ARTIFICIAL INTELLIGENCE IN LOGISTICS |2021

ARTIFICIAL INTELLIGENCE IN LOGISTICS |2021

ARTIFICIAL INTELLIGENCE IN LOGISTICS is a global network of suppliers and customers complicates logistics operations, and logistics companies contain both easily automated tasks and complex processes that can take advantage of artificial intelligence / machine learning algorithms.

The AI theme is becoming the cover story of many trades and general interest magazines. It often turns out that the technology is mature enough not only to replace people in many specialized areas but also to perform better technically.

However, as many experts recognize, AI is still in the early stages of development in terms of applications in operational logistics. Although a few years ago he could be compared to a (grand) child who could easily distinguish (“mother”, “father”), he has now become a teenager who, according to the correct instructions, can be entrusted with more complex tasks.

In addition, many specialized terms such as AI, machine learning, deep learning or neural networks are often used interchangeably and without clear distinction, leading to confusion about concepts among non-specialists.

Although ARTIFICIAL INTELLIGENCE IN LOGISTICS systems are capable of processing much more data and thus much more information than humans, they have so far had the disadvantage of being able to select only known decision-making options. The uniqueness of innovative and creative thinking remains the loyalty of people.

One thing is certain: the current advances in algorithm development, combined with increased processing power and exponential growth in the amount of data available, means that it is now possible to develop systems that can perform tasks that were previously the domain of humans. In addition, these activities are carried out around the clock with greater precision and with constant and unalterable reliability, and without interruptions.

Self-learning systems take on customer service tasks, manage logistics processes, analyze medical data or write messages and compose music. Deep learning, a variant of machine learning, uses layered neural networks (hence the description “deep”) to extend the potential of artificial intelligence to more complex tasks that can only be computed in multiple steps.

Today, machines are able to accurately identify objects and faces in a versatile way, beat humans in challenging games such as Chess and Go, lip reading, and even create natural language. Many companies, including SSI SCHAEFER, see AI as a central part of their strategy and crucial to their future core business.

BASIC CONCEPTS RELATING TO ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE IN LOGISTICS

Artificial intelligence is a branch of information technology that deals with the automation of intelligent behavior. ARTIFICIAL INTELLIGENCE IN LOGISTICS is the attempt to program a computer to process problems on its own, in the same way as with proper training. Problem-solving means making decisions that adequately address the underlying problem within the allotted time, based on data from different sources (databases, sensors, cameras, etc.).

Conventional software programs are coded by developers with specific instructions on what the programs should run. While it works well in many situations that can be defined very precisely, its boundaries are beyond a certain level of complexity. It is not possible for the human programmer to consider all possible future use cases when writing code. When the environment changes, programs will no longer be able to achieve their desired goals or required performance, given the substantially changed underlying conditions.

Machine learning development began as a way to solve this problem: it relies on adaptive algorithms that can learn from data without relying on rules-based programming. The system can detect patterns, create associations and extract information from the data. It, therefore, involves making mostly important connections between input and output using ARTIFICIAL INTELLIGENCE IN LOGISTICS.

The prerequisites for this type of learning process are high processing power and a sufficiently large amount of data. Both were only available last year thanks to Big Data, so it’s no surprise that machine learning has also come a long way in recent years.

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