Deep learning: what's changed?

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Deep learning made the headlines when the UK's AlphaGo team beat Lee Sedol, holder of 18 international titles, in the Go board game. The number of potential moves explodes exponentially so it wasn't possible for computers to use the same techniques used in Chess. In learning Go, the computer would have to create millions of games, competing against itself and discovering new strategies that humans may never have considered. Deep learning itself isn't that new, and researchers have been working on algorithms for many years, refining the approach and developing new algorithms. What has stimulated it recently is the convergence of massively parallel processing, huge data sets and superior performance against traditional machine learning algorithms.


Deep learning: What's changed?

#artificialintelligence

Deep learning made the headlines when the UK's AlphaGo team beat Lee Sedol, holder of 18 international titles, in the Go board game. Go is more complex than other games, such as Chess, where machines have previously crushed famous players. The number of potential moves explodes exponentially so it wasn't possible for computers to use the same techniques used in Chess. In learning Go, the computer would have to create millions of games, competing against itself and discovering new strategies that humans may never have considered. Deep learning itself isn't that new, and researchers have been working on algorithms for many years, refining the approach and developing new algorithms.


When to use Machine Learning or Deep Learning? 7wData

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Understanding which AI technologies to use to advance a project can be challenging given the rapid growth and evolution of the science. This article outlines the differences between machine learning and Deep learning, and how to determine when to apply each one. In both machine learning and Deep learning, engineers use software tools, such as MATLAB, to enable computers to identify trends and characteristics in data by learning from an example data set. In the case of machine learning, training data is used to build a model that the computer can use to classify test data, and ultimately real-world data. Traditionally, an important step in this workflow is the development of features – additional metrics derived from the raw data – which help the model be more accurate.


Distributed Coordination and Network Structure: Experimental Results from Simulation

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Coordination is an important phenomena occurring in a wide variety of social and technical systems. We use simulation to examine the ways in which one important system property, the interaction network, effects overall levels of coordination. In particular, we survey the performance of six different learning algorithms, including reasonable strategies and no regret strategies on networks generated by six different algorithms. Our results suggest that no-regret mechanisms not only perform better but also come closer to replicating human behavior in the network coordination task.


Today's AI 'Revolution' Is More Of An Evolution

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In the science fiction canon, the rise of the machines comes swiftly and visibly. After the initial development of primitive AI systems, the technology advances rapidly, infusing itself throughout society and leading to widespread conflict and displacement as machines rapidly replace humans in a sweeping revolution. The reality is that today's AI revolution is happening far more slowly and relatively silently, with algorithms often displacing traditional human tasks in less visible tasks behind the scenes, while the pace of this revolution is far slower than the public hype around AI might suggest. The Hollywood version of the AI revolution typically revolves around a singular breakthrough in AI technology that leads to exponential growth in machine intelligence, displacing humans and upending the societal structure until, in the blink of an eye, algorithms are in charge. Today's reality is far more mundane.