Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and fluency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Specifically, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentence-tree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output. Experimental results demonstrate that the proposed X2Tree framework outperforms baseline methods over 11.15% increase of acceptance ratio.

I've got this ominous feeling that 2018 could be the year that everything changes dramatically. The incredible breakthroughs we saw in 2017 for deep learning will carry over in a very powerful way in 2018. A lot of work coming from 2017's research will migrate into everyday software applications. As I did last year, I've compiled a list of predictions for where deep learning will go in 2018. Many deep learning hardware startup ventures will begin to finally deliver their silicon in 2018.

Live streaming has become most popular across the world through smartphone applications. Many applications like Periscope, Meerkat, Facebook live etc. had become very popular. Social platforms are focusing on the various technologies to make their step forward in business and success. Recently, Twitter was developing the new technology which recognises the happenings in live video automatically. The Twitter's highly developed learning systems named as cortex which recognises the labelling moving images in the live video streams.

A group of scientists recently developed an AI model which uses Google Street View photographs to determine startlingly accurate social insights about a geographic area. By looking at the cars we drive, the researchers' deep learning network can determine a community's racial, political, and economic makeup. The research was conducted by scientists and based at Stanford university, using an AI training method called a convolutional neural network (CNN). This method involves creating a "gold standard" set of images, checked by humans, which are used to teach a computer how to classify new images on its own. In this case the machine was taught to look for vehicles and separate images of cars and trucks into 2,657 fine-grained categories.

About this course: Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation.

Over the past few years, deep neural networks have become extremely popular. This emerging field of computer science was created around the concept of biological neural networks, and deep learning has become something of a buzzword today. Deep learning scientists and engineers try to mathematically describe various patterns from biological nervous systems. Deep learning systems have been applied to various problems: computer vision, speech recognition, natural language processing, machine translation, and more. It is interesting and exciting that in some tasks, deep learning has outperformed human experts. Today, we will be taking a look at deep learning in the financial sector. One of the more attractive applications of deep learning is in hedge funds. Hedge funds are investment funds, financial organizations that raise funds from investors and manage them. They usually work with time series data and try to make some predictions. There is a special type of deep learning architecture that is suitable for time series analysis: recurrent neural networks (RNNs), or even more specifically, a special type of recurrent neural network: long short-term memory (LSTM) networks.

CapsNets are a hot new architecture for neural networks, invented by Geoffrey Hinton, one of the godfathers of deep learning. It is presented in my video: https://youtu.be/2Kawrd5szHE Therefore, at 16:08, the network should output a vector whose length (not squared length) is longer than 0.9 for digits that are present, or smaller than 0.1 for digits that are absent. I'll clarify this point in my next video on implementing Capsule Networks.

Information technology in 2018 is expected to be more exciting as technologies that were just a concept or in the trial stages in the past years will become a reality, according to technology companies. Commentaries submitted by technology companies had five common predictions related to artificial intelligence, Internet of Things, blockchain, cloud computing, and IT security. According to IDC, revenue growth from information-based products will double the rest of the product and services portfolio for a third of Fortune 500 companies by the end of 2017. "AI became mainstream with consumer products like Amazon Alexa and Apple Siri, and Hitachi believes that it is the collaboration of AI and humans that will bring real benefits to society," said Hitachi Vantara's Hubert Yoshida, chief technology officer, and Russell Skingsley, chief technology officer of Asia Pacific. "Through tools like Pentaho Data Integration, our aim is to democratize the data engineering and data science process to make Machine Intelligence – a combination of Machine Learning and AI – more accessible to a wider variety of developers and engineers. Zakir Ahmed, General Manager of Asia at Oracle NetSuite, notes that to date, AI has mostly been following simple rules. If A B then C else D. This proved to be sufficient for powering devices like smart fridges or cars. On the downside, this predictability also meant that people could easily outsmart AI, often with dire consequences. "Thankfully, in 2018 we will reach a pivoting point, where deep learning will fast become an integral component of AI," said Ahmed, noting that these new intelligent AI 2.0 systems will learn, suggest and automate processes by analysing business patterns and behaviours. "For example, banks can provide tailored financial services based on their customers' investment portfolio and risk appetite.

Even with the support of AI frameworks like TensorFlow or OpenAI, artificial intelligence still requires deep knowledge and understanding compared to a mainstream web developer. If you have built a working prototype, you are probably the smartest guy in the room. Congratulations, you are a member of a very exclusive club. With Kaggle you can even earn decent money by solving real world projects. All in all it is an excellent position to be in, but is it enough to build a business?

Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE's Avitas Systems invested in deep learning supercomputers. These are a few of many milestones in 2017.