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.

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/.

Artificial Intelligence - How Do Autonomous Vehicles Leverage AI?

Using a virtual driver system, driverless automobiles and trucks, also known as self-driving or autonomous vehicles, are capable of moving through settings with little or no human control.

A virtual driver system is a set of characteristics and capabilities that augment or replicate the actions of an absent driver to the point that, at the maximum degree of autonomy, the driver may not even be present.

Diverse technology uses, restricting circumstances, and categorization methods make reaching an agreement on what defines a driverless car difficult.

A semiautonomous system, in general, is one in which the human performs certain driving functions (such as lane maintaining) while others are performed autonomously (such as acceleration and deceleration).

All driving activities are autonomous only under certain circumstances in a conditionally autonomous system.

All driving duties are automated in a fully autonomous system.

Automobile manufacturers, technology businesses, automotive suppliers, and universities are all testing and developing applications.

Each builder's car or system, as well as the technical road that led to it, demonstrates a diverse range of technological answers to the challenge of developing a virtual driving system.

Ambiguities exist at the level of defining circumstances, so that a same technological system may be characterized in a variety of ways depending on factors such as location, speed, weather, traffic density, human attention, and infrastructure.

When individual driving duties are operationalized for feature development and context plays a role in developing solutions, more complexity is generated (such as connected vehicles, smart cities, and regulatory environment).

Because of this complication, producing driverless cars often necessitates collaboration across several roles and disciplines of study, such as hardware and software engineering, ergonomics, user experience, legal and regulatory, city planning, and ethics.

The development of self-driving automobiles is both a technical and a socio-cultural enterprise.

The distribution of mobility tasks across an array of equipment to collectively perform a variety of activities such as assessing driver intent, sensing the environment, distinguishing objects, mapping and wayfinding, and safety management are among the technical problems of engineering a virtual driver system.

LIDAR, radar, computer vision, global positioning, odometry, and sonar are among the hardware and software components of a virtual driving system.

There are many approaches to solving discrete autonomous movement problems.

With cameras, maps, and sensors, sensing and processing can be centralized in the vehicle, or it can be distributed throughout the environment and across other vehicles, as with intelligent infrastructure and V2X (vehicle to everything) capability.

The burden and scope of this processing—and the scale of the problems to be solved—are closely related to the expected level of human attention and intervention, and as a result, the most frequently referenced classification of driverless capability is formally structured along the lines of human attentional demands and intervention requirements by the Society of Automotive Engineers, and has been adopted in 2 years.

These companies use various levels of driver automation, ranging from Level 0 to Level 5.

Level 0 refers to no automation, which means the human driver is solely responsible for longitudinal and latitudinal control (steering) (acceleration and deceleration).

On Level 0, the human driver is in charge of keeping an eye on the environment and reacting to any unexpected safety hazards.

Automated systems that take control of longitudinal or latitudinal control are classified as Level 1, or driver aid.

The driver is in charge of observation and intervention.

Level 2 denotes partial automation, in which the virtual driver system is in charge of both lateral and longitudinal control.

The human driver is deemed to be in the loop, which means that they are in charge of monitoring the environment and acting in the event of a safety-related emergency.

Level 2 capability has not yet been achieved by commercially available systems.

The monitoring capabilities of the virtual driving system distinguishes Level 3 conditional autonomy from Level 2.

At this stage, the human driver may be disconnected from the surroundings and depend on the autonomous system to keep track of it.

The person is required to react to calls for assistance in a range of situations, such as during severe weather or in construction zones.

A navigation system (e.g., GPS) is not required at this level.

To operate at Level 2 or Level 3, a vehicle does not need a map or a specific destination.

A human driver is not needed to react to a request for intervention at Level 4, often known as high automation.

The virtual driving system is in charge of navigation, locomotion, and monitoring.

When a specific condition cannot be satisfied, such as when a navigation destination is obstructed, it may request that a driver intervene.

If the human driver does not choose to interfere, the system may safely stop or redirect based on the engineering approach.

The classification of this situation is based on standards of safe driving, which are established not only by technical competence and environmental circumstances, but also by legal and regulatory agreements and lawsuit tolerance.

Level 5 autonomy, often known as complete automation, refers to a vehicle that is capable of doing all driving activities in any situation that a human driver could handle.

Although Level 4 and Level 5 systems do not need the presence of a person, they still necessitate substantial technological and social cooperation.

While efforts to construct autonomous vehicles date back to the 1920s, Leonardo Da Vinci is credited with the concept of a self-propelled cart.

In his 1939 New York World's Fair Futurama display, Norman Bel Geddes envisaged a smart metropolis of the future inhabited by self-driving automobiles.

Automobiles, according to Bel Geddes, will be outfitted with "technology that would rectify the mistakes of human drivers" by 1960.

General Motors popularized the concept of smart infrastructure in the 1950s by building a "automated highway" with steering-assist circuits.

In 1960, the business tested a working prototype car, but owing to the expensive expense of infrastructure, it quickly moved its focus from smart cities to smart autos.

A team lead by Sadayuki Tsugawa of Tsukuba Mechanical Engineering Laboratory in Japan created an early prototype of an autonomous car.

Their 1977 vehicle operated under predefined environmental conditions dictated by lateral guiding rails.

The truck used cameras to track the rails, and most of the processing equipment was aboard.

The EUREKA (European Research Organization) pooled money and experience in the 1980s to enhance the state-of-the-art in cameras and processing for autonomous cars.

Simultaneously, Carnegie Mellon University in Pittsburgh, Pennsylvania pooled their resources for research on autonomous navigation utilizing GPS data.

Since then, automakers including General Motors, Tesla, and Ford Motor Company, as well as technology firms like ARGO AI and Waymo, have been working on autonomous cars or critical components.

The technology is becoming less dependent on very limited circumstances and more adaptable to real-world scenarios.

Manufacturers are currently producing Level 4 autonomous test cars, and testing are being undertaken in real-world traffic and weather situations.

Commercially accessible Level 4 self-driving cars are still a long way off.

There are supporters and opponents of autonomous driving.

Supporters point to a number of benefits that address social problems, environmental concerns, efficiency, and safety.

The provision of mobility services and a degree of autonomy to those who do not already have access, such as those with disabilities (e.g., blindness or motor function impairment) or those who are unable to drive, such as the elderly and children, is one such social benefit.

The capacity to decrease fuel economy by managing acceleration and braking has environmental benefits.

Because networked cars may go bumper to bumper and are routed according to traffic optimization algorithms, congestion is expected to be reduced.

Finally, self-driving vehicles have the potential to be safer.

They may be able to handle complicated information more quickly and thoroughly than human drivers, resulting in fewer collisions.

Self-driving car negative repercussions may be included in any of these areas.

In terms of society, driverless cars may limit access to mobility and municipal services.

Autonomous mobility may be heavily regulated, costly, or limited to places that are inaccessible to low-income commuters.

Non-networked or manually operated cars might be kept out of intelligent geo-fenced municipal infrastructure.

Furthermore, if no adult or responsible human party is present during transportation, autonomous automobiles may pose a safety concern for some susceptible passengers, such as children.

Greater convenience may have environmental consequences.

Drivers may sleep or work while driving autonomously, which may have the unintended consequence of extending commutes and worsening traffic congestion.

Another security issue is widespread vehicle hacking, which could bring individual automobiles and trucks, or even a whole city, to a halt. 

~ Jai Krishna Ponnappan

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

See also: 

Accidents and Risk Assessment; Autonomous and Semiautonomous Systems; Autonomy and Complacency; Intelligent Transportation; Trolley Problem.

Further Reading:

Antsaklis, Panos J., Kevin M. Passino, and Shyh J. Wang. 1991. “An Introduction to Autonomous Control Systems.” IEEE Control Systems Magazine 11, no. 4: 5–13.

Bel Geddes, Norman. 1940. Magic Motorways. New York: Random House.

Bimbraw, Keshav. 2015. “Autonomous Cars: Past, Present, and Future—A Review of the Developments in the Last Century, the Present Scenario, and the Expected Future of Autonomous Vehicle Technology.” In ICINCO: 2015—12th International Conference on Informatics in Control, Automation and Robotics, vol. 1, 191–98. Piscataway, NJ: IEEE.

Kröger, Fabian. 2016. “Automated Driving in Its Social, Historical and Cultural Contexts.” In Autonomous Driving, edited by Markus Maurer, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, 41–68. Berlin: Springer.

Lin, Patrick. 2016. “Why Ethics Matters for Autonomous Cars.” In Autonomous Driving, edited by Markus Maurer, J. Christian Gerdes, Barbara Lenz, and Hermann Winner, 69–85. Berlin: Springer.

Weber, Marc. 2014. “Where To? A History of Autonomous Vehicles.” Computer History Museum. https://www.computerhistory.org/atchm/where-to-a-history-of-autonomous-vehicles/.