Causal AI
Causal AI is an emerging subset of artificial intelligence that focuses on understanding and applying the principles of cause and effect in its analyses. Unlike traditional AI, which excels at recognizing patterns and making predictions based solely on existing datasets, causal AI seeks to explore the underlying reasons behind those patterns. This capability would enable it to consider additional variables and hypothetical scenarios that may not be directly represented in the data. Pioneering work in this field has been influenced by computer scientist Judea Pearl, who has emphasized the importance of causal reasoning in developing more advanced AI systems.
Currently, AI technologies are often categorized into narrow, general, and super AI, with most prevalent applications being narrow AI that performs specific tasks. Causal AI aims to bridge the gap toward true generative AI by enabling machines to ask meaningful questions about data—such as why certain trends occur—and develop actionable insights. Although fully realized causal AI has not yet been achieved, its potential applications in businesses include the ability to predict issues before they arise and devise effective solutions based on a deeper understanding of causation. This advancement in AI could significantly enhance decision-making processes across various industries.
Causal AI
Causal AI is a subset of artificial intelligence (AI) that understands and incorporates the principles of cause and effect. Most modern AIs can analyze large quantities of data and search for patterns. Many are capable of extrapolating from those patterns, creating accurate predictions. However, modern AIs cannot look beyond their immediate dataset and question why they may be predicting certain results. Though it has yet to be developed, causal AI will be able to examine datasets in new ways, including searching for bias. Casual AI may also be able to provide solutions and complete tasks in a manner that incorporates hypothetical situations that are not directly represented in the AI’s datasets.
Background
Those who work in the field of artificial intelligence (AI) create intelligent machines. To do this, they must create intelligent computer programs. Humans have worked toward the creation of truly intelligent machines since the invention of computers. However, the standards by which a machine can be considered intelligent have changed over time. In 1950, the famous computer scientist Alan Turing proposed that an intelligent program should be indistinguishable from a human operator when communicating through text. This AI milestone is now known as the Turing Test. Some modern AI programs, including Google’s LaMDA and OpenAI’s ChatGPT, have successfully passed the Turing Test. Despite this, some computer experts allege that these programs are not truly intelligent and have simply been trained to effectively mimic writing.
Forms of AI are now used in many industries. Though people are still needed for many processes, AI can perform certain tasks faster and more efficiently than human workers. For example, AI can analyze large datasets and identify patterns faster than skilled human analysts and, in some cases, identify patterns that were never apparent to human analysts. Additionally, AI can perform repetitive, monotonous tasks with greater speed and accuracy than human workers. For these reasons, AI is ideal for interacting with large datasets and optimizing existing processes.
AI programs are commonly sorted into three categories based on their capabilities: narrow AIs, general AIs, and super AIs. Narrow AIs are created to perform a single task intelligently. They have a predefined list of functions and cannot expand beyond those capabilities. Despite these limitations, many narrow AIs have self-learning capabilities, allowing them to become extremely effective at their intended tasks. Popular narrow AI functions include translation and image recognition software.
General AI is capable of learning new tasks, allowing it to apply knowledge and skills in new and innovative ways. If successfully implemented, general AI would be able to perform most of the tasks carried out by humans, potentially in more efficient or effective ways. However, the creation of a true general AI would require programmers to create a full set of cognitive abilities. For this reason, despite large sums of money being invested in researching the idea, scientists have yet to create a general AI.
Super AI is a potential evolution of general AI. It can perform all tasks that people can and many that people cannot. Super AI may one day be created from a general AI that learns to improve itself. Scientists theorize that a super AI could become so intelligent that humans are unable to comprehend its actions.
Overview
Causal AI refers to any AI that can explain and account for cause and effect within its calculations. Most AI currently used by businesses utilizes predictive analytics, meaning that it takes a provided dataset and extrapolates from that data. However, despite recognizing patterns within a dataset and making predictions based on those patterns, the AI does not understand why any of these patterns may be occurring. Without this understanding, the AI is unable to take other circumstances into account when making its predictions.
Some computer scientists consider causal reasoning to be one of the biggest hurdles that must overcome to create true generative AI. To mimic the capabilities of a person, an AI must creatively determine the cause and effect of different parts of a single dataset. The computer must theorize as to why an interaction occurs instead of simply observing that a pattern exists. This might mean considering a variety of factors that may not be included in a dataset, such as relationships, socio-economic status, dependencies, and medical histories.
Though computer scientists have not yet developed causal AIs, they theorize that causal AI could provide significant improvements in AI performance for businesses. For example, whereas a traditional AI can only extrapolate from a given dataset, a causal AI can ask broad questions regarding a business. These questions might include “Why are certain times of day busier than others?” “Why are some customers leaving without making an order?” and “Why are customer satisfaction levels low?” It can then use both its theories and predictions generated from its datasets to create viable solutions for the business. Casual AIs may be able to predict issues before they occur, allowing businesses to better prepare for future economic difficulties.
Modern AI models have created a limited version of causal AI using fault tree analysis (FTA), which was first designed by Bell Laboratories for the US Air Force in 1962. It uses a top-down approach that identifies the component failures that eventually lead to a larger system level failure. Though it is still constructed of a series of logic gates, FTA allows an AI to pair the cause of an event with the event itself.
Bibliography
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