RE-WORK Deep Learning Summit London

BigData Republic
Oct 12, 2017 · 2 min read

On the 21th and 22nd of September we attended the RE-WORK Deep Learning Summit in London. This event was focused on the development of deep learning techniques and business applications based on deep learning. Facebook and Amazon presented their Natural Language Processing research using neural networks, which was a recurring theme in the summit. Bayesian Neural Networks and Generative models were other main topics discussed at the summit.

We saw many interesting start-ups based on innovative applications using deep learning and also some bigger companies with specialized deep learning teams. An overview of some of these companies:

  • DigitalGlobe: analyzes satellite images for various applications. For instance, classifying swimming pools for a cleaning company to determine the best settlement, or determining a flooded area for an insurance company.
  • Alpha-i: Provides a Time Series API which can do forecasting, failure prevention, risk monitoring and anomaly detection on presented data. They use Bayesian Neural Networks of which the certainty of a prediction, for instance predicting stock-market prizes, can be very valuable.
  • Echobox: Generate and post social media messages. Already many companies (mostly news websites, for example “The Guardian”, “Le Monde” and “NewScientist”) use this system. It provides the text, selects an optimal picture and posts the message at the best time of the day. Echobox also has a side project which analyzes trends in social media and use it to predict elections.
  • Jukedeck: an AI that can compose original music. They provided examples of electronic music and even created a Bach-like composition.
  • Automatic front-end development. The model classifies an image of a front-end layout and builds working code for it.
  • Deep Instinct: Prevent cyber-attacks using deep learning. Many anti-virus protectors cannot detect new malware, however many viruses are variations of existing malware with minor modifications. Their model is able to detect these variations with great accuracy.

The variation of use-cases for deep learning is huge. But how is it any different from using other machine learning tools, such as random forests or generalized linear models? The difference is that deep learning works extremely well on unstructured media, such as images, video and sound. Using deep learning we can build learned feature extractors instead of handcrafting a set of features from our input. This requires less work, is more robust for future new data and requires less domain knowledge.

At BigData Republic we have several experts in the deep learning field with many scientific publications. We have worked on various deep learning projects amongst which one was a ProRail case where we built an OCR system to analyze train wagon labels from video footage. Please feel free to contact us if you want to know more about deep learning or have questions about processing your unstructured datasets!



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