Thinking Beyond Transformers: Google Introduces Performers

The key component of the transformer architecture is the attention module. Its job is to figure out the matching pairs (think: Translation) in a sequence through similarity scores. When the length of a sequence increases, calculating similarity scores for all pairs gets inefficient. So, the researchers have come up with the sparse attention technique where it computes only a few pairs and cuts downtime and memory requirements.

According to Google researchers, sparse attention methods still suffer from a number of limitations:

• They require efficient sparse-matrix multiplication operations, which are not available on all accelerators.
• They do not provide rigorous theoretical guarantees for their representation power.
• Optimised primarily for Transformer models and generative pre-training.
• Difficult to use with other pre-trained models as they usually stack more attention layers to compensate for sparse representations, thus requiring retraining and significant energy consumption.
• Not sufficient to address the full range of problems to which regular attention methods are applied, such as Pointer Networks.

Along with these, there are also some operations that cannot be sparsified, such as the commonly used softmax operation, which normalises similarity scores in the attention mechanism and is used heavily in industry-scale recommender systems.

#developers corner #google ai #performers #self attention models #ai

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Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Thinking Beyond Transformers: Google Introduces Performers

The key component of the transformer architecture is the attention module. Its job is to figure out the matching pairs (think: Translation) in a sequence through similarity scores. When the length of a sequence increases, calculating similarity scores for all pairs gets inefficient. So, the researchers have come up with the sparse attention technique where it computes only a few pairs and cuts downtime and memory requirements.

According to Google researchers, sparse attention methods still suffer from a number of limitations:

• They require efficient sparse-matrix multiplication operations, which are not available on all accelerators.
• They do not provide rigorous theoretical guarantees for their representation power.
• Optimised primarily for Transformer models and generative pre-training.
• Difficult to use with other pre-trained models as they usually stack more attention layers to compensate for sparse representations, thus requiring retraining and significant energy consumption.
• Not sufficient to address the full range of problems to which regular attention methods are applied, such as Pointer Networks.

Along with these, there are also some operations that cannot be sparsified, such as the commonly used softmax operation, which normalises similarity scores in the attention mechanism and is used heavily in industry-scale recommender systems.

#developers corner #google ai #performers #self attention models #ai

What Is Google’s Recently Launched BigBird

Recently, Google Research introduced a new sparse attention mechanism that improves performance on a multitude of tasks that require long contexts known as BigBird. The researchers took inspiration from the graph sparsification methods.

They understood where the proof for the expressiveness of Transformers breaks down when full-attention is relaxed to form the proposed attention pattern. They stated, “This understanding helped us develop BigBird, which is theoretically as expressive and also empirically useful.”

Why is BigBird Important?
Bidirectional Encoder Representations from Transformers or BERT, a neural network-based technique for natural language processing (NLP) pre-training has gained immense popularity in the last two years. This technology enables anyone to train their own state-of-the-art question answering system.

#developers corner #bert #bert model #google #google ai #google research #transformer #transformer model

What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.

https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html

#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.

STEP 1:

The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud