The basics of artificial intelligence

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Katharina Rainer

Grasping the fundamentals of artificial intelligence unlocks a world of exciting possibilities: automating tedious tasks, accessing reliable data in the blink of an eye, and spotting potential issues before they escalate. With just a basic understanding of AI, you can harness its power to not only streamline your workflow but also tackle everyday challenges with newfound efficiency and precision. Ready to unlock the future of work? Let’s dive in!

digital brain as a symbol for artificial intelligence

What is Artificial Intelligence?

Artificial Intelligence (AI) refers to systems that can independently and automatically solve certain problems without requiring a predefined algorithm, as is the case with traditional software. Instead, a model is created, fed with data, and trained through machine learning so that it can independently solve the task at hand. This allows AI to process incoming information without needing code adjustments or handle tasks whose exact mechanism is not easily describable.

Modalities: how does AI process data?

The most common method AI uses to process data is natural language processing (NLP). However, AI systems are also capable of working with various types of data.

Audio (sound and music)

Companies like Deutsche Bahn, Google, IBM, and Amazon are already successfully utilizing these AI components. Examples include:

Video

Some examples of video data used in AI systems include:

Structured data

Structured data is also widely used in AI systems. Large companies like Netflix, IKEA, and various banks use AI-based data structuring to:

Natural language (text)

Text Categorization (TC), the automatic assignment of texts to predefined categories, is an important pillar of NLP. NLP is one of the most important modalities for practical AI applications, as every business works with text in one form or another. Practical challenges when implementing TC applications include technical integration efforts, lack of annotated example data, the need for algorithm explainability required by GDPR, and the evolving meaning of words over time.

Examples of successful AI implementations in this area include:

Multimodal (combining all modalities)

AI systems can also combine and cascade the modalities of multiple systems. For example, an audio signal might be converted into text, which is then processed in NLP.

How can AI be used in business?

The possibilities of using AI in business are virtually limitless. However, there are areas where AI is already particularly strong and increasingly being used. The following tasks can be efficiently handled by AI, saving costs and time:

Categorizing
Assigning content to predefined categories (e.g., “Is this content offensive or problematic?”, “Which employee should a message be forwarded to?”, “Does a product meet our quality standards?”)

Recognizing anomalies
Detecting deviations from the norm (e.g., “Is there a fraud attempt or cyberattack?”, “Are our computer systems working correctly?”)

Pattern recognition
Recognizing patterns (e.g., “What information can we generate from unstructured data?”, “Which market segments or customer groups can we identify?”)

Making predictions
Predicting trends (e.g., “How will the demand for a product change?”, “What will be the price of a commodity?”)

Providing answers
Providing direct answers to questions (e.g., “What is a proof of concept?”, “Who is responsible for updating our purchasing policies?”)

Summarizing
Summarizing texts or simplifying language (e.g., “Which keywords describe the content?”, “How can the content be made easier to understand?”)

Generating recommendations
Suggesting relevant items (e.g., “Which article might interest a customer?”, “Which internal policy is relevant for a specific task?”)

Writing text
Proposing, preparing, or modifying texts (e.g., “What are the essential requirements for a Fullstack Developer job posting?”, “What arguments exist for or against a particular purchase?”)

Pitfalls of AI solutions

When selecting AI solutions, companies should pay special attention to data security under current and future frameworks like GDPR and the EU AI Act:

Summary and key takeaways

Artificial intelligence can already solve a wide range of tasks that previously required painstaking effort by humans. This can significantly support businesses in their daily operations, save resources, and accelerate processes.