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Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a multidisciplinary branch of computer science that aims to create machines and systems capable of performing tasks that typically require human intelligence.
In 2016, Google DeepMind unveiled WaveNet, a deep learning network that produced English, Mandarin, and piano music. [49] 15.ai, a free text-to-speech web application launched in March 2020, was an early development in the AI boom that used AI for voice synthesis.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Deep learning spurs huge advances in vision and text processing. 2020s Generative AI leads to revolutionary models, creating a proliferation of foundation models both proprietary and open source, notably enabling products such as ChatGPT (text-based) and Stable Diffusion (image based). Machine learning and AI enter the wider public consciousness.
Over the years, NeurIPS became a premier conference on machine learning and although the 'Neural' in the NeurIPS acronym had become something of a historical relic, the resurgence of deep learning [6] in neural networks since 2012, fueled by faster computers and big data, has led to achievements in speech recognition, object recognition in ...
AlexNet, a deep learning model developed by Alex Krizhevsky, wins the ImageNet Large Scale Visual Recognition Challenge with half as many errors as the second-place winner. [108] This is a turning point in the history of AI; over the next few years dozens of other approaches to image recognition were abandoned in favor of deep learning. [109]
Later, as deep learning becomes widespread, specialized hardware and algorithm optimizations were developed specifically for deep learning. [86] A key advance for the deep learning revolution was hardware advances, especially GPU. Some early work dated back to 2004. [84] [85] In 2009, Raina, Madhavan, and Andrew Ng reported a 100M deep belief ...
Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...
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