Regardless of industry, it seems that nearly every business is asking the same question nowadays: How can artificial intelligence help us? Google Trends data shows that searches for "machine learning" -- a common application of AI that enables machines to process and learn from data inputs -- have skyrocketed over the last several years. It's clear that businesses are eager to get their hands on this technology, which promises to increase efficiency and productivity. Of course, with all the hype around AI and machine learning comes a few key misconceptions about how it can be implemented and leveraged. We asked members of the Forbes Agency Council to help clear things up and explain some realistic, beneficial ways agencies can use AI.
In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Papers published at the Neural Information Processing Systems Conference.
This article was posted by SmileJet on Dev Battles. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing. What is artificial intelligence, or AI?
While computer scientists have been touting artificial intelligence (AI) for more than half a century, the technology is just starting to reveal its potential. In spite of the hype, machine learning, deep learning, computer vision and natural language processing have quietly become entrenched in many people's daily routines.
Machine Comprehension is a very interesting task in both natural language processing and artificial intelligent research but extremely challenging.There are several approaches to NLP tasks in general. With recent breakthroughs allowed in algorithms (deep learning), hardware (GPUs) and user friendly APIs (TensorFlow), some tasks have become feasible up to a certain accuracy. This project report contains TensorFlow implementations of various deep learning models, with a focus on problems in Natural Language Processing. Given the following models implementation and training which are completed in the project done at NIT Srinagar using Intel DevCloud and optimized Intel TensorFlow Framework: 1. Mnist_cnn: A three-layer Convolutional Neural Network for the MNIST Handwritten Digit Classification task. The architecture is a form of Memory Network but unlike the model in that work, it is trained end-to-end, 4. Variational_autoencoder: Variational Autoencoder for the MNIST Handwritten Digits dataset.