The evolution of Google’s MobileNet Architectures to improve computer Vision Models

Computer vision

MobileNetv3 incorporate apply novel ideas such as AutoML and mobile deep learning to computer vision. Mobile deep learning is becoming one of the most active areas of research in the artificial intelligence(AI) space. Designing deep learning models that can execute efficiently on mobile runtimes requiring rethinking many of the architecture paradigms in neural networks. Mobile deep learning models need to balance the accuracy of complex neural network structures with the performance constraints of mobile runtimes.

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The sequence scope: GPT-3 and Large Language Models can get out of control

The Sequence Scope is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Data scientists, scholars, and developers from Microsoft Research, Intel Corporation, Linux Foundation AI, Google, Lockheed Martin, Cardiff University, Mellon College of Science, Warsaw University of Technology, Universitat Politècnica de València and other companies and universities are already subscribed to TheSequence.

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Uber Fiber is an Open Source framework to distribute compute for Reinforcement Learning Models

Uber Fiber

Computational costs are one of the main challenges in the adoption of machine learning models. Some of the recent breakthrough models in areas such as deep reinforcement learning(DRL)… constrained to experiments in big AI research labs. For DRL to achieve mainstream adoption, it has to be accompanied by efficient distributed computation methods that effectively address complex computation requirements. Recently, Uber open sourced Fiber, a scalable distributed computing framework for DRL and population-based methods.

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Six times bigger than GPT-3: Inside Google’s TRILLION parameter switch transformer model

Six Times Bigger than GPT-3: Inside Google’s TRILLION Parameter Switch Transformer Model. OpenAI’s GPT-3 is, arguably , the most famous deep learning models created in the last few years. One of the things that impresses the most about GPT-3 is its size. In some context, GPT-3 is nothing but GPT-2 with a lot of more parameters. With 175 billion parameters, GPT-3 was about four times bigger than its largest predecessor.

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