Artificial Intelligence - The General Problem Solver Software.

 

To arrive at a solution, General Problem Solver is a software for a problem-solving method that employs means-ends analysis and planning.

The software was created in such a way that the problem-solving process is separated from information unique to the situation at hand, allowing it to be used to a wide range of issues.

The software, which was first created by Allen Newell and Herbert Simon in 1957, took over a decade to complete.

George W. Ernst, a graduate student at Newell, wrote the latest edition while doing research for his dissertation in 1966.

The General Problem Solver arose from Newell and Simon's work on the Logic Theorist, another problem-solving tool.

The duo likened Logic Theorist's problem-solving approach to that of people solving comparable issues after inventing it.

They discovered that the logic theorist's method varied significantly from that of humans.

Newell and Simon developed General Problem Solver using the knowledge on human problem solving obtained from these experiments, hoping that their artificial intelligence work would contribute to a better understanding of human cognitive processes.

They discovered that human problem-solvers could look at the intended outcome and, using both backward and forward thinking, decide actions they might take to get closer to that outcome, resulting in the development of a solution.

The General Problem Solver, which Newell and Simon felt was not just reflective of artificial intelligence but also a theory of human cognition, included this mechanism.

To solve issues, General Problem Solver uses two heuristic techniques: 

1. means-ends analysis and 
2. planning.

As an example of means-ends analysis in action, consider the following: 

• If a person coveted a certain book, they would want to be in possession of it.
• The book is currently kept by the library, and they do not have it in their possession.
• The individual has the ability to eliminate the gap between their existing and ideal states.
• They may do so by borrowing the book from the library, and they have other alternatives for getting there, including driving.
• If the book has been checked out by another customer, however, there are possibilities for obtaining it elsewhere.
• To buy it, the consumer may go to a bookshop or order it online.
• The individual must next consider the many possibilities open to them.
• And so on.

The individual is aware of a number of pertinent activities they may do, and if they select the right ones and carry them out in the right sequence, they will be able to receive the book.

Means ends analysis in action is the person who chooses and implements suitable activities.

The programmer sets up the issue as a starting state and a state to be attained when using means-ends analysis with General Problem Solver.

The difference between these two states is calculated using the General Problem Solver (called objects).

• Operators that lessen the difference between the two states must also be coded into the General Problem Solver.

• It picks and implements an operator to address the issue, then assesses if the operation has got it closer to its objective or ideal state.

• If that's the case, it'll go on to the next operator.

• If it doesn't work, it may go back and try another operator.

• The difference between the original state and the target state is decreased to zero by applying operators.

• The capacity to plan was also held by General Problem Solver.

General Problem Solver might sketch a solution to the problem by removing the specifics of the operators and the difference between the starting and desired states.

After a broad solution had been defined, the specifics could be reinserted into the issue, and the subproblems formed by these details could be addressed within the solution guide lines produced during the outlining step.

Defining an issue and operators to program the General Problem Solver was a time-consuming task for programmers.

It also meant that, as a theory of human cognition or an example of artificial intelligence, General Problem Solver took for granted the very actions that, in part, represent intelligence, namely the acts of defining a problem and selecting relevant actions (or operations) from an infinite number of possible actions in order to solve the problem.

In the mid-1960s, Ernst continued to work on General Problem Solver.

He wasn't interested in human problem-solving procedures; instead, he wanted to discover a way to broaden the scope of General Problem Solver so that it could solve issues outside of the logic domain.

In his version of General Problem Solver, the intended state or object was expressed as a set of constraints rather than an explicit specification.

Ernst also altered the form of the operators such that the output of an operator may be written as a function of the starting state or object (the input).

His updated General Problem Solver was only somewhat successful in solving problems.

Even on basic situations, it often ran out of memory.

"We do believe that this specific aggregation of IPL-Vcode should be set to rest, as having done its bit in furthering our knowledge of the mechanics of intelligence," Ernst and Newell proclaimed in the foreword of their 1969 book GPS: A Case Study in Generality and Problem Solving(Ernst and Newell 1969, vii).

Artificial Intelligence Problem Solving

The AI reflex agent converts states into actions. 

When these agents fail to function in an environment where the state of mapping is too vast for the agent to handle, the stated issue is resolved and passed to a problem-solving domain, which divides the huge stored problem into smaller storage areas and resolves them one by one. 

The targeted objectives will be the final integrated action.

Different sorts of issue-solving agents are created and used at an atomic level without any internal state observable with a problem-solving algorithm based on the problem and their working area. 

By describing issues and many solutions, the problem-solving agent executes exactly. 

So we may say that issue solving is a subset of artificial intelligence that includes a variety of problem-solving approaches such as tree, B-tree, and heuristic algorithms.

A problem-solving agent is also known as a goal-oriented agent since it is constantly focused on achieving the desired outcomes.

AI problem-solving steps: 

The nature of people and their behaviors are intimately related to the AI dilemma. 

To solve a problem, we require a set of discrete steps, which makes human labor simple. These are the actions that must be taken to fix a problem:

• Goal formulation is the first and most basic stage in addressing a problem. 

It arranges discrete stages to establish a target/goals that need some action in order to be achieved. 

AI agents are now used to formulate the aim. 

One of the most important processes in issue resolution is problem formulation, which determines what actions should be followed to reach the specified objective. 

This essential aspect of AI relies on a software agent, which consists of the following components to construct the issue. 

Components needed to formulate the problem: 

This state necessitates a beginning state for the challenge, which directs the AI agent toward a certain objective. 

In this scenario, new methods also use a particular class to solve the issue area. 

Action: In this step of issue formulation, all feasible actions are performed using a function with a specified class obtained from the starting state.

Transition: In this step of issue formulation, the actual action performed by the previous action stage is combined with the final stage to be sent to the next stage.

Objective test: This step assesses if the integrated transition model accomplished the given goal or not; if it did, halt the action and go to the next stage to estimate the cost of achieving the goal.

Path costing is a component of problem-solving that assigns a numerical value to the expense of achieving the objective. 

It necessitates the purchase of all gear, software, and human labor.

General Problem Solver Overview

In theory, GPS may solve any issue that can be described as a collection of well-formed formulas (WFFs) or Horn clauses that create a directed graph with one or more sources (that is, axioms) and sinks (that is, desired conclusions). 

Predicate logic proofs and Euclidean geometry problem spaces are two primary examples of the domain in which GPS may be used. 

It was based on the theoretical work on logic machines by Simon and Newell. 

GPS was the first computer software to segregate its issue knowledge (expressed as input data) from its problem-solving method (a generic solver engine). 

IPL, a third-order programming language, was used to build GPS. 

While GPS was able to tackle small issues like the Hanoi Towers that could be adequately described, it was unable to handle any real-world problems since search was quickly lost in the combinatorial explosion. 

Alternatively, the number of "walks" across the inferential digraph became computationally prohibitive. 

(In fact, even a simple state space search like the Towers of Hanoi may become computationally infeasible, however smart pruning of the state space can be accomplished using basic AI methods like A* and IDA*.)

In order to solve issues, the user identified objects and procedures that might be performed on them, and GPS created heuristics via means-ends analysis. 

• It concentrated on the available processes, determining which inputs and outputs were acceptable. 

• It then established sub goals in order to move closer to the ultimate objective.

• The GPS concept ultimately developed into the Soar artificial intelligence framework.

~ Jai Krishna Ponnappan

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

See also: 

Expert Systems; Simon, Herbert A.

Frequently Asked Questions:

In Artificial Intelligence, what is a generic problem solver?

Herbert Simon, J.C. Shaw, and Allen Newell suggested the General Problem Solver (GPS) as an AI software. It was the first useful computer software in the field of artificial intelligence. It was intended to function as a global problem-solving machine.

What is the procedure for using the generic issue solver?

In order to solve issues, the user identified objects and procedures that might be performed on them, and GPS created heuristics via means-ends analysis. It concentrated on the available processes, determining which inputs and outputs were acceptable.

What exactly did the General Problem Solver accomplish?

The General Problem Solver (GPS) was their next effort, which debuted in 1957. GPS would use heuristic approaches (modifiable "rules of thumb") repeatedly to a problem and then undertake a "means-ends" analysis at each stage to see whether it was getting closer to the intended answer.

What are the three key domain universal issue-solving heuristics that Newell and Simon's general problem solver incorporated in 1972?

According to Newell and Simon (1972), every issue has a problem space that is described by three components: 1) the issue's beginning state; 2) a collection of operators for transforming a problem state; 3) a test to see whether a problem state is a solution.

What is heuristic search and how does it work?

Heuristic search is a kind of strategy for finding the best solution to a problem by searching a solution space. The heuristic here use some mechanism for searching the solution space while determining where the solution is most likely to be found and concentrating the search on that region.

What are the elements of a broad issue?

The issue itself, articulated clearly and with sufficient context to explain why it is significant; the way of fixing the problem, frequently presented as a claim or a working thesis; and the goal, declaration of objective, and scope of the paper the writer is writing.

What are the stages of a basic development process employing a problem-solving approach?

Problem-Solving Process in 8 Steps:

Step 1: Identify the issue. What exactly is the issue?

Step 2: Identify the issue.

Step 3: Establish the objectives.

Step 4: Determine the problem's root cause.

Step 5: Make a plan of action.

Step 6: Put your plan into action.

Step 7: Assess the Outcomes

Step 8: Always strive to improve.

 

What's the difference between heuristic and algorithmic problem-solving?

A step-by-step technique for addressing a given issue in a limited number of steps is known as an algorithm. Given the same parameters, an algorithm's outcome (output) is predictable and repeatable (input). A heuristic is an informed assumption that serves as a starting point for further investigation.

What makes algorithms superior than heuristics?

Heuristics entail using a learning and discovery strategy to obtain a solution, while an algorithm is a clearly defined set of instructions for solving a problem. Use an algorithm if you know how to solve an issue.

References and Further Reading:

Barr, Avron, and Edward Feigenbaum, eds. 1981. The Handbook of Artificial Intelligence, vol. 1, 113–18. Stanford, CA: HeurisTech Press.

Ernst, George W., and Allen Newell. 1969. GPS: A Case Study in Generality and Problem Solving. New York: Academic Press.

Newell, Allen, J. C. Shaw, and Herbert A. Simon. 1960. “Report on a General Problem Solving Program.” In Proceedings of the International Conference on Information Processing (June 15–20, 1959), 256–64. Paris: UNESCO.

Simon, Herbert A. 1991. Models of My Life. New York: Basic Books.

Simon, Herbert A., and Allen Newell. 1961. “Computer Simulation of Human Thinking and Problem Solving.” Datamation 7, part 1 (June): 18–20, and part 2 (July): 35–37

Artificial Intelligence - General and Narrow Categories Of AI.

There are two types of artificial intelligence: general (or powerful or complete) and narrow (or limited) (or weak or specialized).

In the actual world, general AI, such as that seen in science fiction, does not yet exist.

Machines with global intelligence would be capable of completing every intellectual endeavor that humans are capable of.

This sort of system would also seem to think in abstract terms, establish connections, and communicate innovative ideas in the same manner that people do, displaying the ability to think abstractly and solve problems.

Such a computer would be capable of thinking, planning, and recalling information from the past.

While the aim of general AI has yet to be achieved, there are more and more instances of narrow AI.

These are machines that perform at human (or even superhuman) levels on certain tasks.

Computers that have learnt to play complicated games have abilities, techniques, and behaviors that are comparable to, if not superior to, those of the most skilled human players.

AI systems that can translate between languages in real time, interpret and respond to natural speech (both spoken and written), and recognize images have also been developed (being able to recognize, identify, and sort photos or images based on the content).

However, the ability to generalize knowledge or skills is still largely a human accomplishment.

Nonetheless, there is a lot of work being done in the field of general AI right now.

It will be difficult to determine when a computer develops human-level intelligence.

Several serious and hilarious tests have been suggested to determine whether a computer has reached the level of General AI.

The Turing Test is arguably the most renowned of these examinations.

A machine and a person speak in the background, as another human listens in.

The human eavesdropper must figure out which speaker is a machine and which is a human.

The machine passes the test if it can fool the human evaluator a prescribed percentage of the time.

The Coffee Test is a more fantastical test in which a machine enters a typical household and brews coffee.

It has to find the coffee machine, look for the coffee, add water, boil the coffee, and pour it into a cup.

Another is the Flat Pack Furniture Test, which involves a machine receiving, unpacking, and assembling a piece of furniture based only on the instructions supplied.

Some scientists, as well as many science fiction writers and fans, believe that once intelligent machines reach a tipping point, they will be able to improve exponentially.

AI-based beings that far exceed human capabilities might be one conceivable result.

The Singularity, or artificial superintelligence, is the point at which AI assumes control of its own self-improvement (ASI).

If ASI is achieved, it will have unforeseeable consequences for human society.

Some pundits worry that ASI would jeopardize humanity's safety and dignity.

It's up for dispute whether the Singularity will ever happen, and how dangerous it may be.

Narrow AI applications are becoming more popular across the globe.

Machine learning (ML) is at the heart of most new applications, and most AI examples in the news are connected to this subset of technology.

Traditional or conventional algorithms are not the same as machine learning programs.

In programs that cannot learn, a computer programmer actively adds code to account for every action of an algorithm.

All of the decisions made along the process are governed by the programmer's guidelines.

This necessitates the programmer imagining and coding for every possible circumstance that an algorithm may face.

This kind of program code is bulky and often inadequate, especially if it is updated frequently to accommodate for new or unanticipated scenarios.

The utility of hard-coded algorithms approaches its limit in cases where the criteria for optimum judgments are unclear or impossible for a human programmer to foresee.

Machine learning is the process of training a computer to detect and identify patterns via examples rather than predefined rules.

This is achieved, according to Google engineer Jason Mayes, by reviewing incredibly huge quantities of training data or participating in some other kind of programmed learning step.

New patterns may be extracted by processing the test data.

The system may then classify newly unknown data based on the patterns it has already found.

Machine learning allows an algorithm to recognize patterns or rules underlying decision-making processes on its own.

Machine learning also allows a system's output to improve over time as it gains more experience (Mayes 2017).

A human programmer continues to play a vital role in this learning process, influencing results by making choices like developing the exact learning algorithm, selecting the training data, and choosing other design elements and settings.

Machine learning is powerful once it's up and running because it can adapt and enhance its ability to categorize new data without the need for direct human interaction.

In other words, the output quality increases as the user gains experience.

Artificial intelligence is a broad word that refers to the science of making computers intelligent.

AI is a computer system that can collect data and utilize it to make judgments or solve issues, according to scientists.

Another popular scientific definition of AI is "a software program paired with hardware that can receive (or sense) inputs from the world around it, evaluate and analyze those inputs, and create outputs and suggestions without the assistance of a person." When programmers claim an AI system can learn, they're referring to the program's ability to change its own processes in order to provide more accurate outputs or predictions.

AI-based systems are now being developed and used in practically every industry, from agriculture to space exploration, and in applications ranging from law enforcement to online banking.

The methods and techniques used in computer science are always evolving, extending, and improving.

Other terminology linked to machine learning, such as reinforcement learning and neural networks, are important components of cutting-edge artificial intelligence systems.

~ Jai Krishna Ponnappan

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

See also: 

Embodiment, AI and; Superintelligence; Turing, Alan; Turing Test.

Further Reading:

Kelnar, David. 2016. “The Fourth Industrial Revolution: A Primer on Artificial Intelligence (AI).” Medium, December 2, 2016. https://medium.com/mmc-writes/the-fourth-industrial-revolution-a-primer-on-artificial-intelligence-ai-ff5e7fffcae1.

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

Mayes, Jason. 2017. Machine Learning 101. https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k/htmlpresent.

Müller, Vincent C., and Nick Bostrom. 2016. “Future Progress in Artificial Intelligence: A Survey of Expert Opinion.” In Fundamental Issues of Artificial Intelligence, edited by Vincent C. Müller, 553–71. New York: Springer.

Russell, Stuart, and Peter Norvig. 2003. Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall.

Samuel, Arthur L. 1988. “Some Studies in Machine Learning Using the Game of Checkers I.” In Computer Games I, 335–65. New York: Springer.

Artificial Intelligence - What Is The Mac Hack IV Program?

 

Mac Hack IV, a 1967 chess software built by Richard Greenblatt, gained notoriety for being the first computer chess program to engage in a chess tournament and to play adequately against humans, obtaining a USCF rating of 1,400 to 1,500.

Greenblatt's software, written in the macro assembly language MIDAS, operated on a DEC PDP-6 computer with a clock speed of 200 kilohertz.

While a graduate student at MIT's Artificial Intelligence Laboratory, he built the software as part of Project MAC.

"Chess is the drosophila [fruit fly] of artificial intelligence," according to Russian mathematician Alexander Kronrod, the field's chosen experimental organ ism (Quoted in McCarthy 1990, 227).

Creating a champion chess software has been a cherished goal in artificial intelligence since 1950, when Claude Shan ley first described chess play as a task for computer programmers.

Chess and games in general involve difficult but well-defined issues with well-defined rules and objectives.

Chess has long been seen as a prime illustration of human-like intelligence.

Chess is a well-defined example of human decision-making in which movements must be chosen with a specific purpose in mind, with limited knowledge and uncertainty about the result.

The processing capability of computers in the mid-1960s severely restricted the depth to which a chess move and its alternative answers could be studied since the number of different configurations rises exponentially with each consecutive reply.

The greatest human players have been proven to examine a small number of moves in greater detail rather than a large number of moves in lower depth.

Greenblatt aimed to recreate the methods used by good players to locate significant game tree branches.

He created Mac Hack to reduce the number of nodes analyzed while choosing moves by using a minimax search of the game tree along with alpha-beta pruning and heuristic components.

In this regard, Mac Hack's style of play was more human-like than that of more current chess computers (such as Deep Thought and Deep Blue), which use the sheer force of high processing rates to study tens of millions of branches of the game tree before making moves.

In a contest hosted by MIT mathematician Seymour Papert in 1967, Mac Hack defeated MIT philosopher Hubert Dreyfus and gained substantial renown among artificial intelligence researchers.

The RAND Corporation published a mimeographed version of Dreyfus's paper, Alchemy and Artificial Intelligence, in 1965, which criticized artificial intelligence researchers' claims and aspirations.

Dreyfus claimed that no computer could ever acquire intelligence since human reason and intelligence are not totally rule-bound, and hence a computer's data processing could not imitate or represent human cognition.

In a part of the paper titled "Signs of Stagnation," Dreyfus highlighted attempts to construct chess-playing computers, among his many critiques of AI.

Mac Hack's victory against Dreyfus was first seen as vindication by the AI community.

~ Jai Krishna Ponnappan

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

See also: 

Alchemy and Artificial Intelligence; Deep Blue.

Further Reading:

Crevier, Daniel. 1993. AI: The Tumultuous History of the Search for Artificial Intelligence. New York: Basic Books.

Greenblatt, Richard D., Donald E. Eastlake III, and Stephen D. Crocker. 1967. “The Greenblatt Chess Program.” In AFIPS ’67: Proceedings of the November 14–16, 1967, Fall Joint Computer Conference, 801–10. Washington, DC: Thomson Book Company.

Marsland, T. Anthony. 1990. “A Short History of Computer Chess.” In Computers, Chess, and Cognition, edited by T. Anthony Marsland and Jonathan Schaeffer, 3–7. New York: Springer-Verlag.

McCarthy, John. 1990. “Chess as the Drosophila of AI.” In Computers, Chess, and Cognition, edited by T. Anthony Marsland and Jonathan Schaeffer, 227–37. New York: Springer-Verlag.

McCorduck, Pamela. 1979. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. San Francisco: W. H. Freeman.

Artificial Intelligence - Who Is Elon Musk?

 

Elon Musk (1971–) is an American businessman and inventor.

Elon Musk is an engineer, entrepreneur, and inventor who was born in South Africa.

He is a dual citizen of South Africa, Canada, and the United States, and resides in California.

Musk is widely regarded as one of the most prominent inventors and engineers of the twenty-first century, as well as an important influencer and contributor to the development of artificial intelligence.

Despite his controversial personality, Musk is widely regarded as one of the most prominent inventors and engineers of the twenty-first century and an important influencer and contributor to the development of artificial intelligence.

Musk's business instincts and remarkable technological talent were evident from an early age.

By the age of 10, he had self-taught himself how program computers, and by the age of twelve, he had produced a video game and sold the source code to a computer magazine.

Musk has included allusions to some of his favorite novels in SpaceX's Falcon Heavy rocket launch and Tesla's software since he was a youngster.

Musk's official schooling was centered on economics and physics rather than engineering, interests that are mirrored in his subsequent work, such as his efforts in renewable energy and space exploration.

He began his education at Queen's University in Canada, but later transferred to the University of Pennsylvania, where he earned bachelor's degrees in Economics and Physics.

Musk barely stayed at Stanford University for two days to seek a PhD in energy physics before departing to start his first firm, Zip2, with his brother Kimbal Musk.

Musk has started or cofounded many firms, including three different billion-dollar enterprises: SpaceX, Tesla, and PayPal, all driven by his diverse interests and goals.

• Zip2 was a web software business that was eventually purchased by Compaq.

• X.com: an online bank that merged with PayPal to become the online payments corporation PayPal.

• Tesla, Inc.: an electric car and solar panel maker 

• SpaceX: a commercial aircraft manufacturer and space transportation services provider (via its subsidiarity SolarCity) 

• Neuralink: a neurotechnology startup focusing on brain-computer connections 

• The Boring Business: an infrastructure and tunnel construction corporation

 • OpenAI: a nonprofit AI research company focused on the promotion and development of friendly AI Musk is a supporter of environmentally friendly energy and consumption.

Concerns over the planet's future habitability prompted him to investigate the potential of establishing a self-sustaining human colony on Mars.

Other projects include the Hyperloop, a high-speed transportation system, and the Musk electric jet, a jet-powered supersonic electric aircraft.

Musk sat on President Donald Trump's Strategy and Policy Forum and Manufacturing Jobs Initiative for a short time before stepping out when the US withdrew from the Paris Climate Agreement.

Musk launched the Musk Foundation in 2002, which funds and supports research and activism in the domains of renewable energy, human space exploration, pediatric research, and science and engineering education.

Musk's effect on AI is significant, despite his best-known work with Tesla and SpaceX, as well as his contentious social media pronouncements.

In 2015, Musk cofounded the charity OpenAI with the objective of creating and supporting "friendly AI," or AI that is created, deployed, and utilized in a manner that benefits mankind as a whole.

OpenAI's objective is to make AI open and accessible to the general public, reducing the risks of AI being controlled by a few privileged people.

OpenAI is especially concerned about the possibility of Artificial General Intelligence (AGI), which is broadly defined as AI capable of human-level (or greater) performance on any intellectual task, and ensuring that any such AGI is developed responsibly, transparently, and distributed evenly and openly.

OpenAI has had its own successes in taking AI to new levels while staying true to its goals of keeping AI friendly and open.

In June of 2018, a team of OpenAI-built robots defeated a human team in the video game Dota 2, a feat that could only be accomplished through robot teamwork and collaboration.

Bill Gates, a cofounder of Microsoft, praised the achievement on Twitter, calling it "a huge milestone in advancing artificial intelligence" (@BillGates, June 26, 2018).

Musk resigned away from the OpenAI board in February 2018 to prevent any conflicts of interest while Tesla advanced its AI work for autonomous driving.

Musk became the CEO of Tesla in 2008 after cofounding the company in 2003 as an investor.

Musk was the chairman of Tesla's board of directors until 2018, when he stepped down as part of a deal with the US Securities and Exchange Commission over Musk's false claims about taking the company private.

Tesla produces electric automobiles with self-driving capabilities.

Tesla Grohmann Automation and Solar City, two of its subsidiaries, offer relevant automotive technology and manufacturing services and solar energy services, respectively.

Tesla, according to Musk, will reach Level 5 autonomous driving capabilities in 2019, as defined by the National Highway Traffic Safety Administration's (NHTSA) five levels of autonomous driving.

Tes la's aggressive development with autonomous driving has influenced conventional car makers' attitudes toward electric cars and autonomous driving, and prompted a congressional assessment of how and when the technology should be regulated.

Musk is widely credited as a key influencer in moving the automotive industry toward autonomous driving, highlighting the benefits of autonomous vehicles (including reduced fatalities in vehicle crashes, increased worker productivity, increased transportation efficiency, and job creation) and demonstrating that the technology is achievable in the near term.

Tesla's autonomous driving code has been created and enhanced under the guidance of Musk and Tesla's Director of AI, Andrej Karpathy (Autopilot).

The computer vision analysis used by Tesla, which includes an array of cameras on each car and real-time image processing, enables the system to make real-time observations and predictions.

The cameras, as well as other exterior and internal sensors, capture a large quantity of data, which is evaluated and utilized to improve Autopilot programming.

Tesla is the only autonomous car maker that is opposed to the LIDAR laser sensor (an acronym for light detection and ranging).

Tesla uses cameras, radar, and ultrasonic sensors instead.

Though academics and manufacturers disagree on whether LIDAR is required for fully autonomous driving, the high cost of LIDAR has limited Tesla's rivals' ability to produce and sell vehicles at a pricing range that allows a large number of cars on the road to gather data.

Tesla is creating its own AI hardware in addition to its AI programming.

Musk stated in late 2017 that Tesla is building its own silicon for artificial-intelligence calculations, allowing the company to construct its own AI processors rather than depending on third-party sources like Nvidia.

Tesla's AI progress in autonomous driving has been marred by setbacks.

Tesla has consistently missed self-imposed deadlines, and serious accidents have been blamed on flaws in the vehicle's Autopilot mode, including a non-injury accident in 2018, in which the vehicle failed to detect a parked firetruck on a California freeway, and a fatal accident in 2018, in which the vehicle failed to detect a pedestrian outside a crosswalk.

Neuralink was established by Musk in 2016.

With the stated objective of helping humans to keep up with AI breakthroughs, Neuralink is focused on creating devices that can be implanted into the human brain to better facilitate communication between the brain and software.

Musk has characterized the gadgets as a more efficient interface with computer equipment, while people now operate things with their fingertips and voice commands, directives would instead come straight from the brain.

Though Musk has made major advances to AI, his pronouncements regarding the risks linked with AI have been apocalyptic.

Musk has called AI "humanity's greatest existential danger" and "the greatest peril we face as a civilisation" (McFarland 2014).

(Morris 2017).

He cautions against the perils of power concentration, a lack of independent control, and a competitive rush to acceptance without appropriate analysis of the repercussions.

While Musk has used colorful terminology such as "summoning the devil" (McFarland 2014) and depictions of cyborg overlords, he has also warned of more immediate and realistic concerns such as job losses and AI-driven misinformation campaigns.

Though Musk's statements might come out as alarmist, many important and well-respected figures, including as Microsoft cofounder Bill Gates, Swedish-American scientist Max Tegmark, and the late theoretical physicist Stephen Hawking, share his concern.

Furthermore, Musk does not call for the cessation of AI research.

Instead, Musk supports for responsible AI development and regulation, including the formation of a Congressional committee to spend years studying AI with the goal of better understanding the technology and its hazards before establishing suitable legal limits.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Bostrom, Nick; Superintelligence.

References & Further Reading:

Gates, Bill. (@BillGates). 2018. Twitter, June 26, 2018. https://twitter.com/BillGates/status/1011752221376036864.

Marr, Bernard. 2018. “The Amazing Ways Tesla Is Using Artificial Intelligence and Big Data.” Forbes, January 8, 2018. https://www.forbes.com/sites/bernardmarr/2018/01/08/the-amazing-ways-tesla-is-using-artificial-intelligence-and-big-data/.

McFarland, Matt. 2014. “Elon Musk: With Artificial Intelligence, We Are Summoning the Demon.” Washington Post, October 24, 2014. https://www.washingtonpost.com/news/innovations/wp/2014/10/24/elon-musk-with-artificial-intelligence-we-are-summoning-the-demon/.

Morris, David Z. 2017. “Elon Musk Says Artificial Intelligence Is the ‘Greatest Risk We Face as a Civilization.’” Fortune, July 15, 2017. https://fortune.com/2017/07/15/elon-musk-artificial-intelligence-2/.

Piper, Kelsey. 2018. “Why Elon Musk Fears Artificial Intelligence.” Vox Media, Novem￾ber 2, 2018. https://www.vox.com/future-perfect/2018/11/2/18053418/elon-musk-artificial-intelligence-google-deepmind-openai.

Strauss, Neil. 2017. “Elon Musk: The Architect of Tomorrow.” Rolling Stone, November 15, 2017. https://www.rollingstone.com/culture/culture-features/elon-musk-the-architect-of-tomorrow-120850/.

AI - Spiritual Robots.

 

In April 2000, Indiana University cognitive scientist Douglas Hofstadter arranged a symposium called "Will Spiritual Robots Replace Humanity by 2100?" at Stanford University.

Frank Drake, astronomer and SETI director, John Holland, creator of genetic algorithms, Bill Joy of Sun Microsystems, computer scientist John Koza, futurist Ray Kurzweil, public key cryptography architect Ralph Merkle, and roboticist Hans Moravec were among the panelists.

Several of the panelists gave their thoughts on the conference's theme based on their own writings.

• Kurzweil's optimistic futurist account of artificial intelligence, The Age of Spiritual Machines, had just been published (1999).
• In Robot: Mere Machine to Transcendent Mind, Moravec presented a positive picture of machine superintelligence (1999).
• Bill Joy had just written a story for Wired magazine called "Why the Future Doesn't Need Us" on the triple technological danger posed by robots, genetic engineering, and nanotechnology (2000).
• Only Hofstadter believed that Moore's Law doublings of transistors on integrated circuits may lead to spiritual robots as a consequence of the tremendous increase in artificial intelligence technologies.

Is it possible for robots to have souls? 

Can they exercise free will and separate themselves from humanity? 

What does it mean to have a soul for an artificial intelligence? 

Questions like these have been asked since the days of golems, Pinocchio, and the Tin Man, but they are becoming more prevalent in modern writing on religion, artificial intelligence, and the Technological Singularity.

Japan's robotics leadership started with puppetry.

Takemoto Giday and playwright Chikamatsu Monzaemon founded the Takemoto-za in Osaka's Dotonbori district in 1684 to perform bunraku, a theatrical extravaganza involving one-half life-size wooden puppets dressed in elaborate costumes, each controlled by three black-clad onstage performers: a principal puppeteer and two assistants.

Bunraku exemplifies Japan's long-standing fascination in bringing inanimate items to life.

Japan is a world leader in robotics and artificial intelligence today, thanks to a grueling postwar rebuilding effort known as gijutsu rikkoku (nation building via technology).

Television was one of the first technologies to be widely used under technonationalism.

The Japanese government hoped that print and electronic media would encourage people to dream of an electronic lifestyle and reconnect with the global economy by encouraging them to employ innovative technology to do so.

As a result, Japan has become a major culture rival to the United States.

Manga and anime, which feature intelligent and humanlike robots, mecha, and cyborgs, are two of Japan's most recognizable entertainment exports.

The notion of spiritual machinery is widely accepted in Japan's Buddhist and Shinto worldviews.

Masahiro Mori, a roboticist at Tokyo Institute of Technology, has proposed that a sufficiently powerful artificial intelligence may one day become a Buddha.

Mindar, a robot based on the Goddess of Mercy Kannon Bodhisattva, is a new priest at Kyoto's Kodaiji temple.

Mindar is capable of presenting a sermon on the popular Heart Sutra ("form is empty, emptiness is form") while moving arms, head, and torso, and costs a million dollars.

Robot partners are accepted because they are among the things thought to be endowed with kami, the spirit or divinity shared by the gods, nature, objects, and people in the Shinto faith.

In Japan, Shinto priests are still periodically summoned to consecrate or bless new and abandoned electronic equipment.

The Kanda-Myokin Shrine, which overlooks Tokyo's Akihabara electronics retail area, provides prayer, rituals, and talismans aimed at purifying or conferring heavenly protection on items like smart phones, computer operating systems, and hard drives.

Americans, on the other hand, are just now starting to grapple with issues of robot identity and spirituality.

This is partly due to the fact that America's leading faiths have their roots in Christian rites and practices, which have traditionally been adverse to science and technology.

However, the histories of Christianity and robotics are intertwined.

In the 1560s, Philip II of Spain, for example, commissioned the first mechanical monk.

Mechanical automata, according to Stanford University historian Jessica Riskin (2010), are uniquely Catholic in origin.

They allowed for computerized reenactments of biblical tales in churches and cathedrals, as well as artificial equivalents of real humans and celestial entities like as angels for study and contemplation.

They also aided Renaissance and early modern Christian thinkers and theologians in contemplating conceptions of motion, life, and the incorporeal soul.

By the middle of the eighteenth century, "There was no dichotomy between machinery and divinity or vitality in the culture of living machinery that surrounded these machines," Riskin writes.

"On the contrary, the automata symbolized spirit in all of its bodily manifestations, as well as life at its most vibrant" (Riskin 2010, 43).

That spirit is still alive and well today.

SanTO, described as a robot with "divine qualities" and "the first Catholic robot," was unveiled at a conference of the Institute of Electrical and Electronics Engineers in New Delhi in 2019. (Trovato et al. 2019).

In reformist churches, robots are also present.

To commemorate the 500th anniversary of the Reformation, the Protestant churches of Hesse and Nassau unveiled the interactive, multilingual BlessU-2 robot in 2017.

The robot, as its name indicates, selects specific blessings for particular attendees.

The Massachusetts Institute of Technology's God and Computers Project intended to establish a conversation between academics developing artificial intelligence and religious experts.

She characterized herself as a "theological counselor" to MIT's Humanoid Robotics Group's emotional AI experimental robots Cog and Kismet.

Foerst concluded that embodied AI becomes engaged in the divine image of God, develops human capabilities and emotional sociability, and shares equal dignity as a creature in the universe via exercises in machine-man connection, intersubjectivity, and ambiguity.

"Victor Frankenstein and his creation may now be pals." 

Frankenstein will be able to accept that his creation, which he saw as a machine and an objective entity, had evolved into a human person" (Foerst 1996, 692).

Deep existential concerns about Christian thinking and conduct are being raised by robots and artificial intelligence.

Since the 1980s, according to theologian Michael DeLashmutt of the Episcopal Church's General Theological Seminary, "proliferating digital technologies have given birth to a cultural mythology that presents a rival theological paradigm to the one presented by kerygmatic Christian theology" (DeLashmutt 2006, i).

DeLashmutt opposes techno-theology for two reasons.

First, technology is not inherently immutable, and as such, it should not be reified or given autonomy, but rather examined.

Second, information technology isn't the most reliable tool for comprehending the world and ourselves.

In the United States, smart robots are often considered as harbingers of economic disruption, AI domination, and even doomsday.

Several times, Pope Francis has brought up the subject of artificial intelligence ethics.

He discussed the matter with Microsoft President Brad Smith in 2019.

The Vatican and Microsoft have teamed together to award a prize for the finest PhD dissertation on AI for social benefit.

In 2014, creationist academics at Matthews, North Carolina's Southern Evangelical Seminary & Bible College bought an Aldebaran Nao humanoid robot to much fanfare.

The seminarians wanted to learn about self-driving cars and think about the ethics of new intelligent technology in the perspective of Christian theology.

The Ethics and Religious Liberty Commission of the Southern Baptist Convention produced the study "Artificial Intelligence: An Evangelical Statement of Principles" in 2019, rejecting any AI's intrinsic "identity, value, dignity, or moral agency" (Southern Baptist Convention 2019).

Jim Daly of Focus on the Family, Mark Galli of Christianity Today, and theologians Wayne Grudem and Richard Mouw were among the signatories.

Some evangelicals argue that transhumanist ideas regarding humanity's perfectibility via technology are incompatible with faith in Jesus Christ's perfection.

The Christian Transhumanist Association and the Mormon Transhumanist Association both oppose this viewpoint.

Both organizations acknowledge that science, technology, and Christian fellowship all contribute to affirming and exalting humanity as beings created in the image of God.

Robert Geraci, a religious studies professor at Manhattan College, wonders if people "could really think that robots are aware if none of them exercise any religion" (Geraci 2007).

He observes that in the United States, Christian sentiment favors virtual, immaterial artificial intelligence software over materialist robot bodies.

He compares Christian faith in the immortality of the soul to transhumanists' desire for entire brain emulation or mind uploading into a computer.

Mind, according to neuroscientists, is an emergent characteristic of the human brain's 86 billion neurons' networking.

Christian longing for transcendence have similarities to this intellectual construct.

Artificial intelligence's eschatology also contains a concept of freedom from death or agony; in this instance, the afterlife is cyberspatial.

New faiths, at least in part inspired by artificial intelligence, are gaining popularity.

The Church of Perpetual Life, based in Hollywood, Florida, is a transhumanist worship institution dedicated to the advancement of life-extension technology.

Cryonics pioneers Saul Kent and Bill Faloon launched the church in 2013.

Artificial intelligence serial entrepreneur Peter Voss and Transhumanist Party presidential candidate Zoltan Istvan are among the professionals in artificial intelligence and transhumanism who have visited the center.

Martine and Gabriel Rothblatt formed the Terasem Movement, a religion related with cryonics and transhumanism.

"Life is intentional, death is voluntary, god is technical, and love is fundamental," the faith's basic doctrines state (Truths of Terasem 2012).

The realistic Bina48 robot, created by Hanson Robotics and modeled after Martine's husband, is in part a demonstration of Terasem's mindfile-based algorithm, which Terasem believes could one day allow legitimate mind uploading into an artificial substrate (and maybe even bring about everlasting life).

Heaven, according to Gabriel Rothblatt, is similar to a virtual reality simulation.

Anthony Levandowski, an engineer who oversaw the teams that produced Google and Uber's self-driving vehicles, launched The Way of the Future, an AI-based religion.

Levandowski is driven by a desire to build a superintelligent, artificial god with Christian morals.

"If anything becomes much, much smarter in the future," he continues, "there will be a changeover as to who is truly in command." 

"What we want is for the planet's control to pass peacefully and peacefully from people to whoever." 

And to make sure that 'whatever' understands who assisted it in getting along" (Harris 2017).

He is driven to ensure that artificial intelligences have legal rights and are fully integrated into human society.

Spiritual robots have become a popular science fiction motif.

Cutie (QT-1) convinces other robots that human people are too mediocre to be their creators in Isaac Asimov's short tale "Reason" (1941).

Instead, Cutie (QT-1) encourages them to worship the power plant on their space station, calling it the Master of both machines and mankind.

The Mission for Saint Aquin (1951), by Anthony Boucher, is a postapocalyptic novelette that pays tribute to Asimov's "Reason."

It follows a priest called Thomas on a postapocalyptic quest to find the famous evangelist Saint Aquin's last resting place (Boucher patterns Saint Aquin after St. Thomas Aquinas, who used Aristotelian logic to prove the existence of God).

Saint Aquin's corpse is said to have never decayed.

The priest rides a robass (robot donkey) with artificial intelligence; the robass is an atheist and tempter who can engage in theological debate with the priest.

When Saint Aquin is finally discovered after many trials, he is revealed to be an incorruptible android theologian.

Thomas is certain of the accomplishment of his quest—he has discovered a robot with a logical brain that, although manufactured by a human, believes in God.

In Stanislaw Lem’s novella “Trurl and the Construction of Happy Worlds” (1965), a box-dwelling robot race created by a robot engineer is persuaded that their habitat is a paradise to which all other creatures should aspire.

The robots form a religion and begin making preparations to drill a hole in the box in order to bring everyone outside the box into their paradise, willingly or unwillingly.

The constructor of the robots is enraged by this idea, and he destroys them.

Clifford D. Simak, a science fiction grandmaster, is also known for his spiritual robots.

Hezekiel is a robot abbot who leads a Christian congregation of other robots in a monastery in A Choice of Gods (1972).

The group has received a communication from The Principle, a god-like creature, although Hezekiel believes that "God must always be a pleasant old (human) gentleman with a long, white, flowing beard" (Simak 1972, 158).

The robot monks in Project Pope (1981) are on the lookout for paradise and the meaning of the cosmos.

John, a mechanical gardener, tells the Pope that he believes he has a soul.

The Pope, on the other hand, is not so sure.

Because humans refuse to let robots to their churches, the robots establish their own Vatican-17 on a faraway planet.

A massive computer serves as the Pope of the Robots.

Androids idolize their creator Simeon Krug in Robert Silverberg's Hugo-nominated novel Tower of Glass (1970), hoping that he would one day free them from harsh slavery.

They leave faith and rebel when they learn Krug is uninterested in their freedom.

Silverberg's Nebula award-winning short story "Good News from the Vatican" (1971) is about an artificially intelligent robot who is elected Pope Sixtus the Seventh as a compromise candidate.

"If he's elected," Rabbi Mueller continues, "he wants an instant time-sharing arrangement with the Dalai Lama, as well as a reciprocal plug-in with the chief programmer of the Greek Orthodox church, just to start" (Silverberg 1976, 269).

Television shows often include spiritual robots.

In the British science fiction comedy Red Dwarf (1988–1999), sentient computers are equipped with belief chips, which convince them of the existence of silicon paradise.

At the animated television series Futurama (1999–2003, 2008–2013), robots worship in the Temple of Robotology, where Reverend Lionel Preacherbot delivers sermons.

The artificial Cylons are monotheists in the popular reboot and reinterpretation of the Battlestar Galactica television series (2003–2009), whereas the humans of the Twelve Colonies are polytheists.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Foerst, Anne; Nonhuman Rights and Personhood; Robot Ethics; Technological Singularity.

References & Further Reading:

DeLashmutt, Michael W. 2006. “Sketches Towards a Theology of Technology: Theological Confession in a Technological Age.” Ph.D. diss., University of Glasgow.

Foerst, Anne. 1996. “Artificial Intelligence: Walking the Boundary.” Zygon 31, no. 4: 681–93.

Geraci, Robert M. 2007. “Religion for the Robots.” Sightings, June 14, 2007. https://web.archive.org/web/20100610170048/http://divinity.uchicago.edu/martycenter/publications/sightings/archive_2007/0614.shtml.

Harris, Mark. 2017. “Inside the First Church of Artificial Intelligence.” Wired, November 15, 2017. https://www.wired.com/story/anthony-levandowski-artificial-intelligence-religion/.

Riskin, Jessica. 2010. “Machines in the Garden.” Arcade: A Digital Salon 1, no. 2 (April 30): 16–43.

Silverberg, Robert. 1970. Tower of Glass. New York: Charles Scribner’s Sons.

Simak, Clifford D. 1972. A Choice of Gods. New York: Ballantine.

Southern Baptist Convention. Ethics and Religious Liberty Commission. 2019. “Artificial Intelligence: An Evangelical Statement of Principles.” https://erlc.com/resource-library/statements/artificial-intelligence-an-evangelical-statement-of-principles/

Trovato, Gabriele, Franco Pariasca, Renzo Ramirez, Javier Cerna, Vadim Reutskiy, Laureano Rodriguez, and Francisco Cuellar. 2019. “Communicating with SanTO: The First Catholic Robot.” In 28th IEEE International Conference on Robot and Human Interactive Communication, 1–6. New Delhi, India, October 14–18.

Truths of Terasem. 2012. https://terasemfaith.net/beliefs/.