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.”

Blockchains are a data buffet for AIs – Fred Ehrsam – Medium

  • And while many of the tech giants working on AI like Google and Facebook have open sourced some of their algorithms, they hold back most of their data.In contrast, blockchains represent and even incent open data.
  • For example: creating a decentralized Uber requires a relatively open dataset of riders and drivers available to coordinate the network.The network effects and economic incentives around these open systems and their data can be more powerful than current centralized companies because they are open standards that anyone can build on in the same way the protocols of the internet like TCP/IP, HTML, and SMTP have achieved far greater scale than any company that sits atop them.
  • And oracle systems (a fancy way of saying getting people all over the world to report real world information to the blockchain in a way we can trust) like Augur will inject more data.This open data has the potential to commoditize the data silos most tech companies like Google, Facebook, Uber, LinkedIn, and Amazon are built on and extract rent from.
  • AIs trained on open data are more likely to be neutral and trustworthy instead of biased by the interests of the corporation who created and trained them.Since blockchains allow us to explicitly program incentive structures, they may make the incentives of AI more transparent.Simplified, AI is driven by 3 things: tools, compute power, and training data.
  • My guess is they shift to 1) creating blockchain protocols and their native tokens and 2) AIs that leverage the open, global data layer of the blockchain.

Sam Altman recently wrote that we are entering an era of hyperscale technology companies. These companies own massive troves of data with strong network effects around them and they are only getting…
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11 Deep Learning Articles, Tutorials and Resources

11 Deep Learning Articles, Tutorials and Resources

  • According to Wikipedia, deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations.
  • Deep learning is sometimes defined as the intersection between machine learning and artificial intelligence.
  • Many articles on deep learning can be found here.

According to Wikipedia, deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set…
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
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Book: Machine Learning Algorithms From Scratch

Book: Machine Learning Algorithms From Scratch | #BigData #MachineLearning #RT

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”