Understanding human behavior better with deep learning algorithms: Behavioral AI

Technological development with an increase in the computing power of computers and the improvement of algorithms have enabled the rise of generative artificial intelligence. Behavioral Artificial Intelligence (Behavioral AI) is a possible application of artificial intelligence that is now technically possible and is also increasingly in demand. The term establishes a link between artificial intelligence and behavioral psychology. Here too, deep learning algorithms are trained with large amounts of data in order to recognize patterns. However, these findings are not used by AI to create new output, but to recognize, understand and predict human behaviour. The data used to train the machine learning algorithms does not have to be in written form, but can also be available as video or audio material. Behavioral AI is based on the knowledge that human behavior does not always follow a predetermined pattern, but can deviate from the norm.

By analyzing large data sets, behavioral AI can determine average values and thus standard values for human behavior and indicate when a person's behavior deviates from the standard values. In this case, the AI can then set adapted mechanisms in motion.

Behavioral AI has a wide range of possible applications

Behavioral AI has a wide range of possible applications and is particularly relevant in areas where the effects of human behavior are at stake. Examples of industries where behavioral AI is used include healthcare, criminology, the advertising industry and (public) security. For example, artificial intelligence can help doctors to recognize symptoms of diseases such as depression, Parkinson's or Alzheimer's based on the voice of patients using voice diagnostics and make an initial diagnosis of the disease. In the area of fraud prevention, AI can be used to detect conspicuous behavioral patterns. For example, AI can be used in the financial sector to uncover fake identities - known as social engineering - by automatically checking emails for suspicious wording. In advertising, Behavioral AI can help marketing departments to display personalized advertisements based on the analysis of internet surfing behaviour, which ultimately leads to more conversions. In public spaces, AI can be used by security experts to identify conspicuous and potentially dangerous behavior and intervene quickly if necessary. Think of parking garages where criminals try to break into cars, department stores in terms of shoplifting or places with lots of people that could be targeted by terrorist attackers. Here, AI that recognizes deviant behaviour could automatically trigger an alarm and call security guards and the police for help.

One area of application that everyone is confronted with in everyday life, and where behavioral AI can also provide valuable services, is road traffic. This is highly susceptible to disruptive factors such as traffic jams or non-compliance with traffic rules by road users. Traffic light systems are designed to coordinate the flow of traffic and ensure a smooth flow of traffic. However, existing traffic light systems are based on more or less rigid rules that do not always match the current traffic situation. As a result, the traffic light systems are unable to react adequately to unforeseen situations. The safety of the people involved should be the top priority for a traffic light system. This is where Behavioral AI comes in: Sensors and real-time AI can be used to track the traffic flow live. The AI is trained with data from road traffic before being used in real traffic. The machine learning algorithms filter out patterns and average values of the various road users. In real road traffic, behaviour that deviates from the norm can be recognized accordingly and the traffic light control can be adapted to the current traffic situation (adaptive traffic light control). A traffic light controlled by behavioral AI not only increases the safety of road users, but also leads to better traffic flow and fewer emissions in road traffic.

Around two years ago, net digital AG and its subsidiary irisnet, which specializes in AI, launched a project to develop a traffic light system using Behavioral AI in collaboration with the Bernard Group and RWTH Aachen University. The aim of the project is to develop a self-learning AI traffic light system for pedestrians that adjusts the duration of green phases based on pedestrian behavior. The traffic light system is capable of recognizing pedestrians and their behaviour as well as bicycles, baby carriages, wheelchairs, etc. and switching the green phase for them accordingly and extending it if necessary. Other deviating behavior, such as a pedestrian unexpectedly falling, should also activate the extension of the green phase. Such flexible switching can improve traffic flow, reduce waiting times at traffic lights and, above all, increase the safety of road users. The traffic light system based on behavioral AI is self-learning and can therefore improve accuracy over time.

Behavioral AI is the future

Behavioral AI will increasingly accompany us in the coming years and has great potential to change and improve the business world and everyday life in the long term. Rigid and rule-based systems such as traffic light systems can be transformed into flexible systems through the use of behavioral AI, which can respond better to often unpredictable human behavior. Germany in particular is still in the early stages of implementing smart traffic lights. In other countries, such as Austria or the UK, comparable traffic light systems are already in use in road traffic, albeit in test or pilot phases. Behavioral AI is distinguished from normal AI by the fact that the algorithms not only evaluate data from the past and in real time to predict road traffic for the next few hours, but can also react to acute, unforeseen situations such as a pedestrian falling. The self-learning machine learning algorithms also register such situations and learn from them.

The potential applications of behavioral AI are diverse and many systems still need to be developed through training with a sufficiently large and valid database. Of course, there are still challenges. One sensitive issue is data protection. This should always be taken into account when implementing behavioral AI projects. In the case of AI traffic lights, this could mean using sensors that represent road users as three-dimensional dots instead of cameras to measure road traffic. This can prevent personal identification and comply with data protection regulations. With careful preparation and cautious testing in real life, nothing should stand in the way of successful implementation.

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