Replicating Particle Collisions at CERN with Kubeflow – this post is interesting for a number of reasons. First, it shows how Kubeflow delivers on the promise of portability and why that matters to CERN. Second, it reiterates that using Kubeflow adds negligible performance overhead as compared to other methods for training. Finally, the post shows another example of how images and deep learning can replace more computationally expensive methods for modelling real-word behaviour. This is the future, today.
AI vs. Machine Learning: The Devil Is in the Details – Need a refresh on what the difference is between artificial intelligence, machine learning and deep learning? Canonical has done a webinar on this very topic, but sometimes a different set of words are useful, so read this article for a refresh. You’ll also learn about a different set of use cases for how AI is changing the world – from Netflix to Amazon to video surveillance and traffic analysis and predictions.
Making Deep Learning User-Friendly, Possible? – The world has changed a lot in the 18 months since this article was published. One of the key takeaways from this article is a list of features to compare several standalone deep learning tools. The exciting news? The output of these tools can be used with Kubeflow to accelerate Model Training. There are several broader questions as well – How can companies leverage the advancements being made within the AI community? Are better tools the right answer? Finding a partner may be the right answer.
Interview spotlight: One of the fathers of AI is worried about its future – Yoshua Bengio is famous for championing deep learning, one of the most powerful technologies in AI. Read this transcript to understand some of his concerns with the direction of AI, as well as the exciting developments in AI. Research that is extending deep learning into things like reasoning, learning causality, and exploring the world in order to learn and acquire information.