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DeepMind AI Supercharges YouTube Shorts Exposure by Auto-Generating Descriptions for Millions of Videos

Marktechpost

” This generated text is stored as metadata, enabling more efficient video classification and facilitating search engine accessibility. Additionally, DeepMind and YouTube teamed up in 2018 to educate video creators on maximizing revenue by aligning advertisements with YouTube’s policies.

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Modern NLP: A Detailed Overview. Part 2: GPTs

Towards AI

Year and work published Generative Pre-trained Transformer (GPT) In 2018, OpenAI introduced GPT, which has shown, with the implementation of pre-training, transfer learning, and proper fine-tuning, transformers can achieve state-of-the-art performance. Basically, it predicts a word with the context of the previous word.

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Modern NLP: A Detailed Overview. Part 3: BERT

Towards AI

Deep contextualized word representations This paper was released by Allen-AI in the year 2018. In the NLP world, there are usually two types of models or tasks broadly, auto-regressive models and auto-encoding models. As for ELMo, no worries, we will start by understanding this concept.

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A Gentle Introduction to GPTs

Mlearning.ai

Along with text generation it can also be used to text classification and text summarization. The auto-complete feature on your smartphone is based on this principle. When you type “how”, the auto-complete will suggest words like “to” or “are”. That’s the precise difference between GPT-3 and its predecessors.

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Google Research, 2022 & Beyond: Language, Vision and Generative Models

Google Research AI blog

This approach outperforms other state-of-the-art models on the ImageNet-1k classification task and various object detection tasks, but with significantly lower computational costs. language models, image classification models, or speech recognition models). Sample attention configurations for multi-modal transformer encoders.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. We recently developed four more new models.

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Google Research, 2022 & beyond: Research community engagement

Google Research AI blog

For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program , where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group. See some of the datasets and tools we released in 2022 listed below.