In the following, you will find out what artificial intelligence is, how it works, where it is used and what its potentials are for companies. We also explain to you what machine learning and deep learning actually have to do with AI.

In order to know how AI can be used in companies and what benefits it brings, we must first understand the technology. At the end of this article, you will have a sound knowledge of AI, ML and DL and, based on this, you can decide whether the use of artificial intelligence could be worthwhile for your company. You will recognize that AI, ML and DL hold a lot of potential that can save time and money in processes in digital companies.

What is artificial intelligence?

Artificial intelligence is a technology that mimics human intelligence and human action using machines. The aim is to develop machines that solve problems independently by being able to react to their environment. It’s not about a voice assistant, for example, giving preprogrammed answers. Rather, the technology should interpret the question itself and respond to it intuitively.

There is no uniform definition of AI, which is probably due to the abstract nature of the term intelligence and the rapid change in the subject area. Characteristic properties for AI, however, are autonomy and adaptivity. AI systems therefore have the ability to perform tasks in complex environments without constant guidance from people. They are able to improve their performance independently because they can learn from experience.

A general distinction is made in AI systems between weak and strong AI, whereby existing AI solutions all belong to the 1st category. Strong AI is mere science fiction these days:

  • Weak AI are machines that can replace a single human cognitive ability. Systems with weak AI do a specific job. In this case, the AI system behaves intelligently without actually being it.
  • Strong AI would be a machine that, on the whole, has the same capabilities as a human or even exceeds them. The system with strong AI could theoretically fulfill any intellectual task. It would be an actually intelligent system that has a consciousness.

How does artificial intelligence work?

Artificial intelligence mimics how the human brain works. This contains innumerable nerve cells, so-called neurons, which perceive sensory impressions via synapses, link them with other neurons and pass them on as electrical impulses through the neural network. By passing on and linking, sensory impressions are processed into information and we learn in this process. This process ultimately leads to the fact that we can apply what we have learned, solve problems and behave intelligently.

Artificial intelligence simulates exactly this process. Artificial neural networks are modeled on certain processes in the human brain in order to be able to learn on the basis of large amounts of data, to solve complex problems or to recognize patterns. Such systems are composed of so-called algorithms, a sequence of different rules in computer language to solve tasks. Information is received as impulses, passed on to artificial neurons on the next layer and combined with other impulses. An example that is often cited is image recognition. Here, systems are trained with the help of large data sets so that they can ultimately distinguish whether person A or person B is in the image.

You can learn more about exactly how image recognition works in the following TED Talk.

What do machine learning and deep learning have to do with it?

Artificial intelligence, machine learning and deep learning are often used interchangeably, although this is not entirely correct. Rather, machine learning and deep learning are sub-areas of AI, with deep learning also being a sub-area of machine learning. AI is the umbrella term for both technologies. In other words, all machine learning is AI, but not all AI is machine learning; all deep learning is machine learning, but not all machine learning is deep learning.

Machine learning

Machine learning enables systems to learn from data independently and to improve without being explicitly programmed. It always works according to the same principle:

  • First of all, data with known relationships is entered
  • This is followed by learning structures in order to apply them later to unknown contexts

In machine learning processes, an algorithm learns to perform a task independently through repetition. The machine is based on a specified information content of the data, whereby, unlike with conventional algorithms, no solution is specified. The machine learns independently to recognize the data structure. For example, robots can learn for themselves how to grab certain objects in order to transport them from A to B. Only A and B are defined for this process. The robot learns through repeated trial and error and through feedback from successful attempts how to actually grab.

Deep learning

Deep learning is a sub-area of machine learning. With deep learning methods, algorithms can be structured in layers in order to create an artificial neural network. This then learns independently and can make intelligent decisions. Deep learning is something like the next evolutionary stage of machine learning and achieves particularly good results when large amounts of data are available for training an artificial neural network.

In contrast to classic machine learning applications, DL algorithms work very well with large amounts of unstructured data. The algorithms no longer require predefined characteristics, but classify the data independently according to logical structures. These are similar to the logical thinking of people. For example, a deep learning algorithm finds differentiating features in unstructured images of dogs themselves, which describe which image shows which dog breed. The models are used for text or image searches on search engines or ensure that autonomous vehicles recognize street signs.

The potential of artificial intelligence and machine learning for companies

In the field of artificial intelligence, machine learning systems are largely used in companies. In the following, we present 5 areas of application for AI and machine learning from which companies can benefit:

Image recognition

Machine vision algorithms can be used to recognize and categorize images. This means that a large amount of data can be processed very quickly, which ultimately makes work much easier.

The following example shows that - in the future - image recognition can recognize and even predict not only faces, but also emotions.

Voice recognition

The recognition and interpretation of verbal language can also be learned via machine learning. These algorithms are used, for example, in voice assistance systems, such as Siri, Google Assistant or Amazon's Alexa.

Semantic voice recognition

Written text can be interpreted semantically via machine learning. This allows contextual translation applications or chatbots that independently generate meaningful solutions.

Pattern recognition

Machine learning can also be used to identify patterns in sequences of events that are not detectable by humans due to the large amount of data and dependencies. For example, artificial intelligence can learn fault patterns of vehicle electronics from data and match these anomalies with behavior during operation. In this way, deviations can be detected so that countermeasures can be taken in good time.

Process optimization

The recognized patterns can also be used as an information basis for optimization processes, for example to enable optimized process control.

The following example also shows process optimization. In Amazon Go supermarkets, the smartphone is scanned when entering the supermarket and AI-equipped sensors recognize who puts which food items in the shopping cart. The payment process is completely automated.

Our conclusion

Artificial intelligence and in particular the sub-area of machine learning are already widespread today. Often, however, we are not even aware of the use of AI: AI can be found in search engines, driver assistance systems, in machine translation programs, language assistants and chatbots. Functions include, for example, image recognition, speech recognition, handwriting recognition and face recognition.

AI applications are also being used more and more frequently in corporate software. For example, virtual personal assistants can be used to talk to the computer. Self-learning systems will soon do a large part of the often annoying office work – similar to what robots already do today in industrial production. We therefore recommend companies to plan the use of AI in business processes in order to remain competitive in the long term.

Are you planning to use artificial intelligence in your company?

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