Is it wise to learn big data

Deep learning: machines learn like humans

Independent “thinking” and decisive software

The goal of deep learning is human-like "thinking" and (automated) decision making software. It is based on an artificial neural network arranged in layers. This processes large amounts of data according to a special algorithm. It "learns" from independently made experiences. The more layers the neural network has, the more powerful deep learning is. This technology has been around since the 1950s. Modern digitization is strongly driving its further development.

How about if software could think like a human? What sounded like science fiction for a long time is no longer a theoretical question. On the contrary: Artificial intelligence (AI) is already in many real applications. Smart voice assistants, autonomous cars or modern image recognition systems would be inconceivable without this technology. Big data makes this possible. The increasing generation of data from countless digital sources is the basis of AI.

But that alone is not enough to turn the interplay of bits and bytes into human or human-like thought processes. This can only be achieved with a special type of information processing. The technical term for this is deep learning. The process is a sub-area of ​​so-called machine learning, which in turn is a sub-category of AI.

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The areas of application of deep learning

Thanks to deep learning, software can “learn” like a human and thus relieve him of many tasks. Naturally, the method shows its strengths where a lot of data is generated. Even today, employees often have to collect and analyze these in a time-consuming manner. Deep learning can usually do this better and faster. Human colleagues gain more space for other, more creative tasks. This already works in these areas of application:
  • Predictive maintenance
  • Smart Manufacturing
  • voice control
  • Quality controls
  • Transport / logistics
  • Security
  • Financial sector
  • administration
  • Insurance
  • Healthcare
  • Customer service
Ready for the inspection? Thanks to deep learning, built-in diagnostic tools relieve people of this decision. (© 2018 Shutterstock / Atstock Productions)

This is how deep learning works today

In these and other areas, programs learn to make their own decisions. From continuously collected training data and "experience", they know, for example, when a certain component will fail in a production line and initiate its replacement in good time. Diagnostic systems work similarly in modern cars, which flexibly calculate the next inspection date based on the driving style and the mileage as well as sensor data. Fixed maintenance intervals are therefore superfluous.

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With insurance companies, they can calculate the probability of damage and risks. In purchasing, they anticipate needs and automatically order supplies at the most favorable terms. In banks, they oversee financial transactions and securities trading. Or they direct the supply chain in logistics. And in service, they communicate with customers as voice bots. These and other options will fundamentally change the work in many departments. How this change affects companies and their employees - for example in the HR department - can be heard in the Telekom Podcast.

Implementing deep learning - a job for professionals

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Small and medium-sized companies can also benefit from these talents - if they provide the right conditions. This primarily includes a lot of usable data and appropriately experienced IT specialists. And: powerful computing power. If you don't want to set up and host your own structures for this, you can tap into scalable capacities via the (multi-) cloud.

External support is also advisable in other ways. Implementing deep learning systems (still) requires special know-how that only a few medium-sized companies should have. All others should rely on experienced, external support from experts for the project. With or without help - this is how the path to the application of deep learning can look like:

  • First, companies should clarify what specific goal they are pursuing with deep learning. The effort is only worthwhile if there is a value-adding, strategic application scenario.
  • If the forecast is positive, an inventory will follow. This involves, for example, the usable data sources. These can be internal (sensors, software), but also external reservoirs, such as databases. Also important: Which parts of Big Data can be used for later use?
  • A suitable model of deep learning is sketched on this basis. It shows how it will work in the future and analyze the incoming data.
  • The next step is the practical embedding of the deep learning software.
  • The algorithm then has to be implemented and fed with training data, on the basis of which it gradually collects empirical values.
  • At the end of this trial phase, it will be analyzed.
  • This is followed by a check of the so-called inference. This means the ability of the system to draw conclusions from the processed data.
  • If the result meets the expectations, the deep learning process can start working. In addition, its performance should be checked regularly.