Have you ever wondered how your phone’s voice assistant understands your commands or how Netflix recommends your next favorite show? These are just two examples of Narrow AI at work. In this article, we’ll define Narrow AI, discuss the top uses, as well as the challenges it faces.
Are you already using Narrow AI?
Generative AI may be all the rage right now, but there are many ways to use Narrow AI. In fact, it’s likely that you’ve already been using Narrow AI without realizing it.
- Have you ever kept your eyes glued to the weather as a storm approaches, and paid close attention to the predictions made by the GFS, GFSX, and of course the European model? Narrow AI is what powers those models.
- When you ask Google (or Siri) to set an alarm while you’re cooking, you are using Narrow AI.
- Do you pay attention to the recommendations Amazon gives you as you’re searching for deals? Amazon uses Narrow AI to determine what to recommend to you.
- Do you use Salesforce or HubSpot to find the effectiveness of your marketing efforts, or to show trends and patterns in your customer data? Narrow AI is what surfaces the information you need to those tools.
Remember, AI isn’t magic, mysterious, or science fiction. It’s good old computer science!
Recap – what is Narrow AI?
Narrow AI is designed and trained to perform a single, specific task. Artificial Narrow Intelligence (ANI) or Weak AI are other names for Narrow AI. In this case, “narrow” refers to the narrow focus of AI applications. Learn about the three types of AI in this post.
That means that these AI applications have a narrow focus. They operate under a pre-defined set of rules, and they can’t apply knowledge to different contexts beyond their specific programming.
Types of Narrow AI
Now that we’ve covered the basics of Narrow AI, let’s delve into its various types and applications.
Reactive AI
Reactive AI is the most basic form of artificial intelligence. This type of AI is designed to respond in real time to specific inputs.
Reactive AI systems have four key characteristics:
- Memory is not stored, and the systems can’t learn from past experiences They operate solely on current input.
- These systems are task-specific, excelling at narrow, well-defined functions
- They provide immediate responses based on pre-programmed rules and algorithms
- They lack adaptability, requiring manual adjustments if task requirements change.
Common applications include chess programs, recommendation engines, and the basic instructions to voice assistants like setting an alarm or playing music.
Generative AI
Generative AI is a form of Narrow AI. These models create new content from existing data. However, these models generate text, images, music, and more based on they way they have been trained with advanced algorithms. It takes vast amounts of data to power the algorithms.
Generative AI tools include GPT-3, which can produce coherent and contextually relevant paragraphs of text based on a prompt. Algorithms like DALL-E can create intricate images from textual descriptions.
However, Generative AI can only operate within a specific domain or task. It can’t “think” about your prompt and “create” text or an image for you out of the blue. It stays within the confines of narrow intelligence because it was trained with lots of data to have a specialized focus.
Predictive AI
Predictive AI analyzes historical data to predict future events or behaviors within a specific domain. These applications analyze patterns and relationships within large datasets to provide insights such as stock market trends, customer buying behaviors, potential equipment failures, and weather prediction.
Algorithms such as regression analysis, decision trees, and neural networks, which are trained to recognize patterns and make predictions with a high degree of accuracy.
Data, both quality and quantity, determines the effectiveness of Predictive AI. It is vital to have continual data collection and refinement to maintain the accuracy and relevancy of Predictive AI.
Descriptive AI
Descriptive AI analyzes past data to describe patterns and trends that have happened. This helps organizations understand what has already happened.
These algorithms are fed historical data to uncover past events, identify patterns, correlations, and anomalies within data sets. Descriptive AI applications can aggregate data from various sources, generate reports, dashboards, and visualizations to present findings. Unlike Predictive AI, it does not forecast future events. Instead, Descriptive AI provides actionable information to inform decision-making processes.
Common applications include business intelligence tools, data dashboards, and analytics platforms.
Diagnostic AI
Diagnostic AI identifies the reasons behind specific outcomes or events based on data analysis. It goes beyond descriptive AI by not only detailing what happened but also exploring why it happened. Diagnostic AI offers deeper insights by explaining the “why” behind data patterns, helping organizations address problems at their source and make more effective strategic decisions.
Diagnostic AI has several key characteristics. To begin with, it performs root cause analysis to find the underlying causes of events or outcomes, finds relationships between variables to find causal links, and engages in deep data exploration to uncover hidden insights. Additionally, it tests various hypotheses to understand the impact of different factors on outcomes and detects unusual patterns or outliers that may indicate problems. Often involving interactive analytics tools, it allows users to explore different scenarios and explanations.
Examples of Diagnostic AI include analyzing patient data in healthcare to identify disease causes and aid in diagnoses, determining root causes of equipment failures in manufacturing to improve production, understanding common issues in customer service through feedback analysis, showing financial anomalies in financial services, and examining factors influencing marketing campaign success or failure.
Limited Memory AI
Limited Memory AI is a more advanced level of Narrow AI. These applications can perform tasks with a certain degree of precision based on past experiences. Unlike Reactive AI, which has no memory, Limited Memory AI can learn from historical data.
However, Limited Memory AI is still a narrow AI. Even though it can use historical data, this memory is typically short-term and relevant to specific tasks. The memory is still task-specific memory and confined to specific tasks or domains. Any information stored is temporary.
Common applications include self-driving cars, which use recent trip data for route planning and obstacle avoidance, and chatbots or virtual assistants like Siri and Alexa, which remember user preferences for personalized responses. Recommendation systems on platforms like Netflix and Amazon use limited memory to suggest content based on user behavior. Game-playing AI, such as AlphaGo, employs past moves to enhance its strategies in future games.
Conclusion
Narrow AI is the only type of AI we have today. It is a broad category of AI applications that can perform specific tasks with high efficiency and accuracy. These applications range from reactive AI, which has no memory or learning capabilities, to generative AI, which can produce original and creative outputs based on data.
However, these applications are still limited by the scope and quality of the data they are trained on, and they cannot transcend beyond their assigned domains or tasks. This is how narrow AI diverges from artificial general intelligence (AGI). However, it’s important to remember that AGI is still just a theory at this time.
AGI is the ultimate goal of AI research. It is theorized that AGI will be able to perform any intellectual task a human can, maybe even surpassing human intelligence. But for now, AGI remains a distant and elusive vision for the future of AI.
How are you using Narrow AI? Let us know in the comments!
