(1) Generalizable
(2) Marches or exceeds top human experts
(3) Can invent knowledge
Schmidt, February 26, 2025, Mr. Schmidt was CEO of Google, (2001-11) and executive chairman of Google and its successor, Alphabet Inc. (2011-17), Wall Street Journal, AI Could Usher In a New Renaissance, https://www.wsj.com/opinion/agi-could-usher-in-a-new-renaissance-physics-math-econ-advancement-ed71a02a?mod=Searchresults_pos1&page=1
Schmidt, February 26, 2025, Mr. Schmidt was CEO of Google, (2001-11) and executive chairman of Google and its successor, Alphabet Inc. (2011-17), Wall Street Journal, AI Could Usher In a New Renaissance, https://www.wsj.com/opinion/agi-could-usher-in-a-new-renaissance-physics-math-econ-advancement-ed71a02a?mod=Searchresults_pos1&page=1
The idea of artificial general intelligence captivated thinkers for decades before it came anywhere near being realized. The concept still conjures popular visions out of science fiction, from C-3PO to Skynet.
Even as the interest has grown, AGI has defied a concise, universally accepted definition. In 1950, Alan Turing proposed the Turing Test to assess machine intelligence. Rather than trying to determine whether machines truly think (a question he deemed intractable), Turing focused on behavior: Could a machine’s actions be indistinguishable from those of a human?
Remarkably, some of today’s AI models pass the Turing Test, in the sense that they produce complex responses that imitate human intelligence. But as the technology has advanced, so has the bar for achieving AGI. Some believe that AGI will be realized when AI moves beyond narrow, focused tasks, growing to possess a generalized ability to understand, learn and perform any intellectual task a human can do. Others define AGI more ambitiously, as intelligence that matches or exceeds the top human minds across domains. Demis Hassabis, CEO of DeepMind Technologies, calls AGI-level reasoning the ability to invent relativity with only the knowledge that Einstein had at the time.
These differing definitions create a moving target for AGI, making it both elusive and tantalizing. To sort through all this, it’s helpful to say what AGI isn’t. It isn’t an infallible intelligence; like other intelligent systems, mistakes can be useful for its learning process. Neither is AGI a singular source of truth—our knowledge of the world is probabilistic and complex, notably at subatomic and intergalactic scales, but also in everyday life. Multiple AGI systems could emerge, each with a distinct capability and way of understanding the world.
Even without a consensus about a precise definition, the contours of an AGI future are beginning to take shape. AI systems capable of performing at the intellectual level of the world’s top scientists are arriving soon—likely by the end of the decade.
A key marker of the shift to AGI will be AI’s ability to produce knowledge based on its own findings, not merely retrieval and recombination of human-generated information. AGI will then move beyond the current limits of knowledge. Glimpses of this capability have already been observed. Since 2020, DeepMind’s AlphaFold can predict protein structures even when no similar structures are previously known. DeepMind also created FunSearch, which in 2023 unveiled new solutions to the cap-set problem, a notoriously difficult mathematics puzzle, by incorporating the power of a large language model with an evaluator, iterating between these components to refine results.
The latest reasoning models from OpenAI and DeepSeek build on this iterative training and are unleashing incredible progress. OpenAI’s o3 model achieved a score of 96.7% on the 2024 American Invitational Mathematics Exam. On the ARC-AGI test (designed to compare models’ reasoning against that of humans) it scored nearly 88%. This is no incremental advancement but a real leap toward AGI.
AGI has all the cognitive capabilities of a person and can come up with new theories
Demis Hassabis, Google Deep Mind, Do we NEED International Collaboration for Safe AGI? Insights from Top AI Pioneers | IIA Davos 2025, https://www.youtube.com/watch?v=U7t02Q6zfdc&t=34s
The way we Define AGI and actually was a term coined by my one of my So-founders Shane leg our chief scientist artificial general intelligence we sort of Define it as a system that uh exhibits all the cognitive capabilities that humans have uh and the reason that’s important is because that’s the only way we’ll know whether the system’s truly General if you want to do a more technical definition of tha then um you know Alan Tobviously famously conjectured and proposed his churing machines and proved that they could compute anything that was computable so what we’re after is a system that is basically a touring machine and as far as we understand it the brain is a kind of touring machine so if we can um mimic and map out the capabilities that the brain has in an artificial system then we probably have a truly uh General system good and how would you distinguish between AGI and super intelligence well AGI um I might my kind of test for AGI would be um could it come up with something like a not just prove a conjecture or prove a theory or prove a formula but could it come up with a theory or a conjecture itself or so for example could it come up with general relativity like Einstein did um with the same knowledge that Einstein had at the time or for example we have systems like alphago that can play amazingly creative moves like famously Mo 37 in game two of the Lisa doll match um which change you know never been seen before in but could it have invented go a game as elegant or as beautiful as go and so far none of our systems can do that level of invention so that’s still missing from AGI and then Super intelligence would be superiorto uh human intelligence across
AGI can generalize and switch between tasks
Singularity.net, June 15, 204, SingularityNET was founded by Dr. Ben Goertzel with the mission of creating a decentralized, democratic, inclusive, and beneficial Artificial General Intelligence (AGI). An AGI is not dependent on any central entity, is open to anyone, and is not restricted to the narrow goals of a single corporation or even a single country. The SingularityNET team includes seasoned engineers, scientists, researchers, entrepreneurs, and marketers. Our core platform and AI teams are further complemented by specialized teams devoted to application areas such as finance, robotics, biomedical AI, media, arts, and entertainment, A Deep Dive on the Differences Between Narrow AI and AGI
Artificial General Intelligence (AGI), also known as Strong AI, is a (so far) theoretical form of AI that possesses the cognitive capabilities of a human being, and/or can display intelligence that is not tied to a highly specific set of tasks. It will be able to generalize what it has learned, (including generalization to contexts qualitatively), take a broad view, and flexibly interpret its tasks at hand in the context of the world at large and its relation thereto.
AGI would be able to understand, learn, and apply knowledge across a wide range of tasks, exhibiting flexibility and adaptability similar to human intelligence. It would demonstrate autonomous learning, reasoning, problem-solving abilities, and an understanding of context and transfer knowledge from one area to another.
While significant progress has been made in developing Narrow AI, achieving AGI poses immense technical and ethical challenges. Companies and researchers at the forefront of developing AGI, such as those at SingularityNET, are still grappling with fundamental questions about how to replicate the full spectrum of human cognition in machines.
The Fundamental Differences Between Narrow AI and AGI
The primary distinction between Narrow AI and AGI lies in their scope, generality, and versatility.
Narrow AI is highly specialized and limited to specific tasks. For instance, an AI trained for image recognition cannot perform natural language processing tasks without retraining. But an AGI would be able to — it would exhibit broad versatility, capable of performing any intellectual task that a human can do; and do it better. AGI will be able to seamlessly switch between tasks and apply knowledge from one area to another.
In terms of learning and adaptability, Narrow AI relies on supervised learning and large datasets to perform tasks. It requires extensive training and often needs retraining for new tasks or changes in its environment. AGI, however, would be capable of autonomous learning and adaptation. It will learn from minimal data, understand new concepts quickly, and adapt to unfamiliar situations without the need to be extensively retrained.
When it comes to understanding and reasoning, Narrow AI operates based on predefined rules and patterns. It lacks true understanding and cannot reason beyond its programmed parameters. AGI, on the other hand, would possess human-like understanding and reasoning abilities. AGI will be able to comprehend complex concepts, make judgments, and reason logically across different contexts.
The ability to transfer knowledge is another important difference we can’t overlook when defining the two forms of AI. Narrow AI is limited in its ability to transfer knowledge between tasks. Each new task often requires separate training and optimization. AGI, however, would be capable of transfer learning, where knowledge gained from one task can be applied to others. This ability makes AGI infinitely more efficient and adaptable.
Narrow AI is widely implemented in the world around us. It’s becoming an increasingly trivial part of our lives and it continues to evolve, driving innovation and efficiency across various industries.
AGI is focused on engineering general intelligence
Goertzel, 2006, Artificial General Intelligence Ben Goertzel & Cassio Pennachin (eds.)
“Only a small community has concentratedon general intelligence. No one has tried to make a thinking machine… The bottom line is that we really haven’t progressed too far toward a truly intelligent machine. We have collections of dumb specialists in small domains; the true majesty of general intelligence still awaits our attack…. We have got to get back to the deepest questions of AI and general intelligence… ” –MarvinMinsky as interviewed in Hal’s Legacy, edited by David Stork, 2000. Our goal in creating this edited volume has been to?ll an apparent gap in the scienti?c literature, by providing a coherent presentation of a body of contemporary research that, in spite of its integral importance, has hitherto kept a very low pro?le within the scienti?c and intellectual community. This body of work has not been given a name before; in this book we christen it “Arti?cial General Intelligence”. What distinguishes AGI work from run-of-the-mill “arti?cial intelligence” research is that it is explicitly focused on engineering general intelligence in the short term. We have been active researchers in the AGI?eld for many years, and it has been a pleasure to gather together papers from our colleagues working on related ideas from their own perspectives. In the Introduction we give a conceptual overview of the AGI?eld, and also summarize and interrelate the key ideas of the papers in the subsequent chapters.
Flexible and general, that is as resourceful and reliable as a human
Gary Marcus, famous AI scientist, https://x.com/GaryMarcus/status/1529457162811936768
Personally, I use it as a shorthand for any intelligence (there might be many) that is flexible and general, with resourcefulness and reliability comparable to (or beyond) human intelligence.
AGI uses reasoning and can learn from human experience
Sebastian Bubeck, Princeton and Now Microsoft, et al, 2023, Sparks of Artificial General Intelligence: Early experiments with GPT-4, https://arxiv.org/pdf/2303.12712
We use AGI to refer to systems that demonstrate broad capabilities of intelligence, including reasoning, planning, and the ability to learn from experience, and with these capabilities at or above human-level. We discuss other definitions of AGI in the conclusion section.
AGI can do any cognitive task a human can do
Science Friday, January 10, 2025, https://www.sciencefriday.com/segments/what-is-agi/#:~:text=Google%20DeepMind%20cofounder%20Dr,matter%20much%20less%20than%20people
Google DeepMind cofounder Dr. Demis Hassabis has described AGI as a system that “should be able to do pretty much any cognitive task that humans can do.”
AGI is autonomous systems that can perform economically valuable work
OpenAI, Charter, https://openai.com/charter/
OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.
Ability to learn anything through self-directed learning
Peter Voss, August 22, 2022, https://www.researchgate.net/publication/225991858_Essentials_of_General_Intelligence_The_Direct_Path_to_Artificial_General_Intelligence
General intelligence comprises the essential, domain-independent skills necessary for acquiring a wide range of domain-specific knowledge — the ability to learn anything. Achieving this with “artificial general intelligence” (AGI) requires a highly adaptive, general-purpose system that can autonomously acquire an extremely wide range of specific knowledge and skills and can improve its own cognitive ability through self-directed learning.
…
Peter Voss, AI Researcher, August 22, 2022, Essentials of General Intelligence, https://www.researchgate.net/publication/225991858_Essentials_of_General_Intelligence_The_Direct_Path_to_Artificial_General_Intelligence
Intelligence can be defined simply as an entity’s ability to achieve goals – with greater intelligence coping with more complex and novel situations. Complexity ranges from the trivial – thermostats and mollusks (that in most contexts don’t even justify the label ‘intelligence’) – to the fantastically complex; autonomous flight control systems and humans. Adaptivity, the ability to deal with changing and novel requirements, also covers a wide spectrum: from rigid, narrowly domain-specific to highly flexible, general purpose. Furthermore, flexibility can be defined in terms of scope and permanence – how much, and how often it changes. Imprinting is an example of limited scope and high permanence, while innovative, abstract problem solving is at the other end of the spectrum. While entities with high adaptivity and flexibility are clearly superior – they can potentially learn to achieve any possible goal – there is a hefty efficiency price to be paid: For example, had Deep Blue also been designed to learn language, direct airline traffic, and do medical diagnosis, it would not have become Chess champion (all other things being equal). buu.
More specifically, this learning ability needs to be autonomous, goal-directed, and highly adaptive:
- Autonomous — Learning occurs both automatically, through exposure to sense data (unsupervised), and through bi-directional interaction with the environment, including exploration/ experimentation (self-supervised).
- Goal-directed – Learning is directed (autonomously) towards achieving varying and novel goals and sub-goals — be they ‘hard-wired’, externally specified, or self-generated. Goal-directedness also implies very selective learning and data acquisition (from a massively data-rich, noisy, complex environment).
- Adaptive – Learning is cumulative, integrative, contextual and adjusts to changing goals and environments. General adaptivity not only copes with gradual changes, but also seeds and facilitates the acquisition of totally novel abilities. General cognitive ability stands in sharp contrast to inherent specializations such as speech- or face-recognition, knowledge databases/ ontologies, expert systems, or search, regression or optimization algorithms. It allows an entity to acquire a virtually unlimited range of new specialized abilities. The mark of a generally intelligent system is not having a lot of knowledge and skills, but being able to acquire and improve them – and to be able to appropriately apply them. Furthermore, knowledge must be acquired and stored in ways appropriate both to the nature of the data, and to the goals and tasks at hand. For example, given the correct set of basic core capabilities, an AGI system should be able to learn to recognize and categorize a wide range of novel perceptual patterns that are acquired via different senses, in many different environments and contexts. Additionally, it should be able to autonomously learn appropriate, goal-directed responses to such input contexts (given some feedback mechanism). We take this concept to be valid not only for high-level human intelligence, but for lower-level animal-like ability. The degree of ‘generality’ (i.e., adaptability) varies along a continuum from genetically ‘hard-coded’ responses (no adaptability), to high-level animal flexibility (significant learning ability as in, say, a dog), and finally to self-aware human general learning ability
Human-level reasoning
Transfer learning
Adaptability
Self-improvvement
General problem solving
Kruthika, December 19, 2024, https://www.f22labs.com/blogs/what-is-agi-artificial-general-intelligence/, What is AGI (Artificial General Intelligence)?
Narrow AI, or weak AI, is designed with a specific function in mind. It’s trained to handle a particular task or set of tasks, based on a fixed dataset, and it operates within strict boundaries.
Virtual assistants like Siri, Alexa, and Google Assistant, for example, are all forms of narrow AI that can understand and respond to certain commands, but their abilities are limited. AGI, is designed to perform any intellectual task that a human could tackle without needing to be explicitly trained for each one.
Understanding AGI
To grasp what AGI truly means, it’s helpful to look at its defining qualities and see how they set it apart from the AI systems we currently use.
Core Characteristics of AGI
AGI is envisioned to have several distinctive traits that make it fundamentally different from narrow AI:
- Human-Level Reasoning: AGI would be able to think, reason, and make decisions much like a human. This means using logic, intuition, and experience to approach complex situations in a nuanced way.
- Transfer Learning: AGI would be able to take what it learns in one area and apply it in another. Just as humans might use their knowledge of math to learn physics, AGI could bridge its understanding across different topics.
- Adaptability Across Domains: One of AGI’s key strengths would be its ability to adapt to new environments, tasks, or challenges without needing constant reprogramming or retraining.
- Self-Improvement: AGI would be capable of learning from its own experiences and refining its performance over time. Like humans, it would learn from its mistakes and make adjustments, allowing it to get better with practice.
- General Problem-Solving: AGI would be equipped to tackle open-ended, complex problems requiring creativity, critical thinking, and practical knowledge, rather than being limited to narrowly defined tasks.
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Matching or exceeding human performance across tasks
Apply knowledge to new problems
Transfer new knowledge to another domain
Nanyi Fei at all, 2017, Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China, https://pmc.ncbi.nlm.nih.gov/articles/PMC9163040/, Towards artificial general intelligence via a multimodal foundation model
Despite not being precisely defined, AGI is broadly agreed to have several key features1 including: (1) matching or exceeding human performance across a broad class of cognitive tasks (e.g., perception, reading comprehension, and reasoning) in a variety of contexts and environments; (2) possessing the ability to handle problems quite different from those anticipated by its creators; and (3) being able to generalize/transfer the learned knowledge from one context to others.
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Self-teach and complete tasks it has not been trained for
Amazon Web Services, What is Artificial General Intelligence?, https://aws.amazon.com/what-is/artificial-general-intelligence/#:~:text=and%20AGI%20efforts%3F-,What%20is%20artificial%20general%20intelligence%3F,necessarily%20trained%20or%20developed%20for.
“Artificial general intelligence (AGI) is a field of theoretical AI research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to be able to perform tasks that it is not necessarily trained or developed for.”
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- Learn anything a human can
- Generalizable
- Common sense knowledge
Google Cloud, https://cloud.google.com/discover/what-is-artificial-general-intelligence
Artificial general intelligence (AGI) refers to the hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can. It is a type of artificial intelligence (AI) that aims to mimic the cognitive abilities of the human brain.
In addition to the core characteristics mentioned earlier, AGI systems also possess certain key traits that distinguish them from other types of AI:
Generalization ability: AGI can transfer knowledge and skills learned in one domain to another, enabling it to adapt to new and unseen situations effectively.
Common sense knowledge: AGI has a vast repository of knowledge about the world, including facts, relationships, and social norms, allowing it to reason and make decisions based on this common understanding.
Match or exceed human cognitive abilities at any task
IBM, What is artificial general intelligence (AGI)?, https://www.ibm.com/think/topics/artificial-general-intelligence, Dave Bergmann Senior Writer, AI Models, IBM, Editorial Lead, AI Models
Artificial general intelligence (AGI) is a hypothetical stage in the development of machine learning (ML) in which an artificial intelligence (AI) system can match or exceed the cognitive abilities of human beings across any task. It represents the fundamental, abstract goal of AI development: the artificial replication of human intelligence in a machine or software.
Solve problems in a non-domain restricted way
IBM, What is artificial general intelligence (AGI)?, https://www.ibm.com/think/topics/artificial-general-intelligence, Dave Bergmann Senior Writer, AI Models, IBM, Editorial Lead, AI Models
In 2007, AI researcher Ben Goertzel popularized the term “artificial general intelligence” (AGI), at the suggestion of DeepMind cofounder Shane Legg, in an influential book of the same name (link resides outside ibm.com). In contrast to what he dubbed “narrow AI,” an artificial general intelligence would be a new type of AI with, among other qualities, “the ability to solve general problems in a non-domain-restricted way, in the same sense that a human can.”
AGI includes consciousness
IBM, What is artificial general intelligence (AGI)?, https://www.ibm.com/think/topics/artificial-general-intelligence, Dave Bergmann Senior Writer, AI Models, IBM, Editorial Lead, AI Models
“Strong AI,” a concept discussed prominently in the work of philosopher John Searle, refers to an AI system demonstrating consciousness and serves mostly as a counterpoint to weak AI. While strong AI is generally analogous to AGI (and weak AI is generally analogous to narrow AI), they are not mere synonyms of one another.
In essence, whereas weak AI is simply a tool to be used by a conscious mind—that is, a human being—strong AI is itself a conscious mind. Though it is typically implied that this consciousness would entail a corresponding intelligence equal or superior to that of human beings, strong AI is not explicitly concerned with relative performance on various tasks. The two concepts are often conflated because consciousness is usually taken to be either a prerequisite or a consequence of “general intelligence.”
AGI requires consciousness
Coursera, What is Artificial General Intelligence? https://www.coursera.org/articles/what-is-artificial-general-intelligence
Artificial general intelligence (AGI) is not yet real–it’s a hypothetical form of artificial intelligence (AI) where a machine learns and thinks like a human does. Ultimately, it would blur the lines between human and machine. Programming AGI requires the machine to develop a kind of consciousness and self-awareness that has started to appear in innovations like self-driving cars that adapt to roads and passing trucks.
AGI can accomplish any task humans can
Coursera, What is Artificial General Intelligence? https://www.coursera.org/articles/what-is-artificial-general-intelligence
Artificial general intelligence is a hypothetical type of intelligent agent that has the potential to accomplish any intellectual task that humans can. In some cases, it outperforms human capabilities in ways beneficial to researchers and companies.
AGI systems can act intelligently without human intervention
Coursera, What is Artificial General Intelligence? https://www.coursera.org/articles/what-is-artificial-general-intelligence
Artificial general intelligence (AGI) is theoretical, even though it is in the midst of being produced and launched, and it should be able to perform a range of intelligence without human intervention–at a human level or surpassing it to solve problems.
Ability to gain complete knowledge
Ability to think abstractly
Ability to have common sense
Ability to understand causation
Coursera, What is Artificial General Intelligence? https://www.coursera.org/articles/what-is-artificial-general-intelligence
AGI is essentially AI that has cognitive computing capability and the ability to gain complete knowledge of multiple subjects the way human brains can. It does not currently exist; it is simply in the process that’s being researched and experimented with. If it were able to surpass human capabilities, AGI could process data sets at speeds beyond what AI is currently capable of. Some of these could include:
The ability to think abstractly
Gathering and drawing from background knowledge of multiple subjects
Common sense and consciousness
Causation–a thorough understanding of cause and effect
In practice, this could include capabilities that humans have that AI does not, such as sensory perception. AGI could recognize colors and depth. Along with this are fine motor skills, like how a human reaches into their pocket to take out a wallet or cook a meal without burning their fingers on the stove. AGI could also develop creativity: Rather than generating a Renaissance painting of a cat, it could think of an idea to paint several cats wearing the clothing styles of each ethnic group in China to represent diversity.
More than just a creative mind, painting cats wearing different Chinese dress patterns requires an understanding of different cultures, symbols, and belief systems. AGI systems would need to handle the subtle nuances of each ethnic group and create a new structure for this task using multiple algorithms at once.
Novel problem solving and reasoning
United Nations, Artificial General Intelligence Issues and Opportunities, https://www.un.org/digital-emerging-technologies/sites/www.un.org.techenvoy/files/GDC-submission_the-millennium-project.pdf
Artificial General Intelligence (AGI) sometimes called strong AI is similar to human capacity for novel problem-solving and reasoning whose goals are set by humans. It can: address complex problems without pre-programing like ANI requires; initiate searches for information worldwide; use sensors and the Internet of Things (IoT) to learn; make phone calls and interview people; make logical deductions; re-write or edit its code to be more intelligent …continually, so it gets smarter and smarter, faster and faster than humans.
AI that can reason across a wide variety of domains
Global Catastrophic Risks Institute, 2020, https://gcrinstitute.org/papers/055_agi-2020.pdf
Artificial general intelligence (AGI) is artificial intelligence (AI) that can reason across a wide range of domains. While most AI research and development (R&D) deals with narrow AI, not AGI, there is some dedicated AGI R&D.
AGI can think, reason, plan
AGI has self control
The Decision Lab, https://thedecisionlab.com/reference-guide/computer-science/artificial-general-intelligence
The Basic Idea Imagine a world where humans and computers are indistinguishable. Believe it or not, we might not be too far off from this futuristic reality.
Artificial general intelligence (AGI), also called strong AI, is a hypothetical type of artificial intelligence (AI) with human-like cognitive abilities. Unlike the AI systems we have today, AGI would be able to think, reason, and learn as well as or even better than humans. This level of human-like intelligence assumes that AGI would have a sense of self-control, self-understanding, and an ability to learn new skills on its own, similar to human consciousness. Some experts even believe that AGI programs would be conscious or sentient.
AGI has capabilities of a human and can replicate human cognitive functions
McKinsey, March 21, 2024, What is artificial general intelligence (AGI)?, https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-artificial-general-intelligence-agi
AGI is AI with capabilities that rival those of a human. While purely theoretical at this stage, someday AGI may replicate human-like cognitive abilities including reasoning, problem solving, perception, learning, and language comprehension. When AI’s abilities are indistinguishable from those of a human, it will have passed what is known as the Turing test, first proposed by 20th-century computer scientist Alan Turing.
AGI matches or surpasses human abilities
Wikipedia, Artificial general intelligence, https://en.wikipedia.org/wiki/Artificial_general_intelligence
Artificial general intelligence (AGI) is a type of artificial intelligence (AI) that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks.
AI that can perform any task and replicate human reasoning
Institute of Data, October 6, 2023, Exploring the Differences Between Narrow AI, General AI, and Superintelligent AI, https://www.institutedata.com/us/blog/exploring-the-differences-between-narrow-ai-general-ai-and-superintelligent-ai/
General AI, also known as strong AI or human-level AI, represents the concept of machines that possess the ability to understand, learn, and perform any intellectual task that a human can do. General AI aims to replicate human-level intelligence and reasoning
AGI is human level intelligence, can perform any task humans can
Rosemary Kasiobi Nwadike in Tech — Aug 7, 2024, https://blog.obiex.finance/agi-vs-narrow-ai-key-differences/
General AI, or Artificial General Intelligence (AGI), refers to machines that possess human-level intelligence and can perform any intellectual task that a human can do. This type of AI is still theoretical and is the focus of much research and debate.
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AGI is the ability to learn and apply it to new tasks
Geeks for Geeks, January 4, 2025, Difference between AGI vs AI, https://www.geeksforgeeks.org/difference-between-agi-vs-ai/
Artificial General Intelligence (AGI) is the hypothetical type of AI that would have an understanding and ability to learn with the knowledge and apply it to the wide range of tasks much like the cognitive ability in humans.
Problem Solving: An AGI would look at complicated data sets trying to understand the trends and making predictions. A data analyst does this work. Learning and Adaptation: Even if the AGI discovers a new type of software or tool it may use it to its advantage once it has learned how to use it without needing specific programming for the tool.
Interpersonal Skills: An AGI can interact with customers in the same way as a human representative by using natural language processing to understand inquiries and respond accordingly.
| Category | AGI | AI |
| Definition | AGI refers to systems capable of understanding, learning and performing any intellectual task at human-level capability. | AI refers to systems designed to perform specific tasks or solve particular problems efficiently. |
| Type | AGI referred to as strong AI or general AI. | AI referred to as narrow AI or weak AI. |
| Capabilities | Capable of understanding, learning, and applying knowledge like a human. | Performs specific tasks, such as image recognition, chatbots. |
| Current Status | Theoretically developed but not realized in practice. | Highly applied in current applications today (e.g., virtual assistants). |
| Application | AGI Would be able to transform and integrate into almost every aspect of human life. | Aimed to specific industries, such as healthcare, finance, and transportation. |
| Examples | Examples of AGI is hypothetical systems are believed to perform any intellectual task a human being can do. | Examples of AI are IBM Watson, self-driving cars, recommendation systems. |
AGi can generalize, learn, adapt, and reason
Qbotica, Understanding Artificial General Intelligence (AGI): An In-Depth Overview, https://qbotica.com/understanding-artificial-general-intelligence-agi-an-in-depth-overview/
AGI stands out from narrow AI because it has several key capabilities:
Generalization: AGI can apply knowledge from one area to another, similar to how humans do. For example, understanding language nuances in different contexts.
Learning: It can continuously learn from new experiences without requiring explicit reprogramming.
Adaptability: AGI can quickly adjust to unfamiliar environments or tasks.
Reasoning: It’s capable of making informed decisions based on incomplete or ambiguous information.
All the domains in which we are intelligent
John Werner, MIT Senior Fellow, January 10, 2025, More Than One AI Revolution? Yann LeCun On Tech Trajectorieshttps://www.forbes.com/sites/johnwerner/2025/02/11/more-than-one-ai-revolution-yann-lecun-on-tech-trajectories/?ss=ai
I asked LeCun about the prospect of artificial general intelligence, and he said he didn’t like the term. He prefers ‘AMI’ (advanced machine intelligence) and had a pretty bold answer for the clang of voices suggesting that we’re going to get to a place where “AI is smarter than us.” “The idea that somehow intelligence is kind of a linear scale is nonsense,” he said. “Your cat is smarter than you are on certain things, and you’re smarter than it on certain things, A $30 gadget that you can bu, that can beat you at chess, is smarter than you at chess. So…the idea that somehow it’s a linear scale, that at some point it’s going to be an event when we reach AGI, is complete nonsense. It’s going to be progressive.” n the end, he suggested, AI will be “smart” in human ways, but there’s no single tipping point. What he described was much more like the notion of an eventual singularity. “There is no question that at some point in the future, we will have systems that are as intelligent as humans, in pretty much all of the domains where humans are intelligent,” he said.
AGI is achieved when we come up with problems regular people cannot solve
Shane Legg, Google Deep Mind, Francis Cholet, ARC AGI Contest Sponsor, January 10, 2025, https://x.com/ShaneLegg/status/1877674960770007042
Chollet –
Pragmatically, we can say that AGI is reached when it’s no longer easy to come up with problems that regular people can solve (with no prior training) and that are infeasible for AI models. Right now it’s still easy to come up with such problems, so we don’t have AGI
Shane Legg –
Ive been saying this within DeepMind for at least 10 years, with the additional clarification that it’s about cognitive problems that regular people can do. By this criteria we’re not there yet, but I think we might get there in the coming years.