AI behavioral prediction: Machines designed to predict the future

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  Quantumrun Foresight
• May 17, 2023

Devices powered by machine learning (ML) algorithms are rapidly changing how we work and communicate. And with the introduction of next-generation algorithms, these devices may begin achieving higher levels of reasoning and comprehension that can support proactive actions and suggestions for their owners.

AI behavioral prediction context

In 2021, Columbia Engineering researchers revealed a project that applies predictive ML based on computer vision. They trained machines to predict human behavior up to a few minutes into the future by using thousands of hours’ worth of movies, TV shows, and sports videos. This more intuitive algorithm takes unusual geometry into account, allowing machines to make predictions that aren't always bound by the traditional rules (e.g., parallel lines never crossing). 

This kind of flexibility allows robots to substitute related concepts if they're unsure what will happen next. For example, if the machine is uncertain whether people would shake hands after an encounter, they would recognize it as a "greeting" instead. This predictive AI technology can find various applications in everyday life, from helping people with their day-to-day tasks to predicting outcomes in certain scenarios. Previous efforts to apply predictive ML typically concentrated on anticipating a single action at any given time, with the algorithms attempting to categorize this action, such as offering a hug, handshake, high-five, or no action. However, due to the inherent uncertainty involved, most ML models cannot identify similarities between all potential outcomes.

Disruptive impact

Since current algorithms are still not as logical as humans (2022), their reliability as co-workers is still relatively low. While they can perform or automate specific tasks and activities, they can't be counted to make abstractions or strategize. However, emerging AI behavioral prediction solutions will change this paradigm, especially in how machines work alongside humans over the coming decades.

For example, AI behavioral prediction will enable software and machines to propose novel and worthwhile solutions when met with uncertainties. In the service and manufacturing industries, in particular, cobots (collaborative robots) will become able to read situations well in advance instead of following a set of parameters, as well as suggest options or improvements to their human coworkers. Other potential use cases are in cybersecurity and healthcare, where robots and devices may increasingly be trusted to take immediate action based on potential emergencies.

Companies will become even better equipped to offer tailored services to their customers to create a more individualized experience. It could potentially become commonplace for businesses to provide highly personalized offers. Additionally, AI will allow firms to gain deeper insights into customer behavior to optimize marketing campaigns for maximum efficiency or effectiveness. However, the widespread adoption of behavioral prediction algorithms could lead to new ethical considerations related to privacy rights and data protection laws. As a result, governments may be forced to legislate additional steps to regulate the use of this AI behavioral prediction solutions.

Applications for AI behavioral prediction

Some applications for AI behavioral prediction may include:

• Self-driving vehicles that can better predict how other cars and pedestrians will behave on the road, leading to fewer collisions and other accidents.
• Chatbots that can anticipate how customers will react to complex conversations and will propose more customized solutions.
• Robots in healthcare and assisted care facilities that can accurately predict patients’ needs and immediately address emergencies.
• Marketing tools that can predict user trends on social media platforms, allowing companies to adjust their strategies accordingly.
• Financial service firms using machines to identify and forecast future economic trends.
• Politicians utilizing algorithms to determine which area is likely to have the most engaged voter base and anticipate political outcomes.
• Machines that can analyze demographic data and provide insight into communities’ needs and preferences.
• Software that can identify the next best technological advancement for a particular sector or industry, such as predicting the need for a new product category or service offering in an emerging market.
• Identification of areas where labor shortages or skills gaps exist, preparing organizations for improved talent management solutions.
• Algorithms being used to pinpoint areas of deforestation or contamination that may need special attention when planning conservation efforts or environmental protection efforts.
• Cybersecurity tools that can detect any suspicious behavior before it becomes a threat, assisting with early preventive measures against cybercrime or terrorist activities.

Questions to consider

• How else do you think AI behavioral prediction will change how we interact with robots?
• What are the other use cases for predictive machine learning?