AI

Speech recognition has been successfully depolyed on various smart devices, and is changing the way we interact with them. Traditional phonetic-based recognition approaches require training of separate components such as pronouciation, acoustic and language model.

Learning to answer open-ended questions about images, a task known as visual question answering (VQA), has received much attention over the last several years. VQA has been put forth as a benchmark for complete scene understanding and flexible reasoning, two fundamental goals of AI.

When humans generate novel neural architectures, they go through a surprisingly large amount of trial and error.

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Most neural architectures for machine translation use an encoder-decoder model consisting of either convolutional or recurrent layers. The encoder layers map the input to a latent space and the decoder, in turn, uses this latent representation to map the inputs to the targets.

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Over the past few years, neural networks have driven rapid improvements in accuracy and quality for natural language tasks.

A vast amount of today’s information is stored in relational databases. These databases provide the foundation of systems such as medical records, financial markets, and electronic commerce.

There are times when word vectors are initialized to lists of random numbers before a model is trained for a specific task, but it is also quite common to initialize the word vectors of a model with those obtained by running methods like word2vec, GloVe, or FastText.

Sultan Mahmood is the Global Financial Services leader of artificial intelligence and robotics at PwC. He also leads the development of PwC UK’s AI solutions and advises clients across many industries on identifying and delivering business results enabled by disruptive technologies such as AI and