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Big Data Trends in 2016, Part 4 of 3: Advanced Machine Learning

TUM PM62 LEADdig

During the last few weeks, we've combined expertise with forward thinking to create a forecast of trends in 2016, consisting of the three next big things in the data world: Data Science on scale, Machine Learning/Deep Learning as a Service and Human Data Interfaces. But we aren't the only ones looking to the future: The specialist magazine LEADdigital has published “The 10 most important tech trends for 2016” and since it features our Chief Data Scientist, Klaas Bollhoefer, we'll now share with you the fifth trend – making it the fourth part of our trilogy, you could say…

Advanced Machine Learning: applicable Deep Learning

"Which knowledge – that is actually applicable – can machines gain from people, behaviors and examples? And what's more: In the future, machines are expected to have autonomous experiences and to learn from them, in order to be able to apply these experiences in appropriate situations. They can help companies automate processes and design them more efficiently.

“In 2016 this will be implemented in a variety of ways. Namely, wherever machines are able to use neural networks (DNNs) and self-learning algorithms to recognize and match patterns in images, text files and audio files. This is what makes machines intelligent. '2016 will be the year of action. In the context of the Internet of Things and Industry 4.0, machine learning experts will have the chance to be able to translate these huge, technical opportunities and options into specific business plans and data projects in a tailor-made manner,' says Klaas Bollhoefer from the cloud and big data service provider The unbelievable Machine Company with confidence.

"Here's an example of how a specific business process could look: An independent electronics merchant has a large product range and a whole variety of components, spare parts, etc. in stock. A customer needs a spare part for his outdated appliance. Individual components are in the store but none of the standard components are correct. The staff member's search is painstaking, time-consuming and sometimes absolutely impossible. Thanks to the introduction/connection of a neural network, the problem can be solved: The staff member uses a smartphone and an app to take a photo of the old part, which doesn't have an order number, and the matching part is then identified from the photo – regardless of how old or unusual the corresponding appliance or how severely disfigured the part that needs replacing."

This abstract is taken from the 01/2016 edition of LEAD digital. Unfortunately the article is no longer available online. However, Deep Learning and the solutions it provides for automated identification and classification of objects using image data will also be addressed by our Data Scientists Til Breuer and Dr. Christian Nietner at the BITKOM Big Data Summit.

Here’s something else that may interest you:

Trends in 2016: The next big things in the data world (Part 1) – Data Science on scale

Trends in 2016: The next big things in the data world (Part 2) – Machine Learning/Deep Learning as a Service

Trends in 2016: The next big things in the data world (Part 3) – Human Data Interfaces (HDI)

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