Skip to content

Machine Learning

Machine Learning (short: ML) increasingly defines the business world, because it offers a reliable prediction in many areas that have enough and high quality data. The goal of this chapter is to understand and be able to distinguish the most basic algorithms in the field of artificial intelligence. To do this, we need to know what artificial intelligence does exactly.

Tasks of Machine Learning

Machine learning makes it possible for computer systems to learn how to solve tasks without this solution being explicitly programmed. The models are fed with data and decide in many training steps how they can further improve their prediction to get as close as possible to reality.

The first of these systems already existed in the 1950s. As early as 1959, Arthur Lee Samuels published an article in the IBM Journal of Research and Development about a self-learning algorithm for the game of checkers.

Some of our Articles in the Field of Machine Learning

Adagrad

What is Adagrad?

Discover Adagrad: The Adaptive Gradient Descent for efficient machine learning optimization. Unleash the power of dynamic learning rates!

Line Search

What is the Line Search?

Discover Line Search: Optimize Algorithms. Learn techniques and applications. Improve model convergence in machine learning.

Sarsa

What is SARSA?

Discover SARSA: a potent RL algorithm for informed decision-making. Learn how it enhances AI capabilities. Unveil SARSA's potential in ML!

Monte Carlo Methods / Monte Carlo Simulation

What are the Monte Carlo Methods?

Discover the power of Monte Carlo methods in problem-solving. Learn how randomness drives accurate approximations.

Verlustfunktion / Loss Function

What is a Loss Function?

Exploring Loss Functions in Machine Learning: Their Role in Model Optimization, Types, and Impact on Robustness and Regularization.

Binary Cross-Entropy

What is the Binary Cross-Entropy?

Dive into Binary Cross-Entropy: A Vital Loss Function in Machine Learning. Discover Its Applications, Mathematics, and Practical Uses.

Although Arthur Lee Samuels’ paper was published over 60 years ago, the topic of machine learning has really taken off in recent years. There are several reasons why development was largely paused for many years:

  • The power of computer processors has increased significantly and we are now able to have more computer power in less space.
Machine Learning / Künstliche Intelligenz: Das Diagramm zeigt das sogenannte Moore's Law für Künstliche Intelligenz (Machine Learning). Es beschreibt die Entwicklung der Rechenleistung pro Jahr.
Moore’s Law for Processing Power | Photo: evobsession.com
  • Large amounts of data can now be stored and managed much more cheaply. In addition, new storage technologies have made it easier to analyze data sets.
  • In the meantime, we use entire computer clusters for many applications, on which computationally intensive tasks are divided. This has additionally reduced the computation time of the models and large data analyses can be handled in a short time.
  • As a result of the points already mentioned, the willingness to collect data has also increased sharply beyond companies. As a result, large amounts of social media data, weather data or medical data are available in addition to machine and company data.
  • The latest developments are accessible to very many people via Python libraries, for example. In addition, you can access computationally powerful machines free of charge in Google Colab. As a result, a large community has formed worldwide that actively applies and further develops ML.

What does Machine Learning mean?

The term AI cannot be defined unambiguously. This is due to the fact that the term intelligence cannot be definitively defined either. In general, a distinction is made between so-called “Broad AI” and “Narrow AI”.

Broad AI is about models and algorithms that solve general problems, i.e. reading, calculating, writing, making predictions, etc.. Without wanting to anticipate the individual articles here: This is not yet possible today and also still in the distant future. Algorithms that are very well suited for all linguistic applications (translation, language understanding, etc.) cannot calculate and vice versa.

All the models we have at the moment are classified as Narrow AI. They are very well suited for a clearly defined problem and may even be better at it than a human. The classic example of this are various algorithms for board games, in which the machine has already beaten the human.

In addition, part of the current literature is now also concerned with ensuring that Machine Learning algorithms also develop a real understanding of what they are doing. Until now, many models have been trained to recognize and map statistical correlations. Now, however, the first algorithms are also being trained to develop an understanding of the data.

Conclusion

Nowadays, we encounter machine learning in almost every area of life, whether at work, while surfing the net privately, or via e-mails and other messages that are perfectly tailored to our tastes. Therefore, it is quite helpful to develop a basic understanding of the individual models and algorithms. This can help you understand the limits of artificial intelligence or even discover possibilities for your own company.

Cookie Consent with Real Cookie Banner