What Is Artificial General Intelligence?

Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the AGI system to solve problems when presented with new tasks. 

In other words, it's AI's capacity to learn similarly to humans.

Strong AI, full AI, and general intelligent action are some names for it. 

The phrase "strong AI," however, is only used in few academic publications to refer to computer systems that are sentient or aware. 

These definitions may change since specialists from many disciplines see human intelligence from various angles. 

For instance, computer scientists often characterize human intelligence as the capacity to accomplish objectives. 

On the other hand, general intelligence is defined by psychologists in terms of survival or adaptation.

Weak or narrow AI, in contrast to strong AI, is made up of programs created to address a single issue and lacks awareness since it is not meant to have broad cognitive capacities. 

Autonomous cars and IBM's Watson supercomputer are two examples. 

Nevertheless, AGI is defined in computer science as an intelligent system having full or comprehensive knowledge as well as cognitive computing skills.

As of right now, there are no real AGI systems; they are still the stuff of science fiction. 

The long-term objective of these systems is to perform as well as humans do. 

However, due to AGI's superior capacity to acquire and analyze massive amounts of data at a far faster rate than the human mind, it may be possible for AGI to be more intelligent than humans.

Artificial intelligence (AI) is now capable of carrying out a wide range of functions, including providing tailored suggestions based on prior web searches. 

Additionally, it can recognize various items for autonomous cars to avoid, recognize malignant cells during medical inspections, and serve as the brain of home automation. 

Additionally, it may be utilized to find possibly habitable planets, act as intelligent assistants, be in charge of security, and more.

Naturally, AGI seems to far beyond such capacities, and some scientists are concerned this may result in a dystopian future. 

Elon Musk said that sentient AI would be more hazardous than nuclear war, while Stephen Hawking advised against its creation because it would see humanity as a possible threat and act accordingly.

Despite concerns, most scientists agree that genuine AGI is decades or perhaps centuries away from being developed and must first meet a number of requirements (which are always changing) in order to be achieved. 

These include the capacity for logic, tact, puzzle-solving, and making decisions in the face of ambiguity. 

Additionally, it must be able to plan, learn, and communicate in natural language, as well as represent information, including common sense. 

AGI must also have the capacity to detect (hear, see, etc.) and output the ability to act, such as moving items and switching places to explore. 

How far along are we in the process of developing artificial general intelligence, and who is involved?

In accordance with a 2020 study from the Global Catastrophic Risk Institute (GCRI), academic institutions, businesses, and different governmental agencies are presently working on 72 recognized AGI R&D projects. 

According to the poll, projects nowadays are often smaller, more geographically diversified, less open-source, more focused on humanitarian aims than academic ones, and more centered in private firms than projects in 2017. 

The comparison also reveals a decline in projects with academic affiliations, an increase in projects sponsored by corporations, a rise in projects with a humanitarian emphasis, a decline in programs with ties to the military, and a decline in US-based initiatives.

In AGI R&D, particularly military initiatives that are solely focused on fundamental research, governments and organizations have very little roles to play. 

However, recent programs seem to be more varied and are classified using three criteria, including business projects that are engaged in AGI safety and have humanistic end objectives. 

Additionally, it covers tiny private enterprises with a variety of objectives including academic programs that do not concern themselves with AGI safety but rather the progress of knowledge.

One of the most well-known organizations working on AGI is Carnegie Mellon University, which has a project called ACT-R that aims to create a generic cognitive architecture based on the basic cognitive and perceptual functions that support the human mind. 

The project may be thought of as a method of describing how the brain is structured such that different processing modules can result in cognition.

Another pioneering organization testing the limits of AGI is Microsoft Research AI, which has carried out a number of research initiatives, including developing a data set to counter prejudice for machine-learning models. 

The business is also investigating ways to advance moral AI, create a responsible AI standard, and create AI strategies and evaluations to create a framework that emphasizes the advancement of mankind.

The person behind the well-known video game franchises Commander Keen and Doom has launched yet another intriguing endeavor. 

Keen Technologies, John Carmack's most recent business, is an AGI development company that has already raised $20 million in funding from former GitHub CEO Nat Friedman and Cue founder Daniel Gross. 

Carmack is one of the AGI optimists who believes that it would ultimately help mankind and result in the development of an AI mind that acts like a human, which might be used as a universal remote worker.

So what does AGI's future hold? 

The majority of specialists are doubtful that AGI will ever be developed, and others believe that the urge to even develop artificial intelligence comparable to humans will eventually go away. 

Others are working to develop it so that everyone will benefit.

Nevertheless, the creation of AGI is still in the planning stages, and in the next decades, little progress is anticipated. 

Nevertheless, throughout history, scientists have debated whether developing technologies with the potential to change people's lives will benefit society as a whole or endanger it. 

This proposal was considered before to the invention of the vehicle, during the development of AC electricity, and when the atomic bomb was still only a theory.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

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

Cyber Security - Ransomware Guidance For Financial Firms.

 

DFS Ransomware Guidance - Financial Sector.

The Department of Financial Services (DFS) of New York released a letter outlining how regulated organizations need to cooperate to thwart and lessen ransomware attacks. 

The letter outlines nine controls that need to be implemented by regulated companies. 

Both regulated financial institutions and the MSSPs (Managed Security Service Provider) who supply them with services should take note of the letter. 

MSSPs will need to demonstrate how they can assist their customers in following DFS's guidelines in order to engage with regulated businesses.

Fighting ransomware requires technology that codifies efficient procedures, such as those suggested by DFS, promptly interprets data from various security instruments, and coordinates the necessary reaction.

DFS Analysis of Financial Services Ransomware Attacks.

The advisory letter states that the "good news" is that most ransomware assaults can be avoided, which is unusual when discussing ransomware. 

This is due to the fact that ransomware perpetrators often utilize the same methods. 

Attackers gained access to the target's network in the 74 recent assaults that the DFS examined by using: 

1. phishing, 
2. remote desktop protocols, 
3. or unpatched vulnerabilities. 

In order to launch their ransomware, the attackers would then increase their privileges, often by acquiring and decrypting encrypted passwords.

The advice letter points out that there are well-known defenses worth considering against the ransomware attackers' common tactics that might assist shield their intended victims.

DFS's Ransomware Security Controls for Financial Services Companies Subject to Regulation

The letter outlines nine particular measures that regulated businesses are required to put in place wherever feasible. 

The first seven concentrate on avoiding ransomware while the latter two discuss preparing for a ransomware occurrence. The nine controls are listed below:

1. Anti-Phishing education and email filtering.

The advice emphasizes the need of employing both technical and educational tools to safeguard against phishing emails.

2. Patch and Vulnerability Management.

According to the recommendations, businesses should have a written program for controlling vulnerabilities that includes regular security fixes and upgrades.

3. Two-factor identification.

According to the advice, employing MFA for user accounts is successful at preventing hackers from entering the network and growing their rights.

4. Turn off RDP Access.

The guidelines advise regulated companies to limit access to remote desktop protocol to whitelisted sources and deactivate it wherever feasible.

5. Password administration.

Access control and password management are essential for limiting dangerous threat actors, including ransomware.

6. Manage Privileged Access.

According to the recommendations, businesses should rigorously guard, audit, and limit the usage of privileged accounts, and individuals should be granted the least amount of access necessary to carry out their tasks.

7. Response and monitoring.

According to the recommendations, businesses need to have ways to keep an eye on their systems and react to any questionable behavior. 

EDR and SIEM are among the proposed techniques.

8. Backups that have been tested and separated.

The advice stipulates that regulated organizations should store several backups, at least one set of which should be separated from the network, in the first control that deals with preparing for an incident (the first seven addressed preventing an occurrence). 

Businesses should routinely check their ability to recover systems using backups.

9. An emergency action plan.

Companies should develop incident response plans that particularly target ransomware, according to the recommendations for the second incident preparedness control.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read and learn more Cyber Security Systems here.

Cyber Security - What Methods Do Hackers Use To Hack In 2022?

We devote a lot of effort to attempting to explain to different organizations and people the many sorts of hackers that exist and how people are likely to come into touch with them, as this is the root cause from a social engineering perspective. 

The sorts of hackers to be on the lookout for are listed below, along with some information on how they could attempt to take advantage of you or your company.

Which Major Hacker Organizations Should We Be Aware Of?

1. Nation States:

We won't include any names for political reasons, but you can probably guess which nations are engaged in global cyberwarfare and attempting to hack into pretty about everywhere they believe they can gain an advantage.

A list of target names, nations, and industries will be managed by highly sophisticated industrial style espionage, sabotage, and ransom attack type activities in accordance with the nation state's present agenda.

Please keep in mind, though, that western governments won't be totally blameless in this.

2. Organized Crime:

Most of us are probably most familiar with organized crime, which consists of groups or people whose only goal is to steal money from anybody they can hack. Rarely is it personal or political; they usually simply ask "where they can get money from."

3. Hacktivists: 

While it might be difficult to forecast the kind of targets that these organizations will attack, in reality, they are self-described cyber warriors that attack political, organizational, or private targets in order to promote their "activist" agendas.

What Are The Most Likely Ways That You Could Be Hacked?

1. Device Exploits: 

This is one of the most typical methods of hacking. Basically, all that occurs is that you will get a link to click on that seems safe but really tries to execute some local malware to attack a weakness on your computer.

Therefore, you are vulnerable since you haven't properly updated Windows Updates (or any other device you're using), handled vulnerabilities in the software you've placed on your devices, or misconfigured software that you've installed (I.e all macros enabled in your Microsoft Office or something like that).

Once the attacker has "got you," which is often done with a remote access trojan of some kind, they will look for another place to hide inside your network, prolonging their capacity to take advantage of you. 

They will often search for whatever on your network they can obtain a remote shell on since they will effectively know that the way they got you in the first place (through your computer) can be readily fixed (i.e a printer or an old switch or something).

2. IP address exploits:  

Discovering your office's, data center's, or home's exterior endpoints is another frequent method of hacking. 

Your IP addresses are initially determined using a variety of techniques; sadly, this is relatively readily done via internet lookups or rather often by simple social engineering.

It would be simple for someone to call your workplace and claim to be from your IP service provider in an effort to persuade you to reveal your office's IP address. 

For nation governments and bigger organized criminal organizations, they will simply efficiently maintain databases of known ports and known susceptible software operating on those ports while continuously scanning through millions of IP addresses depending on the nations and regions they are interested in.

Millions upon millions of IP addresses, ports, and known vulnerabilities are posted on Shodan, which is essentially a "Hacker Search Engine," and are available for anyone to see and query at any time. 

In reality, anybody with access to the Shodan API may quickly search across the whole Shodan database, gaining instant access to millions of entries.

3. Cloud / SaaS Phishing: 

Multi-factor authentication is thus beginning to fend against this issue, however many organizational accounts continue to exist throughout the globe without it enabled.

In actuality, you or a member of your team might be the target of an attack on your Office 365, Google G-Suite, or even your online accounting platform. 

In many cases, you will simply get a link to something that seems absolutely innocuous or nice in order to "re-enter" your login information for a crucial platform (something you wouldn't want the bad guys to have access to).

Once within the platform, the bad guys may do a wide range of things to attempt to take advantage of you; a popular tactic is to send emails pretending to be a senior staff member in order to transfer money to an account.

The hackers will continue to keep an eye on you in an attempt to uncover new methods to cause havoc in your digital life. They may even just discreetly send communications for a senior member of staff to another external anonymous account.

In reality, anybody may strike you at any moment. However, how you should approach your defense will rely on your cyber security risk profile (i.e., what you could have that adversaries might attempt to exploit). 

To begin with, it's wise to maintain tabs on anyone you suspect of wanting to hack you and their motivations.

What Are Some Examples Techniques Used By Hackers?

You are more likely to be targeted by a "Nation State" if you work for a government contractor on specialized intellectual property. 

This doesn't have to be drugs or weapons; it might be anything that a Nation State would want to duplicate or own for itself.

You're far more likely to be targeted by organized crime if you're the CEO of a corporation or the finance department (which granted can also be a Nation State). 

You are probably aware that in phishing campaigns and other situations where bad guys use LinkedIn and Google to scrape information about people's job titles and seniorities in order to figure out how to target their attacks more precisely to the most valuable targets, hackers will target business leaders more frequently.

If you're the CEO of a large company, national security hackers will attempt to target your children's or family's gadgets in an effort to gain access to your house for espionage or other similar operations. 

This is why it makes sense to have a closed network at home/private spaces that are only for the gadgets of family/children.

At the lower end of the spectrum, all of us are sometimes targeted by hackers using phishing emails. 

As indicated above, emails sent requesting us to click on links are also used to attempt to run remote access trojans in order to allow the bad guys access to your workstations, so we need to be aware that this isn't only for our credentials (i.e., that Multi-Factor authentication may save us from). 

Once a back door has been built, gangs may manually disseminate ransomware using this.

Hacktivists are likely to attack you if you work as an executive for a corporation that pollutes foreign rivers and ecosystems.

Therefore, the main goal of this blog isn't to spook people or incite worry, but rather, we believe that having a basic awareness of the many kinds of adversaries out there may help individuals frame how they should be thinking about their own security.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read and learn more Cyber Security Systems here.

Erasure Error Correction Key To Quantum Computers

 

Overview of an erasure-converted neutral atom quantum computer that is fault-tolerant. A plane of atoms beneath a microscope objective that is used to image fluorescence and project trapping and control fields is shown in the schematic of a neutral atom quantum computer. b Individual 171Yb atoms are used as the physical qubits. The Rydberg state |r|rleft|rright|rangle, which is accessible by a single-photon transition ( = 302 nm) with Rabi frequency, is used to conduct two-qubit gates. The qubit states are encoded in the metastable 6s6p 3P0F = 1/2 level (subspace Q). With a total rate of = B + R + Q, decays from |r|rleft|rrightrangle are the most common faults during gates. The remaining decays are either blackbody (BBR) transitions to neighbouring Rydberg states (B/ 0.61) or radiative decays to the ground state 6s2 1S0 (R/ 0.34), with just a tiny percentage (Q/ 0.05) returning to the qubit subspace. These events may be identified and turned into erasing errors at the end of a gate by observing the fluorescence from ground state atoms (subspace R) or by autoionizing any leftover Rydberg population and seeing the fluorescence on the Yb+ transition (subspace B). c A section of the XZZX surface code that was the subject of this study, displaying stabiliser operations, data qubits, and ancilla qubits in the order shown by the arrows. A quantum circuit utilising ancilla A1 and interspersed erasure conversion steps to simulate a measurement of a stabiliser on data qubits D1 through D4. After every gate, erasure detection is used, and when necessary, erased atoms are restored using a movable optical tweezer pulled from a reservoir. Although just the atom that was discovered to have left the subspace has to be replaced, doing so also guards against the risk of leakage on the second atom being unnoticed. Nature Communications (2022). DOI: 10.1038/s41467-022-32094-6

Why is "erasure" essential to creating useful quantum computers?

Researchers have uncovered a brand-new technique for fixing mistakes in quantum computer computations, possibly eliminating a significant roadblock to a powerful new computing domain.

Error correction in traditional computers is a well-established discipline. In order to transmit and receive data across clogged airwaves, every cellphone has to be checked and fixed. 

Using very ephemeral subatomic particle characteristics, quantum computers have the incredible potential to tackle certain difficult problems that are intractable for traditional computers. 

Even peeking into these computing activities to seek for problems might bring the whole system crashing down since they are so fleeting.

A multidisciplinary team led by Jeff Thompson, an associate professor of electrical and computer engineering at Princeton, and including collaborators Yue Wu, Shruti Puri, and Shimon Kolkowitz from Yale University and the University of Wisconsin-Madison demonstrated how they could significantly increase a quantum computer's tolerance for faults and decrease the amount of redundant information. 

The new method doubles the allowed error rate, from 1% to 4%, and makes it workable for developing quantum computers.

The operations you wish to perform on quantum computers are noisy, according to Thompson, which means that computations are subject to a variety of failure scenarios.

An error in a traditional computer may be as basic as a memory bit mistakenly switching from a 1 to a 0, or it can be complex like many wireless routers interfering with one another. 

Building in some redundancy to ensure that each piece of data gets examined with duplicate copies is a popular strategy for addressing these problems. 

However, such strategy calls for more data and raises the likelihood of mistakes. Therefore, it only functions when the great majority of the available information is accurate. 

Otherwise, comparing incorrect data to incorrect data just serves to deepen the inaccuracy.

According to Thompson, redundancy is a terrible technique if your baseline error rate is too high. The biggest obstacle is lowering that barrier.

Thompson's team simply increased the visibility of mistakes rather than concentrating just on lowering the amount of errors. 

The researchers studied the physical sources of mistake in great detail and designed their system such that the most frequent source of error effectively destroys the damaged data rather than merely corrupting it. 

According to Thompson, this behavior is an example of a specific kind of mistake known as a "erasure error," which is inherently simpler to filter out than damaged data that still seems to be all the other data.

In a traditional computer, it might be dangerous to presume that the slightly more common 1s are accurate and the 0s are incorrect if a packet of presumably duplicate information appears as 11001. 

However, the argument is stronger if the information appears as 11XX1, where the damaged bits are obvious.

Because you are aware of the erasure mistakes, Thompson said that they are much simpler to fix. "They could not participate in the majority vote. That is a significant benefit."

Erasure faults in conventional computing are widely recognized, but researchers hadn't previously thought about attempting to construct quantum computers to turn errors into erasures, according to Thompson.

Their device could, in fact, sustain an error rate of 4.1%, which Thompson claimed is well within the range of possibilities for existing quantum computers. 

The most advanced error correction in prior systems, according to Thompson, could only tolerate errors of less than 1%, which is beyond the capacity of any existing quantum system with a significant number of qubits.

The team's capacity to produce erasure mistakes ended up being a surprising advantage of a decision Thompson made in the past. 

His work examines "neutral atom qubits," in which a single atom is used to store a "qubit" of quantum information. 

They were the ones who invented this use of the element ytterbium. As opposed to the majority of other neutral atom qubits, which contain only one electron in their outermost layer of electrons, ytterbium possesses two in this layer, according to Thompson.

As an analogy, Thompson remarked, "I see it as a Swiss army knife, and this ytterbium is the larger, fatter Swiss army knife." "You get a lot of new tools from that additional little bit of complexity you get from having two electrons."

Eliminating mistakes turned out to be one application for those additional tools. 

The group suggested boosting ytterbium electrons from their stable "ground state" to excited levels known as "metastable states," which may be long-lived under the appropriate circumstances but are fundamentally brittle. 

The researchers' proposal to encode the quantum information using these states is counterintuitive.

The electrons seem to be walking a tightrope, Thompson said. Additionally, the system is designed such that the same elements that lead to inaccuracy also result in electrons slipping off the tightrope.

A collection of ytterbium qubits may be illuminated, but only the defective ones light up because, as an added bonus, the electrons scatter light extremely visibly after they reach the ground state. 

Those that illuminate need to be discounted as mistakes.

This development requires merging knowledge from the theory of quantum error correction and the hardware of quantum computing, drawing on the multidisciplinary character of the research team and their close cooperation.

Although the physics of this configuration are unique to Thompson's ytterbium atoms, he said that the notion of building quantum qubits to produce erasure mistakes might be a desirable objective in other systems—of which there are many being developed all over the world—and the group is still working on it.

According to Thompson, other organizations have already started designing their systems to turn mistakes into erasures. 

"We view this research as setting out a type of architecture that might be utilized in many various ways," Thompson said. "We already have a lot of interest in discovering adaptations for this task," said the researcher.

Thompson's team is now working on a demonstration of the transformation of mistakes into erasures in a modest operational quantum computer that integrates several tens of qubits as a next step.

The article was published in Nature Communications on August 9 and is titled "Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays."

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Quantum Computing here.

References And Further Reading:

• Yue Wu et al, Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays, Nature Communications (2022). DOI: https://dx.doi.org/10.1038/s41467-022-32094-6

• Ponnappan, JK. 2022. Quantum Computing Error Correction - Improving Encoding Redundancy Exponentially Drops Net Error Rate. Technologists In Sync (TS-QC-Jan 22). https://www.technologistsinsync.com/2021/12/quantum-computing-error-correction.html

• Ponnappan, JK. 2022. Fault Tolerance For Quantum Computing Errors. Technologists In Sync (TS-QC-Jan 22). https://www.technologistsinsync.com/2021/10/fault-tolerance-for-quantum-computing.html

• Hilder, J., Pijn, D., Onishchenko, O., Stahl, A., Orth, M., Lekitsch, B., Rodriguez-Blanco, A., Müller, M., Schmidt-Kaler, F. and Poschinger, U.G., 2022. Fault-tolerant parity readout on a shuttling-based trapped-ion quantum computer. Physical Review X, 12(1), p.011032.

• Nakazato, T., Reyes, R., Imaike, N., Matsuda, K., Tsurumoto, K., Sekiguchi, Y. and Kosaka, H., 2022. Quantum error correction of spin quantum memories in diamond under a zero magnetic field. Communications Physics, 5(1), pp.1-7.

• Krinner, S., Lacroix, N., Remm, A., Di Paolo, A., Genois, E., Leroux, C., Hellings, C., Lazar, S., Swiadek, F., Herrmann, J. and Norris, G.J., 2022. Realizing repeated quantum error correction in a distance-three surface code. Nature, 605(7911), pp.669-674.

• Ajagekar, A. and You, F., 2022. New frontiers of quantum computing in chemical engineering. Korean Journal of Chemical Engineering, pp.1-10.

AI Glossary - What Is The ART 1 Algorithm?

 

What Is ART 1?

The initial Adaptive Resonance Theory (ART) model was designated as ART 1. 

It has the ability to cluster binary input variables.

What Is The Architecture And Design Of ART 1?

The Design of ART1

The following two units make up ART1:

Parameters Used Above:

n − Number of components in the input vector

m − Maximum number of clusters that can be formed

bij − Weight from F1b to F2 layer, i.e. bottom-up weights

tji − Weight from F2 to F1b layer, i.e. top-down weights

ρ − Vigilance parameter

||x|| − Norm of vector x

1. Computational Unit Of ART 1

It consists of the following:

(i) Unit of input (F1 layer) 

It also includes the next two parts:

1. F1 a layer Input portion – In ART1, this part would merely include the input vectors with no processing. It has an F1b layer interface portion connection.

2. F1b layer interface portion - The signal from the input section and the signal from the F2 layer are combined at the F1b layer interface portion. Bottom up weights bij connect the F1b layer to the F2 layer, while top down weights tji link the F2 layer to the F1b layer.

2. Cluster Unit (F2 layer): 

This is a layer that is in competition. To learn the input pattern, the unit with the highest net input is chosen. All other cluster units have their activation set to 0.

3. Reset Method: 

This mechanism operates by comparing the input vector's similarity to the top-down weight. The cluster will not be permitted to learn the pattern if the degree of similarity is less than the vigilance parameter, and a rest will take place.

4. Supplement Unit: 

In reality, the problem with the reset mechanism is that the layer F2 has to be suppressed under certain circumstances and also needs to be accessible while learning occurs. Because of this, the supplementary units G1 and G2 as well as the reset unit R were introduced. Gain control units are what they are known as. These units communicate with the other units in the network by receiving and sending signals. An inhibitory signal is denoted by a "," whereas an excitatory signal is denoted by a "+."

What Is The Adaptive Resonance Theory ART 1 Algorithm?

Step 1 − Initialize the learning rate, the vigilance parameter, and the weights as follows −

α>1and0<ρ≤1

0<bij(0)<αα−1+nandtij(0)=1

Step 2 − Continue step 3-9, when the stopping condition is not true.

Step 3 − Continue step 4-6 for every training input.

Step 4 − Set activations of all F1a and F1 units as follows

F2 = 0 and F1a = input vectors

Step 5 − Input signal from F1a to F1b layer must be sent like

si=xi

Step 6 − For every inhibited F2 node

yj=∑ibijxi the condition is yj ≠ -1

Step 7 − Perform step 8-10, when the reset is true.

Step 8 − Find J for yJ ≥ yj for all nodes j

Step 9 − Again calculate the activation on F1b as follows

xi=sitJi

Step 10 − Now, after calculating the norm of vector x and vector s, we need to check the reset condition as follows −

If ||x||/ ||s|| < vigilance parameter ρ,⁡then⁡inhibit ⁡node J and go to step 7

Else If ||x||/ ||s|| ≥ vigilance parameter ρ, then proceed further.

Step 11 − Weight updating for node J can be done as follows −

bij(new)=αxiα−1+||x||

tij(new)=xi

Step 12 − The stopping condition for algorithm must be checked and it may be as follows −

Do not have any change in weight.

Reset is not performed for units.

Maximum number of epochs reached.

 

~ 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.

Frequently Asked Questions:

What distinguishes ARTs 1 and 2 from one another?

The ART1 architecture is the most basic and straightforward. 

It can cluster input values with binary data. 

ART2 is an enhancement of ART1 that can cluster input data with continuous values.

What is the Process of Adaptive Resonance Theory?

A cognitive and neurological theory called adaptive resonance theory, or ART, explains how the brain develops its own ability to attend to, classify, identify, and anticipate items and events in a dynamic environment. 

The current most comprehensive set of cognitive and neurological theories for explanation and prediction is ART.

What Is The ART Network?

The ART network is essentially a vector classifier that receives an input vector and categorizes it into one of the categories based on which stored pattern it most closely matches.

What Is Fuzzy ART?

Fuzzy ART uses fuzzy set theory calculations to train the ART 1 neural network to classify solely binary input patterns.

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.