Google Brain’s new super fast and highly accurate AI: the Mixture of Experts Layer.

Google Brain’s new super fast and highly accurate #AI: the Mixture of Experts Layer

  • Google Brain’s new super fast and highly accurate AI: the Mixture of Experts Layer.Conditional Training on unreasonable large networks.One of the big problems in Artificial Intelligence is the gigantic amount of GPUs (or computers) needed to train large networks.The training time of neural networks grows quadratically (think squared) in function of their size.
  • Therefore, we have to build giant neural networks to process the ton of data that corporations like Google Microsoft have.Well, that was the case until Google released their paper Mixture of Experts Layer.The Mixture of Experts Layer as shown in the original Paper.The rough concept is to keep multiple experts inside the network.
  • Each expert is itself a neural network.
  • This does look similar to the PathNet paper, however, in this case, we only have one layer of modules.You can think of experts as multiple humans specialized in different tasks.In front of those experts stands the Gating Network that chooses which experts to consult for a given data (named x in the figure).
  • The Gating Network also decides on output weights for each expert.The output of the MoE is then:ResultsIt works surprisingly well.Take for example machine translation from English to French:The MoE with experts shows higher accuracy (or lower perplexity) than the state of the art using only 16% of the training time.ConclusionThis technique lowers the training time while achieving better than state of the art accuracy.

One of the big problems in Artificial Intelligence is the gigantic amount of GPUs (or computers) needed to train large networks. The training time of neural networks grows quadratically (think…
Continue reading “Google Brain’s new super fast and highly accurate AI: the Mixture of Experts Layer.”

Google Brain’s new super fast and highly accurate AI: the Mixture of Experts Layer.

Google Brain’s new super fast and highly accurate #AI: the Mixture of Experts Layer

  • Google Brain’s new super fast and highly accurate AI: the Mixture of Experts Layer.Conditional Training on unreasonable large networks.One of the big problems in Artificial Intelligence is the gigantic amount of GPUs (or computers) needed to train large networks.The training time of neural networks grows quadratically (think squared) in function of their size.
  • Therefore, we have to build giant neural networks to process the ton of data that corporations like Google Microsoft have.Well, that was the case until Google released their paper Mixture of Experts Layer.The Mixture of Experts Layer as shown in the original Paper.The rough concept is to keep multiple experts inside the network.
  • Each expert is itself a neural network.
  • This does look similar to the PathNet paper, however, in this case, we only have one layer of modules.You can think of experts as multiple humans specialized in different tasks.In front of those experts stands the Gating Network that chooses which experts to consult for a given data (named x in the figure).
  • The Gating Network also decides on output weights for each expert.The output of the MoE is then:ResultsIt works surprisingly well.Take for example machine translation from English to French:The MoE with experts shows higher accuracy (or lower perplexity) than the state of the art using only 16% of the training time.ConclusionThis technique lowers the training time while achieving better than state of the art accuracy.

One of the big problems in Artificial Intelligence is the gigantic amount of GPUs (or computers) needed to train large networks. The training time of neural networks grows quadratically (think…
Continue reading “Google Brain’s new super fast and highly accurate AI: the Mixture of Experts Layer.”

Google Is Making AI That Can Make More AI

Google Is Making #AI That Can Make More AI

  • What’s more, using AIs to build more AIs may also increase the speed at which new AIs can be made.
  • Once you’ve trained an AI to accomplish a certain goal, you can’t necessarily crack it open and see how it is doing it.
  • The downside is that AI building more AIs sure seems like it’s inviting a runaway cascade and, eventually, Skynet.
  • Google Is Making AI That Can Make More AI
  • The lab is reportedly building AI software that can build more AI software , with the goal of making future AI cheaper and easier.

There’s no way this could go wrong.
Continue reading “Google Is Making AI That Can Make More AI”

DeepMind’s new computer can learn from its own memory

DeepMind's new computer can learn from its own memory

  • DeepMind says its new AI model, called a differentiable neural computer (DNC), can be fed with things like a family tree and a map of the London Underground network, and can answer complex questions about the relationships between items in those data structures.
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  • By augmenting an AI’s capabilities with the power of learning from memory, it’ll likely be able to complete far more complex tasks on its own.
  • DeepMind, an artificial intelligence firm that was acquired by Google in 2014 and is now under the Alphabet umbrella, has developed a computer than can refer to its own memory to learn facts and use that knowledge to answer questions.
  • It’s the networks that helped DeepMind’s AlphaGo AI defeat world champions at the complex game of Go .

DeepMind has developed a computer than can refer to its own memory to learn facts and use that knowledge to answer questions.
Continue reading “DeepMind’s new computer can learn from its own memory”