the ARTMAP match tracking algorithm, which governs search after a predictive mistake, makes prediction with sparse or inconsistent data easier. 

The new approach (MT-) significantly compresses memory without sacrificing speed while improving the accuracy of the real-time network differential equations as compared to the old match tracking algorithm (MT+). 

Simulated analyses of four medical databases—Pima Indian diabetes, breast cancer, heart disease, and gallbladder removal—examine the prognostic accuracy of these conditions. 

Results using logistic regression, K closest neighbor (KNN), the ADAP preceptron, multisurface pattern separation, CLASSIT, instance-based (IBL), and C4 are comparable to or superior to those from ARTMAP-IC. 

The dynamics of ARTMAP are quick, reliable, and scalable. 

By repeatedly training the system on various input set orderings, a voting technique enhances prediction. 

Confidence intervals for competing predictions are derived from voting, instance counting, and distributed representations.

HOW DOES ARTMAP-IC NEURAL NETWORK CLASSIFIER FUNCTION?

In an ART-based network, information reverberates between the network’s layers. 

Learning is possible in the network, when resonance of the neuronal activity occurs. ART1 was developed to perform clustering on binary-valued patterns. 

By interconnecting two ART1 modules, ARTMAP was the first ART-based architecture suited for classification tasks. 

ARTMAP- IC adds to the basic ARTMAP system new capabilities designed to solve the problem with inconsistent cases, which arises in prediction, where similar input vectors correspond to cases with different outcomes, (Carpenter, Grossberg, and Reynolds, 1991), (Carpenter and Markuzon, 1998). 

It modifies the ARTMAP search algorithm to allow the network to encode inconsistent cases (IC). 

Below figure, adapted from (Carpenter and Markuzon, 1998), shows the architecture of an ARTMAP-IC network. 

Simplified ARTMAP-IC Architecture

It consist of fully connected layers of nodes: an M-node input layer F1, an Nnode competitive layer F2, an N-node instance counting layer F3, an L-node output layer F0 b , and an L-node map field Fab that links F3 and F0 b . 

In ARTMAP-IC an input a=(a1, a2, … , aM) learns to predict an outcome b=(b1, b2, …, bL), , where only one component bK=1, placing the input a in class K. 

With fast learning, β=1, ARTMAP-IC represents category K as hyper-rectangle ℜK that just encloses all the training set patterns a to which it has been assigned. 

A set of real weights W={wji: j=1,…,N; i=1,…,M} is associated with the F1 - F2 layer connections. Each F2 node j represents a category in the input space, and stores a prototype vector wj=(wj1, wj2, …,wjM). 

The F2 layer is connected, through associative links to F3, which in turn is connected to the map field Fab by associative links with binary weights Wab=(wjk ab:j=1,…,N; k=1,…,L}. 

The vector wj ab=(wj1 ab, wj2 ab, …,wjL ab) relates F2 node j to one of the L output classes. Instance counting biases distributed predictions according to the number of training set inputs classified by each F2 node. 

During testing the F2->F3 input yj is multiplied by the counting weight cj to produce normalized F3 activity, which projects to the map field Fab for prediction. 

How Does The ARTMAP-IC Algorithm Operate In Classifier Mode?

The following algorithm describes the operation of an ARTMAP-IC classifier in learning mode: 

1. Initialization: 

Initially, all the neurons of F2 are uncommitted, all weight values wji are initialized to 1, and all weight values wjk of Fab are set to 0. 

2. Input pattern coding: 

When a training pair (a,b) is presented to the network, a undergoes preprocessing, and yields pattern A=(A1,A2,…,A2M). 

The vigilance parameter ρ is reset to its baseline value. 

3. Prototype selection: 

Pattern A activates layer F1 and is propagated through weighted connections W to layer F2. 

Activation of each node j in the F2 layer is determined by the choice function Tj(A)=|A∧wj|/(α+|wj|). 

The F2 layer produces a winner-take-all pattern of activity y=(y1,y2,…,yN) such that only node j=J with the greatest activation value remains active (yJ=1). 

Node J propagates its prototype vector wJ back onto F1 and the vigilance test |A∧wj|≥ρM is performed. 

This test compares the degree of match between wJ and A to the vigilance parameter ρ∈[0,1]. 

If this test is satisfied, node J remains active and resonance is said to occur. 

Otherwise, the network inhibits the active F2 node and searches for another node J that passes the vigilance test. 

If such a node does not exist, an uncommitted F2 node becomes active and undergoes learning (step 5). 

4. Class prediction: 

Pattern b is fed directly to the map field Fab, while the F2 activity pattern y is propagated to the map field via associative connections Wab. 

The latter input activates Fab nodes according to the prediction function ∑= = N j ab j jk ab Sk y y w 1 ( ) and the most active Fab node K yields the class prediction (K=k(J)). 

If node K constitutes an incorrect class prediction, a match tracking signal raises vigilance just enough to induce another search among F2 nodes (step 3). 

This search continues until either an uncommitted F2 node becomes active (learning ensues at step 5), or a node J that has  previously learned the correct class prediction K becomes active. 

5. Learning: 

Learning input a involves updating prototype vector wJ, and if J corresponds to a newly committed node, creating a permanent associative link to Fab. 

A new association between F2 node J and Fab node K (K=k(J)) is learned by setting wJk ab=1 for k=K, where K is the target class label for a. 

Once the weights (W and Wab) have converged for the training set patterns, ARTMAP can predict a class label for an input pattern by performing steps 2, 3 and 4 without any testing. 

A pattern a that activates node J is predicted to belong to the class K=k(J)

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

Be sure to refer to the complete & active AI Terms Glossary here.

You may also want to read more about Artificial Intelligence here.

Reference And Further Reading

• Tayyebi, S. and Soltanali, S., Fuzzy Modeling System Based on Ga Fuzzy Rule Extraction and Hybrid of Differential Evolution and Tabu Search Approaches: Application in Synthesis Gas Conversion to Valuable Hydrocarbons Process. Saeed, Fuzzy Modeling System Based on Ga Fuzzy Rule Extraction and Hybrid of Differential Evolution and Tabu Search Approaches: Application in Synthesis Gas Conversion to Valuable Hydrocarbons Process.

• Tang, Y., Qiu, J. and Gao, M., 2022. Fuzzy Medical Computer Vision Image Restoration and Visual Application. Computational and Mathematical Methods in Medicine, 2022.

• Dymora, P., Mazurek, M. and Bomba, S., 2022. A Comparative Analysis of Selected Predictive Algorithms in Control of Machine Processes. Energies 2022, 15, 1895.

• Naosekpam, V. and Sahu, N., 2022, April. IFVSNet: Intermediate Features Fusion based CNN for Video Subtitles Identification. In 2022 IEEE 7th International conference for Convergence in Technology (I2CT) (pp. 1-6). IEEE.

• Boga, J. and Kumar, V.D., 2022. Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning. Expert Systems, p.e12988.

• Župerl, U., Stepien, K., Munđar, G. and Kovačič, M., 2022. A Cloud-Based System for the Optical Monitoring of Tool Conditions during Milling through the Detection of Chip Surface Size and Identification of Cutting Force Trends. Processes, 10(4), p.671.

• Neto, J.B.C., Ferrari, C., Marana, A.N., Berretti, S. and Bimbo, A.D., 2022. Learning Streamed Attention Network from Descriptor Images for Cross-resolution 3D Face Recognition. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM).

• Chattopadhyay, S., Dey, A., Singh, P.K., Ahmadian, A. and Sarkar, R., 2022. A feature selection model for speech emotion recognition using clustering-based population generation with hybrid of equilibrium optimizer and atom search optimization algorithm. Multimedia Tools and Applications, pp.1-34.

• Kanagaraj, R., Elakiya, E., Rajkumar, N., Srinivasan, K. and Sriram, S., 2022, January. Fuzzy Neural Network Classification Model for Multi Labeled Electricity Consumption Data Set. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1037-1041). IEEE.

AI Glossary - What Is ARTMAP?

 

What Is ARTMAP AI Algorithm?

The supervised learning variant of the ART-1 model is ARTMAP.

It learns binary input patterns that are given to it.

The suffix "MAP" is used in the names of numerous supervised ART algorithms, such as Fuzzy ARTMAP.

Both the inputs and the targets are clustered in these algorithms, and the two sets of clusters are linked.

The ARTMAP algorithms' fundamental flaw is that they lack a way to prevent overfitting, hence they should not be utilized with noisy data.

How Does The ARTMAP Neural Network Work?

A novel neural network architecture called ARTMAP automatically picks out recognition categories for any numbers of arbitrarily ordered vectors depending on the accuracy of predictions. 

A pair of Adaptive Resonance Theory modules (ARTa and ARTb) that may self-organize stable recognition categories in response to random input pattern sequences make up this supervised learning system. 

The ARTa module gets a stream of input patterns ([a(p)]) and the ARTb module receives a stream of input patterns ([b(p)]), where b(p) is the right prediction given a (p). 

An internal controller and an associative learning network connect these ART components to provide real-time autonomous system functioning. 

The remaining patterns a(p) are shown during test trials without b(p), and their predictions at ARTb are contrasted with b. (p). 

The ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms when tested on a benchmark machine learning database in both on-line and off-line simulations, and achieves 100% accuracy after training on less than half the input patterns in the database. 

It accomplishes these features by using an internal controller that, on a trial-by-trial basis, links predictive success to category size and simultaneously optimizes predictive generalization and reduces predictive error, using only local operations. 

By the smallest amount required to rectify a predicted inaccuracy at ARTb, this calculation raises the alertness parameter an of ARTa. 

To accept a category or hypothesis triggered by an input a(p), rather than seeking a better one via an autonomously controlled process of hypothesis testing, ARTa must have a minimal level of confidence, which is calibrated by the parameter a. 

The degree of agreement between parameter a and the top-down learnt expectation, or prototype, which is read out after activating an ARTa category, is compared. 

If the degree of match is less than a, search is initiated. 

The self-organizing expert system known as ARTMAP adjusts the selectivity of its hypotheses depending on the accuracy of its predictions. 

As a result, even if they are identical to frequent occurrences with distinct outcomes, unusual but significant events may be promptly and clearly differentiated. 

In the intervals between input trials, a returns to baseline alertness. 

When is big, the system operates in a cautious mode and only makes predictions when it is certain of the result. 

At no step of learning, therefore, do many false-alarm mistakes happen, yet the system nonetheless achieves asymptote quickly. 

Due to the self-stabilizing nature of ARTMAP learning, it may continue to learn one or more databases without deteriorating its corpus of memories until all available memory has been used.

What Is Fuzzy ARTMAP?

For incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analogue or binary input vectors, which may represent fuzzily or crisply defined sets of characteristics, a neural network architecture is developed. 

By taking advantage of a close formal resemblance between the computations of fuzzy subsethood and ART category choosing, resonance, and learning, the architecture, dubbed fuzzy ARTMAP, accomplishes a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks. 

In comparison to benchmark backpropagation and general algorithm systems, fuzzy ARTMAP performance was shown using four simulation classes. 

A letter recognition database, learning to distinguish between two spirals, identifying locations inside and outside of a circle, and incremental approximation of a piecewise-continuous function are some of the simulations included in this list. 

Additionally, the fuzzy ARTMAP system is contrasted with Simpson's FMMC system and Salzberg's NGE systems.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

Be sure to refer to the complete & active AI Terms Glossary here.

You may also want to read more about Artificial Intelligence here.

References And Further Reading:

• Moreira-Júnior, J.R., Abreu, T., Minussi, C.R. and Lopes, M.L., 2022. Using Aggregated Electrical Loads for the Multinodal Load Forecasting. Journal of Control, Automation and Electrical Systems, pp.1-9.

• Ferreira, W.D.A.P., Grout, I. and da Silva, A.C.R., 2022, March. Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. In 2022 International Electrical Engineering Congress (iEECON) (pp. 1-4). IEEE.

• La Marca, A.F., Lopes, R.D.S., Lotufo, A.D.P., Bartholomeu, D.C. and Minussi, C.R., 2022. BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network. Sensors, 22(11), p.4027.

• Santos-Junior, C.R., Abreu, T., Lopes, M.L. and Lotufo, A.D., 2021. A new approach to online training for the Fuzzy ARTMAP artificial neural network. Applied Soft Computing, 113, p.107936.

• Ferreira, W.D.A.P., 2021. Rede neural ARTMAP fuzzy implementada em hardware aplicada na previsão da qualidade do ar em ambiente interno.

Artificial Intelligence - Who Is Sherry Turkle?

 

 

 

Sherry Turkle(1948–) has a background in sociology and psychology, and her work focuses on the human-technology interaction.

While her study in the 1980s focused on how technology affects people's thinking, her work in the 2000s has become more critical of how technology is utilized at the expense of building and maintaining meaningful interpersonal connections.

She has employed artificial intelligence in products like children's toys and pets for the elderly to highlight what people lose out on when interacting with such things.

Turkle has been at the vanguard of AI breakthroughs as a professor at the Massachusetts Institute of Technology (MIT) and the creator of the MIT Initiative on Technology and the Self.

She highlights a conceptual change in the understanding of AI that occurs between the 1960s and 1980s in Life on the Screen: Identity inthe Age of the Internet (1995), substantially changing the way humans connect to and interact with AI.

She claims that early AI paradigms depended on extensive preprogramming and employed a rule-based concept of intelligence.

However, this viewpoint has given place to one that considers intelligence to be emergent.

This emergent paradigm, which became the recognized mainstream view by 1990, claims that AI arises from a much simpler set of learning algorithms.

The emergent method, according to Turkle, aims to emulate the way the human brain functions, assisting in the breaking down of barriers between computers and nature, and more generally between the natural and the artificial.

In summary, an emergent approach to AI allows people to connect to the technology more easily, even thinking of AI-based programs and gadgets as children.

Not just for the area of AI, but also for Turkle's study and writing on the subject, the rising acceptance of the emerging paradigm of AI and the enhanced relatability it heralds represents a significant turning point.

Turkle started to employ ethnographic research techniques to study the relationship between humans and their gadgets in two edited collections, Evocative Objects: Things We Think With (2007) and The Inner History of Devices (2008).

She emphasized in her book The Inner History of Devices that her intimate ethnography, or the ability to "listen with a third ear," is required to go past the advertising-based clichés that are often employed when addressing technology.

This method comprises setting up time for silent meditation so that participants may think thoroughly about their interactions with their equipment.

Turkle used similar intimate ethnographic approaches in her second major book, Alone Together: 

Why We Expect More from Technology and Less from Each Other (2011), to argue that the increasing connection between people and the technology they use is harmful.

These issues are connected to the increased usage of social media as a form of communication, as well as the continuous degree of familiarity and relatability with technology gadgets, which stems from the emerging AI paradigm that has become practically omnipresent.

She traced the origins of the dilemma back to early pioneers in the field of cybernetics, citing, for example, Norbert Weiner's speculations on the idea of transmitting a human person across a telegraph line in his book God & Golem, Inc.(1964).

Because it reduces both people and technology to information, this approach to cybernetic thinking blurs the barriers between them.

In terms of AI, this implies that it doesn't matter whether the machines with which we interact are really intelligent.

Turkle claims that by engaging with and caring for these technologies, we may deceive ourselves into feeling we are in a relationship, causing us to treat them as if they were sentient.

In a 2006 presentation titled "Artificial Intelligence at 50: From Building Intelligence to Nurturing Sociabilities" at the Dartmouth Artificial Intelligence Conference, she recognized this trend.

She identified the 1997 Tamagotchi, 1998 Furby, and 2000 MyReal Baby as early versions of what she refers to as relational artifacts, which are more broadly referred to as social machines in the literature.

The main difference between these devices and previous children's toys is that these devices come pre-animated and ready for a relationship, whereas previous children's toys required children to project a relationship onto them.

Turkle argues that this change is about our human weaknesses as much as it is about computer capabilities.

In other words, just caring for an item increases the likelihood of not only seeing it as intelligent but also feeling a connection to it.

This sense of connection is more relevant to the typical person engaging with these technologies than abstract philosophic considerations concerning the nature of their intelligence.

Turkle delves more into the ramifications of people engaging with AI-based technologies in both Alone Together and Reclaiming Conversation: The Power of Talk in a Digital Age (2015).

She provides the example of Adam in Alone Together, who appreciates the appreciation of the AI bots he controls over in the game Civilization.

Adam appreciates the fact that he is able to create something fresh when playing.

Turkle, on the other hand, is skeptical of this interaction, stating that Adam's playing isn't actual creation, but rather the sensation of creation, and that it's problematic since it lacks meaningful pressure or danger.

In Reclaiming Conversation, she expands on this point, suggesting that social partners simply provide a perception of camaraderie.

This is important because of the value of human connection and what may be lost in relationships that simply provide a sensation or perception of friendship rather than true friendship.

Turkle believes that this transition is critical.

She claims that although connections with AI-enabledtechnologies may have certain advantages, they pale in contrast to what is missing: 

• the complete complexity and inherent contradictions that define what it is to be human.

A person's connection with an AI-enabled technology is not as intricate as one's interaction with other individuals.

Turkle claims that as individuals have become more used to and dependent on technology gadgets, the definition of friendship has evolved.

• People's expectations for companionship have been simplified as a result of this transformation, and the advantages that one wants to obtain from partnerships have been reduced.

• People now tend to associate friendship only with the concept of interaction, ignoring the more nuanced sentiments and arguments that are typical in partnerships.

• By engaging with gadgets, one may form a relationship with them.

• Conversations between humans have become merely transactional as human communication has shifted away from face-to-face conversation and toward interaction mediated by devices. 

In other words, the most that can be anticipated is engagement.

Turkle, who has a background in psychoanalysis, claims that this kind of transactional communication allows users to spend less time learning to view the world through the eyes of another person, which is a crucial ability for empathy.

Turkle argues we are in a robotic period in which people yearn for, and in some circumstances prefer, AI-based robotic companionship over that of other humans, drawing together these numerous streams of argument.

For example, some people enjoy conversing with their iPhone's Siri virtual assistant because they aren't afraid of being judged by it, as evidenced by a series of Siri commercials featuring celebrities talking to their phones.

Turkle has a problem with this because these devices can only respond as if they understand what is being said.

AI-based gadgets, on the other hand, are confined to comprehending the literal meanings of data stored on the device.

They can decipher the contents of phone calendars and emails, but they have no idea what any of this data means to the user.

There is no discernible difference between a calendar appointment for car maintenance and one for chemotherapy for an AI-based device.

A person may lose sight of what it is to have an authentic dialogue with another human when entangled in a variety of these robotic connections with a growing number of technologies.

While Reclaiming Communication documents deteriorating conversation skills and decreasing empathy, it ultimately ends on a positive note.

Because people are becoming increasingly dissatisfied with their relationships, there may be a chance for face-to-face human communication to reclaim its vital role.

Turkle's ideas focus on reducing the amount of time people spend on their phones, but AI's involvement in this interaction is equally critical.

• Users must accept that their virtual assistant connections will never be able to replace face-to-face interactions.

• This will necessitate being more deliberate in how one uses devices, prioritizing in-person interactions over the faster and easier interactions provided by AI-enabled devices.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Blade Runner; Chatbots and Loebner Prize; ELIZA; General and Narrow AI; Moral Turing Test; PARRY; Turing, Alan; 2001: A Space Odyssey.

References And Further Reading

• Haugeland, John. 1997. “What Is Mind Design?” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 1–28. Cambridge, MA: MIT Press.

• Searle, John R. 1997. “Minds, Brains, and Programs.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 183–204. Cambridge, MA: MIT Press.

• Turing, A. M. 1997. “Computing Machinery and Intelligence.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 29–56. Cambridge, MA: MIT Press.

Artificial Intelligence - What Is The Turing Test?

 

 

The Turing Test is a method of determining whether or not a machine can exhibit intelligence that mimics or is equivalent and likened to Human intelligence. 

The Turing Test, named after computer scientist Alan Turing, is an AI benchmark that assigns intelligence to any machine capable of displaying intelligent behavior comparable to that of a person.

Turing's "Computing Machinery and Intelligence" (1950), which establishes a simple prototype—what Turing calls "The Imitation Game," is the test's locus classicus.

In this game, a person is asked to determine which of the two rooms is filled by a computer and which is occupied by another human based on anonymized replies to natural language questions posed by the judge to each inhabitant.

Despite the fact that the human respondent must offer accurate answers to the court's queries, the machine's purpose is to fool the judge into thinking it is human.

According to Turing, the machine may be considered intelligent to the degree that it is successful at this job.

The fundamental benefit of this essentially operationalist view of intelligence is that it avoids complex metaphysics and epistemological issues about the nature and inner experience of intelligent activities.

According to Turing's criteria, little more than empirical observation of outward behavior is required for predicting object intelligence.

This is in sharp contrast to the widely Cartesian epistemological tradition, which holds that some internal self-awareness is a need for intelligence.

Turing's method avoids the so-called "problem of other minds" that arises from such a viewpoint—namely, how to be confident of the presence of other intelligent individuals if it is impossible to know their thoughts from a presumably required first-person perspective.

Nonetheless, the Turing Test, at least insofar as it considers intelligence in a strictly formalist manner, is bound up with the spirit of Cartesian epistemol ogy.

The machine in the Imitation Game is a digital computer in the sense of Turing: a set of operations that may theoretically be implemented in any material.

A digital computer consists of three parts: a knowledge store, an executive unit that executes individual orders, and a control that regulates the executive unit.

However, as Turing points out, it makes no difference whether these components are created using electrical or mechanical means.

What matters is the formal set of rules that make up the computer's very nature.

Turing holds to the core belief that intellect is inherently immaterial.

If this is true, it is logical to assume that human intellect functions in a similar manner to a digital computer and may therefore be copied artificially.

Since Turing's work, AI research has been split into two camps: 

1. those who embrace and 
2. those who oppose this fundamental premise.

To describe the first camp, John Haugeland created the term "good old-fashioned AI," or GOFAI.

Marvin Minsky, Allen Newell, Herbert Simon, Terry Winograd, and, most notably, Joseph Weizenbaum, whose software ELIZA was controversially hailed as the first to pass the Turing Test in 1966.

Nonetheless, detractors of Turing's formalism have proliferated, particularly in the past three decades, and GOFAI is now widely regarded as a discredited AI technique.

John Searle's Minds, Brains, and Programs (1980), in which Searle builds his now-famous Chinese Room thought experiment, is one of the most renowned criticisms of GOFAI in general—and the assumptions of the Turing Test in particular.

In the latter, a person with no prior understanding of Chinese is placed in a room and forced to correlate Chinese characters she receives with other Chinese characters she puts out, according to an English-scripted software.

Searle thinks that, assuming adequate mastery of the software, the person within the room may pass the Turing Test, fooling a native Chinese speaker into thinking she knew Chinese.

If, on the other hand, the person in the room is a digital computer, Turing-type tests, according to Searle, fail to capture the phenomena of understanding, which he claims entails more than the functionally accurate connection of inputs and outputs.

Searle's argument implies that AI research should take materiality issues seriously in ways that Turing's Imitation Game's formalism does not.

Searle continues his own explanation of the Chinese Room thought experiment by saying that human species' physical makeup—particularly their sophisticated nerve systems, brain tissue, and so on—should not be discarded as unimportant to conceptions of intelligence.

This viewpoint has influenced connectionism, an altogether new approach to AI that aims to build computer intelligence by replicating the electrical circuitry of human brain tissue.

The effectiveness of this strategy has been hotly contested, although it looks to outperform GOFAI in terms of developing generalized kinds of intelligence.

Turing's test, on the other hand, may be criticized not just from the standpoint of materialism, but also from the one of fresh formalism.

As a result, one may argue that Turing tests are insufficient as a measure of intelligence since they attempt to reproduce human behavior, which is frequently exceedingly dumb.

According to certain variants of this argument, if criteria of rationality are to distinguish rational from illogical human conduct in the first place, they must be derived a priori rather than from real human experience.

This line of criticism has gotten more acute as AI research has shifted its focus to the potential of so-called super-intelligence: forms of generalized machine intelligence that far outperform human intellect.

Should this next level of AI be attained, Turing tests would seem to be outdated.

Furthermore, simply discussing the idea of superintelligence would seem to need additional intelligence criteria in addition to severe Turing testing.

Turing may be defended against such accusation by pointing out that establishing a universal criterion of intellect was never his goal.

Indeed, according to Turing (1997, 29–30), the purpose is to replace the metaphysically problematic issue "can machines think" with the more empirically verifiable alternative: 

"What will happen when a computer assumes the role [of the man in the Imitation Game]" (Turing 1997, 29–30).

Thus, Turing's test's above-mentioned flaw—that it fails to establish a priori rationality standards—is also part of its strength and drive.

It also explains why, since it was initially presented three-quarters of a century ago, it has had such a lengthy effect on AI research in all domains.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Blade Runner; Chatbots and Loebner Prize; ELIZA; General and Narrow AI; Moral Turing Test; PARRY; Turing, Alan; 2001: A Space Odyssey.

References And Further Reading

Haugeland, John. 1997. “What Is Mind Design?” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 1–28. Cambridge, MA: MIT Press.

Searle, John R. 1997. “Minds, Brains, and Programs.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 183–204. Cambridge, MA: MIT Press.

Turing, A. M. 1997. “Computing Machinery and Intelligence.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 29–56. Cam￾bridge, MA: MIT Press.