Chatbots were simple in the beginning, often limited to web browser pop-ups that could only send straightforward greetings like "Hi, how are you?" to website visitors. Because of AI and cloud technology, chatbots are no longer just a nice-to-have greeting tool, but a key element in fostering a more engaging customer experience for businesses across every industry. By tapping into deep learning technologies, these tools learn, converse, and understand the world similarly to the way humans do, making customer service simpler and more efficient. Consumers are encountering these technologies more frequently -- according to Gartner, by 2018, 30 percent of our interactions with new technologies will be through "conversations" with smart machines. Chatbots can be found everywhere, helping customers with nearly any task, from online shopping to planning a wedding.

Artificial intelligence is hot, and you can tell that because both giant companies and tiny startups are excited about it. Nvidia, which had $6.9 billion in revenues last year, is in touch with more than 2,000 AI startups around the world. And this week, the graphics chip maker and AI company took a step in figuring out which ones are the best. Jen-Hsun Huang, CEO of Nvidia, hosted a Shark Tank style event called Nvidia Inception to find the best AI startups. Huang and a panel of judges listened to pitches from 14 AI startups across three categories.

I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the "Best Python library for neural networks", and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years. The library I recommended in July 2014, pylearn2, is no longer actively developed or maintained, but a whole host of deep learning libraries have sprung up to take its place. Each has its own strengths and weaknesses. We've used most of the technologies on this list in production or development at indico, but for the few that we haven't, I'll pull from the experiences of others to help give a clear, comprehensive picture of the Python deep learning ecosystem of 2017. Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do not fully exploit the local stationarity structures of user/item graphs, and the number of parameters to learn is linear w.r.t. the number of users and items. We propose a novel approach to overcome these limitations by using geometric deep learning on graphs. Our matrix completion architecture combines graph convolutional neural networks and recurrent neural networks to learn meaningful statistical graph-structured patterns and the non-linear diffusion process that generates the known ratings. This neural network system requires a constant number of parameters independent of the matrix size. We apply our method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.

ABSTRACT Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often "overshadowed" by voice characteristics relating to emotional intensity or emotional activation. In this work we explore a representation learning approach that automatically derives discriminative representations of emotional speech. In particular, we investigate two machine learning strategies to improve classifier performance: (1) utilization of unlabeled data using a deep convolutional generative adversarial network (DCGAN), and (2) multitask learning. Our speakerindependent classification experiments show that in particular the use of unlabeled data in our investigations improves performance of the classifiers and both fully supervised baseline approaches are outperformed considerably. We improve the classification of emotional valence on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which is competitive to state-of-the-art performance. Index Terms-- Machine Learning, Affective Computing, Semisupervised Learning, Deep Learning 1. INTRODUCTION Machine Learning, in general, and affective computing, in particular, rely on good data representations or features that have a good discriminatory faculty in classification and regression experiments, such as emotion recognition from speech.

Amazon.com, Inc. 's AMZN Amazon Web Services (AWS) has made Amazon Lex, the machine learning technology behind Alexa, available to all customers. Amazon Lex algorithms facilitate natural language understanding, automatic speech recognition and text to speech. Amazon is now offering these technologies as a fully managed service. With Amazon Lex, developers can build conversational apps easily, which were otherwise extremely difficult to create as these involved complicated deep learning algorithms on enormous amount of data. Moreover, the integration of Amazon Lex with AWS Lambda (Amazon's event-driven, serverless computing platform) will enable developers to run serverless codes, apply business logic and fetch data from enterprise applications and AWS services like Amazon DynamoDB.

Data Scientist Jeremy Howard has studied machine learning for 25 years. He says artificial intelligence can help achieve amazing things. But he warns the impact on jobs may cause a great deal of social instability. Jeremy Howard is a data scientist and the founding researcher at fast.ai -- a company dedicated to making deep learning accessible. Previously, Jeremy was the CEO of Enlitic, an advanced machine learning company. He was also the president and chief scientist at Kaggle, a community and competition platform of over 200,000 data scientists.

Deep learning, a subfield of machine learning, has become one of the most known areas in the ongoing AI hype. Having led to many important publications and impressive results, it is applied to dozens of different scenarios and has already yielded interesting results like human-like speech generation, high accuracy object detection, advanced machine translation, super resolution and many more. There is a steady flow of papers and publications that describe the latest advances in network design, compare existing architectures or describe unseen approaches leading to even better results than the current state-of-the-art. At the same time more and more companies and developers jump on the deep learning bandwagon and deploy the ideas and architectures to real world production systems. This article describes our approach to applying deep learning to our image editing product, the struggle we had with finding the right architecture and the experience we made while developing a system that can be deployed to mobile devices.

SANTA CLARA, CA--(Marketwired - Apr 17, 2017) - NVIDIA ( NASDAQ: NVDA) today announced that its deep learning platform is now available as part of Baidu Cloud's deep learning service, giving enterprise customers instant access to the world's most adopted AI tools. The new Baidu Cloud offers the latest GPU computing technology, including Pascal architecture-based NVIDIA Tesla P40 GPUs and NVIDIA deep learning software. It provides both training and inference acceleration for open-source deep learning frameworks, such as TensorFlow and PaddlePaddle. "Baidu and NVIDIA are long-time partners in advancing the state of the art in AI," said Ian Buck, general manager of Accelerated Computing at NVIDIA. "Baidu understands that enterprises need GPU computing to process the massive volumes of data needed for deep learning. Through Baidu Cloud, companies can quickly convert data into insights that lead to breakthrough products and services."

Data Scientist Jeremy Howard has studied machine learning for 25 years. He says super-intelligent machines can help us achieve amazing things. But he warns they might bring the end for our species. Jeremy Howard is a data scientist and the founding researcher at fast.ai -- a company dedicated to making deep learning accessible. Previously, Jeremy was the CEO of Enlitic, an advanced machine learning company. He was also the president and chief scientist at Kaggle, a community and competition platform of over 200,000 data scientists.