Interest in artificial intelligence (AI) has surged with the emergence of such intuitive tools as ChatGPT. But, ChatGPT and the slew of related breakthrough projects that we’ve witnessed in AI still take the form of what can be called “narrow” intelligence. 

Hyperbole aside, we have only scratched the surface of what the new technology may eventually become. ChatGPT has the markings of artificial narrow intelligence (ANI). That is, AI that is designed to perform specific tasks. 

This advanced tool has arisen from ongoing research in the domain of natural language processing (NLP). ANI stands in contrast to artificial general intelligence (AGI), the multidecade effort to bring generalized human-like intelligence to machines.

AI: Narrowing things down

In his book, The singularity is near: When humans transcend biology, computer science and futurist Ray Kurzweil used the term "narrow AI" to describe the development of systems that exhibit "intelligent" behaviors in specific contexts. In contrast to natural, generally intelligent systems like humans, ANI systems require human reprogramming or reconfiguration when the context or behavior specification changes, even slightly. This is because they lack the ability to adapt to new goals or circumstances, and generalize knowledge from one context to another, which humans do through transfer learning. 

In theory, ANI is a type of AI designed to perform a single or a narrow set of related tasks at a high level of proficiency. It is also referred to as weak AI, narrow AI, limited AI or even specialized AI. ANI systems are typically trained on a large dataset and are able to make decisions or perform actions based on this training.

ANI systems can be classified into two categories: supervised learning systems and unsupervised learning systems. Supervised learning systems are trained on labeled datasets that enable the system to learn the relationship between the input data and the desired output. On the other hand, unsupervised learning systems are trained on unlabeled datasets and can identify the patterns and relationships in the data without guidance. 

Evolution of ANI

The concept of ANI dates back to the 1950s, when researchers first began investigating the possibility of creating machines capable of performing cognitive tasks. Some might trace its development back to one of the first AI attempts to create a program known as the General Problem Solver (GPS). This was designed to solve problems in a manner similar to that of humans.

While the GPS was not a huge success, it did lay the groundwork for future AI research and development. By the 1960s, we saw the development of NLP systems such as ELIZA, which was able to hold simple conversations with humans. 

Furthermore, the development of expert systems such as Dendral and MYCIN in the 1970s marked a major milestone in the field of AI because they were able to mimic the decision-making processes of human experts and had a wide range of applications in drug design and healthcare. Significant advances in machine learning (ML) occurred in the 1980s and 1990s, paving the way for the development of more advanced ANI systems. During this period, one notable achievement was the development of the AI system Deep Blue, which defeated world champion chess player Garry Kasparov in a match in 1997.

The 2000s kicked off with the introduction of Siri and Google Translate. Developed in 2011, Siri uses NLP to understand and respond to voice commands. On the other hand, Google Translate is an NLP system that can translate text and speech from one language to another. 

In the 2020s, advanced NLP systems such as OpenAI’s GPT-3 hit the market. These had an amazing ability to generate human-like text. OpenAI also launched DALL-E and DALL-E 2, which uses a neural network to generate images based on a given text prompt.

In 2022, OpenAI launched ChatGPT, an AI system that can understand and respond to user input in a conversational manner, which makes it well-suited for use in chatbot applications. There has also been significant progress in the use of ANI in healthcare, with the development of AI systems such as DeepMind's AlphaFold, which is able to predict the 3D structure of proteins.

8 types of ANI systems

Broadly speaking, there are are several types of ANI, including:

    Top 3 applications of ANI

          Top 7 use cases of ANI

                        Pros and cons of ANI

                        Like any technology, ANI has both benefits and drawbacks. Some of the pros:

                              However, ANI also has some drawbacks:

                                        The next phase of AI: Artificial general intelligence (AGI)

                                        At the same time that ANI continues to produce results, the pursuit of artificial general intelligence (AGI) still captures the imagination of the tech community. AGI refers to machines that possess human-like intelligence, with the ability to perform a wide range of tasks, think abstractly and adapt to new situations. This stands in contrast to ANI, which is designed to perform specific tasks.

                                        While AGI remains largely theoretical at this point, the idea has garnered significant attention and investment, with notable figures such as Bill Gates, Stephen Hawking and Elon Musk expressing concerns about the potential threat of such advanced AI. 

                                        But opinions on the feasibility and timeline of AGI vary widely. Some researchers argue that a realistic timeline might place such an advance in 2040, with the pessimistic timeline being 2075.