LaBSE: Language-Agnostic BERT Sentence Embedding by Google AI

Multilingual Embedding Models are the ones that map text from multiple languages to a shared vector space (or embedding space). This implies that in this embedding space, related or similar words will lie closer to each other, and unrelated words will be distant (refer to the figure above).

In this article, we will discuss LaBSE: Language-Agnostic BERT Sentence Embedding, recently proposed in Feng et. al. which is the state of the art in Sentence Embedding.

Existing Approaches

The existing approaches mostly involve training the model on a large amount of parallel data. Models like LASER: Language-Agnostic SEntence Representations and m-USE: Multilingual Universal Sentence Encoder essentially map parallel sentences directly from one language to another to obtain the embeddings. They perform pretty well across a number of languages. However, they do not perform as good as dedicated bilingual modeling approaches such as Translation Ranking (which we are about to discuss). Moreover, due to limited training data (especially for low-resource languages) and limited model capacity, these models cease to support more languages.

Recent advances in NLP suggest training a language model on a masked language modeling (MLM) or a similar pre-training objective and then fine-tuning it on downstream tasks. Models like XLM are extended on the MLM objective, but on a cross-lingual setting. These work great on the downstream tasks but produce poor sentence-level embeddings due to the lack of a sentence-level objective.

Rather, the production of sentence embeddings from MLMs must be learned via fine-tuning, similar to other downstream tasks.

Language-Agnostic BERT Sentence Embedding

#deep-learning #machine-learning #towards-data-science #data-science #artificial-intelligence

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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

Google Announces General Availability Of AI Platform Prediction

Recently, the developers at Google Cloud announced the general availability of the AI Platform Prediction. The platform is based on a Google Kubernetes Engine (GKE) backend and is said to provide an enterprise-ready platform for hosting all the transformative ML models.

Emerging technologies like machine learning and AI have transformed the way most processes and industries work around us. Machine learning has brought various significant features that require predictions, such as identifying objects in images, recommending products, optimising market campaigns and more.

However, building a robust and enterprise-ready machine learning environment can include various issues like it being time-consuming, costly as well as complex. Google’s AI Platform Prediction takes into account all these issues to provide a robust environment for ML-based tasks.

In March this year, the tech giant launched the AI Platform Pipelines in beta version to ensure in delivering an enterprise-ready and a secure execution environment for the machine learning workflows.

According to the developers, this new platform is designed for various functions in machine learning models such as improved reliability, more flexibility via new hardware options such as Compute Engine machine types and NVIDIA accelerators, reduced overhead latency, and improved tail latency.m.

#google ai #google ai platform #google kubernetes #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

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

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