What is Narrow AI?

Narrow AI, also called weak AI, is artificial intelligence that can perform specific digital tasks intelligently but cannot self-learn and adapt to learn new tasks across various domains. Examples of narrow AI include chatbots, image recognition, image generation, and other AI applications you use daily. Narrow AI technologies have been trained on vast datasets. They can recognize complex patterns in data - but they are still considered narrow or weak because they don't possess general cognitive abilities or the ability to learn, innovate, and problem-solve in ways that humans can.

What is the difference between narrow AI and strong AI?

Strong artificial intelligence (Strong AI), also known as artificial general intelligence (AGI), is a theoretical AI system that can reason, self-teach, and perform tasks across various problem domains, similar to human beings. It can generalize and apply knowledge across functions, plan using current knowledge, and modify behavior to adapt to changing environments. Strong AI remains a theoretical goal due to various processing and evaluation limitations.

In practice, all AI systems are narrow AI. Narrow AI performs well when completing set tasks on which it has training data. Any gaps in its training data cause it to struggle with that aspect of the task. For example, a narrow AI facial recognition technology cannot recognize faces from the demographic it has not been trained in.

What are the benefits of narrow AI?

Businesses can extract several benefits from narrow AI in their workflows and operations.

Increase efficiency

Narrow AI can automate repetitive or laborious tasks, removing the need for businesses to allocate resources to them. Without the need to complete these mundane tasks, humans can focus on other areas to improve overall productivity. Another benefit of narrow artificial intelligence systems is that they are available 24/7 for businesses. This is a major benefit for time-consuming tasks that require constant monitoring.

Provide cost savings

Businesses can save money on human labor by automating tasks with narrow AI. It can also perform tasks extremely well, providing further cost benefits to businesses. For example, a recommendation engine could use consumer behavior to recommend content to customers, enhancing their satisfaction and deriving value for the company. Both in terms of saving money and providing value, narrow AI is an effective economic choice.

Reduce human errors

Human errors in fields like cybersecurity or medicine can cause serious problems. Even in data entry, small mistakes could create larger problems in the future. By automating tasks with weak AI, businesses can eliminate the possibility of humans introducing errors into their processes.

Effective data analysis

One of the strengths of narrow AI is its ability to rapidly process large volumes of data. Businesses can use this function to analyze data and create data-driven insights. Teams that frequently use data, like marketing or accounting, can use narrow AI to enhance their existing workflows.

How does narrow AI work?

Narrow AI uses deep learning models that train on huge volumes of data. Due to their wide learning context, these deep learning or foundational models (FMs) can perform various generalized tasks.

Transformers

Foundation models are built on transformers—neural network architecture that transforms input data into a computer-readable output sequence. Neural networks mimic the human brain to simulate human intelligence. Transformers are vital in weak AI systems related to natural language processing, as they change typed input into a mathematical representation of that sentence. A transformer model takes an input sentence like “The grass is green” and transforms it into a signifier representing the relationship between each word in the sentence. This mathematical representation enables natural language processing so AI systems can understand the user’s input sentence and generate an appropriate response.

Training

Researchers customize foundation models to build narrow artificial intelligence systems. Depending on the function a company wants a narrow AI to complete, it trains on more specific data to develop proficiency.

For example, a company wants to build a chatbot on its internal documents. By training a large language model FM on internal documents, developers create a narrow AI system that performs language-based tasks like text generation and summarization. It will have the knowledge customers or employees need when interacting with it.

How are narrow artificial intelligence applications developed?

Narrow artificial intelligence applications go through several phases before launching. Here are the main development steps that narrow AI goes through before being able to perform specific tasks.

Exploratory data analysis

Data scientists research the AI use case and identify the most relevant data sets necessary for that use case. They create data visualizations and identify patterns or outliers. If relevant, they may also collect information from new sources. Data engineers clean the data, removing duplicates or inconsistencies to ensure accurate and unbiased data is available for the narrow AI system.

Model selection

Various FMs are available that perform differently for different use cases. Some FMs work only with text data, while others can process images, videos, or audio, too. Data scientists choose the best model by studying model performance benchmarks, experimenting, and evaluating results from different models.

After selecting the FM, organizations use the identified data sets for further model customization.

Model customization

There are various approaches to customizing the foundation model for your use case.

Fine-tuning

Fine-tuning adjusts model parameters to a new data set for a new task. For example, fine-tuning a customer review dataset for sentiment classification. However, you must ensure that data is carefully handled and the model does not begin overfitting.

Prompt engineering

Prompt engineering is adjusting the input you give a narrow AI model to enhance its output accuracy. Researchers freeze the pre-existing model parameters during this process, only changing the model input. They may use a series of request-response chains to guide the AI system in a more controlled manner.

Retrieval augment generation

Typically, narrow artificial intelligence relies entirely on its training data to produce responses. In retrieval augment generation(RAG), models can search against an index of additional resources outside their training data to expand their knowledge base.

Using RAG increases the scope of what a narrow AI can do while remaining cost-effective. Enhancing with RAG will also permit a model to access current information, overcoming one of the main problems with narrow AI—data drift. Data drift occurs when narrow AI outputs become irrelevant due to outdated training data.

Model deployment

After narrow AI models have gone through each stage, they are ready for deployment. This stage connects an AI model to the computational resources it needs to handle requests. At this stage, businesses can integrate a narrow artificial intelligence model into their applications or workflows and use its capabilities.

What are the challenges with narrow AI implementations?

While narrow AI has numerous use cases and benefits, it also has challenges that impact implementation.

Data drift

Narrow AI trains on large volumes of historical data. Over time, this data may become less relevant. As consumer opinions, social norms, perspectives, or preferences shift, artificial narrow intelligence's historical data may become outdated. As a model’s data becomes increasingly outdated, the quality of responses it generates or its accuracy in a task decreases.

Hallucinations

When responding to a prompt, artificial intelligence systems may generate incorrect or irrelevant information. These misleading results could come from a narrow AI that doesn’t have access to enough training data or form incorrect conclusions from the AI. Even small biases in AI training data can cause hallucinations.

Cost

The training process and the running of an artificial general intelligence system are extremely computationally intensive. To train AI models, researchers must store huge volumes of data and process them at high speeds. Without substantial computation resources, narrow AI may not be feasible to run or may incur high costs.

User trust

As artificial intelligence is still a fairly new technology for many people, one of the largest challenges with narrow AI implementations is a lack of user trust. Without fully understanding how narrow artificial intelligence works, people may hesitate to use it and incorporate it into processes.

One potential solution is for researchers to include AI explainability in their models. The weak AI model provides a transparent explanation of how it arrived at its answer. For example, it explains which parts of a prompt it placed more importance on and traces the steps it used to formulate its response.

How can AWS help with your narrow AI requirements?

AWS provides managed artificial intelligence services that help you train, deploy, and scale narrow AI applications. Organizations use our AI tools and foundational models to innovate AI systems with their own data for personalized use cases.

Amazon Bedrock is a fully managed service wherein developers can use API calls to access generative AI models they deploy. You can select, customize, train, and deploy industry-leading foundational models on Bedrock to work with proprietary data.

Amazon SageMaker Jumpstart helps software teams accelerate AI development by building, training, and deploying foundational models in a machine-learning hub.

Use Amazon Elastic Compute Cloud UltraClusters to power your generative AI workloads with supercomputing GPUs to process massive datasets with low latency.

Get started with narrow AI on AWS by creating a free account today.

What Is Narrow AI?