Artificial intelligence (AI) is a branch of computer science that refers to the simulation of human intelligence by machines, especially computer systems. AI technologies include machine learning, neural networks, deep learning, and generative AI. These systems analyze vast amounts of data, learn from patterns, and make independent decisions or recommendations.
The idea of AI originated as early as 1950, when scientists such as Alan Turing developed the first concepts for thinking machines. The term “artificial intelligence” was first coined at a conference in the summer of 1956. The first expert systems were developed in the 1980s thanks to advances in computing power.
But it was only with the advent of machine learning and big data processing in the 2010s that AI made its breakthrough. AI systems can now be found in almost every industry, from marketing to cybersecurity, healthcare, manufacturing, and retail.
A distinction is made between two types of artificial intelligence, which are often mistakenly confused by the general public:
- AGI (Artificial General Intelligence): General intelligence that, similar to humans, can learn flexibly in very different domains, transfer knowledge, and is capable of building new tools for itself.
- ANI (Artificial Narrow Intelligence): Basically, any AI that currently exists that can only really cope in a specific field. ChatGPT, Microsoft’s Copilot, and others are only ANI and not AGI. They have no real intrinsic motivation, no consciousness, and cannot really build new tools independently in the sense of AGI.
Artificial Narrow Intelligence (ANI) has been around for quite some time - longer than most people currently believe. Even simple spam filters from 15 years ago were already self-learning systems. However, it is only in recent years that ANI has become significantly more powerful. But only AGI would represent a real, massive breakthrough.
The progression of an artificial intelligence can be categorized into several distinct phases:
Phase 1 – Data collection and preparation: The first step is to identify the problem to be solved and to collect, prepare, and clean the data that can contribute to solving this problem.
Phase 2 – Model development: This phase consists of selecting the right algorithms and technologies to develop, train, and implement an AI model.
Phase 3 – Model validation: To ensure that the models work as expected, various tests and validation steps such as cross-validation, A/B testing, and statistical tests are used. It should also be ensured that the models continue to function correctly even with new, unseen data.
Phase 4 – Deployment: The validated models are integrated into the existing system environment or application platform. Strategic planning is important here to ensure that the AI models can be seamlessly integrated into the existing systems.
Phase 5 – Monitoring and maintenance: To ensure that the AI systems function correctly and deliver accurate results, model performance must be continuously monitored after deployment. In addition, maintenance and update processes must be established to adapt to changes over time and keep the system up to date.
Artificial intelligence is already being used in many areas, such as:
- Autonomous systems such as self-driving cars or robots,
- Speech recognition in voice assistants such as Siri or Alexa,
- Language models, such as ChatGPT or Google’s Gemini, and
- Medical diagnosis and treatment, including the ability to detect diseases.
The use of artificial intelligence offers the following advantages, among others:
- Increased efficiency: AI helps to increase efficiency and productivity by automating repetitive tasks. This saves companies time and money.
- Improved decision-making: Artificial intelligence possesses the capability to analyze vast quantities of data with greater speed and accuracy than human counterparts, thereby enabling organizations to make more informed and effective decisions.
- Improved customer experience: Virtual assistants and AI-powered chatbots provide customers with personalized support in real time, improving the customer experience and helping companies reduce response times and human error.
- Operational efficiency: AI can be used to detect security breaches, anomalies, and fraud more quickly to minimize potential losses.
- New products and services: By helping companies analyze data and identify trends, AI systems can enable the development of new products and services.
In addition to the advantages mentioned above, however, its use also carries certain risks that must be recognized and eliminated.
- Security and data protection risks: AI often processes highly sensitive data, which requires high security standards.
- Hallucinations and discrimination: AI systems formulate convincing but incorrect answers, even to simple questions, or distort results, leading to discrimination and injustice.
- Deepfakes: Deepfakes are used to manipulate media content such as videos, images, or audio recordings, often in a remarkably realistic way, and are sometimes used for misinformation and identity fraud.
- High implementation costs: For smaller companies, the development and implementation of AI can be costly.
- Lack of skilled workers: Since AI as it is currently used has not been in use for very long, qualified AI experts are rare and expensive.
- Transparency and accountability: AI models can make complex decisions. However, the processes, especially neural networks, are difficult to understand, which can lead to trust issues.
- Market dominance: A few large companies dominate the market and thus the development of AI by specifically buying up AI startups. They reinforce their monopoly-like market position with all the associated disadvantages.
Artificial intelligence offers great opportunities for increasing efficiency and innovation in almost every industry. The key is to have a well-thought-out strategy for how AI can be used in business processes to overcome challenges such as costs, data protection, and skills shortages, and to remain competitive in the long term.