So what’s new in AI?
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I graduated with a degree in AI when the cost of the equivalent computational power to an iPhone was $50 million. A lot has changed but surprisingly much is still the same.
Source: towardsdatascience.com

I graduated with a degree in AI when the cost of the equivalent computational power to an iPhone was $50 million. A lot has changed but surprisingly much is still the same.
Source: towardsdatascience.com
Academics, economists, and AI researchers often undervalue the role of intuition in science. Here’s why they’re wrong.
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A few examples of images from the Google-Landmarks dataset, including landmarks such as Big Ben, Sacre Coeur Basilica, the rock sculpture of Decebalus and the Megyeri Bridge, among others.
Read MoreOur new paper proposes a new learning paradigm called ‘Scheduled Auxiliary Control (SAC-X)’ which seeks to overcome the issue of exploration in control tasks. SAC-X is based on the idea that to learn complex tasks from scratch, an agent has to learn to explore and master a set of basic skills first. Just as a baby must develop coordination and balance before she crawls or walks—providing an agent with internal (auxiliary) goals corresponding to simple skills increases the chance it can understand and perform more complicated tasks.
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