As the hype around Generative AI seems to be settling down, industry leaders come to a swift realization of the eminent age of artificial intelligence. With a market valuation of $197 billion in 2023, the global AI industry continues to surge, and forecasts indicate it is set to reach an astounding $1,812 billion by 2030. Those numbers, regardless of how stunning, are merely scratching the surface of the real potential of AI.
We have already discussed the current use cases of AI in our blog, including the application of AI in modern workplaces, the use of large language models, and more. In essence, all of those topics are deeply interconnected and they demonstrate the unprecedented transformative power of AI across industry verticals.
Today, we'll take a look at the future of AI and the potential rise of artificial general intelligence, a technology that is guaranteed to produce an even bigger impact than the current Generative AI tools.
What is artificial general intelligence?
Artificial general intelligence (AGI) is a subfield of AI research dedicated to creating systems that can mimic human-level cognitive abilities, including the capacity for self-improvement. However, there's a division within the AI research community, as not all experts believe achieving AGI is feasible, and there's an ongoing debate about how to define and accurately measure intelligence.
AGI, if realized, would possess the capability to independently tackle diverse complex problems across various fields of knowledge. Basically, this is a type of intelligence that would closely resemble that of humans, with unmatched adaptability and capacity for self-improvement. As of now, the closest thing we have to the AGI artificial general intelligence are the large language models like GPT-4. Such models produce human-like prompt responses and sustain a continuous dialogue with the user. However, these models are still far from having even rudimentary cognitive abilities and the capacity for self-improvement.
Common approaches in artificial general intelligence research
Computer scientists and AI researchers are actively advancing theoretical frameworks to address the unresolved challenge of AGI. There are various high-level approaches that have emerged in the field of AGI research, categorizing them as follows:
- Symbolic approach: This approach to AGI asserts that symbolic thought is at the core of human general intelligence and is precisely what enables broad generalization. Abstract thinking is an integral characteristic of human intelligence, and it might become a characteristic of artificial intelligence too.
- Emergentist approach: The emergentist approach to AGI focuses on the notion that the human brain consists of simple elements (neurons) that self-organize into complex patterns in response to sensory experiences. This approach suggests that a similar form of intelligence might arise by replicating such a structure.
- Hybrid approach: In the hybrid approach to AGI, the brain is viewed as a composite system where various components and principles collaborate to create an outcome greater than the sum of its parts. Hybrid AGI research encompasses a wide range of diverse approaches.
- Universalist approach: The universalist approach to AGI centers on the mathematical essence of general intelligence and suggests that once AGI is theoretically solved, the principles used for its solution can be scaled down and applied to create AGI in practical applications.
All of these approaches are aimed at building a stronger understanding of the human brain and intelligence and then extrapolating that understanding upon AI.
What are the real-life examples of artificial general intelligence?
There are no real examples of AGI artificial general intelligence on the market, but there are examples of narrow artificial intelligence systems that approximate or even surpass human capabilities in specific domains. Artificial intelligence research is dedicated to these systems and their potential for AGI in the future. Here are examples of such systems:
- IBM's Watson: Watson and similar supercomputers possess immense computing power and utilize AI to tackle previously unmanageable scientific and engineering tasks, like modeling the Big Bang theory or simulating the human brain.
- Expert systems: These AI-based systems mimic human judgment, capable of tasks such as recommending medication based on patient data or predicting molecular structures.
- Self-driving cars: AI-driven vehicles recognize and interact with other vehicles, pedestrians, and objects on the road while adhering to traffic rules.
- ROSS Intelligence: Known as the AI attorney, ROSS is a legal expert system capable of mining data from a billion text documents, analyzing information, and providing precise responses to complex legal questions in seconds.
- AlphaGo: AlphaGo excels at a specific problem-solving task—playing the board game Go. It famously defeated the world champion Lee Sedol in 2016.
- Language Model Generative Pre-trained Transformer (GPT): GPT-3 and GPT-4 are versions of OpenAI's language model capable of generating human-like text. However, they often produce flawed output despite their ability to emulate general human intelligence.
- Music AIs: Dadabots, for instance, is an AI algorithm that can create its own approximation of music when given a corpus of existing compositions.
Although AGI has not been realized in these examples, its application could enhance their capabilities. For instance, self-driving cars currently require human intervention in ambiguous situations, as do music algorithms, language models, and legal systems. These domains involve tasks that AI can automate but also require a higher level of abstraction and human-like intelligence.
What is the difference between artificial general intelligence (AGI) and artificial intelligence (AI)?
While artificial intelligence (AI) already encompasses a wide array of technologies and research fields related to machine and computer cognition, the concept of artificial general intelligence (AGI) remains a theoretical objective. AGI is expected and believed to have the capacity to learn virtually anything while exhibiting autonomous, goal-directed, and highly adaptive learning. Not unlike humans, AGI will be able to independently learn, draw conclusions, and even generate new insight from the acquired information.
In contrast, most existing AI systems are considered narrow AI, tailored for specific tasks and applications, though they can still be quite powerful and intricate, relying on human programming for training and accuracy. Take large language models that can be used for a large number of tasks, including the creation of chatbots and digital assistants. They may sustain human-like conversations and perform a number of complex tasks, such as summarizing large volumes of text. However, they lack the cognitive capacity to analyze the information they perceive and draw insights from it. All of that will be accomplished by artificial general intelligence.
Narrow AI
Artificial General Intelligence
Task-specific AI with limited capabilities. Can handle a range of highly-specific, pre-trained tasks with high probability of error or “hallucination”
General, human-like, and versatile type of intelligence. Can handle a wide range of cognitive tasks with low error margin
Trained by data scientists. Connects questions to specific data sets to produce an answer
Can learn, generalize, adapt, and even plan for the future. Generates unique response based on previous observations
No self-awareness or ability to think
Can pass the Turing Test, possesses common sense, a degree of self-awareness, personal opinion, and even expresses emotions
How will AGI work and how will it impact enterprises?
Since AGI remains a theoretical idea, there's a divergence of viewpoints regarding its potential realization. However, in a broad sense, AGI is loosely defined as AI systems having a reasonable level of self-awareness, autonomous decision-making, and the capability to solve complex problems across various contexts, even those they were unaware of when initially created. The enterprises already benefit from the current AI capabilities, and these benefits will only be extended once AGI becomes a reality.
Enhanced automation
AGI will automate a wide range of tasks, including data entry, analysis, and decision-making. This leads to increased operational efficiency, reduced manual labor, and lower operational costs. We already use AI tools for faster document processing, summarization and classification of data, content creation, analytics, and more. All of these capabilities will expand through artificial general intelligence research.
Advanced problem-solving and risk mitigation
AGI will be able to handle complex and dynamic business challenges. It can process vast datasets, identify patterns, and provide actionable insights that may not be apparent through traditional analysis methods. It will continuously monitor data for anomalies and potential threats, making it valuable for risk management.
What's more, AGI can detect cybersecurity breaches, fraud attempts, or operational irregularities and take immediate action to mitigate these risks. Unlike current models, AGI will be able to come up with new solutions rather than suggesting familiar options.
Accelerated innovation
True intelligence will speed up the innovation process by generating creative ideas, suggesting improvements, and optimizing research and development efforts. It will explore various possibilities rapidly, potentially leading to breakthrough innovations. Just like humans, it will be able to experiment with different approaches to problem-solving, learn from failures, make adjustments, and succeed in its innovative pursuits. The major difference, however, will be the speed of that process.
Enterprises that adopt AGI will leverage its capabilities to innovate, optimize processes, and adapt quickly to changing market conditions. This can provide a significant competitive edge in industries where agility and innovation are critical. For instance, marketing teams will be able to make much more informed decisions on audience preferences, supply chain managers will be able to analyze alternative routes for delivering the goods, and financial institutions will get high-quality insights into market trends. These are just some of the examples of how artificial general intelligence will disrupt industries.
Cost and resource optimization
By automating tasks and streamlining processes, AGI will lead to cost savings. For example, it can reduce customer support costs through chatbots, minimize inventory holding costs through demand forecasting, and lower energy consumption through smart building management. AGI can analyze data in real time to make informed decisions about resource allocation. For example, it can optimize supply chain logistics, allocate human resources efficiently, and manage inventory levels more effectively.
Continuous learning and adaptation
Future intelligence systems can learn from new data and adapt to evolving business needs over time. This will ensure that they remain relevant and valuable, even as the business environment changes. AGI will also provide access to expert-level knowledge and insights across various domains. This can be especially valuable in industries where specialized expertise is required but scarce or expensive to obtain.
Given the evolving nature of AI research and the AGI concept, different theoretical approaches have emerged for its development. Some involve techniques like neural networks, while others propose creating comprehensive simulations of the human brain through computational neuroscience methods. In the future, potential applications of AGI may encompass advanced chatbots, digital assistants, and autonomous vehicles, all of which require a high degree of reasoning and autonomous decision-making capabilities.
Adopt AI today to gain a strong advantage tomorrow
The future of AI presents both possibilities and obligations, and our current position marks the inception of a significant transformation. Preparing for the impact of artificial general intelligence on industries, businesses, and everyday life is the most prudent course of action for leaders as they progress toward the innovations of tomorrow.
At Trinetix, we assist enterprises with harnessing technology to drive meaningful business transformations. We leverage the capabilities of AI to help our clients predict future trends, detect risk factors, support decision-making, and enable real-time analytics. Our forward-thinking methodology empowers enterprises to use existing AI capabilities in a way that best suits their unique needs—today and in the future.
Let’s chat about the current uses of AI for your business and see how it can propel your workflows to a new level of efficiency.
