AI glossary: key terms explained simply
Artificial intelligence (AI) has made significant progress in recent years and plays a key role in many areas of our lives. From machine language processing to image generation, AI is permeating numerous industries. In this article, we present 25 key AI terms that improve our understanding of this technology and have a significant impact on today’s developments.
Key Takeaways:
- The AI transformation is profoundly changing business models and sustainably optimizing corporate processes.
- AI leads to increased efficiency, automation and opens up new business models and competitive advantages for companies.
- A clear AI strategy, data quality and strong leadership are crucial for success.
- Studies show: Companies that invest in AI achieve significant productivity gains and accelerate innovation.
- An important factor in the AI transformation: AI training and workshops to build up the necessary AI skills in the company.
Artificial intelligence (AI)
Artificial intelligence (AI) refers to the ability of machines to perform tasks that normally require human intelligence. This includes learning, problem solving and understanding natural language.
AI technologies are used in many areas, such as voice assistants, medical diagnostics and autonomous driving.
Machine learning (ML)
Machine learning is a branch of AI that focuses on the development of algorithms that can learn from data and improve themselves automatically. Examples of machine learning include spam filters that recognize which emails are harmful or recommendation algorithms on platforms such as Netflix.
Deep learning
Deep learning is a subcategory of machine learning that works with artificial neural networks. These networks consist of several layers of neurons and enable machines to recognize complex patterns in large data sets. Deep learning is often used in areas such as image recognition and natural language processing.
Adversarial Learning
Adversarial learning is a machine learning technique in which a model is trained using hostile inputs (adversarial examples) in order to be more robust against attacks. This method helps to arm AI models against deliberate manipulation, which could otherwise lead to incorrect results.
Reinforcement Learning
Reinforcement learning is a method of machine learning in which a model learns through interaction with its environment. The model receives feedback in the form of rewards or punishments and optimizes its decisions accordingly. This technique is often used in autonomous systems or robotics.
Supervised Learning
Supervised learning is a form of machine learning in which a model is trained with labeled data. This means that there is a known output for each example in the training data set. The model learns to recognize these input-output relationships and apply them to new data.
Unsupervised Learning
In contrast to supervised learning, unsupervised learning works with unlabeled data. The aim is to recognize patterns or structures in the data without specific categories having been defined in advance. Applications include cluster analysis or anomaly detection.
Neural networks
Neural networks are inspired by the way the human brain works. They consist of nodes (neurons) arranged in layers. These networks process input, such as images or text, and output predictions, e.g. for classifying objects or analyzing sentiment in texts.
Generative AI
Generative AI describes algorithms that are able to generate new data. This data can be text, images, music or videos based on existing data. Applications of generative AI can be found in text and image generators, such as DALL-E or ChatGPT.
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Large Language Models (LLM)
Large Language Models (LLM) are special AI models that are trained on extensive text datasets to understand and generate natural language. Examples include GPT-3 and GPT-4, which are used in numerous applications such as chatbots, translation tools and language assistants.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of AI that deals with the interaction between computers and human language. NLP is used in chatbots, machine translation and speech recognition software. NLP techniques enable computers to analyze and understand texts.
Algorithm
An algorithm is a series of instructions that solve a problem step by step. In AI, algorithms are used to analyze data and make decisions. Different types of algorithms, such as decision trees or neural networks, help to solve problems in different areas, from image classification to text analysis.
Bias in AI
Bias in AI refers to distortions or bias in the predictions of a model. These can be caused by unbalanced training data or by the design of the algorithms. Bias can lead to discriminatory results, e.g. in credit scoring or facial recognition, and should therefore be actively minimized.
Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is a technology that makes it possible to automate repeatable, rule-based tasks in business processes using software robots. These robots perform manual, time-consuming tasks, such as processing data, filling out forms or transferring information between different systems.
RPA helps companies to increase efficiency, minimize errors and free up human resources for more value-adding tasks. RPA is used particularly in finance, customer service and human resources.
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Computer Vision
Computer vision is an area of AI that enables machines to understand and interpret visual data such as images and videos. This technology is used in many areas, including facial recognition, autonomous vehicles and medical image processing.
Explainable AI
Explainable AI is an approach that aims to make AI decisions more comprehensible. While many modern AI models are considered“black boxes“, Explainable AI makes it possible to understand the inner processes and decision-making paths of AI. This is particularly important in areas such as healthcare or the justice system, where transparency is crucial.
Data Mining
Data mining describes the process of analyzing large data sets in order to discover patterns, correlations or trends. In AI, data mining is used to gain insights that can lead to improved predictions or decisions, such as in the analysis of customer behavior in e-commerce.
Prompt Engineering
Prompt engineering is a technique that was specially developed for working with large language models (LLMs) such as GPT. It involves formulating precise and optimized input commands – so-called“prompts” – in order to obtain the desired answers or results from AI models.
This approach is often used to generate specific answers, creative content or problem-oriented solutions. Prompt engineering plays an important role in the development of applications such as chatbots, content creation tools and customized AI solutions, as it significantly improves the quality and relevance of AI responses.
Prompt Injections
Prompt injections are targeted inputs that aim to manipulate the behavior of an AI model in unforeseen ways. This technique allows users to trick AI models into generating unexpected or potentially harmful responses.
Prompt injections pose a particular challenge to the security and robustness of AI applications, as they can exploit vulnerabilities.
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GANs) are an innovative approach in the field of generative AI. They consist of two competing networks: a generator, which creates new data, and a discriminator, which distinguishes between real and generated data. GANs are often used to create realistic images, videos and audio.
Edge Computing
Edge computing refers to the processing of data close to its source in order to reduce latency. This is particularly beneficial in real-time applications such as autonomous driving or IoT technology, as it enables fast processing of sensor data.
Predictive analytics
Predictive analytics is a technique that uses historical data to predict future events. In AI, predictive analysis helps companies to make better decisions by recognizing trends and making predictions about customer preferences, market changes or machine maintenance.
Retrievel Augmented Generations
Retrieval Augmented Generation (RAG) combines the power of language models with an external database to generate more accurate and contextualized answers. In this technique, the model first searches an external knowledge source to retrieve relevant information on a specific topic and integrates it into the answer generation.
Hybrid AI
Hybrid AI combines data-based machine learning with symbolic approaches such as knowledge representation and logical reasoning. This enables greater flexibility in solving complex problems, as it can draw not only on statistical patterns but also on rule-based knowledge.
This method improves decision-making and enables a better understanding of contexts. Hybrid AI is used in areas such as medical diagnostics, financial analysis and autonomous systems.
AI competence
AI competence describes the knowledge and skills required to successfully develop, implement and use artificial intelligence. This includes knowledge in areas such as machine learning, data analysis, ethics in AI and the application of specific AI tools and frameworks. AI expertise is crucial in order to understand the opportunities and challenges of the technology and to use it optimally.
Conclusion
Artificial intelligence (AI) is fundamentally changing our everyday lives and the business world. Whether in the automation of processes, the analysis of large amounts of data or in customer service – AI offers immense opportunities that companies can use in a targeted manner to become more successful. However, the full potential of AI can only be unlocked if both employees and the company itself have the necessary skills. This is precisely why targeted training is so important.
Our AI training courses at Roover Consulting help you to develop these crucial skills. Our experienced trainers not only impart theoretical knowledge, but also provide your teams with practical training on how to use AI technologies safely and efficiently. From machine learning to generative models – we show your employees how AI can make their day-to-day work more productive and easier.
AI skills are a key success factor today. Let’s work together to develop the skills that will make your teams strong and your company competitive. If you would like to find out more about how we can support you, please contact us!
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