AGI is the logical conclusion of AI research—a set of digital algorithms capable of approaching problems and critically evaluating information with the flexibility and depth of human cognition. In other words, AGI doesn't simply mimic intelligence; it demonstrates it as clearly and undeniably as any person.
If this sounds far-fetched, that's because AGI is still very much a theoretical field; no true AGI has ever been created. Today's artificial intelligence is impressive, but it does not demonstrate real cognitive ability, meaning it cannot actually think for itself in the way that its human users can. It is not aware of itself as a thinking being.
This is the main point differentiating AI and AGI. More specifically, the differences between artificial general intelligence vs. artificial intelligence can be summed up in the following way:
- AI operates based on predefined rules and data patterns. It can make decisions only within the boundaries of its training data.
- AGI would hypothetically be capable of sophisticated reasoning across domains, potentially developing novel problem-solving approaches that might differ from human reasoning patterns. It would be able to handle abstract concepts, engage in complex logical analysis and potentially discover new ways of thinking that humans haven't considered.
The development of AI is rooted firmly in the desire to create systems that can automate complex tasks. AI and AGI would have different limitations in terms of what they would be capable of accomplishing.
- AI is designed for specific tasks like image recognition or language translation. It cannot generalise knowledge or transfer skills across different domains.
- AGI would perform a wide range of tasks, mimicking the versatility of human intelligence. AGI would adapt to new tasks autonomously without needing retraining or more specific programming.
All AI models are built on initial training data, but they may also be able to improve their performance based on new data, evolving and becoming better over time.
- AI learns from large datasets within a narrow domain. Some AI incorporates machine learning to identify patterns when new data is encountered, enhancing their ability to perform specific tasks with increasing accuracy.
- AGI would be capable of incorporating learnings from new data but would not be limited to a specific domain. Similar to human learning, AGI would be able to learn and evolve in regard to any subject it is exposed to.
Understanding context means interpreting and responding to information based on the surrounding circumstances and nuances, taking into account the kinds of nuanced information that can be difficult to quantify.
AI is limited contextual understanding, and often struggles with nuances. AI typically requires clear and structured input to function accurately.
AGI is intended to comprehend and interpret context in a way that aligns with human understanding. This capability would enable it to interact more naturally with humans.
General problem solving involves addressing a wide variety of challenges using a versatile approach. It is the ability to effectively evaluate novel problems and to begin constructing viable solutions.
AI solves specific problems it was trained for. It lacks the flexibility to address unanticipated issues outside its programming.
AGI would theoretically solve a broad spectrum of problems, employing general intelligence using human-level problem-solving skills. AGI could dynamically apply its knowledge and skills to novel and diverse challenges.
Artificial general intelligence as a term was first coined in 2007, and it has existed as an unnamed concept for much longer than that. During that time, it has always been more of a theory than anything close to reality. GenAI brought this concept back into the realm of possibility for many theorists and may be the first notable steps in years towards realising true AGI.
GenAI's ability to apply deep learning techniques to create new content (text, images, video, audio etc.) that is categorically similar to its training data while still being original and distinct brings the world closer to AGI. That said, generative AI and AGI are not the same thing.
Despite the impressive capabilities of GenAI, these systems still present the same limitations of AI. In contrast, artificial general intelligence could theoretically replicate the full range of human cognitive abilities and more; it would possess the flexibility to understand, learn and apply knowledge across diverse tasks and contexts, demonstrating reasoning and problem-solving skills on par with human intelligence. This could also be applied towards enhancing generative capabilities, beyond even what today's GenAI can do.
Given the astonishing strides AI has made in the past year alone, it can be easy to look at AGI as inevitable—or even imminent. The reality is that there are still several advancements that will need to occur before AI can finally make that leap to true general intelligence. The following elements are crucial to bridging the gap between narrow AI and the versatile, human-like thought processes promised by AGI:
AI systems must be able to interpret and process sounds with the same spatial awareness and nuance as humans. This includes distinguishing between overlapping sounds and identifying sound sources in complex environments, which is essential for applications like advanced virtual assistants and autonomous systems operating in dynamic settings.
AI must accurately recognise and interpret visual inputs, including subtle differences in colour and texture, to perform tasks such as advanced medical imaging, quality control in manufacturing and real-time video analysis.
AI systems need real spatial intelligence, allowing them to navigate and interact with physical environments as humans do. This is about more than simply 'seeing’ the physical world; it means fully understanding 3D space, recognising object relationships and predicting physical interactions. AGI with spatial intelligence would interpret spatial dynamics in real time, adjusting actions based on the layout and changes in its surroundings, rather than relying solely on GPS or predefined maps.
AGI must possess the ability to recognise problems and devise effective solutions independently. This involves not only understanding the problem context but also applying common sense and predictive reasoning to solve never-before-encountered issues, like how humans troubleshoot and innovate as part of the problem-solving process.
Mobile AI systems need to navigate complex environments autonomously and safely without human intervention. This includes not just following GPS coordinates but also dynamically adjusting to new obstacles, changing conditions, and unforeseen events—all of which are crucial for applications such as fully autonomous vehicles and robotic delivery systems.
Although less important for purely computer-based systems, developing fine motor skills is essential for physical tasks that require precision and dexterity. This advancement will enable robots to perform such intricate activities as surgical procedures and delicate assembly work in manufacturing.
Before AGI can exist, AI needs to move beyond processing isolated pieces of information and achieve full context comprehension. This includes understanding the implied meanings, social cues and complex language structures prevalent in human language.
Generative AI can mimic creativity, but it is not creative. AGI must be capable of generating completely novel ideas and creative solutions, which requires a diverse knowledge set and the ability to synthesise that knowledge in innovative ways.
Lastly (and possibly the furthest out of all these advancements) is the ability for AGI to interact seamlessly with humans on an emotional level. Recognising and responding to unspoken emotional signals and understanding social dynamics is an extremely subtle and complex area of social interaction—one that many humans have difficulty with. This makes it all the more complicated when it comes to attempting to teach these interactions to AI systems.
AGI is still a way off, but advanced AI solutions are available today. ServiceNow delivers these AI solutions to your business, through the power of the Now Platform®. ServiceNow's comprehensive suite of centralised applications integrates AI at their core, empowering organisations of all sizes.
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