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Algorithms | AI | Explanations

Recap: ML Conference 2019 in Munich

On 17.06. another round of the semi annual ML Conference started in Munich. As usual, it started with a day-long workshop with joint live coding, giving the participants an approachable introduction into Machine Learning and Deep Learning. The second and third conference days were filled to bursting with lectures. As always, the main problem was […]

Understanding AI – Part 5: Supervised & Unsupervised Learning in ML

In the previous article we introduced the basic concepts of Machine Learning and how the training of an ML model works, using a simple but practical algorithm. Next, we want to take a closer look at the different types of Machine Learning. ML can be further distinguished based on a variety of aspects. Let’s start […]

BLG symposium 2019 – lecture “AI and Magic”

“Any sufficiently advanced technology is indistinguishable from magic.” – Arthur C. Clarke JAX 2019 is barely over, but Christoph is already on the podium for the next talk. At the symposium of the BLG (Federal Association of Industrial Photographic Laboratories), his lecture will cover “AI and Magic – How does Artificial Intelligence work? Christoph will […]

JAX 2019

JAX 2019 is approaching and once again Christoph is contributing two sessions. This year he’s focussing on Neural Networks and explains how to use TensorFlow-Training while working with JVM. The first session on May 7th will focus on the benefits of TensorFlow while working with JVM. Although powerful and comprehensiveL frameworks exist for JVM (DL4J, […]

Understanding AI – Part 4: The basics of Machine Learning

After shedding some light onto Symbolic AI in the previous article, we’re now moving on to take a closer look at Machine Learning (ML). When it comes to Symbolic AI, breaking down a problem as minutely as possible is key for successfully solving it. Only this enables the computer to correctly access the “learned” answers […]

Understanding AI – Part 3: Methods of symbolic AI

Reading time approx. 10 minutes: In the previous article we added two distinctions to our initial definition of AI: On the one hand we distinguish between strong and weak AI (Terminator & Science Fiction vs. the scientific status quo). Also we pointed out the difference between symbolic AI and Machine Learning. Let’s remember: Symbolic AI attempts […]

Understanding AI – Part 2: Symbolic AI, Neural Networks and Deep Learning

Reading time approx. 10 minutes: Artificial Intelligence (AI) is as old as computer science itself. Calculations, logical deductions, complex assignments… all this was once restricted to humans, until computers came forth. Even the performance of the early and (from today’s perspective) primitive computing systems seemed to imitate the human mind and it’s accomplishments. No wonder Alan […]