Attention and Memory in Deep Learning and NLP – WildML

A recent trend in Deep Learning are Attention Mechanisms.

Attention Mechanisms in Neural Networks are loosely based on the visual attention mechanism found in humans.

If we look a bit more look closely at the equation for attention we can see that attention comes at a cost.

An alternative approach to attention is to use Reinforcement Learning to predict an approximate location to focus to.

Interpreted another way, the attention mechanism is simply giving the network access to its internal memory, which is the hidden state of the encoder.

When the networks parameter weights are tied in a certain way, the memory mechanism inEnd-to-End Memory Networks identical to the attention mechanism presented here, only that it makes multiple hops over the memory.

It’s likely that in the future we will see a clearer distinction between memory and attention mechanisms, perhaps along the lines of Reinforcement Learning Neural Turing Machines, which try to learn access patterns to deal with external interfaces.

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Top minds in machine learning predict where AI is going in 2020

As we begin a new year and decade, VentureBeat turned to some of the keenest minds in AI to revisit progress made in 2019 and look ahead to how machine learning will mature in 2020.

While some predict advances in subfields like semi-supervised learning and the neural symbolic approach, virtually all the ML luminaries VentureBeat spoke with agree that great strides were made in Transformer-based natural language models in 2019 and expect continued controversy over tech like facial recognition.

Like most of the other industry leaders VentureBeat spoke with for this article, Chintala predicts the AI community will place more value on AI model performance beyond accuracy in 2020 and begin turning attention to other important factors, like the amount of power it takes to create a model, how output can be explained to humans, and how AI can better reflect the kind of society people want to build.

Unequivocally, one of the biggest machine learning trends of 2019 was the continued growth and proliferation of natural language models based on Transformer, the model Chintala previously referred to as one of the biggest breakthroughs in AI in recent years.

Dean pointed to the progress that has been made, saying ” that whole research thread I think has been quite fruitful in terms of actually yielding machine learning models that [let us now] do more sophisticated NLP tasks than we used to be able to do.

The development of more efficient AI models was an emphasis at NeurIPS, where IBM Research introduced techniques for deep learning with an 8-bit precision model.

In the year ahead, Gil is particularly interested in neural symbolic AI. IBM will look to neural symbolic approaches to power things like probabilistic programming, where AI learns how to operate a program, and models that can share the reasoning behind their decisions.

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AI creativity will bloom in 2020, all thanks to true web machine learning

An end-to-end open source machine learning library that is capable of, among other features, running pre-trained AI directly in a web browser.

Js brings TensorFlow’s server-side AI solution directly into the web, if I were to build this project today, I could run a pre-trained model that lets the AI recognize the given logo in the user’s phone browser.

Projects show how developers can get much more inventive when machine learning becomes a properly integrated part of the web.

Thanks to true web AI, this propensity to see mobiles as assistants will become fully entrenched once websites – especially mobile websites – start performing instantaneous machine learning.

Hypothetically speaking, we could also use true web AI to develop websites that adapt to people’s ways of using them.

Js with the Web Storage API, a website could gradually personalize its color palette to appeal more to each user’s preferences.

Now that we finally have the gift of true web machine learning, 2020 could well be the year that developers unleash their AI creativity.

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