Artificial intelligence (AI) is a field of computer science concerned with the development of systems and technologies designed to emulate or simulate human-like thought and action. AI has become a catch-all term for applications and is often used synonymously with its subfields, which include Machine Learning and Deep Learning.
The goal of an AI application is to give machines the ability to solve problems, learn, draw conclusions, and make decisions that were previously only possible with human intelligence. Artificial intelligence uses various approaches for this purpose, such as machine learning, neural networks, expert systems, and natural language processing (NLP).
Developments in AI have the potential to improve many aspects of the workplace and drive innovation across a wide range of industries.
An algorithm is an abstract step-by-step instruction or procedure for solving a problem or task. In computer science, algorithms are a fundamental component to solve problems efficiently. An algorithm can be viewed as a set of clear and concise instructions that are executed in sequence to achieve a particular result. There are different types of algorithms that vary depending on their application in AI and other fields.
Efficiency: The efficiency of an algorithm is a crucial factor. Relevant here are the speed and resource consumption required by an algorithm to perform a given task. In concrete terms, this means that an efficient algorithm solves a problem in a reasonable amount of time and consumes only necessary resources.
Correctness: The correctness of an algorithm is crucial. An algorithm must provide the desired result and work for all possible input values.
Robustness: A robust algorithm should produce correct results even in the presence of unexpected or invalid inputs, or at least have adequate error handling. Robust algorithms are resistant to interference and can function properly even under slightly different conditions.
Scalability: The scalability of an algorithm refers to its ability to handle growing data sets or input conditions without severely degrading performance. A scalable algorithm maintains its efficiency and performance regardless of how large the input is.
Parallelization: In today’s world, where many computers have multiple processors or cores, the ability of an algorithm to be parallelized is of great importance. An algorithm can actually benefit from using multiple processors or cores simultaneously to increase the speed of execution.
Applied artificial intelligence describes the practical implementation of AI in various fields and refers to the integration of AI into real-world applications and systems to solve specific problems or enhance human-like capabilities. This area of AI focuses on the practical application of techniques such as machine learning, pattern recognition, and data mining to make relevant advances in areas such as food, healthcare, finance, transportation, industry, and many others. Applied AI leverages insights from research and develops customized solutions to meet the specific needs of a particular field or business.
Applied Artificial Intelligence offers a wide range of opportunities and possibilities in various fields. However, for the profitable use of Applied AI, it is crucial to understand how the underlying technologies work in order to subsequently enable business process improvement.
Increasing efficiency: Applied AI can greatly contribute to optimizing processes and workflows in companies and organizations. By using AI-powered automation solutions, repetitive tasks can be automated, increasing efficiency and freeing up human resources for more complex and strategic tasks.
Better decision making: AI systems can analyze large amounts of data and identify patterns that might be difficult for humans to detect. This allows for informed decisions based on facts and data, which can lead to optimized results.
Personalized experiences: Applied AI enables companies to offer personalized experiences and services. For example, based on a customer’s previous purchases and preferences, an online grocery platform could offer personalized recommendations for new products or recipes that match the customer’s tastes and habits.
Advances in research: AI can be used in scientific research to model complex problems, analyze data, and gain new insights. This can lead to progress in areas such as, sustainable agriculture, circular economy or energy efficiency.
AI applications already exist in the food industry that can improve efficiency, enhance quality, and provide personalized experiences. Here are some examples:
Inventory and supply chain management
AI can be used in the food industry to optimize inventory and the supply chain. By analyzing historical sales data, weather conditions, holidays and other factors, demand can be better predicted. This enables companies to optimize their inventories, avoid overstocks and bottlenecks, and plan deliveries more efficiently.
AI-based image recognition systems can be used in food production to monitor product quality and detect defects. AI can automatically analyze images of the food produced and identify impurities, cracks or other defects. This can ensure product quality and reduce waste. (® , ®, ® )
AI can be used to monitor the condition of machinery and production equipment and predict when maintenance needs to be performed. By analyzing sensor data and historical failure patterns, AI can provide early warning of potential problems and reduce unplanned downtime.
Production planning and optimization AI can help optimize production planning by analyzing historical sales data, seasonal patterns, and other factors to predict demand. Based on this, production volume and scheduling can be adjusted to avoid overproduction or shortages. (.)
AI can also help with the efficient use of resources in food production. By analyzing data on energy consumption, water demand and material use, AI can suggest ways to save resources and reduce waste.
Personalized product development
AI can be used in food production to develop personalized products that meet consumers’ individual needs and preferences. AI can analyze consumer data to create customized recipes and food combinations.
Automation of production processes
AI-controlled robots and automation systems can be used in food production to automate manual workflows. This can not only increase the production speed, but also improve the precision and quality of the manufactured products.
Traceability and food safety
AI can be used to improve food traceability and ensure food safety. By tracking food batches and supply chains, AI can help identify potentially dangerous products and facilitate recalls.
These examples show that Applied Artificial Intelligence in food production can help optimize processes, improve product quality, increase efficiency and ensure food safety. By using AI, food manufacturers can modernize their production and become more competitive.
Closely related to artificial intelligence is Big Data. Both technologies complement and reinforce each other. Big Data refers to the immense amount of data generated in our digital world, while Artificial Intelligence aims to give machines human-like intelligence and capabilities. The connection between the two lies in their interdependence and the significant role Big Data plays in the development and success of AI.
What exactly does this coexistence look like? Artificial intelligence requires large amounts of data to function effectively and achieve optimal results. These data sets are often generated by a variety of sources, including sensors, social media, e-commerce transactions, IoT devices and more.
Big data serves as the basis for machine learning, in which algorithms learn from experience and patterns. The more data an AI system can analyze, the better it can recognize complex patterns, understand relationships and make predictions. Big Data provides the necessary information to train, refine and improve AI models. That’s because AI systems need to be able to process large amounts of data quickly in order to act in real time, or at least near real time. Big Data technologies such as distributed databases, data warehousing, and cloud computing provide the infrastructure needed to manage the vast amount of data and make it accessible to AI applications.
Big Data is characterized by the so-called “3Vs”: Volume (the amount of data), Variety (the way the data is generated) and Velocity (the speed at which the data is generated and processed). The combination of Big Data and Artificial intelligence also has a critical impact on the applications and benefits of AI in various fields. This is illustrated by an example from food safety and traceability:
The food industry generates enormous amounts of data throughout the food production chain, from farming to processing to sales. With the help of Big Data and AI , this data can be analyzed to improve food safety and ensure food traceability. This enables rapid response to foodborne illness outbreaks and more efficient food recalls.
In the field of demand forecasting and inventory management , Big Data and AI also play a major role when it comes to optimizing processes: analyzing large volumes of sales and consumer data enables companies to predict demand for specific foods. This allows them to optimize their inventories and avoid overstocks or shortages. This helps reduce food waste and improves supply chain efficiency.
The final example is quality assurance in food production: By collecting and analyzing data during the production process, manufacturers can identify potential quality problems early on and take corrective action. AI systems can help identify errors or deviations before they lead to major problems.
ChatGPT is a program that can automatically create messages and texts.
We are happy to support you in realizing and understanding the full potential of ChatGPT by offering a potential workshop . In this workshop, we will explain the benefits of ChatGPT and help you integrate it into your business processes to improve customer experience and increase efficiency.
ChatGPT was trained in two steps: Pre-training and fine-tuning.
During pre-training , the model learns to generate text by analyzing a large amount of Internet text. It tries to recognize patterns in the data, such as the next word in a sentence based on the previous words.
Pre-training is followed by fine-tuning, where the model learns specific tasks. It is trained on a special dataset containing human conversations to learn conversational skills. The model does not know any specific context or personal information of the users, it generates answers based on the patterns it has learned during training.
It is important to note that ChatGPT does not store information about users or access personal data. It is designed to respect and ensure data privacy.
ChatGPT is based on the technology called GPT (Generative Pretrained Transformer) developed by OpenAI. It is an example of an Artificial Neural Network, more precisely a Transformer-based model.
An example of the use of ChatGPT in business processes in IT support
Chatbots are programs that can automatically respond to messages from users. In IT support, chatbots can be used to automatically respond to user requests and resolve issues.
This means that human employees no longer have to handle every request, saving time and resources. For example, chatbots can provide troubleshooting instructions or solve problems using automated solutions.
Your company can customize ChatGPT and use it in different areas to improve these processes and save time and resources.