A Close-Up Look at Artificial General Intelligence and Its Mechanisms

As the buzz around Generative AI starts to settle, industry leaders are growing aware of the upcoming era of artificial intelligence. With a market value of $197 billion in 2023, the AI industry is on the rise, projected to reach $1,812 billion by 2030.

In this article, I will focus on the future of AI and the potential rise of artificial general intelligence—a technology that will have a bigger global impact than current Generative AI tools.

What is artificial general intelligence?

Artificial General Intelligence is a branch of AI research focused on developing systems that have the capability to closely copy human-level cognitive abilities, including unparalleled adaptability and the potential for self-improvement.

However, the closest approximation to AGI is now found in large language models like GPT-4, which generate human-like responses to prompts and engage in continuous dialogue with users. Nevertheless, these models still lack even basic cognitive abilities.

There are 4 common approaches in artificial general intelligence research, categorizing them as follows:

1. Symbolic approach: This AGI approach considers symbolic thought the core of human general intelligence, enabling broad generalization. Abstract thinking, a key aspect of human intelligence, may also play a role in artificial intelligence.

   
   

2. Emergentist approach: The emergentist approach to AGI states that the human brain, consisting of neurons, self-organizes into complex patterns based on sensory experiences. Replicating such a structure might lead to a similar form of intelligence.

   
   

3. Hybrid approach: The hybrid AGI approach views the brain as a composite system where different components and principles collaborate to achieve a more powerful outcome. Hybrid AGI research covers a lot of diverse approaches.

   
   

4. Universalist approach: The universalist approach to AGI focuses on the mathematical essence of general intelligence. It suggests that once AGI is theoretically solved, the principles used can be scaled down for practical applications.

The purpose of these approaches is to help us understand human intelligence and apply this knowledge to advance AI.

How is it different from artificial intelligence?

While artificial intelligence covers various technologies related to machine and computer understanding, the idea of artificial general intelligence is still a theoretical goal. Similar to humans, AGI will learn, make conclusions, and generate new insights from acquired information.

Currently, most AI systems have a narrow focus. They are designed for specific tasks and applications and are dependent on human programming for training and accuracy. For example, large language models can engage in human-like conversations and handle complex tasks, but they lack the cognitive ability to analyze information and draw insights, a capability expected from artificial general intelligence.

Are there any real-life examples of AGI?

Even though the AGI concept hasn’t fully left the theoretical territory, there are some examples of narrow AI that can outperform humans in specific areas:

1. IBM's Watson: Supercomputers like Watson use AI to handle complex scientific tasks, from modeling the Big Bang theory to simulating the human brain.

   
   

2. Self-driving cars: AI-powered vehicles recognize and interact with other traffic elements, following road rules to navigate.

   
   

3. ROSS Intelligence: ROSS mines data from different text documents, analyzes information and provides precise responses to complex legal questions in seconds.

   
   

4. AlphaGo: Specializing in playing the board game Go, AlphaGo famously defeated world champion Lee Sedol in 2016.

   
   

5. Music AIs: Dadabots is an AI algorithm that can create its own music when given a set of existing compositions.

   
   

What are AGI benefits for enterprises?

AGI is loosely described as AI systems that have a reasonable level of self-awareness, making decisions autonomously, and solving complex problems across different contexts. Enterprises are already gaining benefits from current AI capabilities, and these advantages will only grow once AGI becomes a reality.

Improved automation

AGI will automate a broad range of tasks, from data entry and analysis to decision-making. This will enhance operational efficiency, reduce manual labor, and decrease operational costs. Organizations already use AI tools for quick document processing, data summarization, content creation, and analytics. Through research on artificial general intelligence, all these capabilities will expand.

Upgraded problem-solving and risk management

AGI will handle complex business challenges, process extensive datasets, and offer actionable insights for effective risk management. It will improve detection of cybersecurity breaches, fraud attempts, or operational irregularities, providing immediate mitigation. Unlike current models, AGI will also be able to generate new solutions.

Accelerated innovation

Improved artificial intelligence will speed up innovation by generating creative ideas, optimizing research, and exploring business possibilities. The introduction of AGI will let business leaders experiment with problem-solving, learn from failures, and succeed in timely innovative pursuits.

Additionally, AGI will significantly influence industries through its transformative speed and impact. Enterprises adopting AGI will achieve a competitive edge, innovating, optimizing processes, and adapting to changing market conditions. For example, marketing teams will be able to make more informed decisions, while supply chain managers will have more opportunities for analyzing alternative delivery routes. Financial institutions will gain high-quality insights into market trends.

Cost and resource optimization

AGI will let businesses achieve better cost savings by automating tasks and streamlining processes. For example, the use of AGI systems will allow cutting customer support costs with chatbots, minimizing inventory holding costs through demand forecasting, and reducing energy consumption with smart building management. AGI's real-time data analysis will inform resource allocation decisions, optimizing supply chain logistics, human resource allocation, and inventory management.

Continuous learning and adaptation

AGI will be able to learn from new data, providing access to expert-level knowledge and insights across diverse domains, particularly valuable in industries requiring rare or costly expertise. In the future, potential AGI applications may include advanced chatbots, digital assistants, and autonomous vehicles, all requiring high reasoning and autonomous decision-making capabilities.

The future of AI

The future of AI is going to bring a major shift in technology with various possibilities and responsibilities to take care of. Artificial general intelligence will be a key part of this change, as it has the power to change industries, businesses, and our everyday routines. If industry leaders want to make the most of this change, their priority should be to prepare for AGI's impact and open their minds to new ideas and improvements that lie ahead.