Artificial General Intelligence vs Narrow AI

As the advancement in AI technologies continues in light speed, we started to see the term "Artificial General Intelligence" more frequently, feeling getting close to it but leaving many wondering about its meaning and differences. In this article I tried to demystify the concepts of AGI and Narrow AI while highlighting the distinctions between the two. I delve into the defining characteristics of AGI and Narrow AI that allow us to assess recent advancements. Answer some of the key questions you may have such as: With Generative AI, are we close to giving a birth to AGI? How far we are to achieve human-level AGI? What are the ethical and social implications of AGI?

What is Artificial General Intelligence?

Artificial General Intelligence (AGI) refers to a hypothetical (perhaps not anymore) technology that would be capable of performing any intellectual task that a human being can do. In order to be considered AGI, the technology would need to exhibit a broad range of cognitive abilities, such as:

Ability to learn from experience, adapt to new situations, improve its own performance. Being able to reason, plan, and solve problems in complex in dynamic environments. Exhibiting human level creativity, curiosity, and a capacity for abstract reasoning. Having a sense of self-awareness and consciousness, with excellent natural language processing and multimodal interactions.

AGI systems would be able to generalize from one domain to another and adapt to new situations and environments WITHOUT additional programming or human intervention. AGI is sometimes referred as "Strong AI" because it is not limited to a specific domain and can exhibit intelligence outside of its designated area of focus.

Common Sense is a such a fundamental aspect of AGI. It encompasses the ability to possess general knowledge and understanding across various domains, such as learning new things, comprehending natural language, or making intuitive judgments on complex situations. While humans may excel in specific areas, they possess a breadth of capabilities that extend across different domains. In contrast, narrow AI (NA) systems are task-specific and lack the holistic understanding that AGI aims to achieve. Questions arise regarding the necessary advancements, ethical considerations, and potential implications of achieving AGI. Exploring these examples toward AGI pushes us to ponder the boundaries and possibilities of artificial intelligence.

While humans possess common sense naturally, AI systems still evolving to acquire common sense. Common sense reasoning is important for AI systems for several reasons. Firstly, common sense knowledge helps AI systems to avoid producing meaningless or absurd outputs, otherwise known hallucinations when faced with unusual problem data. Unlike human experts who can rely on their common sense to recognize invalid information, AI systems without common sense knowledge may provide incorrect or nonsensical responses. Secondly, common sense reasoning provides a broader perspective that allows AI systems to make decisions beyond their domain-specific knowledge. Human experts often use common sense to override their specialist domain knowledge in certain circumstances. For instance, a medical expert considering life-threatening surgery on an elderly patient with a short life expectancy might rule out the surgery based on their common sense understanding of the situation. AI systems without common sense would lack this broader perspective and might not make the same informed decision.

Let's just take one example, AGI can transform the healthcare industry by introducing a personalized health assistant capable of monitoring, diagnosing, and treating patients across diverse domains. This AI-powered assistant would utilize its extensive knowledge from various sources, such as medical records, scientific literature, and social media, to gain a comprehensive understanding of each patient's history, preferences, and unique needs. Leveraging common sense reasoning, the assistant would infer symptoms, mood, and behaviour by analysing speech, facial expressions, and body language. Through transfer learning, it would apply skills and knowledge from one domain to another, employing natural language processing for communication, computer vision for medical image analysis, and machine learning for treatment recommendations. It also performs across different domains, incorporating financial analysis capabilities to assess cost-effectiveness and considering factors like patient preferences, lifestyle, and socioeconomic background.

What is Narrow AI?

The term "Narrow AI" refers to AI systems that are designed to perform specific tasks, rather than being capable of general intelligence or human-like cognition. Such as; recognizing faces in images or predicting stock prices on historical data. These AI systems are designed to operate within a defined set of parameters and are not capable of adapting to new situations or learning new skills without additional programming or human intervention. Narrow AI is sometimes referred to as "weak AI" because it is limited to a specific domain and cannot exhibit intelligence outside of its designated area of focus.

Narrow AI utilizes technologies like Machine Learning, Cognitive Search, Form Recognizer, OCR, Bot frameworks, Vision, Language, Speech, Video Analytics…. services to learn from data and make predictions within specific domains. These technologies function by leveraging the power of algorithms and mimicking the workings of the human brain's neural system. We find numerous examples such as teaching machines to play chess, predicting weather patterns, doing sales forecasts, developing algorithms for accurate crop forecasts in agriculture. Narrow AI applications extend to diverse fields, including speech recognition, facial recognition, autonomous vehicles, natural language processing, and more. The versatility and practicality of narrow AI solutions have driven remarkable advancements in these areas.

For example, in retail and ecommerce, Narrow AI solutions can analyse a customer's purchase behaviour, and preferences. Using this data, can generate personalized product recommendations. For example, a customer who frequently purchases running shoes might receive recommendations for the latest running shoe models, accessories, and related gear. This personalized recommendation engine enhances the customer's shopping experience and increases the likelihood of making relevant purchases.

Similar examples represent narrow AI because they are purpose-built for specific domains or tasks within their respective industries. These applications excel in their specialized areas but lack the capacity for general intelligence or human-like cognition. They are not designed to learn from experience or tackle a broad spectrum of tasks outside their defined expertise. Narrow AI systems exhibit high proficiency within their limited scope, delivering valuable outcomes and enhancing efficiency within their targeted domains.

Challenges for building AGI

The current state of AGI is still in its infancy, but evolving and researchers are exploring the possibilities and challenges involved in achieving true AGI. There has been significant progress in the field of AI, and many narrow AI systems have been developed that can perform specific tasks at a level that rivals or surpasses human performance, there are still challenges to overcome in order to achieve AGI.

Achieving AGI is developing a system that can understand and reason about the world in the same way that humans do has been a dream of many for a long time. This requires the ability to integrate information from multiple sources, recognize patterns and relationships, and make decisions based on incomplete or uncertain information, adapt to new situations and environments, without additional programming or human intervention.

Another key challenge in building AGI has been the lack of a unified theory of intelligence in our collective societal and tech environment (as simple measurements like IQ does not define the boundaries of human Intelligence).

But slowly being formed. We have made significant progress in developing AI systems, building AGI remains a tough task. However, with continued research and development, we may be able to unlock the secrets of human-like intelligence in the not-too-distant future.

Ethical and Societal Implications of AGI

As we develop AGI, we must consider the ethical and societal implications of this technology. Yes, AGI has the potential to revolutionize many industries, but it can also lead to widespread job displacement and economic disruption. There are also concerns about the safety and security of AGI, and the potential for these systems to be misused or weaponized. Finally, there are questions about the relationship between AGI and human beings, and the potential impact of AGI on our society and our way of life.

Is Generative AI getting us close to AGI?

The question of whether Generative AI, particularly GPT-4 models and beyond, are bringing us closer to AGI is a topic of widespread discussion. Generative AI, exemplified by ChatGPT and upcoming versions like GPT-4+ LLM derivations, plays important role in advancing AI technologies.

Generative AI technologies through its natural language understanding and generation capabilities, enabling humanlike responses that clearly shows deep comprehension of context. Creative and innovative responses go beyond pre-programmed answers, feels us bringing us closer to human-level intelligence. The expansion into artistic domains, such as AI led music, art, and literature...creation further pushes the boundaries of AI capabilities and aligns with the multifaceted nature of human intelligence.

Having said that, Generative AI is a step towards AGI. While Generative AI may have some overlap with AGI, there are still significant differences between the two. Generative AI still relies on humans for its sustainability and evolution. There is still work to do to show signs of consciousness or self-awareness and have cross domain human common sense without hallucinations. But we are getting very close.

As we move forward in the journey to Artificial General Intelligence from Generative AI before Narrow AI, the next few years will be crucial. It's our chance to responsibly bring AGI to life, ensuring that innovation respects human values and shapes a better future.