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Artificial Narrow Intelligence

What is Artificial Narrow Intelligence?

Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI, is the only type of artificial intelligence we have successfully realized to date. Narrow AI is goal-oriented, designed to perform singular tasks - i.e. facial recognition, speech recognition/voice assistants, driving a car, or searching the internet - and is very intelligent at completing the specific task it is programmed to do.

While these machines may seem intelligent, they operate under a narrow set of constraints and limitations, which is why this type is commonly referred to as weak AI. Narrow AI doesn’t mimic or replicate human intelligence, it merely simulates human behaviour based on a narrow range of parameters and contexts.

Consider the speech and language recognition of the Siri virtual assistant on iPhones, vision recognition of self-driving cars, and recommendation engines that suggest products you make like based on your purchase history. These systems can only learn or be taught to complete specific tasks.

Narrow AI has experienced numerous breakthroughs in the last decade, powered by achievements in machine learning and deep learning. For example, AI systems today are used in medicine to diagnose cancer and other diseases with extreme accuracy through replication of human-esque cognition and reasoning.

Narrow AI’s machine intelligence comes from the use of natural language processing (NLP) to perform tasks. NLP is evident in chatbots and similar AI technologies. By understanding speech and text in natural language, AI is programmed to interact with humans in a natural, personalized manner.

Narrow AI can either be reactive or have limited memory. Reactive AI is incredibly basic; it has no memory or data storage capabilities, emulating the human mind’s ability to respond to different kinds of stimuli without prior experience. Limited memory AI is more advanced, equipped with data storage and learning capabilities that enable machines to use historical data to inform decisions.

What is the difference between general AI and narrow AI?

AI is an artificial intelligence system that can learn tasks and solve problems without being explicitly instructed on every single detail. It should be able to do reasoning and abstraction, and easily transfer knowledge from one domain to another.

Creating an AI system that satisfies all those requirements is very difficult, researchers have learned throughout the decades. The original vision of AI, computers that imitate the human thinking process, has become known as artificial general intelligence.

Artificial General Intelligence is “a machine that has the capacity to understand or learn any intellectual task that a human being can.” Scientists, researchers, and thought leaders believe that AGI is at least decades away.

But in their continued endeavors to fulfill the dream of creating thinking machines, scientists have managed to invent all sorts of useful technologies. Narrow AI is the umbrella term that encompasses all these technologies.

Narrow AI systems are good at performing a single task, or a limited range of tasks. In many cases, they even outperform humans in their specific domains. But as soon as they are presented with a situation that falls outside their problem space, they fail. They also can’t transfer their knowledge from one field to another.

For instance, a bot developed by the Google-owned AI research lab DeepMind can play the popular real-time strategy game StarCraft 2 at championship level. But the same AI will not be able to play another RTS game such as Warcraft or Command & Conquer.

While narrow AI fails at tasks that require human-level intelligence, it has proven its usefulness and found its way into many applications.  Your Google Search queries are answered by narrow AI algorithms. A narrow AI system makes your video recommendations in YouTube and Netflix, and curates your Weekly Discovery playlist in Spotify. Alexa and Siri, which have become a staple of many people’s lives, are powered by narrow AI.

In fact, in most cases that you hear about a company that “uses AI to solve problem X” or read about AI in the news, it’s about artificial narrow intelligence.

Different types of Narrow AI 

1. Symbolic Artificial Intelligence

Symbolic artificial intelligence, also known as good old-fashioned AI (GOFAI), was the dominant area of research for most of AI’s history. Symbolic AI requires programmers to meticulously define the rules that specify the behavior of an intelligent system. Symbolic AI is suitable for applications where the environment is predictable and the rules are clear-cut. Although symbolic AI has somewhat fallen from grace in the past years, most of the applications we use today are rule-based systems.

2. Machine Learning

Machine learning, the other branch of narrow artificial intelligence, develops intelligent systems through examples. A developer of a machine learning system creates a model and then “trains” it by providing it with many examples. The machine learning algorithm processes the examples and creates a mathematical representation of the data that can perform prediction and classification tasks.

For instance, a machine-learning algorithm trained on thousands of bank transactions with their outcome (legitimate or fraudulent) will be able to predict if a new bank transaction is fraudulent or not.

Machine learning comes in many different flavors. Deep learning is a specialized type of machine learning that has become especially popular in the past years. Deep learning is especially good at performing tasks where the data is messy, such as computer vision and natural language processing.

Reinforcement learning, another subset of machine learning, is the type of narrow AI used in many game-playing bots and problems that must be solved through trial-and-error such as robotics.

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Artificial Narrow Intelligence

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is Artificial Narrow Intelligence?

Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI, is the only type of artificial intelligence we have successfully realized to date. Narrow AI is goal-oriented, designed to perform singular tasks - i.e. facial recognition, speech recognition/voice assistants, driving a car, or searching the internet - and is very intelligent at completing the specific task it is programmed to do.

While these machines may seem intelligent, they operate under a narrow set of constraints and limitations, which is why this type is commonly referred to as weak AI. Narrow AI doesn’t mimic or replicate human intelligence, it merely simulates human behaviour based on a narrow range of parameters and contexts.

Consider the speech and language recognition of the Siri virtual assistant on iPhones, vision recognition of self-driving cars, and recommendation engines that suggest products you make like based on your purchase history. These systems can only learn or be taught to complete specific tasks.

Narrow AI has experienced numerous breakthroughs in the last decade, powered by achievements in machine learning and deep learning. For example, AI systems today are used in medicine to diagnose cancer and other diseases with extreme accuracy through replication of human-esque cognition and reasoning.

Narrow AI’s machine intelligence comes from the use of natural language processing (NLP) to perform tasks. NLP is evident in chatbots and similar AI technologies. By understanding speech and text in natural language, AI is programmed to interact with humans in a natural, personalized manner.

Narrow AI can either be reactive or have limited memory. Reactive AI is incredibly basic; it has no memory or data storage capabilities, emulating the human mind’s ability to respond to different kinds of stimuli without prior experience. Limited memory AI is more advanced, equipped with data storage and learning capabilities that enable machines to use historical data to inform decisions.

What is the difference between general AI and narrow AI?

AI is an artificial intelligence system that can learn tasks and solve problems without being explicitly instructed on every single detail. It should be able to do reasoning and abstraction, and easily transfer knowledge from one domain to another.

Creating an AI system that satisfies all those requirements is very difficult, researchers have learned throughout the decades. The original vision of AI, computers that imitate the human thinking process, has become known as artificial general intelligence.

Artificial General Intelligence is “a machine that has the capacity to understand or learn any intellectual task that a human being can.” Scientists, researchers, and thought leaders believe that AGI is at least decades away.

But in their continued endeavors to fulfill the dream of creating thinking machines, scientists have managed to invent all sorts of useful technologies. Narrow AI is the umbrella term that encompasses all these technologies.

Narrow AI systems are good at performing a single task, or a limited range of tasks. In many cases, they even outperform humans in their specific domains. But as soon as they are presented with a situation that falls outside their problem space, they fail. They also can’t transfer their knowledge from one field to another.

For instance, a bot developed by the Google-owned AI research lab DeepMind can play the popular real-time strategy game StarCraft 2 at championship level. But the same AI will not be able to play another RTS game such as Warcraft or Command & Conquer.

While narrow AI fails at tasks that require human-level intelligence, it has proven its usefulness and found its way into many applications.  Your Google Search queries are answered by narrow AI algorithms. A narrow AI system makes your video recommendations in YouTube and Netflix, and curates your Weekly Discovery playlist in Spotify. Alexa and Siri, which have become a staple of many people’s lives, are powered by narrow AI.

In fact, in most cases that you hear about a company that “uses AI to solve problem X” or read about AI in the news, it’s about artificial narrow intelligence.

Different types of Narrow AI 

1. Symbolic Artificial Intelligence

Symbolic artificial intelligence, also known as good old-fashioned AI (GOFAI), was the dominant area of research for most of AI’s history. Symbolic AI requires programmers to meticulously define the rules that specify the behavior of an intelligent system. Symbolic AI is suitable for applications where the environment is predictable and the rules are clear-cut. Although symbolic AI has somewhat fallen from grace in the past years, most of the applications we use today are rule-based systems.

2. Machine Learning

Machine learning, the other branch of narrow artificial intelligence, develops intelligent systems through examples. A developer of a machine learning system creates a model and then “trains” it by providing it with many examples. The machine learning algorithm processes the examples and creates a mathematical representation of the data that can perform prediction and classification tasks.

For instance, a machine-learning algorithm trained on thousands of bank transactions with their outcome (legitimate or fraudulent) will be able to predict if a new bank transaction is fraudulent or not.

Machine learning comes in many different flavors. Deep learning is a specialized type of machine learning that has become especially popular in the past years. Deep learning is especially good at performing tasks where the data is messy, such as computer vision and natural language processing.

Reinforcement learning, another subset of machine learning, is the type of narrow AI used in many game-playing bots and problems that must be solved through trial-and-error such as robotics.

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