What is artificial narrow intelligence (ANI)?

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. 

>>Follow VentureBeat’s ongoing ChatGPT coverage<<

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:

1. Rule-based systems: Designed to follow a set of predetermined rules to perform a specific task.
2. Expert systems: Perform tasks that normally require human expertise, such as diagnosing medical conditions or identifying the best course of action in a given situation.
3. Decision-tree systems: Use a tree-like structure to make decisions based on a set of predetermined rules.
4. Artificial neural networks: Inspired by the way the human brain works, these AI systems can recognize patterns and make decisions based on that information.
5. Genetic algorithms: Use principles of genetics and natural selection to improve performance over time.
6. Evolutionary computation: Uses principles of evolution and natural selection to adapt and improve performance.
7. Fuzzy logic systems: Use fuzzy logic, a type of logic that allows for the representation of uncertain or imprecise information to make decisions.
8. Bayesian networks: Use probabilistic reasoning to make decisions based on uncertain or incomplete information.

Top 3 applications of ANI

1. NLP: ANI is used in NLP systems to help computers understand and interpret human language. For example, the virtual assistant Siri uses NLP to understand and respond to voice commands in natural language.

2. Image and speech recognition: ANI is used in image and speech recognition systems to identify objects, people and other elements in images and audio recordings. For example, the facial recognition technology used by Meta applies narrow AI to identify and tag people in photos.

3. Decision-making: ANI is used in decision-making systems to help businesses and organizations make more informed decisions. For example, a narrow AI system might be used by a bank to analyze financial data and make recommendations on whether to approve a loan application.

Top 7 use cases of ANI

1. Healthcare: ANI is used to assist with tasks such as diagnosing medical conditions and predicting patient outcomes. For example, an ANI system might be used to analyze medical images and identify potential signs of cancer.

2. Finance: ANI is used to analyze financial data and make predictions about market trends and other economic factors. For example, a narrow AI system might be used by a hedge fund to identify investment opportunities.

3. Manufacturing: ANI is used to assist with tasks such as quality control and product inspection. For example, an ANI system might be used to inspect products on an assembly line and identify defects.

4. Customer service: ANI is used to assist with tasks such as answering customer inquiries and resolving problems. For example, an ANI system might be used to answer common questions and provide solutions for customers through a chatbot on a company’s website.

5. Supply chain management: ANI is used to optimize logistics and improve efficiency. For example, an AI system might be used to analyze data on delivery times and routes to identify bottlenecks and improve delivery times.

6. Transportation: ANI is used to assist with tasks such as route planning and traffic management. For example, an AI system might be used to optimize routes for delivery trucks and reduce fuel consumption.

7. Agriculture: ANI is used to assist with tasks such as crop monitoring and pest control. For example, an AI system might be used to analyze images of crops to identify pests and determine the most effective treatment.

Pros and cons of ANI

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

• Increased accuracy: These systems are able to perform tasks with a high degree of accuracy, which can help reduce errors and improve efficiency.

• Increased speed: They are able to process large amounts of data quickly, which can help speed up tasks and decision-making processes.

• Cost savings: In some cases, the use of ANI can help reduce labor costs by automating tasks that would otherwise be performed by humans.

However, ANI also has some drawbacks:

• Narrow AI is only able to perform the specific task it was designed for. It is not able to adapt or learn to perform new tasks, and it is not able to think or make decisions in the same way that a human can.

• It is reliant on the data it is trained on. If the data is biased or incomplete, the narrow AI system may make biased or inaccurate decisions. This can be a major concern in fields such as healthcare and criminal justice, where biased algorithms can have serious consequences.

• It can be brittle and inflexible. It is designed to perform a specific task and may not be able to handle variations in the input or unexpected situations.

• ANI systems can be computationally intensive, requiring powerful hardware and significant amounts of energy to run. This can be a barrier to the widespread adoption of narrow AI systems.

• There are concerns about the ethical implications of narrow AI and the potential for it to be used in harmful or malicious ways. There is a need for regulations and oversight to ensure that narrow AI is used responsibly.

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.