New York University (NYU) & Facebook Artificial Intelligence Research (FAIR) researchers, including Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto, have introduced DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 is an upgraded version of DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels.
Read more: https://analyticsindiamag.com/what-is-drq-algorithm-by-nyu-facebook-ai-research/
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.
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The process of benchmarking is considered to be one of the most crucial assets for the progress of AI and machine learning research. The benchmark datasets are usually fixed sets of data, which are manually, semi-automatically as well as automatically generated to form a representative sample for these specific tasks to be solved by a model.
Recently, researchers from the Institute for Artificial Intelligence and Decision Support, Vienna claimed that the considerable part of metrics currently used to evaluate classification AI benchmark tasks might be inconsistent. It may result in a poor reflection in the performance of a classifier, especially when used with imbalanced datasets.
For the research, they analysed the present aspect of performance metrics that are based on data covering more than 3500 ML model performance results from a web-based open platform.
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The researchers at Facebook believe that this algorithm will have real-world applications, including dealing with negotiations, fraud detection, and even #cybersecurity
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Researchers at Facebook AI recently introduced and open-sourced a new framework for self-supervised learning of representations from raw audio data known as wav2vec 2.0. The company claims that this framework can enable automatic speech recognition models with just 10 minutes of transcribed speech data.
Neural network models have gained much traction over the last few years due to its applications across various sectors. The models work with the help of vast quantities of labelled training data. However, most of the time, it is challenging to gather labelled data than unlabelled data.
The current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance. There are around 7,000 languages in the world and many more dialects. It can be said that the availability of the transcribed speech for a vast majority of languages is still negative.
To mitigate such issues, researchers open-sourced the wave2vec framework. The framework has the capability to make efficient development in Automatic Speech Recognition (ASR) for the low-resource languages.).
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