In a nutshell: What are machine learning and AI?
Machine learning and artificial intelligence (AI) are interlinked. Machine learning is a sub-area of AI. Thanks to machine learning, a machine - such as a computer - acquires the ability to analyze data independently, learn from it and make decisions.
Artificial intelligence extends the capabilities of machine learning, as AI is based on neural networks and other technologies in addition to machine learning.
Definition and example of machine learning
In machine learning, applications and machines (e.g. computers) are programmed with algorithms so that they can examine, use and develop data. Machine learning is always based on rigid algorithms. They are also referred to as learning algorithms. How each individual learning algorithm is structured depends on the individual application.
The learning algorithm projects the data obtained in the learning process onto a mathematical model. Using this model, the algorithm can adapt the data so that it can be used for different areas of application. Ultimately, the algorithm and the model help to develop solutions for various problems.
One example of machine learning is its use in customer relationship management (CRM). Machine learning can be used to segment data on all customers. Among other things, the preferences of customers and their previous interactions with the company and its offers can be evaluated.
In this example, machine learning can be used to optimize the user experience.
Definition and example of artificial intelligence
Artificial intelligence is not yet clearly defined, as the very concept of intelligence is malleable and a fully-fledged artificial intelligence that can really act completely like a human being has not yet been achieved.
The aspect of humanity is clearly at the forefront of artificial intelligence. AI therefore refers to all applications, machines and other inventions that imitate human abilities.
In order to imitate human abilities and solve complex problems, artificial intelligence must be developed in a complex and computationally intensive process. The development of an AI includes static training with data, the development of neural networks, machine learning and deep learning, robotics and numerous other aspects.
All in all, artificial intelligences are elaborately programmed applications that have numerous areas of application.
Probably the most popular example of an AI is the chatbot ChatGPT. It allows users to hold conversations. They can also have images and texts created by the AI. It is also possible for the chatbot to generate marketing plans.
Another example of AI is the optimization of educational offerings using tailored and specially developed AI applications. If, for example, a university programs an AI and feeds it with knowledge data relating to a specific subject area during training, the AI can evaluate the university's educational offerings and make suggestions for optimization.
Similarities between machine learning and artificial intelligence
Both machine learning and artificial intelligence pursue the goal of solving problems. A given task (i.e. the problem) is to be solved using the acquired knowledge. How this knowledge is learned differs between machine learning and AI. This will be discussed in more detail later.
Another thing that machine learning and AI have in common is the development process. The developers of ML and AI solutions program them in such a way that both are able to learn independently from data and make decisions based on the data.
The application of machine learning models and AI models is possible in numerous areas. For example, both machine learning and artificial intelligence are used for predictions (e.g. weather, sports results), marketing tools, optimizing work processes and formulating suggestions for action.
Although there are similarities between machine learning and artificial intelligence, the two technologies are fundamentally different. Artificial intelligence is a broad category with diverse methods and technologies, while machine learning is only a small part of it. So let's take a closer look at the differences between machine learning and AI.
Differences between machine learning and AI
Machine learning and artificial intelligence pursue opposing main objectives.
The core objective of machine learning is to analyze, learn and make decisions based on data. Based on special algorithms, the machine extracts information from large amounts of data, learns autonomously from this information and then makes the right decisions.
The core objective when creating an artificial intelligence is for it to be able to solve complex human tasks and generally act in a human-like manner.
Machine learning as a subfield of artificial intelligence
Machine learning is divided into supervised learning and unsupervised learning.
- Supervised learning is based on the use of algorithms to analyze data. The machine uses the programmed algorithms to analyze the specified data.
- Unsupervised learning, unlike supervised learning, is initiated by the machine itself and is exploratory. The machine attempts to discover patterns in the data records and learn in this way.
Both supervised learning and unsupervised learning are autonomous, which means that the machine can analyze data itself on the basis of the stored algorithms, learn from it and make decisions.
This gives the machine far-reaching capabilities for differentiated decision-making. However, with machine learning it is not possible to imitate the decision-making structures of the human brain.
With its autonomy in the learning process and independent handling of data volumes, machine learning has characteristics of artificial intelligence. However, it is not capable of solving complex problems.
In addition, machine learning lacks many other functions that artificial intelligence has due to its large pool of methods.
AI comprises a much larger pool of methods
The number of methods used to create artificial intelligence is far greater than for machine learning. As already mentioned, machine learning is a sub-area of artificial intelligence. Other sub-areas include natural language processing (NLP), robotics, deep learning, the development of neural networks and genetic algorithms - the latter for simulating evolutionary problems.
The difference between machine learning and deep learning is that deep learning uses artificial neural networks to enable complex tasks to be solved. The development of neural networks in artificial intelligence makes it possible to process individual experiences and link them with existing knowledge.
- Like a human, AI gains experience during certain tasks.
- AI uses this experience to learn lessons and work more efficiently in the future.
- The AI may also transfer its experience to other tasks.
The wealth of methods of artificial intelligence provides more possibilities for use. The latest developments such as the chatbots Google Gemini and ChatGPT show the range of functions that AI applications can have: from analyzing and creating texts to creating images and developing marketing plans.
Microsoft's Copilot also demonstrates the wide range of possible applications for artificial intelligence.
We have created a comprehensive AI workshop on the use of artificial intelligence in the software solutions provided by Microsoft: SmartAI365 - Artificial Intelligence (AI) & Copilot in M365. Find out more on the linked page and contact us for an individual consultation!
Great effort in the development of artificial intelligence
The effort required to develop artificial intelligence is far greater than that required to develop machine learning.
Machine learning requires the selection and preparation of data for training. After that, only the strategy or model for machine learning needs to be selected. In all of this, a single server can be sufficient to carry out machine learning successfully.
The development of artificial intelligence, on the other hand, is of a much more complex nature. In order to train and create an AI with a large scope of functions and knowledge , IT systems with hundreds of computers may be required for high-computing use cases, for example.
This high level of effort in the development of artificial intelligence also justifies the prices for the various AI solutions currently available on the market.
Areas of application of machine learning and AI
Machine learning is limited in its use to analyzing data, evaluating it and making decisions based on the data. ML software can also generate new data in this process.
Using learning algorithms and static models, ML software is able to correct itself and deliver increasingly precise results when processing data. The Fraunhofer Institute lists the following areas of application for machine learning, among others:
- Recognition of images
- Recognition of patterns in data
- Process optimization
Machine learning algorithms that use computer vision can recognize and categorize images. This opens up potential applications in autonomous driving, facial recognition and the translation of old characters.
By recognizing patterns in data, machine learning algorithms can detect errors in systems. Anomalies generally become more recognizable and error analysis is accelerated by machine learning.
Process optimization is the last example: In data engineering and process mining, data is informative for evaluating the use of resources. In particular, the uninterrupted mapping of processes can provide insightful findings for optimization from the data on the interfaces in the various work processes.
Depending on the effort involved in development and the amount of data from training , an AI can simulate human intelligence more or less comprehensively. This even makes it possible to solve complex tasks that cannot be solved with rigid algorithms. An AI has a certain degree of creativity and can imitate some patterns of human thought.
Since an AI can work with all types of data - whether structured, semi-structured or unstructured data - it is suitable for the following areas of application:
- Analysis of files and development of arguments in jurisprudence
- Use of autonomous weapon systems for self-control
- Customer support in online marketing in the form of chatbots
- Simulation of non-player characters (NPCs) in gaming
- Solving mathematical problems
- Creation of images, artworks and videos
- Development of designs for products
- Creation of creative and informative texts
- Evaluation of satellite images to derive potential for improvement in climate protection
Conclusion: Increase productivity with the right use of machine learning and AI
Both machine learning and artificial intelligence open up a great deal of scope for organizations to increase productivity and simplify work processes. Just taking out a subscription to Microsoft 365 with integrated AI can fundamentally improve day-to-day work in companies.
The use of modern technologies such as machine learning and AI not only has advantages for the productivity of companies, but also has a positive impact on the quality of work and the employee experience. There is therefore much to be said for the use of ML software and AI applications in suitable areas of application.
