AI Glossary - What Is Artificial Intelligence in Medicine Or AIM?

 

AIM is an abbreviation for Artificial Intelligence in Medicine.

It is included in the field of Medical Informatics.

Artificial Intelligence in Medicine (the journal) publishes unique papers on the theory and application of artificial intelligence (AI) in medicine, medically-oriented human biology, and health care from a range of multidisciplinary viewpoints.

The study and implementation of ways to enhance the administration of patient data, clinical knowledge, demographic data, and other information related to patient care and community health is known as medical informatics. 

It is a relatively new science, having arisen in the decades after the 1940s discovery of the digital computer.

What Is Artificial intelligence's Importance in Healthcare and Medicine?

• Artificial intelligence can help physicians choose the best cancer therapies from a variety of possibilities. 

• AI helps physicians identify and choose the right drugs for the right patients by capturing data from various databases relating to the condition. 

• AI also supports decision-making processes for existing drugs and expanded treatments for other conditions, as well as expediting clinical trials by finding the right patients from a variety of data sources.

What role does artificial intelligence play in medicine and healthcare?

Medical imaging analysis is aided by AI.

It aids in the evaluation of photos and scans by a doctor. 

This allows radiologists and cardiologists to find crucial information for prioritizing urgent patients, avoiding possible mistakes in reading electronic health records (EHRs), and establishing more exact diagnoses.

What Are The Advantages of AI in Healthcare?

Artificial intelligence (AI) has emerged as the most potent agent of change in the healthcare business over the previous decade. 

Learn how healthcare professionals might profit from artificial intelligence.

There are several potential for healthcare institutions to use AI to offer more effective, efficient, and precise interventions to their patients, ranging from diagnosis and risk assessment to treatment technique selection.

AI is positioned to generate innovations and benefits throughout the care continuum as the amount of healthcare data grows. 

This is based on AI technologies and machine learning (ML) algorithms' capacity to provide proactive, intelligent, and often concealed insights that guide diagnostic and treatment decisions.

When used in the areas of improved treatment, chronic illness management, early risk detection, and workflow automation and optimization, AI may be immensely valuable to both patients and clinicians. 

Below are some advantages of adopting AI in healthcare to assist providers better grasp how to use it in their ecosystem.

Management of Population Health Using AI.

Healthcare companies may utilize artificial intelligence to gather and analyze patient health data in order to proactively detect and avoid risk, reduce preventative care gaps, and get a better understanding of how clinical, genetic, behavioral, and environmental variables influence the population. 

Combining diagnostic data, exam results, and unstructured narrative data provides a complete perspective of a patient's health, as well as actionable insights that help to avoid illness and promote wellness. 

AI-powered systems may help compile, evaluate, and compare a slew of such data points to population-level trends in order to uncover early illness risks.

As these data points are accumulated to offer a picture into the population, predictive analytics may be obtained. 

These findings may subsequently be used to population risk stratification based on genetic and phenotypic variables, as well as behavioral and social determinants. 

Healthcare companies may use these insights to deliver more tailored, data-driven treatment while also optimizing resource allocation and use, resulting in improved patient outcomes.

Making Clinical Decisions Using AI.

Artificial intelligence may help minimize the time and money required to assess and diagnose patients in some healthcare procedures. 

Medical workers may save more lives by acting quicker as a result of this. 

Traditional procedures cannot detect danger as quickly or accurately as machine learning (ML) algorithms can. 

These algorithms, when used effectively, may automate inefficient, manual operations, speeding up diagnosis and lowering diagnostic mistakes, which are still the leading cause of medical malpractice lawsuits.

Furthermore, AI-enabled technologies may assemble and sift through enormous amounts of clinical data to provide doctors a more holistic perspective of patient populations' health state. 

These technologies provide the care team with real-time or near-real-time actionable information at the proper time and location to improve treatment outcomes dramatically. 

The whole care team may operate on top of licensing by automating the gathering and analysis of the terabytes of data streaming inside the hospital walls.

Artificial Intelligence-Assisted Surgery

Surgical robotics applications are one of the most inventive AI use cases in healthcare. 

AI surgical systems that can perform the slightest motions with flawless accuracy have been developed as AI robotics has matured. 

These devices can carry out difficult surgical procedures, lowering the typical procedure wait time as well as the danger of blood loss, complications, and other adverse effects.

Machine learning may also help to facilitate surgical procedures. 

It may give surgeons and healthcare workers with real-time data and sophisticated insights into a patient's present status. 

This AI-assisted data allows them to make quick, informed choices before, during, and after surgeries to assure the best possible results.

Improved Access to Healthcare Using AI.

As a consequence of restricted or no healthcare access, studies indicate considerable differences in average life expectancy between industrialized and developing countries. 

In terms of implementing and exploiting modern medical technology that can provide proper treatment to the public, developing countries lag behind their peers. 

In addition, a lack of skilled healthcare personnel (such as surgeons, radiologists, and ultrasound technicians) and appropriately equipped healthcare facilities has an influence on care delivery in these areas. 

To encourage a more efficient healthcare ecosystem, AI can offer a digital infrastructure that allows for speedier identification of symptoms and triage of patients to the appropriate level and modality of treatment.

In healthcare, AI may assist alleviate a scarcity of doctors in rural, low-resource locations by taking over some diagnostic responsibilities. 

Using machine learning for imaging, for example, enables for quick interpretation of diagnostic investigations like X-rays, CT scans, and MRIs. 

Furthermore, educational institutions are increasingly using these technologies to improve student, resident, and fellow training while reducing diagnostic mistakes and patient risk.

AI To Improve Operational Efficiency and Performance Of Healthcare Practices.

Modern healthcare operations are a complicated web of intricately linked systems and activities. 

This makes cost optimization challenging while also optimizing asset usage and guaranteeing minimal patient wait times.

Artificial intelligence is rapidly being used by health systems to filter through large amounts of big data inside their digital environment in order to generate insights that might help them improve operations, increase efficiency, and optimize performance. 

For example, AI and machine learning can: 

(1) improve throughput and effective and efficient use of facilities by prioritizing services based on patient acuity and resource availability, 

(2) improve revenue cycle performance by optimizing workflows such as prior authorization claims and denials, and 

(3) automate routine, repeatable tasks to better deploy human resources when and where they are most needed.

When used effectively, AI and machine learning may give administrators and clinical leaders with the knowledge they need to enhance the quality and timeliness of hundreds of choices they must make every day, allowing patients to move smoothly between different healthcare services.

The rapidly growing amount of patient data both within and outside of hospitals shows no signs of slowing down. 

Healthcare organizations are under pressure from ongoing financial challenges, operational inefficiencies, a global shortage of health workers, and rising costs. 

They need technology solutions that drive process improvement and better care delivery while meeting critical operational and clinical metrics.

The potential for AI in healthcare to enhance the quality and efficiency of healthcare delivery by analyzing and extracting intelligent insights from vast amounts of data is boundless and well-documented.

What role does AI play in medicine and informatics in the future?

According to Accenture Consulting, the artificial intelligence (AI) industry in healthcare is estimated to reach $6.6 billion by 2021. 

From AI-based software for managing medical data to Practice Management software to robots helping surgeries, this creative technology has led to numerous improvements.

~ 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.

AI - SyNAPSE

 

 

Project SyNAPSE (Systemsof Neuromorphic Adaptive Plastic Scalable Electronics) is a collaborativecognitive computing effort sponsored by the Defense Advanced Research ProjectsAgency to develop the architecture for a brain-inspired neurosynaptic computercore.

The project, which began in 2008, is a collaboration between IBM Research, HRL Laboratories, and Hewlett-Packard.

Researchers from a number of universities are also involved in the project.

The acronym SyNAPSE comes from the Ancient Greek word v, which means "conjunction," and refers to the neural connections that let information go to the brain.

The project's purpose is to reverse-engineer the functional intelligence of rats, cats, or potentially humans to produce a flexible, ultra-low-power system for use in robots.

The initial DARPA announcement called for a machine that could "scale to biological levels" and break through the "algorithmic-computational paradigm" (DARPA 2008, 4).

In other words, they needed an electronic computer that could analyze real-world complexity, respond to external inputs, and do so in near-real time.

SyNAPSE is a reaction to the need for computer systems that can adapt to changing circumstances and understand the environment while being energy efficient.

Scientists at SyNAPSE are working on neuromorphicelectronics systems that are analogous to biological nervous systems and capable of processing data from complex settings.

It is envisaged that such systems would gain a considerable deal of autonomy in the future.

The SyNAPSE project takes an interdisciplinary approach, drawing on concepts from areas as diverse as computational neuroscience, artificial neural networks, materials science, and cognitive science.

Basic science and engineering will need to be expanded in the following areas by SyNAPSE: 

• Architecture—to support structures and functions that arise in biological systems;

•  simulation—for the digital replication of systems in order to verify functioning prior to the installation of material neuromorphological systems.

In 2008, IBM Research and HRL Laboratories received the first SyNAPSE grant.

Various aspects of the grant requirements were subcontracted to a variety of vendors and contractors by IBM and HRL.

The project was split into four parts, each of which began following a nine-month feasibility assessment.

The first simulator, C2, was released in 2009 and operated on a BlueGene/P supercomputer, simulating cortical simulations with 109 neurons and 1013 synapses, similar to those seen in a mammalian cat brain.

Following a revelation by the Blue Brain Project leader that the simulation did not meet the complexity claimed, the software was panned.

Each neurosynaptic core is 2 millimeters by 3 millimeters in size and is made up of materials derived from human brain biology.

The cores and actual brains have a more symbolic than comparable relationship.

Communication replaces real neurons, memory replaces synapses, and axons and dendrites are replaced by communication.

This enables the team to explain a biological system's hardware implementation.

HRL Labs stated in 2012 that it has created the world's first working memristor array layered atop a traditional CMOS circuit.

The term "memristor," which combines the words "memory" and "transistor," was invented in the 1970s.

Memory and logic functions are integrated in a memristor.

In 2012, project organizers reported the successful large-scale simulation of 530 billion neurons and 100 trillion synapses on the Blue Gene/Q Sequoia machine at Lawrence Livermore National Laboratory in California, which is the world's second fastest supercomputer.

The TrueNorth processor, a 5.4-billion-transistor chip with 4096 neurosynaptic cores coupled through an intrachip network that includes 1 million programmable spiking neurons and 256 million adjustable synapses, was presented by IBM in 2014.

Finally, in 2016, an end-to-end ecosystem (including scalable systems, software, and apps) that could fully use the TrueNorth CPU was unveiled.

At the time, there were reports on the deployment of applications such as interactive handwritten character recognition and data-parallel text extraction and recognition.

TrueNorth's cognitive computing chips have now been put to the test in simulations like a virtual-reality robot driving and playing the popular videogame Pong.

DARPA has been interested in the construction of brain-inspired computer systems since the 1980s.

Dharmendra Modha, director of IBM Almaden's Cognitive ComputingInitiative, and Narayan Srinivasa, head of HRL's Center for Neural and Emergent Systems, are leading the Project SyNAPSE project.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Cognitive Computing; Computational Neuroscience.

References And Further Reading

Defense Advanced Research Projects Agency (DARPA). 2008. “Systems of Neuromorphic Adaptive Plastic Scalable Electronics.” DARPA-BAA 08-28. Arlington, VA: DARPA, Defense Sciences Office.

Hsu, Jeremy. 2014. “IBM’s New Brain.” IEEE Spectrum 51, no. 10 (October): 17–19.

Merolla, Paul A., et al. 2014. “A Million Spiking-Neuron Integrated Circuit with a Scalable Communication Network and Interface.” Science 345, no. 6197 (August): 668–73.

Monroe, Don. 2014. “Neuromorphic Computing Gets Ready for the (Really) Big Time.” Communications of the ACM 57, no. 6 (June): 13–15.

AI - Milind Tambe

 

Milind Tambe (1965–) is a pioneer in artificial intelligence research for social good.

Public health, education, safety and security, housing, and environmental protection are some of the frequent areas where AI is being used to solve societal issues.

Tambe has developed software that preserves endangered species in game reserves, social network algorithms that promote healthy eating habits, and applications that track social ills and community difficulties and provide suggestions to help people feel better.

Tambe was up in India, where the robot novels of Isaac Asimov and the first Star Trek series (1966–1969) inspired him to study about artificial intelligence.

Carnegie Mellon University's School of Computer Science awarded him his PhD.

His first study focused on the creation of AI software for security.

After the 2006 Mumbai commuter train attacks, he got interested in the possibilities of artificial intelligence in this subject.

His doctoral research revealed important game theory insights into the nature of random encounters and collaboration.

Tambe's ARMOR program generates risk assessment scores by randomly scheduling human security patrols and police checkpoints.

Following random screening processes, Los Angeles Airport police uncovered a vehicle carrying five rifles, ten pistols, and a thousand rounds of ammunition in 2009.

Federal air marshals and port security patrols utilize more latest versions of the program to arrange their flights.

Today, Tambe's group uses deep learning algorithms to aid wildlife conservation agents in distinguishing between poachers and animals captured by infrared cameras on unmanned drone aircraft in real time.

Within three-tenths of a second of their arrival near animals, the Systematic Poacher Detector (SPOT) can identify poachers.

SPOT was tested in Zimbabwe and Malawi park reserves before being deployed in Botswana.

PAWS, a successor technology that predicts poacher activities, has been implemented in Cambodia and might be used in more than 50 nations across the globe in the future years.

Tambe's algorithms can simulate population migrations and epidemic illness propagation in order to improve the efficacy of public health campaigns.

Several nonobvious patterns have been discovered by the algorithm, which will help to enhance illness management.

Tambe's team created a third algorithm to assist drug misuse counselors in dividing addiction rehabilitation groups into smaller subgroups where healthy social ties may flourish.

Climate change, gang violence, HIV awareness, and counterterrorism are among the other AI-based answers.

Tambe is the Helen N. and Emmett H. Jones Professor of Engineering at the University of Southern California's Viterbi School of Engineering (USC).

He is the cofounder and codirector of USC's Center for Artificial Intelligence in Society, and he has received several awards, including the John McCarthy Award and the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

Both the Association for the Advancement of Artificial Intelligence (AAAI) and the Association for Computing Machinery have named him a Fellow (ACM).

Tambe is the cofounder and director of research of Avata Intelligence, a company that sells artificial intelligence management software to help companies with data analysis and decision-making.

LAX, the US Coast Guard, the Transportation Security Administration, and the Federal Air Marshals Service all employ his methods.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Predictive Policing.

References And Further Reading

Paruchuri, Praveen, Jonathan P. Pearce, Milind Tambe, Fernando Ordonez, and Sarit Kraus. 2008. Keep the Adversary Guessing: Agent Security by Policy Randomization. Riga, Latvia: VDM Verlag Dr. Müller.

Tambe, Milind. 2012. Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned. Cambridge, UK: Cambridge University Press.

Tambe, Milind, and Eric Rice. 2018. Artificial Intelligence and Social Work. Cambridge, UK: Cambridge University Press.

AI - Technological Singularity

 

The emergence of technologies that could fundamentally change humans' role in society, challenge human epistemic agency and ontological status, and trigger unprecedented and unforeseen developments in all aspects of life, whether biological, social, cultural, or technological, is referred to as the Technological Singularity.

The Singularity of Technology is most often connected with artificial intelligence, particularly artificial general intelligence (AGI).

As a result, it's frequently depicted as an intelligence explosion that's pushing advancements in fields like biotechnology, nanotechnology, and information technologies, as well as inventing new innovations.

The Technological Singularity is sometimes referred to as the Singularity, however it should not be confused with a mathematical singularity, since it has only a passing similarity.

This singularity, on the other hand, is a loosely defined term that may be interpreted in a variety of ways, each highlighting distinct elements of the technological advances.

The thoughts and writings of John von Neumann (1903–1957), Irving John Good (1916–2009), and Vernor Vinge (1944–) are commonly connected with the Technological Singularity notion, which dates back to the second half of the twentieth century.

Several universities, as well as governmental and corporate research institutes, have financed current Technological Singularity research in order to better understand the future of technology and society.

Despite the fact that it is the topic of profound philosophical and technical arguments, the Technological Singularity remains a hypothesis, a guess, and a pretty open hypothetical idea.

While numerous scholars think that the Technological Singularity is unavoidable, the date of its occurrence is continuously pushed back.

Nonetheless, many studies agree that the issue is not whether or whether the Technological Singularity will occur, but rather when and how it will occur.

Ray Kurzweil proposed a more exact timeline for the emergence of the Technological Singularity in the mid-twentieth century.

Others have sought to give a date to this event, but there are no well-founded grounds in support of any such proposal.

Furthermore, without applicable measures or signs, mankind would have no way of knowing when the Technological Singularity has occurred.

The history of artificial intelligence's unmet promises exemplifies the dangers of attempting to predict the future of technology.

The themes of superintelligence, acceleration, and discontinuity are often used to describe the Technological Singularity.

The term "superintelligence" refers to a quantitative jump in artificial systems' cognitive abilities, putting them much beyond the capabilities of typical human cognition (as measured by standard IQ tests).

Superintelligence, on the other hand, may not be restricted to AI and computer technology.

Through genetic engineering, biological computing systems, or hybrid artificial–natural systems, it may manifest in human agents.

Superintelligence, according to some academics, has boundless intellectual capabilities.

The curvature of the time curve for the advent of certain key events is referred to as acceleration.

Stone tools, the pottery wheel, the steam engine, electricity, atomic power, computers, and the internet are all examples of technological advancement portrayed as a curve across time emphasizing the discovery of major innovations.

Moore's law, which is more precisely an observation that has been viewed as a law, represents the increase in computer capacity.

"Every two years, the number of transistors in a dense integrated circuit doubles," it says.

People think that the emergence of key technical advances and new technological and scientific paradigms will follow a super-exponential curve in the event of the Technological Singularity.

One prediction regarding the Technological Singularity, for example, is that superintelligent systems would be able to self-improve (and self-replicate) in previously unimaginable ways at an unprecedented pace, pushing the technological development curve far beyond what has ever been witnessed.

The Technological Singularity discontinuity is referred to as an event horizon, and it is similar to a physical idea linked with black holes.

The analogy to this physical phenomena, on the other hand, should be used with care rather than being used to credit the physical world's regularity and predictability to technological singularity.

The limit of our knowledge about physical occurrences beyond a specific point in time is defined by an event horizon (also known as a prediction horizon).

It signifies that there is no way of knowing what will happen beyond the event horizon.

The discontinuity or event horizon in the context of technological singularity suggests that the technologies that precipitate technological singularity would cause disruptive changes in all areas of human life, developments about which experts cannot even conjecture.

The end of humanity and the end of human civilization are often related with technological singularity.

According to some research, social order will collapse, people will cease to be major actors, and epistemic agency and primacy would be lost.

Humans, it seems, will not be required by superintelligent systems.

These systems will be able to self-replicate, develop, and build their own living places, and humans will be seen as either barriers or unimportant, outdated things, similar to how humans now consider lesser species.

One such situation is represented by Nick Bostrom's Paperclip Maximizer.

AI is included as a possible danger to humanity's existence in the Global Catastrophic Risks Survey, with a reasonably high likelihood of human extinction, placing it on par with global pandemics, nuclear war, and global nanotech catastrophes.

However, the AI-related apocalyptic scenario is not a foregone conclusion of the Technological Singularity.

In other more utopian scenarios, technology singularity would usher in a new period of endless bliss by opening up new opportunities for humanity's infinite expansion.

Another element of technological singularity that requires serious consideration is how the arrival of superintelligence may imply the emergence of superethical capabilities in an all-knowing ethical agent.

Nobody knows, however, what superethical abilities might entail.

The fundamental problem, however, is that superintelligent entities' higher intellectual abilities do not ensure a high degree of ethical probity, or even any level of ethical probity.

As a result, having a superintelligent machine with almost infinite (but not quite) capacities but no ethics seems to be dangerous to say the least.

A sizable number of scholars are skeptical about the development of the Technological Singularity, notably of superintelligence.

They rule out the possibility of developing artificial systems with superhuman cognitive abilities, either on philosophical or scientific grounds.

Some contend that while artificial intelligence is often at the heart of technological singularity claims, achieving human-level intelligence in artificial systems is impossible, and hence superintelligence, and thus the Technological Singularity, is a dream.

Such barriers, however, do not exclude the development of superhuman brains via the genetic modification of regular people, paving the door for transhumans, human-machine hybrids, and superhuman agents.

More scholars question the validity of the notion of the Technological Singularity, pointing out that such forecasts about future civilizations are based on speculation and guesswork.

Others argue that the promises of unrestrained technological advancement and limitless intellectual capacities made by the Technological Singularity legend are unfounded, since physical and informational processing resources are plainly limited in the cosmos, particularly on Earth.

Any promises of self-replicating, self-improving artificial agents capable of super-exponential technological advancement are false, since such systems will lack the creativity, will, and incentive to drive their own evolution.

Meanwhile, social opponents point out that superintelligence's boundless technological advancement would not alleviate issues like overpopulation, environmental degradation, poverty, and unparalleled inequality.

Indeed, the widespread unemployment projected as a consequence of AI-assisted mass automation of labor, barring significant segments of the population from contributing to society, would result in unparalleled social upheaval, delaying the development of new technologies.

As a result, rather than speeding up, political or societal pressures will stifle technological advancement.

While technological singularity cannot be ruled out on logical grounds, the technical hurdles that it faces, even if limited to those that can presently be determined, are considerable.

Nobody expects the technological singularity to happen with today's computers and other technology, but proponents of the concept consider these obstacles as "technical challenges to be overcome" rather than possible show-stoppers.

However, there is a large list of technological issues to be overcome, and Murray Shanahan's The Technological Singularity (2015) gives a fair overview of some of them.

There are also some significant nontechnical issues, such as the problem of superintelligent system training, the ontology of artificial or machine consciousness and self-aware artificial systems, the embodiment of artificial minds or vicarious embodiment processes, and the rights granted to superintelligent systems, as well as their role in society and any limitations placed on their actions, if this is even possible.

These issues are currently confined to the realms of technological and philosophical discussion.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Bostrom, Nick; de Garis, Hugo; Diamandis, Peter; Digital Immortality; Goertzel, Ben; Kurzweil, Ray; Moravec, Hans; Post-Scarcity, AI and; Superintelligence.

References And Further Reading

Bostrom, Nick. 2014. Superintelligence: Path, Dangers, Strategies. Oxford, UK: Oxford University Press.

Chalmers, David. 2010. “The Singularity: A Philosophical Analysis.” Journal of Consciousness Studies 17: 7–65.

Eden, Amnon H. 2016. The Singularity Controversy. Sapience Project. Technical Report STR 2016-1. January 2016.

Eden, Amnon H., Eric Steinhart, David Pearce, and James H. Moor. 2012. “Singularity Hypotheses: An Overview.” In Singularity Hypotheses: A Scientific and Philosophical Assessment, edited by Amnon H. Eden, James H. Moor, Johnny H. Søraker, and Eric Steinhart, 1–12. Heidelberg, Germany: Springer.

Good, I. J. 1966. “Speculations Concerning the First Ultraintelligent Machine.” Advances in Computers 6: 31–88.

Kurzweil, Ray. 2005. The Singularity Is Near: When Humans Transcend Biology. New York: Viking.

Sandberg, Anders, and Nick Bostrom. 2008. Global Catastrophic Risks Survey. Technical Report #2008/1. Oxford University, Future of Humanity Institute.

Shanahan, Murray. 2015. The Technological Singularity. Cambridge, MA: The MIT Press.

Ulam, Stanislaw. 1958. “Tribute to John von Neumann.” Bulletin of the American Mathematical Society 64, no. 3, pt. 2 (May): 1–49.

Vinge, Vernor. 1993. “The Coming Technological Singularity: How to Survive in the Post-Human Era.” In Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace, 11–22. Cleveland, OH: NASA Lewis Research Center.

AI - Symbolic Logic

 

In mathematical and philosophical reasoning, symbolic logic entails the use of symbols to express concepts, relations, and positions.

Symbolic logic varies from (Aristotelian) syllogistic logic in that it employs ideographs or a particular notation to "symbolize exactly the item discussed" (Newman 1956, 1852), and it may be modified according to precise rules.

Traditional logic investigated the truth and falsehood of assertions, as well as their relationships, using terminology derived from natural language.

Unlike nouns and verbs, symbols do not need interpretation.

Because symbol operations are mechanical, they may be delegated to computers.

Symbolic logic eliminates any ambiguity in logical analysis by codifying it entirely inside a defined notational framework.

Gottfried Wilhelm Leibniz (1646–1716) is widely regarded as the founding father of symbolic logic.

Leibniz proposed the use of ideographic symbols instead of natural language in the seventeenth century as part of his goal to revolutionize scientific thinking.

Leibniz hoped that by combining such concise universal symbols (characteristica universalis) with a set of scientific reasoning rules, he could create an alphabet of human thought that would promote the growth and dissemination of scientific knowledge, as well as a corpus containing all human knowledge.

Boolean logic, the logical underpinnings of mathematics, and decision issues are all topics of symbolic logic that may be broken down into subcategories.

George Boole, Alfred North Whitehead, and Bertrand Russell, as well as Kurt Gödel, wrote important contributions in each of these fields.

George Boole published The Mathematical Analysis of Logic (1847) and An Investigation of the Laws of Thought in the mid-nineteenth century (1854).

Boole zoomed down on a calculus of deductive reasoning, which led him to three essential operations in a logical mathematical language known as Boolean algebra: AND, OR, and NOT.

The use of symbols and operators greatly aided the creation of logical formulations.

Claude Shannon (1916–2001) employed electromechanical relay circuits and switches to reproduce Boolean algebra in the twentieth century, laying crucial foundations in the development of electronic digital computing and computer science in general.

Alfred North Whitehead and Bertrand Russell established their seminal work in the subject of symbolic logic in the early twentieth century.

Their Principia Mathematica (1910, 1912, 1913) demonstrated how all of mathematics may be reduced to symbolic logic.

Whitehead and Russell developed a logical system from a handful of logical concepts and a set of postulates derived from those ideas in the first book of their work.

Whitehead and Russell established all mathematical concepts, including number, zero, successor of, addition, and multiplication, using fundamental logical terminology and operational principles like proposition, negation, and either-or in the second book of the Principia.

In the last and third volumes, Whitehead and Russell were able to demonstrate that the nature and reality of all mathematics is built on logical concepts and connections.

The Principia showed how every mathematical postulate might be inferred from previously explained symbolic logical facts.

Only a few decades later, Kurt Gödel's On Formally Undecidable Propositions in the Principia Mathematica and Related Systems (1931) critically analyzed the Principia's strong and deep claims, demonstrating that Whitehead and Russell's axiomatic system could not be consistent and complete at the same time.

Even so, it required another important book in symbolic logic, Ernst Nagel and James Newman's Gödel's Proof (1958), to spread Gödel's message to a larger audience, including some artificial intelligence practitioners.

Each of these seminal works in symbolic logic had a different influence on the development of computing and programming, as well as our understanding of a computer's capabilities as a result.

Boolean logic has made its way into the design of logic circuits.

The Logic Theorist program by Simon and Newell provided logical arguments that matched those found in the Principia Mathematica, and was therefore seen as evidence that a computer could be programmed to do intelligent tasks via symbol manipulation.

Gödel's incompleteness theorem raises intriguing issues regarding how programmed machine intelligence, particularly strong AI, will be realized in the end.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Symbol Manipulation.

References And Further Reading

Boole, George. 1854. Investigation of the Laws of Thought on Which Are Founded the Mathematical Theories of Logic and Probabilities. London: Walton.

Lewis, Clarence Irving. 1932. Symbolic Logic. New York: The Century Co.

Nagel, Ernst, and James R. Newman. 1958. Gödel’s Proof. New York: New York University Press.

Newman, James R., ed. 1956. The World of Mathematics, vol. 3. New York: Simon and Schuster.

Whitehead, Alfred N., and Bertrand Russell. 1910–1913. Principia Mathematica. Cambridge, UK: Cambridge University Press.