There's a lot of chatter about chatbots these days and how we might be able to use them in the future. The biggest question seems to be whether chatbots can be useful enough to convincingly replace human conversation. Before chatbots can reach that point, they'll need to develop and mature into a technology that enables human communication with a computer using natural language. Most bots today are not at the level where they can flawlessly replicate conversation. Some chatbots today are not fed enough data.

Today, a lot of high-profile deep machine learning projects in the cloud are powered by GPUs; specifically NVIDIA GPUs. Even Facebook uses them for its own machine learning work behind-the-scenes. GPUs are able to handle the massive amounts of computing power required to train deep neural networks that facilitate these projects. But Huawei deputy chairman and rotating CEO Eric Xu believes the future of machine learning lies in dedicated processors. Read on to find out more. In the past few years, NVIDIA has made a big push to dethrone CPUs in the deep machine learning space.

PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.

Artificial intelligence is very interesting topic of research for many modern scientists. The concept of machine intelligence is really fascinating. It gives human a power to design something that can live on its own. The AI technology has become really advanced and its only matter of time when the machines will be able to learn almost anything. The machine learning algorithms are already very smart, however the processing power has been a challenge in last decade.

Is Humanising Chatbots the Next Inevitable Improvement?

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.

Working out how to zap a tumor with radiation is a laborious process for physicians. Google's machine-learning division, DeepMind, thinks AI can help ease the burden. When medics apply radiotherapy to a cancer patient, they have to carefully determine which parts of the body should be exposed to radiation in order to kill the tumor while ensuring that as much healthy surrounding tissue as possible is preserved. The process, known as segmentation, requires the doctor to manually draw areas that can and can't be treated onto a 3-D scan of the patient's tumor site. The process is particularly complex for head and neck cancers, in which the tumor often sits immediately next to many important anatomical features.

Like all financial companies PayPal worries about fraudsters, armed with stolen credentials, logging into a legitimate customer's account and using a credit card linked to it. "We want to stop these guys at our door," said Hui Wang, senior director of global risk and data sciences at PayPal. The company has more reason than most to worry, given its high visibility and massive payment volumes. It generates 10,900 in payments every second, and it handled 4.9 billion payments in 2015 for 188 million customers in 202 countries. To detect suspicious activity, and more importantly to separate false alarms from true fraud, PayPal uses a homegrown artificial intelligence engine built with open-source tools.

Serving more than a billion people a day, Facebook has its work cut out for it when providing customized news feeds. That is where the social network giant takes advantage of deep learning to serve up the most relevant news to its vast user base. Facebook is challenged with finding the best personalized content, Andrew Tulloch, Facebook software engineer, said at the company's recent @scale conference in Silicon Valley. "Over the past year, more and more, we've been applying deep learning techniques to a bunch of these underlying machine learning models that power what stories you see." Applying such concepts as neural networks, deep learning is used in production in event prediction, machine translation models, natural language understanding, and computer vision services. Event prediction, in particular, is one of the largest machine learning problems at Facebook, which must serve the top couple of stories out of thousands of possibilities for users, all in a few hundred milliseconds. "Predicting relevance in and of itself is a very challenging problem in general and relies on understanding multiple content modalities like text, pixels from images and video, and the social context," Tulloch said.

Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant tags such as'bridge', 'railway' and'tennis' while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well. ConvNets, therefore, are an important tool for most machine learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be an intimidating experience.