In 2013, DeepMind Technologies, then a little-known company, published a groundbreaking paper showing how a neural network could learn to play 1980s video games the way humans do by looking at the screen. These networks then went on to thrash the best human players.
Source: technologyreview.com
Recently, we discovered two exoplanets by training a neural network to analyze data from NASA’s Kepler space telescope and accurately identify the most promising planet signals. And while this was only an initial analysis of ~700 stars, we consider this a successful proof-of-concept for using machine learning to discover exoplanets, and more generally another example of using machine learning to make meaningful gains in a variety of scientific disciplines (e.g. healthcare, quantum chemistry, and fusion research)
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Spoiler alert: Morse code doesn’t really need cracking. Its useful because messages can be sent using this code with minimal equipment, and I say it doesn’t need cracking because the code is well known and what the combinations of dots and dashes stand for is no secret. But, in theory, it is a substitution cipher — where each letter of the alphabet (and each digit) has some representation using dots and dashes, as illustrated below.
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We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task. This method performs as well as MAML, a broadly applicable meta-learning algorithm, while being simpler to implement and more computationally efficient.
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