Chatbots, those computer programs that mimic human conversation using artificial intelligence, are transforming the way people interact with the Internet. It's not surprising that chat apps are considered the new browsers and chatbots, the new websites. Siri, Alexa, Google Now, and Cortana are all examples of chatbots. And Facebook has opened up Messenger, making it not merely an app but a platform upon which developers can build an entire bot ecosystem. Chatbots are designed to be the ultimate virtual assistant, helping you accomplish tasks ranging from answering questions, getting driving directions, turning up the thermostat in your smart home, to playing your favorite tunes.

NEW YORK (Reuters) - Spanish lender Banco Santander SA has invested in two artificial-intelligence companies, part of the financial industry's increased focus on technology smart enough to mimic human thinking, sources familiar with the deals told Reuters. The bank's venture arm, Santander InnoVentures, bought stakes in Personetics Technologies, which provides automated customer service, and Gridspace, whose software can learn and interpret language the way a person would, the sources said. The size of the investments could not be determined and the sources asked not to be named because they were not allowed to disclose the information publicly. The deals underscore how lenders have become more interested in using artificial intelligence for a wide variety of tasks, including hiring, spotting fraud, improving call centers and recommending products for customers. Personetics, which has offices in New York, London and Tel Aviv, creates "chatbots" that can respond to customer questions through popular messaging platforms like Facebook Inc's Messenger.

Mark Russinovich, Chief Technology Officer, Microsoft Azure, Microsoft Learn the latest frameworks, techniques and the unconventional machine-learning algorithms that Microsoft uses to disrupt attackers and protect its cloud infrastructure and customers.

The Global Scientific Information and Computing Center at the Tokyo Institute of Technology has been at the forefront of accelerated computing, and well before GPUs came along and made acceleration not only cool but affordable and normal. But its latest system, Tsubame 3.0, being installed later this year, the Japanese supercomputing center is going to lay the hardware foundation for a new kind of HPC application that brings together simulation and modeling and machine learning workloads. The hot new idea in HPC circles is not just being able to run machine learning workloads side by side with simulations, but to use machine learning to further accelerate the simulation, and we have a future feature story underway, based on conversations with researchers at TiTech and at Oak Ridge National Laboratory, where the "Summit" hybrid CPU-GPU system is being built for the US Department of Energy, about this very topic. Suffice it to say, the idea is to integrate machine learning into the simulation, to do some of the computationally intensive stuff in a new way. So, as part of a climate model, you teach the system using machine learning to predict the weather by watching movies of the weather, or in astronomy, you use machine learning to remove the noise from the signal to find the interesting bits of a star field.

With a thrilling blend of customer feedback, digital interfaces, algorithms, and nuanced brewing, a British company has concocted four beers using artificial intelligence, and they are now available in the United Kingdom. The company, IntelligentX, created a survey system that uses Facebook Messenger chat bots to gather feedback from consumers on their taste preferences for beer. The data is fed into an algorithm to develop a beer recipe that is passed on to actual humans who brew, bottle, and share the beers. The whole system is like crowdsourcing a recipe for the perfect beer, and IntelligentX thinks it can master the process. A taste tester from Bloomberg sampled some of the beers and consulted with other testers to see how well the automated brewing intelligence (or ABI) system performed.

This conference aims to bring together researchers running machine learning algorithms on a variety of computing platforms to foster discussions between them. The goal is to encourage algorithm designers for these platforms to help each other scale and transplant ideas between the platforms.

A post showing how to perform Image Segmentation using Fully Convolutional Networks that were trained on PASCAL VOC using our framework. In this post we want to present Our Image Segmentation library that is based on Tensorflow and TF-Slim library, share some insights and thoughts and demonstrate one application of Image Segmentation. To be more precise, we trained FCN-32s, FCN-16s and FCN-8s models that were described in the paper "Fully Convolutional Networks for Semantic Segmentation" by Long et al. on PASCAL VOC Image Segmentation dataset and got similar accuracies compared to results that are demonstrated in the paper. We provide all the training scripts and scripts to convert PASCAL VOC into easier-to-use .tfrecords Moreover, it is very easy to apply the same scripts to a custom dataset of your own.

The European Parliament has voted on a resolution to regulate the development of artificial intelligence and robotics across the European Union. Based on a raft of recommendations drafted in a report submitted in January to the legal affairs committee, the proposed rules include establishing ethical standards for the development of artificial intelligence, and introducing an insurance scheme to cover liability for accidents involving driverless cars.

Brands of all stripes have tried to capitalize on the rise of chatbots, coming up with everything from beauty advice and cocktail recipe bots to financial assistant bots. But given the sheer volume of inquiries brands get on their social channels, these automated interfaces have perhaps been most useful for brands' customer service initiatives. "Brands can use deep learning algorithms on top of their customer service logs through these chatbots to provide personalized responses in real-time," said Mikhail Naumov, president and co-founder at DigitalGenius. "Chatbots are very useful for customer service when done right." We asked four brands to share their biggest takeaways from using chatbots for customer service. Kia Motors Kia launched NiroBot in November 2016, tied to the introduction of its hybrid utility car, the 2017 Niro.

An algorithm that's able to accurately predict autism diagnoses in young kids could enable potental interventions to be made earlier. A team of researchers at the University of North Carolina at Chapel Hill have developed a deep learning algorithm that can accurately predict whether a child at high risk of autism is likely to be diagnosed with the disorder in early childhood. The algorithm was able to predict with 81 percent accuracy whether a diagnosis of autism would be made for a child with an autistic sibling,. The deep learning tool was developed in conjunction with computer scientists from the College of Charleston as part of the Infant Brain Imaging Study, which focuses on early brain development among children with autism. By scanning their brains at 6 months old, a year old, and 2 years old, they were able to make some interesting discoveries.