What is Artificial General Intelligence (AGI)? - Brief Guide

Explore the answer to “What is artificial general intelligence?” and uncover more aspects around it.

Artificial intelligence stands at the forefront of most groundbreaking technological advancements today. However, what if ‘artificial general intelligence’ became a reality? What will be the kind of technological advancements we’ll be witnessing? What will be the technologies used? the questions around it are endless. But, before we get our knees deep into the questions above with this editorial, let’s answer “What is artificial general intelligence?”

Artificial general intelligence (AGI) is the intelligence of machines that allows machines to comprehend, learn, and perform intellectual tasks similar to humans.

AGI allows machines to simulate human thought processes and behavior to tackle challenging issues. Since these machines will be built with extensive knowledge bases and cognitive computing powers, they will operate just like humans.

Elements that Would Make Up Artificial General Intelligence

In contrast to other AI systems, which are made for specialized tasks, artificial general intelligence (AGI) seeks to mimic human problem-solving abilities. Even though AGI is still mostly theoretical, the following describes the kind of functionalities it would require to become a reality:

Types of Artificial General Intelligence - Covering the Entire Spectrum

Unlike specialized AI systems, AGI is not limited to specific narrow tasks. Instead, it is a broad category of different types of AI with sentient capabilities. Below, we have mentioned the types in a table:

Development of Artificial General Intelligence - Investigating Plausible Approaches

For scientists and researchers, achieving artificial general intelligence systems is a long-term goal. In fact, in the search to develop the tech, several fundamental paradigms and approaches have been investigated. Each of these approaches has its own guiding idea and principles. So, let’s explore those approaches in detail.

1. Symbolic AI (Classic AI)

Symbolic AI, also known as classical or rule-based AI, is one of the earliest approaches to AI. It focuses on using formal logic and symbol manipulation to perform reasoning and express knowledge. 

This method uses symbols to represent knowledge, which can be manipulated through rules or algorithms to help in problem-solving and decision-making. 

Symbolic AI is best shown by expert systems, which make decisions in certain fields based on the knowledge of rules. Symbolic AI is strong at some tasks, but it has trouble with ambiguity and gaining knowledge from a variety of data. This is the reason it is used in combination with technologies like machine learning and deep learning.

2. Connectionist AI (Neural Networks)

Connectionist AI, often associated with neural networks and machine learning, has gained prominence in recent years. The composition and operation of the human brain serve as the model for this strategy. 

Artificial neural networks are used to describe complicated interactions in data. They are made up of interconnected nodes or neurons. 

A subset of this strategy called deep learning uses multiple-layered deep neural networks to learn hierarchical data representations automatically. This method has demonstrated impressive results in tasks including image identification, natural language processing, and reinforcement learning.

3. Deep Reinforcement Learning (RL)

Deep Reinforcement Learning (DRL) is a technique that tackles complex problems like robotics control and game playing by combining reinforcement learning with deep neural networks. People who advocate for AGI would contend that DRL has the potential to be a key component in the development of general intelligence.

4. Evolutionary Algorithms

Evolutionary algorithms draw inspiration from biological evolution to optimize solutions. This method uses selection, recombination, and mutation to evolve a population of potential solutions iteratively. One such example is the employment of genetic algorithms for parameter optimization and neural network architectural evolution. 

5. Hybrid Approaches

Certain scholars suggest hybrid methodologies that integrate several components of previously listed paradigms. For example, neural network-based learning and symbolic reasoning can be combined to build systems capable of both symbolic reasoning and pattern recognition. These hybrid systems aim to use the advantages of both methods to produce more flexible problem-solving abilities.

6. Cognitive Architectures

Comprehensive frameworks called cognitive architectures are created to simulate human-like cognition. These architectural designs integrate several cognitive processes into a unified system, including memory, perception, reasoning, and decision-making. 

ACT-R (Adaptive Control of Thought-Rational) illustrates this. Although these architectures provide a methodical framework for understanding intelligence, they often do not meet AGI's learning and scalability needs.

7. Neuromorphic Computing

Neuromorphic computing seeks to build AI systems using hardware that mimics the structure and function of the human brain more closely. These specialized neuromorphic chips have the potential to accelerate the development of artificial general intelligence (AGI) by processing information in a way that is more parallel and energy-efficient by nature.

8. Bayesian Networks and Probabilistic Reasoning

Bayesian networks and probabilistic reasoning models handle uncertainty and probabilistic reasoning. They are useful for jobs like autonomous driving and medical diagnosis that require reasoning under ambiguity. 

Enhancing AGI systems with probabilistic reasoning can help them become more resilient and flexible.

Possible Artificial General Intelligence Use Cases in Different Industries

AGI, as of now, is mostly theoretical. However, if it turns into reality, there are multiple possibilities. Exploring those possibilities, here are a few artificial general intelligence use cases:

1. Healthcare

By examining large datasets from patient records, medical literature, and genetic data to find trends and suggest tailored medicine, artificial intelligence (AGI) has the potential to completely transform healthcare diagnostics and treatment regimens. 

This may result in novel insights into intricate illnesses and the creation of effective treatments, greatly enhancing patient outcomes and the effectiveness of healthcare systems.

2. Education

AGI could provide personalized learning experiences that adjust to each student's needs, learning preferences, and strengths. AGI has the ability to evaluate a student's progress in real time and adjust the curriculum, tempo, and instructional strategies, just like a real teacher. This might completely transform the educational system by giving every student equal access to the best possible learning experiences.

3. Scientific Research

By creating new theories and integrating existing information, AGI can speed up scientific discoveries. AGI could study data more thoroughly than human researchers in domains like chemistry, physics, and climate science. 

By doing so, it could find insights more quickly and effectively, accelerating the rate of innovation and comprehension.

4. Environmental Conservation

Artificial general intelligence applications to environmental conservation may result in improved resource management, environmental change forecasting, and the creation of long-term fixes. 

AGI could aid in the development of plans for addressing climate change, protecting biodiversity, and guaranteeing sustainable development practices by analyzing complicated environmental data.

5. Everyday Life and Work

AGI might become a necessary component of day-to-day living, serving as sophisticated personal assistants that can handle a variety of responsibilities, from communication and scheduling to supporting decision-making. 

By offering insights from a wide range of knowledge domains, artificial general intelligence (AGI) has the potential to automate intricate decision-making processes, improve efficiency, and stimulate innovative problem-solving in the workplace.

6. Ethics and Governance

The emergence of AGI also brings up important questions about governance, ethics, and the effects of this technology on society. To maximize AGI's benefits and minimize the risks related to the autonomy and decision-making abilities of AGI systems, its implementation must be compliant with ethical norms and social values.

Artificial General Intelligence Examples - Possible Future

Whenever we talk about artificial general intelligence examples, the most common names, we hear are from our favorite movies like 2001: A Space Odyssey, Her, Chappie, etc. However, similarities can be drawn between AGI and the existing AI systems, to conceptually illustrate artificial general intelligence examples. So, based on this endeavor, here are some examples:

1. Adaptive Learning and Problem Solving 

Imagine an AGI system in a research lab. This AGI can read and understand scientific papers, develop hypotheses, design experiments, and even conduct these experiments using robotic systems. 

It continuously learns from new data, refines its hypotheses, and adapts its approach. Unlike specialized AI that might only analyze data or control lab equipment, this AGI understands the broader implications of the research and can even contribute creatively to new scientific theories.

2. Multifaceted Personal Assistant

Envision a personal assistant AGI that not only manages your schedule and communications (like current AI) but also understands your personal preferences, emotional state, and long-term goals. 

It can make complex decisions like rearranging your schedule in a way that optimizes your work-life balance, suggesting career moves based on your aspirations, and even engaging in meaningful conversations to provide emotional support.

3. Integrated Global Systems Management

Consider an AGI tasked with managing global environmental systems. This AGI would analyze vast amounts of data from various sources like weather patterns, economic models, and ecological studies. 

It could then make complex decisions to balance economic needs with environmental sustainability, predict and mitigate natural disasters, and even coordinate global responses to crises like pandemics, all while adapting to new data and changing circumstances.

4. Autonomous Innovation and Design

An AGI in the field of engineering could revolutionize the way products are designed and manufactured. Such a system would not only understand the principles of engineering and materials science but could also innovate new designs, predict the success of these designs in the real world, and even oversee their production. 

This AGI could work across different fields of engineering, invent new materials, and optimize manufacturing processes for efficiency and sustainability.

5. Intercultural and Diplomatic Negotiator

An AGI serving as an intercultural negotiator could understand and interpret the nuances of different languages, cultures, and social norms. It would be capable of facilitating diplomatic discussions, resolving conflicts by finding mutually beneficial solutions, and even predicting the long-term outcomes of these agreements. Unlike human diplomats who are limited by their cultural perspectives, this AGI would have a comprehensive, unbiased understanding of all parties involved.

Artificial General Intelligence vs Artificial Intelligence - Understanding The Difference

Artificial General Intelligence (AGI) and Artificial Intelligence (AI) are related concepts, but they represent different levels of capabilities and applications within the field of computer science. Let's delve into the detailed differences between AGI and AI through a table.

Artificial General Intelligence Models 

There are few artificial general intelligence models that will serve as the foundation for developing systems that can comprehend, acquire, and use information in a wide range of contexts—not just specialized ones. Let’s learn about them.

1. Cognitive Architecture Approach

The cognitive architecture technique is one of the main development methodologies for AGI models. This approach attempts to mimic how people perceive and absorb information by taking cues from human psychology and brain architecture. The aim of cognitive architectures such as ACT-R and SOAR is to provide a flexible and well-organized framework that emulates the cognitive processes and learning skills of humans.

2. Neural Networks and Deep Learning

Deep learning methods and sophisticated neural networks constitute another important strategy. In contrast to conventional AI, which depends on explicit programming for certain tasks, these models pick up patterns and make judgments by learning from enormous volumes of data. 

Improving neural networks to not only carry out certain tasks but also to generalize their learning capacities across many domains is necessary to make the leap toward AGI.

3. Hybrid Models

Scholars are increasingly focusing on hybrid models that incorporate aspects of neural networks and cognitive structures, realizing the shortcomings of singular approaches. 

4. Evolutionary and Generative Models

Another area of advancement in AGI is represented by generative models and evolutionary algorithms. By simulating the mechanisms leading to intelligence's evolution, these models enable artificial intelligence (AI) systems to grow and improve with time. These models adapt and evolve, hopefully leading to the creation of general intelligence by examining a wide range of possible answers.

The goal of these models is to combine the adaptive, data-driven insights of neural networks with the structured, rule-based reasoning of cognitive architectures. The combination of these methods has the potential to create AGI systems that are more flexible and adaptive.

Diving Into The Future of Artificial General Intelligence

AGI, which refers to machines that may demonstrate intelligence on par with human abilities in a variety of tasks, is still purely theoretical at this point. Therefore, estimating its future, as of now, will be pure speculation. However, considering its possible technical, ethical, and societal implications, here are a few directions where AGI can make a difference in the future.

1. Technological Advancements

Significant technological developments in domains including machine learning, natural language processing, robotics, and cognitive science are necessary for the creation of artificial general intelligence (AGI). Advancements in these domains may result in the development of systems that not only emulate human intellect but also outperform it in terms of learning velocity, flexibility, and problem-solving ability.

2. Impact on Industries

Unprecedented breakthroughs and efficiencies could result from the integration of AGI into a variety of sectors. Industries can leverage automation of complex tasks, enhanced decision-making capabilities, and the ability to learn and adapt rapidly could transform sectors like healthcare, finance, manufacturing, and more.

3. Ethical Considerations

The pursuit of AGI raises profound ethical questions. Ethical frameworks and regulations will be crucial to guide the responsible development and deployment of AGI. So, careful thought will be given to issues related to employment displacement, privacy, security, and the misuse of sophisticated AI systems.

4. Societal Transformation

There may be profound changes in society as a result of AGI. As AGI systems become essential components of society, there may be changes to the labor market, educational programs, and everyday living. Preparing for these changes requires proactive efforts in education, retraining, and fostering a societal understanding of AGI's implications.

5. Collaboration and Governance

AGI development requires international cooperation and oversight. Given the potential global impact, international standards and agreements will be essential to address challenges, ensure ethical practices, and avoid the misuse of AGI technologies.

6. Unforeseen Challenges

The path to AGI is full of challenges, some of which may be unforeseen. Overcoming issues related to explainability, safety, and the alignment of AGI goals with human values will require continuous research and vigilance for unforeseen issues.

7. Human-Machine Integration

In addition to creating independent, intelligent systems, the future of AGI might entail fusing AI with human abilities. AGI-assisted human-machine symbiosis has the potential to promote a more peaceful and fruitful cooperation.

8. Research and Innovation

Innovation and ongoing research are essential to the evolution of AGI. Our understanding of intelligence and consciousness will advance significantly as a result of investments in fundamental research, interdisciplinary collaboration, and the investigation of innovative methodologies.

Ethical Concerns of Artificial General Intelligence

Multimodal AI systems like DALL-E, GPT-4, etc., are the closest we have to come to achieving AGI in real life. The inception of AGI will raise tons of ethical concerns, some of which are discussed through media like movies, while others are pure contention. So, let’s understand the ethical concerns of artificial general intelligence if they become a reality.

1. Existential Risk

The creation of strong AI would be the biggest event in human history. Unfortunately, it might also be the last unless we learn how to avoid the risks.

-Stephen Hawking (English Theoretical Physicist and Cosmologist)

AGI, if it becomes a reality, is bound to surpass human intelligence. However, it also means a system that might have abstract thinking capabilities, gradually leading to a crisis associated with self-identity. 

The world today is ready for a slave AGI, but its capabilities can eventually lead to it thinking, why am I not the master? Aside from this, there can be differences in political views, different understanding of human rights, different perspectives towards sustaining peace, etc.

If this doesn’t paint a picture, consider what happened to Ultron (Marvel Villain). If the AGI suffers from an existential crisis like humans, it can lead it to disrupt boundaries set by humans for it.

2. Bias and Discrimination

A US criminal justice system, COMPAS (risk assessment tool), has showcased racial bias, labeling black defendants as high-risk in comparison to white. These biases and discrimination can propagate if AGI becomes a reality. This can have huge implications if AGI systems are used in the legal landscape, leading to wrong convictions and misuse for propagating an agenda towards a particular race, religion, caste, color, or creed.

3. Job Displacement

A 2023 Goldman Sachs report states that AI could possibly impact over 300 million jobs by the next 10 years. In fact, as per their report, around 6% of US jobs will be completely substituted by AI. This is a shocking revelation for anyone working in corporates, feeling the chills of sustaining their job in the future. And the AI systems that will substitute those jobs are nowhere close to AGI.

An AGI system wouldn’t require any prompts or direction from any user. Instead, it will be able to learn tasks and perform them, way faster and efficiently in comparison to a human. So, it is highly likely its inception would put millions, if not billions, in jeopardy.

4. Loss of Control

AI systems learn from the dataset provided to them. But that is only the first stage. Systems like ChatGPT can gather data and learn the preferences of its users. These are systems that are constantly evolving.

AGI, in this aspect, would be unhinged. Why? Well, for it to work, it needs access to a massive repository of data, the Internet. These systems will have automated access to cloud systems to expand, and they will be constantly evolving at a rapid pace, becoming so sophisticated that their control goes out of our hands. And, this can lead to a situation where the humans are no longer in charge.

5. Privacy Concerns

Government bodies like the NSA (National Security Agency) already use surveillance tools like PRISM, Upstream, etc., to collect data from the internet within a legal framework. However, an AGI system that has gone rogue because of any unforeseen event with similar power can disrupt the privacy of billions of users around the world. This can include interception of private data that ranges from the name of the person to his/her internet history.

6. Weaponization

There are several cyber warfare incidents like Stuxnet (2010), Ukraine Power Grid Attacks (2015,2016), NotPetya (2017), etc., that have happened in the past. But, with the power of AGI, these infiltration capabilities will increase tenfold. It can lead to attacks from one nation to another to steal confidential information, infrastructural attacks, the rise of non-state actors, etc., leading to denser conflicts.

7. Unequal Access

AGI, at least in its earlier release, won’t be affordable to a major chunk of the population. The system would require resources that might break the pocket of a common person. Contrarily, people with power and money will have access to it, creating a huge divide between the people with power and the ones with no such power. 

Artificial General Intelligence - Compliances Required to Release It Today!

The entire editorial talks about AGI as a hypothetical concept so there are no real compliances in place, as of now. However, hypothetically, if AGI were to be released today, the desired compliances one would require would be:

1. GDPR (General Data Protection Regulation): An EU centric regulation that mandates data protection and privacy of users. For AGI, it would demand data minimization, purpose limitation, and the right to be forgotten.
2. CCPA (California Consumer Privacy Act): A California law that gives rights to consumers to control their personal data, including the right to know, basis of collection, and opting out.
3. NIST (National Institute of Standards and Technology): NIST is developing standards and guidelines for AI that are related to fairness and bias.
4. ISO 26262: It is an international standard applied to the functional safety of an automotive system.
5. NIST Cybersecurity Framework: These are guidelines that work as guidance to protect systems against cybersecurity threats. 
6. IEEE Ethically Aligned Design: This is a framework that is used for designing and developing ethical AI systems that emphasize human well-being, accountability, and transparency.
7. OECD Principles on AI: This principle asks for responsible stewardship for trustworthy AI that respects human rights and democratic values.

Conclusion: Unlocking the Potential of Artificial General Intelligence (AGI)

In conclusion, Artificial General Intelligence (AGI) stands at the forefront of technological evolution, embodying the quest to create machines with cognitive abilities akin to human intelligence. The pursuit of AGI holds immense promise for revolutionizing industries, streamlining processes, and addressing complex challenges. However, as we peer into the horizon of possibilities, it is imperative to tread with caution.

With this editorial, we hope you may have gotten a gist of the question “What is artificial general intelligence?” and its ethical, societal, and technical implications and prowess. And, if you want more similar resources, you can check out our dedicated artificial intelligence blog section.