Artificial General Intelligence: a Way To Smarter Machines

Artificial intelligence is a concept that draws inspiration from human intelligence. With the appearance of the first computers, scientists and philosophers began discussing the fundamental structure of the human brain and the possibility of recreating it on a machine.

AI systems strive to be as intelligent and agile as a human brain. Most algorithms and machine learning architectures try to achieve human-level performance. That's why a concept of strong AI or artificial general intelligence (AGI) appeared, however only as a concept. Further, we will define artificial general intelligence and discuss its benefits and ethical considerations regarding machine intelligence.

What is Artificial General Intelligence?

Artificial general intelligence, the pinnacle of artificial intelligence, promises to solve any problem a human can and perhaps even challenge the intellect of geniuses. This potential, however, remains untapped, as true AGI has yet to be realized.

According to the paper, What is Meant by AGI? On the Definition of Artificial General Intelligence, there is no one true definition for AGI. It'll be given once it becomes available, and most researchers will recognize it as true AGI.

For now, AI is at a level of artificial intelligence development that will enable AI systems to solve any task with the help of generalized human cognitive abilities.

While not yet perfected, current AI systems include some language models that exhibit features of AGI. For instance, GPT -3, a language model developed by OpenAI, can generate human-like text based on a given prompt. It can process data such as human language faster than we can. Yet, it's still incapable of abstract thinking, strategic reasoning, or using its thoughts and memories to make rational decisions or develop original concepts, key aspects of AGI.

Certainly, some LLM models can produce the necessary information when prompted to develop, for example, a marketing strategy or to draw a conclusion from a given body of text. However, what would appear to be deliberate and thoughtful is actually an attempt by the AI model to guess the sequence of words rather than a reasoned decision since such systems have no consciousness.

While the capabilities demonstrated by large language models, text-to-image, text-to-audio, and speech-to-text systems are impressive, they do not qualify as artificial general intelligence. These systems lack self-awareness and fail to replicate human intelligence in its entirety. This raises important ethical considerations and underscores the need for responsible AI development.

Our human intelligence makes us superior to machines. At the same time, the processes of our cognitive mechanisms are the most difficult to understand and, therefore, the most difficult to reproduce.

According to some predictions, true AGI can already be expected in the foreseeable future. Such a future type of AI will be able to do a whole range of tasks and make independent conclusions based on the information that is fed to it, not only learn but even become aware of its own existence at some point in time. AGI tools will even be able to master such abilities as sensory perception and fine motor skills, given that it has a robotic body. While the potential benefits of AGI are vast, including advancements in healthcare, transportation, and education, there are also significant risks and ethical considerations, such as job displacement, privacy concerns, and the potential for misuse of AGI technology, that need to be carefully addressed.

A strong AI is expected to be able to reason, integrate prior knowledge into decision-making, cope with challenges, use judgment in the face of uncertainty, plan, and generate creative ideas. To pursue these ambitions, however, AI researchers must find a way to grant the machines consciousness. This raises significant ethical considerations and societal implications. Granting machines consciousness could potentially lead to a new life form with its own rights and responsibilities. It's a complex and controversial topic that requires careful consideration and discussion.

Although AGI is only a concept for now, the next stage of AI development that supersedes human cognitive abilities, called super AI, is already being discussed. Let's take a closer look at other stages of AI development.

Three Stages of AI Development

A general definition of AI technology is a large branch of computer science that seeks to make it appear that a machine, i.e., a computer, has human intelligence. So if a machine exhibits cognitive abilities inherent in humans, it is called AI. Scientists have come up with three stages of AI development:

• Weak AI or narrow AI

• Strong AI or general AI

• Super AI

Weak AI

Weak AI, also known as narrow AI, illustrates that if a machine can behave intelligently, it does not prove that it is actually as intelligent as a human being. Weak AI has limited functionality. The advanced algorithms used at the core of weak AI perform specific tasks to solve problems that do not cover the full range of human cognitive abilities.

Weak AI responds to inputs based on algorithms. Tools like this may appear capable of reasoning, but they simply cannot do it. For example, voice assistants such as Siri, Cortana, and Alexa don't comprehend the words their users say and their meaning. These tools listen to audio cues and follow programmed instructions to respond accordingly.

Super AI

While no Artificial General Intelligence (AGI) system has been achieved yet, the next stage of AI development has been theorized. Super artificial intelligence will not just be able to do whatever a human does but will outperform any genius. In addition to surpassing humanity's best minds in all fields, this kind of AI will likely be able to re-configure itself while improving and even developing new systems and algorithms.

Technologies to develop AGI

Deep learning models

Deep learning models, a subset of machine learning models, are at the forefront of recent advancements in AI due to their ability to learn complex patterns and representations from large amounts of data. They are a key technology in the development of AGI, as they enable machines to learn and understand complex information, similar to how the human brain does. Most commonly, they are represented by multilayered neural networks. Examples of deep learning models include Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) or Transformers for natural language processing, both of which are crucial for AGI development.

Natural Language Processing

Natural language processing (NLP) is a critical area of AI research and development that enables machines to understand, interpret, and generate human language. It is foundational for achieving artificial general intelligence because language is a primary medium through which humans communicate complex ideas and knowledge. NLP models utilize computational linguistics and machine learning to convert language data into basic units known as tokens and comprehend the context in which these tokens appear.

Generative AI

Generative AI models play a significant role in driving the development of AGI by enabling machines to create new data, explore diverse possibilities, and simulate creative processes. AGI systems must exhibit generative capabilities to develop novel solutions, adapt to new environments, and interact with humans and the world. By leveraging generative AI, AGI researchers aim to imbue artificial systems with creativity, innovation, and adaptability akin to human behavior.

Computer vision

AGI systems require the ability to reason about visual information and predict future events based on observed patterns. Computer vision (CV) techniques, such as image captioning, visual question answering, and scene prediction, support visual reasoning by bridging the gap between perception and cognition. Computer vision enables AGI to understand complex scenes by analyzing the spatial relationships between objects, their attributes, and contextual information. Scene understanding facilitates higher-level reasoning and decision-making, allowing AGI systems to comprehend the context in which they operate. CV plays a crucial role in embodied AI by providing real-time feedback on the robot's perception of its surroundings, guiding its actions and decision-making processes.

Robotics

Robotics is of considerable significance in the development of artificial general intelligence by providing physical embodiments for intelligent systems to interact with and learn from the environment. Embodied AI involves integrating AGI systems with physical bodies or robotic platforms, enabling them to interact with the physical world.

Robotics provides AGI systems with physical bodies, allowing them to perceive and act upon the world in ways analogous to humans. Embodied AGI systems can gather sensorimotor experiences, interact with objects, and navigate real-world environments.

Human intelligence is a guiding principle and inspiration for AGI research, informing intelligent systems' design, development, and evaluation. By understanding and replicating key aspects of human intelligence, researchers strive to create AI systems capable of emulating human-like cognitive abilities and achieving the level of general intelligence exhibited by humans.

Approaches to AGI

Attempts to create a real AGI system have included several diverse and multifaceted approaches, reflecting the complexity and ambition of creating machines that possess human-like cognitive abilities. The following are some basic examples of how researchers can reach AGI in the near future.

Connectionist Approach

Connectionist or neural network-based approaches model intelligence inspired by the structure and function of the human brain. Connectionist systems consist of interconnected nodes (neurons) organized in layers, capable of learning from data through iterative training processes.

Symbolic approach

The symbolic approach in artificial intelligence is grounded in the use of logic networks and symbolic representations to encode knowledge and facilitate learning. This approach is based on the premise that intelligent behavior can be achieved by processing symbolic representations of the world. The manipulation of symbols is often governed by logic networks, which include rules such as "if-then" statements. These rules define how symbols interact and how knowledge is inferred from existing information.

Whole organism approach

Unlike traditional approaches that focus solely on software-based models, the whole organism architecture emphasizes the integration of AI systems with physical embodiments resembling human bodies. Proponents of the whole organism architecture believe that AGI is best achieved when systems learn from physical interactions with the world rather than solely from simulated environments or digital data.

AGI systems embodied in physical robots can undergo developmental stages, starting with basic sensorimotor skills and gradually progressing to more complex cognitive abilities through independent exploration and learning.

Human brain emulation

Whole brain emulation is a theoretical approach to achieving artificial general intelligence by replicating a human brain's detailed structure and function in a computational framework. The idea is to create a digital model that mimics the human brain's neural activities and cognitive processes, potentially capturing human-like consciousness and intelligence.

Benefits of AGI

Developing and deploying artificial general intelligence has the potential to significantly benefit society.

Complex Problem Solving in various domains. AGI will be able to tackle complex and large-scale problems that are currently beyond human capabilities, such as climate change, pandemics, and global poverty. It will also be able to integrate knowledge from different fields to create innovative solutions that might not be apparent from a single-disciplinary perspective;

Accelerating Scientific Discovery. AGI is expected to process and analyze large datasets faster than humans, leading to scientific discoveries and innovations. AGI can identify correlations and insights that humans might miss, opening up new areas of research;

Risk Assessment. AGI system will evaluate risks and accurately predict outcomes, aiding in strategic planning and crisis management. It will analyze vast amounts of data to provide insights and recommendations, helping humans make better-informed decisions in business and daily life;

Creative Assistance. By analyzing existing research papers and market trends, AGI will be capable of identifying gaps and opportunities in different domains, accelerating innovation. AGI can provide creative professionals with new tools and inspirations, aiding in the creation of art, music, literature, and design;

Threat Detection. AGI will enhance security systems by detecting potential threats and suspicious activities in real-time, improving public safety. It will manage automated surveillance systems, reducing the need for human monitoring and increasing efficiency. AGI will enhance early warning systems for natural disasters like earthquakes, tsunamis, and hurricanes, thus improving preparedness and response.

Requirements for AGI

Although strong AI does not yet exist, some of the qualities required for an AI to be considered an AGI have already been defined. The requirements for artificial general intelligence encompass several aspects that define its capabilities and characteristics:

• AGI must be able to perform a wide range of tasks currently carried out by humans without the need for specialized adjustments for each new task;

• A strong AI has to outperform humans intellectually, being able to learn at the same speed as or faster than humans;

• AGI must be proficient in solving various problems across different domains, from mathematical puzzles to complex real-world scenarios. It should easily switch between tasks and apply knowledge from one domain to another, demonstrating true versatility;

• AGI should be capable of learning from experience and adapting to new situations and changes in the environment. In other words, it should be able to reuse previously gained experience. This includes the ability to self-learn and autonomously improve its skills;

• AGI must understand and generate natural language at a level comparable to humans, which includes text, speech processing, and complex forms of communication and interaction;

• AGI should possess cognitive abilities such as attention, memory, planning, reasoning, problem-solving, and creativity. It has to demonstrate creativity in generating novel ideas, solutions, and artistic expressions.

Above all else, AGI must be developed with ethical norms and standards to ensure its actions are safe for society and individuals. This includes measures to prevent harm, uphold human rights and freedoms, and ensure transparency and accountability.

Ethical Considerations

Without adequately drafted guidelines and regulations for Artificial General Intelligence (AGI), systems with advanced capabilities and the potential to surpass human thought processes could pose significant risks. While the idea of AGI deciding humans are enemies and taking harmful actions is speculative, ethical considerations are critical to ensure AGI's beneficial and safe integration into society.

If developed without robust ethical frameworks and regulations, AGI could lead to unintended consequences and potential risks. However, the notion of AGI autonomously deciding to harm humans is speculative and not grounded in the current understanding or capabilities of AI systems.

Ethical guidelines and regulatory frameworks must address issues such as fairness, transparency, accountability, privacy, security, and the alignment of AI goals with human values. These considerations are essential to promote responsible AI development and deployment, safeguarding against potential harm.

An Artificial General Intelligence (AGI) that surpasses human intelligence could pose significant risks if its goals are misaligned with human values. This misalignment could lead to uncontrollable behavior, potentially becoming an existential threat to humanity.

Furthermore, an AGI capable of recursive self-improvement could rapidly evolve beyond human understanding and control. If safeguards and regulatory measures are not in place, this could result in unpredictable outcomes and potentially catastrophic scenarios.

While these concerns are valid and reflect ongoing discussions in AI ethics and safety, it's important to note that AGI's actual development and behavior remain hypothetical. Current AI systems, including those with advanced capabilities, are far from achieving AGI and the level of autonomy and self-improvement described in the statement.

Ethical considerations, robust regulatory frameworks, transparency, and ongoing research into AI safety are essential components of responsible AI development to mitigate potential risks associated with AGI in the future.

Future of AGI

Addressing the ethical challenges associated with AGI requires a proactive and collaborative approach. Society can work towards overcoming the moral problems posed by AGI in the future by establishing comprehensive ethical frameworks, promoting transparency, ensuring fairness, prioritizing safety, implementing ethical oversight, and encouraging responsible innovation.

Creating AGI could potentially change everything—how we work, live, and make decisions. So, we need to tread carefully to ensure this powerful technology benefits everyone and doesn’t harm anyone. Solving these problems is a collective effort from scientists, policymakers, businesses, and everyday people. Combining diverse perspectives helps create solutions that consider different viewpoints and potential impacts.

The future of AGI holds immense promise but also significant challenges. Machine learning innovations, particularly deep learning, will drive progress toward more advanced AGI systems. Technological advancements and careful consideration of ethical, societal, and economic implications will be crucial in guiding the development of AGI.