The Impact of Artificial Intelligence in the Mobility Sector :It is clear and widely accepted that Artificial Intelligence (AI) is going to change our lives, but it is not known for sure when it will happen. By using these Artificial Intelligence Techniques will allow companies and any individuals to perform more tasks with very little effort, by automatic complex processes. This article explores the impact of AI on the mobility Sector.

What is Artificial Intelligence and why is it Important?

Artificial intelligence (AI) is the basis on which human intelligence is imitated. For this purpose, algorithms are created, applied, and integrated into a dynamic computing environment. Simply put, AI is an attempt to make computers think and behave humanly.

The Artificial Intelligence, defined in 1956 by John McCarthy university professor, is “the science and engineering to create intelligent machines, especially intelligent computer programs.” A subset of AI is machine learning, which is described as the field of study that gives computers the ability to learn without being explicitly programmed.”

According to McKinsey, the application of Artificial Intelligence will allow increased productivity and other benefits not only for companies, but also for the entire economy. Estimates from a macroeconomic point of view indicate that automation alone could boost productivity growth globally from 0.8% to 1.4% each year.

Artificial Intelligence will have a transformative impact similar to that of electricity 100 years ago. It will change every sector, from healthcare to transportation, communications, and manufacturing processes.

Although the idea of Artificial Intelligence is not new, the pace of recent progress is.

The Main AI Factors that Promote this Acceleration

1. Computing Capability

Advances beyond the current generation of central processing units (CPUs) and graphics processing units (GPUs) are emerging. This capability has been added in highly scalable data centers and can be accessed through the cloud.

2. Big Data

Huge amounts of information (images, voice, video, location, sensor information …) are collected through IoT processes and can be used to form artificial intelligence models.

3. Machine Learning

Algorithms have advanced considerably by developing deep learning based on neural networks. Furthermore, the accessibility of these algorithms through toolkits, such as sci-kit-learn and Tensorflow, is helping developers and data analysts.

Artificial Intelligence and the Adoption of Technology

Companies should take into account a series of pertinent considerations before starting with Artificial Intelligence. The technology is complex and its success depends on the environment (that is, the quality of the data, the business culture, or the appetite of the customers). Normally, Artificial Intelligence affects to some extent the transparency of decisions and the General Data Protection Regulation, which forces companies to clearly indicate what they do with the data, can increase complexity.

There is also considerable concern about security. The guarantee that an Artificial Intelligence will not be hacked or programmed maliciously must be taken into account in the early stages. In a statement in Toronto, An emerging global artificial intelligence center, Prime Minister of Canada Justin Trudeau reinforced the importance of ethics applied to Artificial Intelligence and suggested a “working group” in Canada, where there is a strong moral and ethical code that favors diversity.

How Does Artificial Intelligence Affect Jobs?

An interesting debate centers on how people’s jobs will be affected. According to McKinsey , only a small percentage of jobs (about 5%) can be fully automated using current Artificial Intelligence technologies, while 60% of jobs have at least 30% of activities that can be automated. This also applies to CEOs, as they spend about a quarter of their time on activities that machines can do, such as analyzing reports and data to inform decisions.

Instead of replacing jobs, Artificial Intelligence will reduce some tasks in many professions, and workers will have more time to focus on activities that add more value. By eliminating less creative tasks from your job, artificial intelligence can increase employee job satisfaction.

Applications of Artificial Intelligence in the Field of Mobility

Artificial intelligence enables a multitude of intelligent applications that make mobility safer, more comfortable, more efficient and more resource-efficient. Artificial Intelligence and machine learning technologies will greatly impact the automotive and mobility sectors as they introduce new products and business models rather than just productivity improvements.

It acts like a multiplier and

Optimizes the control of traffic, systems and vehicles,

Improves the accuracy of traffic forecasts and weather forecasts,

Increases the efficiency of logistics processes

Detects accidents with foresight.

“According to Microsoft , in 2025, 100% of new cars will be connected, and in 2030, 15% of new cars will be autonomous, as they will send, receive, and analyze “huge amounts of data.”

Cars are data centers on wheels. Many stakeholders can use all that information (traffic authorities, leasing companies, municipalities, car manufacturers, insurers, workshops, emergency services, etc.) in the field of mobility to improve processes.

Some Examples of Artificial Intelligence in the Mobility Sector

1. Reduction of Occupational Accidents

AA techniques will allow learning the different causes of road accidents: distraction, weather conditions, fatigue, vehicle breakdowns … By having IoT sensors on board, We will be able to understand in depth the reasons behind an accident, as well as predict when a similar accident can occur (with a reasonable probability) and notify the driver and the company, which improves the safety of the fleet .

2. Public Transport on Demand

You will take into account both historical and real-time data to plan routes based on demand and capacity. Systems that “communicate” with each other: Combined support has improved routing and traffic patterns.

3. Predictive Maintenance Algorithms

They will detect a potential error based on statistical data from the same models with similar driving conditions and will schedule a workshop according to the owner’s schedule.

4. Smart Logistics

Tracking operator patterns (order picking, inventory control, and field staff), combined with predictive orders in certain areas, can reduce delivery times and optimize inventory levels in warehouses.