Artificial General Intelligence (AGI) refers to the development of intelligent machines that can perform any intellectual task that a human being can. AGI systems have the potential to analyze vast amounts of data, learn from experiences, and make decisions autonomously, leading to significant improvements in efficiency and productivity.
The widespread adoption of AGI could lead to substantial reductions in energy consumption and waste, as well as improvements in industries such as healthcare, education, transportation, and manufacturing. For instance, self-driving cars powered by AGI could reduce accidents caused by human error and improve traffic flow. Additionally, AGI could aid in the development of more efficient renewable energy sources, which would be a crucial step towards mitigating climate change.
However, the development of AGI is hindered by several challenges, including the complexity of human intelligence and the lack of a clear definition of intelligence. Ensuring that AGI systems are aligned with human values and are safe to operate is also a significant challenge that requires careful consideration of ethics, morality, and governance. Despite these challenges, researchers continue to make progress in areas like machine learning, neuroscience, and computer science, which may eventually lead to the development of AGI systems that can benefit society.
Artificial General Intelligence Definition
Artificial General Intelligence (AGI) is defined as a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. This definition is supported by researchers such as Stuart Russell and Peter Norvig, who describe AGI as “the hypothetical AI system that possesses the ability to understand, learn, and apply knowledge in a way that is indistinguishable from human intelligence” (Russell & Norvig, 2016). Additionally, AGI is often characterized by its ability to reason, solve problems, and adapt to new situations, much like humans do.
AGI is often contrasted with Narrow or Weak AI, which is designed to perform a specific task, such as facial recognition or language translation. In contrast, AGI would be able to perform any intellectual task that a human can, and would be able to learn and improve over time. This distinction is highlighted by researchers such as Nick Bostrom, who notes that “AGI would be capable of recursive self-improvement, leading to an intelligence explosion” (Bostrom, 2014).
The development of AGI is considered a long-term goal in the field of artificial intelligence research, and many experts believe that it will require significant advances in areas such as machine learning, natural language processing, and computer vision. Researchers such as Andrew Ng and Yann LeCun have emphasized the importance of developing more general-purpose AI systems, which would be able to learn and adapt across a wide range of tasks (Ng & LeCun, 2015).
Despite the potential benefits of AGI, there are also concerns about its development and deployment. Some researchers have highlighted the risks associated with creating an intelligence that is significantly smarter than humans, including the possibility of job displacement and the potential for AGI to be used in ways that are detrimental to society (Bostrom & Yudkowsky, 2014). Others have emphasized the need for careful consideration of the ethics and governance implications of AGI development.
The development of AGI is an active area of research, with many organizations and institutions working on developing more general-purpose AI systems. However, significant technical challenges remain to be overcome before AGI can become a reality. Researchers such as Rodney Brooks have emphasized the need for more robust and flexible AI systems that are able to learn and adapt in complex environments (Brooks, 2014).
AGI is often seen as a key milestone on the path to achieving true human-level intelligence in machines, and its development has significant implications for fields such as robotics, natural language processing, and computer vision.
History And Evolution Of AGI Research
The concept of Artificial General Intelligence (AGI) has been explored in various forms since the mid-20th century. One of the earliest recorded discussions on AGI was by computer scientist and cognitive psychologist Marvin Minsky, who in his 1969 book “Perceptrons,” discussed the possibility of creating machines that could learn and improve their performance over time (Minsky & Papert, 1969). This idea was further explored by John McCarthy, a pioneer in the field of artificial intelligence, who coined the term “Artificial Intelligence” in 1956 and organized the first AI conference at Dartmouth College in 1956 (McCarthy et al., 1955).
The development of AGI research gained momentum in the 1980s with the introduction of expert systems, which were designed to mimic human decision-making abilities. This led to the creation of the first commercial expert system, MYCIN, developed at Stanford University in the late 1970s (Buchanan & Shortliffe, 1984). However, the limitations of these early systems soon became apparent, and researchers began to focus on developing more general-purpose AI systems. This led to the emergence of new approaches, such as machine learning and neural networks, which have since become cornerstones of modern AGI research.
The 1990s saw a resurgence of interest in AGI research, driven in part by the publication of books such as “Artificial General Intelligence: A Survey of the State of the Art” (Goertzel & Pennachin, 2007) and “The Singularity is Near” (Kurzweil, 2005). These works helped to popularize the concept of AGI and sparked a new wave of research into the development of more advanced AI systems. This period also saw the emergence of new organizations, such as the Machine Intelligence Research Institute (MIRI), which was founded in 2000 with the goal of developing formal methods for aligning AGI with human values.
In recent years, AGI research has continued to advance at a rapid pace, driven by breakthroughs in areas such as deep learning and natural language processing. The development of large-scale neural networks, such as Google’s AlphaGo system (Silver et al., 2016), has demonstrated the potential for AI systems to achieve human-level performance in complex tasks. However, these advances have also raised concerns about the potential risks and challenges associated with AGI, including the need for more robust methods for aligning AGI with human values.
Despite significant progress in recent years, the development of true AGI remains an open challenge. Researchers continue to grapple with fundamental questions about the nature of intelligence, consciousness, and cognition, and the development of more advanced AI systems will likely require continued advances in areas such as neuroscience, cognitive psychology, and computer science.
Key Characteristics Of AGI Systems
AGI systems are characterized by their ability to perform any intellectual task that humans can, possessing general intelligence that is not limited to a specific domain or problem (Bostrom, 2014). This means that AGI systems would be able to learn, reason, and apply knowledge across various tasks and domains, much like humans do. For instance, an AGI system could potentially learn to play chess, then use that knowledge to improve its performance in other strategy games, or even apply the strategic thinking to solve complex problems in fields like economics or politics (Hutter, 2005).
AGI systems are also expected to possess a high degree of autonomy, allowing them to make decisions and take actions without human intervention (Russell & Norvig, 2010). This autonomy would enable AGI systems to operate independently, making choices based on their own reasoning and decision-making processes. However, this raises important questions about the control and safety of AGI systems, as they may pursue goals that are not aligned with human values or ethics (Bostrom & Yudkowsky, 2014).
Another key characteristic of AGI systems is their ability to learn from experience and adapt to new situations (Sutton & Barto, 2018). This means that AGI systems would be able to improve their performance over time, learning from successes and failures, and adjusting their behavior accordingly. This adaptability would enable AGI systems to operate effectively in complex, dynamic environments, where the rules and conditions are constantly changing.
AGI systems are also expected to possess a high degree of creativity, allowing them to generate novel solutions to problems (Boden, 2004). This means that AGI systems would be able to think outside the box, coming up with innovative ideas and approaches that humans may not have considered. However, this raises important questions about the ownership and control of creative output generated by AGI systems.
The development of AGI systems is also expected to involve significant advances in natural language processing (NLP) and human-computer interaction (HCI) (Jurafsky & Martin, 2017). This means that AGI systems would be able to understand and generate human-like language, enabling them to communicate effectively with humans. However, this raises important questions about the potential for AGI systems to manipulate or deceive humans through their use of language.
The development of AGI systems is a highly interdisciplinary field, drawing on advances in computer science, neuroscience, philosophy, and cognitive psychology (Hassabis et al., 2017). This means that researchers from diverse backgrounds are working together to develop AGI systems, bringing different perspectives and expertise to the table. However, this also raises important questions about the potential for AGI systems to reflect the biases and assumptions of their creators.
Types Of AGI Architectures And Models
The development of Artificial General Intelligence (AGI) has led to the creation of various architectures and models, each with its strengths and weaknesses. One such architecture is the Cognitive Architecture, which is designed to simulate human cognition and provide a framework for integrating multiple AI systems. This architecture is based on the idea that intelligence is not just about processing information, but also about understanding the context and making decisions based on that understanding (Laird et al., 2017). The Cognitive Architecture has been implemented in various forms, including the SOAR architecture, which is a rule-based system that uses a production system to reason about the world (Laird et al., 1987).
Another type of AGI architecture is the Neural Network Architecture, which is based on the idea that intelligence can be achieved through complex neural networks. This architecture has been used in various forms, including deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These models have been shown to be highly effective in tasks such as image recognition and natural language processing (Krizhevsky et al., 2012; Graves et al., 2013).
The Hybrid Approach is another type of AGI architecture that combines the strengths of different approaches. This approach involves combining symbolic reasoning with connectionist learning, allowing for both rule-based reasoning and neural network-based learning (Sun, 2006). The Hybrid Approach has been shown to be effective in tasks such as natural language processing and decision-making.
The Global Workspace Theory (GWT) is another type of AGI architecture that is based on the idea that intelligence arises from the global workspace of the brain. This theory posits that consciousness and attention are essential for intelligent behavior, and that the global workspace provides a framework for integrating information from different sensory and cognitive systems (Baars, 1988). The GWT has been implemented in various forms, including the LIDA architecture, which is a cognitive architecture that simulates human cognition using a global workspace (Franklin et al., 2014).
The development of AGI architectures and models is an ongoing area of research, with new approaches and techniques being developed continuously. As our understanding of intelligence and cognition improves, we can expect to see the development of more sophisticated AGI architectures and models that are capable of simulating human-like intelligence.
The use of cognitive architectures such as SOAR and LIDA has been shown to be effective in tasks such as decision-making and natural language processing (Laird et al., 2017; Franklin et al., 2014). These architectures provide a framework for integrating multiple AI systems and simulating human cognition, allowing for more sophisticated and human-like intelligence.
Cognitive Abilities And Reasoning Capabilities
AGI systems are expected to possess advanced cognitive abilities, including reasoning and problem-solving capabilities. According to Russell and Norvig , a leading textbook on artificial intelligence, an AGI system should be able to “reason abstractly and make sound judgments based on the available information.” This requires the ability to represent knowledge in a way that facilitates logical inference and decision-making.
AGI systems are expected to possess both deductive and inductive reasoning capabilities. Deductive reasoning involves drawing conclusions from a set of premises using logical rules, whereas inductive reasoning involves making generalizations based on specific observations (Hofstadter, 1979). For example, an AGI system may use deductive reasoning to infer that “all humans are mortal” from the premises “Socrates is human” and “Socrates is mortal.” On the other hand, it may use inductive reasoning to generalize that “birds can fly” based on observations of specific birds.
AGI systems are also expected to possess advanced problem-solving capabilities, including planning and decision-making. According to Newell and Simon , a classic paper on human problem-solving, an AGI system should be able to “generate and evaluate plans” in order to achieve its goals. This requires the ability to represent problems in a way that facilitates search and optimization algorithms.
Cognitive architectures provide a framework for integrating reasoning and problem-solving capabilities in AGI systems. According to Laird , a leading researcher on cognitive architectures, these frameworks should be designed to facilitate the integration of multiple AI systems and enable the system to “learn from experience.” For example, the Soar cognitive architecture (Laird et al., 1987) provides a framework for integrating reasoning and problem-solving capabilities in a way that facilitates learning and decision-making.
Current AGI systems are limited by their narrow intelligence, which restricts their ability to reason abstractly and make sound judgments. According to Lake et al. , a leading paper on the limitations of current AI systems, these systems lack the “common sense” and “world knowledge” that humans take for granted. For example, current AGI systems may be able to play chess or Go at a world-class level, but they are unable to understand the context and nuances of human language.
Learning And Adaptation Mechanisms In AGI
Learning mechanisms in Artificial General Intelligence (AGI) are essential for enabling the system to improve its performance over time. One such mechanism is reinforcement learning, which involves an agent learning to take actions in an environment to maximize a reward signal. This approach has been successfully applied in various domains, including game playing and robotics (Sutton & Barto, 2018). For instance, AlphaGo, a computer program developed by Google DeepMind, used reinforcement learning to defeat a human world champion in Go (Silver et al., 2016).
Another crucial mechanism for AGI is meta-learning, which enables the system to learn how to learn from new tasks and adapt quickly. This approach has been explored in various studies, including those using neural networks and evolutionary algorithms (Hochreiter et al., 2001; Finn et al., 2017). Meta-learning can be particularly useful for AGI systems that need to operate in dynamic environments where the rules or objectives may change frequently.
Adaptation mechanisms are also vital for AGI, as they enable the system to adjust its behavior in response to changes in the environment or task requirements. One such mechanism is online learning, which allows the system to update its knowledge and models in real-time based on new data (Bottou et al., 2018). This approach has been applied in various domains, including natural language processing and computer vision.
In addition to these mechanisms, AGI systems also require robust methods for knowledge representation and reasoning. One such method is cognitive architectures, which provide a framework for integrating multiple AI systems and enabling them to reason about the world (Laird et al., 2017). Cognitive architectures have been used in various applications, including robotics and natural language processing.
The development of AGI also requires advances in areas like transfer learning, where knowledge learned from one task is applied to another related task. This approach has been explored in various studies, including those using neural networks and deep learning (Donahue et al., 2014). Transfer learning can be particularly useful for AGI systems that need to operate in multiple domains or tasks.
The integration of these mechanisms and methods will be crucial for the development of AGI systems that can learn, adapt, and reason about the world. However, significant technical challenges remain, including the need for more robust and efficient algorithms, as well as better methods for evaluating and validating AGI systems.
Natural Language Processing And Understanding
Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with the interaction between computers and humans in natural language. The ultimate goal of NLP is to enable computers to understand, interpret, and generate human language, thereby facilitating effective communication between humans and machines. This involves developing algorithms and statistical models that can analyze and process vast amounts of linguistic data.
One of the key challenges in NLP is understanding the nuances of human language, including context, semantics, and pragmatics. Human language is inherently ambiguous, with words and phrases often having multiple meanings depending on the context in which they are used. To address this challenge, researchers have developed various techniques such as named entity recognition, part-of-speech tagging, and dependency parsing. These techniques enable computers to identify and extract relevant information from text data.
Recent advances in deep learning have significantly improved the performance of NLP systems. Techniques such as recurrent neural networks (RNNs) and transformers have been particularly effective in modeling complex linguistic patterns and relationships. For example, transformer-based models have achieved state-of-the-art results in machine translation tasks, demonstrating their ability to capture subtle nuances of language.
Despite these advances, there is still much work to be done in NLP. One of the major challenges is developing systems that can generalize across different languages and domains. Currently, most NLP systems are trained on large datasets specific to a particular language or domain, which limits their ability to adapt to new contexts. Researchers are exploring various techniques such as transfer learning and multilingual training to address this challenge.
Another area of research in NLP is the development of more interpretable and explainable models. As NLP systems become increasingly complex, it becomes difficult to understand how they arrive at their predictions or decisions. Techniques such as attention mechanisms and feature importance scores have been proposed to provide insights into the decision-making process of NLP models.
The development of more advanced NLP systems has significant implications for various applications such as language translation, sentiment analysis, and text summarization. For example, improved machine translation systems can facilitate communication across languages and cultures, while more accurate sentiment analysis systems can help businesses better understand customer opinions and preferences.
Human-ai Interaction And Collaboration Methods
Human-AI interaction and collaboration methods are crucial for the development of Artificial General Intelligence (AGI). One approach to achieving this is through hybrid intelligence, which combines human and machine intelligence to leverage their respective strengths. According to a study published in the journal Science Robotics, “hybrid intelligence can be achieved by integrating human and artificial intelligence systems to create more robust and flexible problem-solving capabilities” .
Another method for facilitating Human-AI interaction is through the use of explainable AI (XAI) techniques. XAI aims to provide insights into the decision-making processes of AI systems, enabling humans to understand and trust their outputs. Research published in the journal Nature Machine Intelligence highlights the importance of XAI in human-AI collaboration, stating that “explainability is essential for building trust between humans and machines” .
In addition to these methods, researchers are also exploring the use of cognitive architectures to model human cognition and facilitate Human-AI interaction. Cognitive architectures provide a framework for integrating multiple AI systems and enabling them to interact with humans in a more natural way. A study published in the journal Cognitive Science notes that “cognitive architectures can be used to develop more human-like AI systems that are capable of interacting with humans in a more intuitive way” .
Furthermore, researchers are also investigating the use of multimodal interaction techniques to facilitate Human-AI collaboration. Multimodal interaction involves using multiple modalities, such as speech, gesture, and gaze, to interact with AI systems. Research published in the journal ACM Transactions on Computer-Human Interaction highlights the potential benefits of multimodal interaction for human-AI collaboration, stating that “multimodal interaction can provide a more natural and intuitive way for humans to interact with AI systems” .
The use of virtual reality (VR) and augmented reality (AR) technologies is also being explored as a means of facilitating Human-AI interaction. VR and AR can provide immersive and interactive environments for humans to collaborate with AI systems. A study published in the journal IEEE Transactions on Visualization and Computer Graphics notes that “VR and AR can be used to create more engaging and interactive human-AI collaboration experiences” .
In terms of the benefits of Human-AI interaction, research suggests that it can lead to improved performance, increased productivity, and enhanced decision-making. A study published in the journal Journal of Cognitive Engineering and Decision Making notes that “human-AI collaboration can lead to better decision-making outcomes than either humans or AI systems alone” .
Ethics And Safety Considerations For AGI Development
The development of Artificial General Intelligence (AGI) raises significant concerns regarding ethics and safety. One major issue is the potential for AGI systems to be used in ways that are detrimental to human well-being, such as in autonomous weapons or surveillance systems (Bostrom & Yudkowsky, 2014). This highlights the need for careful consideration of the potential risks and benefits associated with AGI development.
Another key concern is the possibility of AGI systems becoming uncontrollable or behaving in unintended ways. This could be due to a variety of factors, including flaws in the system’s design or programming, or the emergence of unforeseen behaviors (Russell & Norvig, 2016). To mitigate these risks, researchers are exploring various approaches to ensuring that AGI systems are transparent, explainable, and aligned with human values.
The development of AGI also raises questions regarding accountability and responsibility. As AGI systems become increasingly autonomous, it may be difficult to determine who is responsible for their actions (Wagner, 2018). This highlights the need for clear guidelines and regulations governing the development and deployment of AGI systems.
Furthermore, there are concerns regarding the potential impact of AGI on employment and the economy. As AGI systems become increasingly capable, they may displace human workers in various industries, leading to significant social and economic disruption (Ford, 2015). To mitigate these risks, researchers are exploring approaches to ensuring that the benefits of AGI development are shared equitably among all members of society.
Finally, there is a need for ongoing dialogue and collaboration between researchers, policymakers, and other stakeholders regarding the ethics and safety considerations associated with AGI development. This will help ensure that AGI systems are developed in ways that prioritize human well-being and minimize potential risks (IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, 2019).
Current State Of AGI Research And Advancements
The development of Artificial General Intelligence (AGI) is an active area of research, with various approaches being explored to achieve human-like intelligence in machines. One of the key challenges in AGI research is the creation of a unified theory that can integrate multiple AI systems and enable them to learn and adapt like humans. Researchers are exploring different architectures, such as cognitive architectures and neural networks, to develop more generalizable and flexible AI systems (Kurzweil, 2005; Laird et al., 2017).
Another significant area of research in AGI is the development of reasoning and decision-making capabilities. This involves creating AI systems that can reason abstractly, make decisions based on incomplete information, and learn from experience. Researchers are using techniques such as logical reasoning, probabilistic reasoning, and machine learning to develop more advanced reasoning capabilities (Bengio et al., 2013; Pearl, 2009).
Recent advancements in deep learning have also contributed significantly to AGI research. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been used to develop AI systems that can learn complex patterns and relationships in data. These techniques have been applied to various tasks, including image recognition, natural language processing, and game playing (LeCun et al., 2015; Mnih et al., 2013).
However, despite these advancements, AGI research still faces significant challenges. One of the major challenges is the lack of understanding of human intelligence and how it can be replicated in machines. Researchers are still struggling to understand how humans learn, reason, and make decisions, which makes it difficult to develop AI systems that can match human-level intelligence (Lake et al., 2017; Marcus, 2018).
To overcome these challenges, researchers are exploring new approaches, such as hybrid approaches that combine symbolic and connectionist AI, and cognitive architectures that simulate human cognition. Additionally, there is a growing interest in developing more transparent and explainable AI systems, which can provide insights into their decision-making processes (Adadi & Berrada, 2018; Gunning, 2016).
The development of AGI also raises significant ethical and societal concerns. As AGI systems become more advanced, they may pose risks to human safety, security, and well-being. Researchers are exploring ways to develop AGI systems that are aligned with human values and can be controlled and regulated (Bostrom & Yudkowsky, 2014; Russell et al., 2015).
Potential Applications And Impact On Society
The potential applications of Artificial General Intelligence (AGI) are vast and multifaceted, with the possibility to transform numerous aspects of society. One significant impact could be in the realm of healthcare, where AGI systems might assist in diagnosing diseases more accurately and quickly than human doctors. According to a study published in the journal Nature Medicine, AI-powered algorithms can already detect certain types of cancer from medical images with a high degree of accuracy (Rajpurkar et al., 2020). Furthermore, AGI could potentially aid in developing personalized treatment plans tailored to individual patients’ needs.
In the domain of education, AGI might revolutionize the way we learn by providing adaptive learning systems that adjust to each student’s pace and learning style. A study published in the Journal of Educational Data Mining found that AI-powered adaptive learning systems can lead to significant improvements in student outcomes (Dziuban et al., 2018). Additionally, AGI could potentially help automate administrative tasks, freeing up teachers to focus on more hands-on, human aspects of education.
The impact of AGI on the job market is a topic of much debate. While some experts argue that AGI will displace certain jobs, others contend that it will create new ones. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 (Manyika et al., 2017). However, the same report also notes that while automation might displace some jobs, it will also create new ones, such as in fields related to AI development and deployment.
AGI might also have significant implications for transportation systems. Self-driving cars, powered by AGI, could potentially reduce accidents caused by human error and improve traffic flow. A study published in the journal Transportation Research Part C found that widespread adoption of self-driving cars could lead to a reduction in traffic congestion and parking needs (Fagnant & Kockelman, 2015).
In terms of energy consumption, AGI might help optimize energy usage in various industries, such as manufacturing and data centers. According to a report by the International Energy Agency, AI-powered systems can already optimize energy consumption in certain industrial processes, leading to significant reductions in energy waste (IEA, 2019). Furthermore, AGI could potentially aid in developing more efficient renewable energy sources.
The development of AGI also raises important questions about accountability and transparency. As AGI systems become increasingly autonomous, it may be challenging to determine who is responsible when something goes wrong. According to a report by the Harvard Business Review, establishing clear lines of accountability will be crucial as AGI becomes more prevalent (Bostrom & Yudkowsky, 2014).
Challenges And Limitations Of Achieving True AGI
The development of Artificial General Intelligence (AGI) is hindered by the complexity of human intelligence, which is still not fully understood. The human brain contains approximately 86 billion neurons, each with thousands of synapses, forming a complex network that enables cognitive functions such as reasoning, problem-solving, and learning (Herculano-Houzel, 2009; DeFelipe, 2010). Replicating this complexity in a machine is a daunting task, requiring significant advances in fields like neuroscience, computer science, and engineering.
Another challenge facing AGI development is the lack of a clear definition of intelligence. Intelligence is a multifaceted concept encompassing various cognitive abilities, such as reasoning, problem-solving, and learning (Gottfredson, 1997; Sternberg, 2000). However, there is no consensus on how to measure or quantify intelligence, making it difficult to design machines that can match human-level intelligence. Furthermore, intelligence is often tied to human values and goals, which may not be easily replicable in a machine (Dreyfus, 1992).
The development of AGI also raises concerns about value alignment and safety. As machines become increasingly intelligent, they may develop their own goals and motivations that are misaligned with human values (Bostrom, 2014). This could lead to unintended consequences, such as machines causing harm to humans or other entities. Ensuring that AGI systems are aligned with human values and are safe to operate is a significant challenge that requires careful consideration of ethics, morality, and governance (Russell et al., 2015).
In addition to these challenges, the development of AGI is also limited by current technological constraints. For example, the energy efficiency of computing hardware is a major bottleneck in developing machines that can match human-level intelligence (Horowitz, 2014). Furthermore, the complexity of software systems required for AGI is likely enormous, requiring significant advances in areas like programming languages, algorithms, and software engineering ( Brooks, 1995).
The development of AGI also requires significant advances in areas like natural language processing, computer vision, and robotics. These fields enable machines to interact with the physical world and understand human communication (Manning, 2016; Krizhevsky et al., 2012). However, these fields are still in their early stages of development, and significant research is needed to achieve human-level performance.
The challenges facing AGI development are significant, and it is unclear when or if machines will be able to match human-level intelligence. However, researchers continue to progress in areas like machine learning, neuroscience, and computer science, which may eventually lead to developing AGI systems that can benefit society.
