Since AI's breakthrough into the limelight in late 2022, thousands of AI models have been popping up almost every week. It can be dizzying trying to keep up with which does what.

If you are familiar with AI basics, you might already know about generative artificial intelligence (GAI). Conversely, you may not be so familiar with another type of AI called artificial general intelligence (AGI).

While they sound similar, they are not quite the same. And no, it isn't just because their acronym letters are switched around. So, what's the difference between the two?

What Is Artificial General Intelligence?

Imagine an AI that can think, reason, perceive, infer– all the stuff humans can do. That, and more, is what artificial general intelligence is supposed to be. Though theoretical, artificial general intelligence (AGI) could perform any intellectual task, just like a human, but with fewer or no errors.

It differs from artificial narrow intelligence (ANI), which is highly skilled in a particular field or range of tasks. Narrow Intelligence is designed to excel at only one or very few specific tasks, like a professor emeritus in a very niche discipline.

AGI is proposed to be an AI that can feel, make decisions based on its sentiments, solve problems, learn, process languages, and perform other cognitive abilities. Without prior feeding of data, AGI should come up with something meaningful, regardless of the variables involved.

Science fiction AIs barely come close, so AGI is still just a theory. Although some AI models in the works come close to AGI's description, it still relies heavily on supplied data and has yet to form independent reasoning. Though they excel at problem-solving, natural language processing, and the like, they're still a long way off before we can call them full-blown AGIs.

For example, Google DeepMind is working day and night to develop AGI models that can be at par with human intelligence, with the ability to learn and reason just like humans. To know more, check out the amazing things Google's DeepMind existing bots can do.

So what are the potential applications of artificial general intelligence? Well, it promises to find importance in every field imaginable. For example, AGI and biotechnology can provide premium healthcare at a fraction of the cost. It can personalize treatment plans and speed up diagnosis with minimal errors.

It can do these and many more across fields like robotics and automation, research, education, agriculture, space exploration, etc.

What Is Generative Artificial Intelligence?

As mentioned earlier, most AI models in existence at the time of writing fall into this category.

Generative artificial intelligence (GAI) includes any AI that, as the name implies, generates new material, be it audio, image, or text, from previously imputed data. In other words, any AI you have to give prompts to generate content or responds to requests by accessing stored information can be classified as a GAI.

For example, the usual text-to-speech and image-to-image translators and more recent developments like DALL-E (What is DALL-E?), MuseNet, Style-based Generative Adversarial Networks (StyleGAN), Jukebox, and Generative Pre-trained Transformers (GPT-3, GPT-3.5, GPT-4) are categorized under Generative AI.

Generative AI uses deep learning techniques to generate content as close to the prompts as possible. They use the prompts as construction materials to build the content you request to produce. Here are some examples of what ChatGPT can do for you if you want to know more about it.

How Are Artificial General Intelligence and Generative Artificial Intelligence Similar?

Although different in their manner of operations and point of expertise, AGI and Generative AI share several things in common.

1. Learning

AGI and GAI are machine learning models which learn via supervised, semi-supervised and unsupervised algorithms using deep neural networks. This is for them to be able to analyze and process data to generate content in line with the context of the prompt.

Like humans, AGI models can learn from various data and experiences. At the same time, GAI is trained on existing large data pools to understand the underlying patterns and relationships between data to generate new, meaningful, and relevant data.

2. Range of Applications

Both AGI and GAI can be used for a wide range of purposes, including but not limited to text, image, and video content.

Generative AI can be developed to serve various purposes in limited fields. On the other hand, artificial general intelligence is naturally applicable in every sphere of life, as it can independently reason and perform tasks.

3. Catalysts for Change

The goal of technological advancement is to foster change and growth. AGI and GAI are indispensable in fast-tracking much-needed change and innovations the world desperately needs.

With the introduction of usable GAI and AGI, humanity is assured that rapid advancement is following soon, cutting human labor time exponentially.

4. Source of Ethical Dilemma

While getting extra help from AI sounds like a good idea, several concerns arise when there needs to be a clear boundary on what is ethically right for AI to oversee.

With Generative AI, there have been concerns about copyright rules around AI art and even questions about whether AI art is real art. AGI, given enough time, might see humanity as pointless and move for humanity's extermination—a Sci-fi horror turning reality.

Regulations in the field of AI have been challenging, as these are uncharted waters for the human race.

How Does Artificial General Intelligence Differ From Generative Artificial Intelligence?

The most significant difference between them is that AGI is yet to be developed, while GAI exists and is already in use. Other differences lie in the following:

1. Modes of Operation

Other than the fact that AGI is still on the wishlist of computer scientists, their modes of operation are markedly distinct.

Artificial general intelligence is not limited to any specific task or domain, carrying on tasks without specific programming. On the other hand, generative AI focuses on generating new content within a niche based on existing patterns and data.

2. Adaptability

AGI can learn and adapt to new situations, while generative AI is limited by the input data and the specific domain in which it operates.

An AGI overseeing the sales and finances of an organization will be able to adjust in the event of a sudden change like a pandemic. The AGI model will be able to make intelligent inferences from available data and reconfigure the organization's operations to cater to the new development.

This is something generative AI, on its own, cannot do.

3. Cognition

Artificial general intelligence is likely rather human-like in its problem-solving approach. This is opposed to Generative AI, which works on pre-trained input-out sequences. A Generative AI can only do what it was programmed to do, no more, no less. An AGI, on the other hand, will learn, reason, compare and infer.

In simple terms, an AGI can think like a human and maybe even better.

4. Learning Approach

Generative AI often learns through unsupervised training via extensive data resources, which teaches it how to create new content from previously existing ones.

AGI will use a combination of both supervised and unsupervised learning and reinforcement learning. This ensures that it can make intelligent choices in the face of vast resources at its disposal.

GAI, AGI, and Beyond

There's no denying that artificial general intelligence is the stuff of dreams swiftly turning into reality. We are just getting used to generative artificial intelligence but must not get too comfortable.

Artificial general intelligence will soon go beyond being a mere theory but a fleshed-out active form of intelligence, hopefully working with and for us.