It depends who you ask. AI might be a hot topic but you'll still need to justify those projects. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to recognise what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI.

You hear a lot of different terms bandied about these days when it comes to new data processing techniques. One person says they're using machine learning, while another calls it artificial intelligence. Still others may claim to be doing deep learning, while "cognitive" is the favored phrase for so...

Abstract: Three different classes of data mining methods (k-Nearest Neighbour, Ridge Regression and Multilayer Perceptron Feed-Forward Neural Networks) are applied for the purpose of quantitative trading on 10 simulated time series, as well as real world time series of 10 currency exchange rates ranging from 1.11.1999 to 12.6.2015. Each method is tested in multiple variants. The k-NN algorithm is applied alternatively with the Euclidian, Manhattan, Mahalanobis and Maximum distance function. The Ridge Regression is applied as Linear and Quadratic, and the Feed-Forward Neural Network is applied with either 1, 2 or 3 hidden layers. In addition to that Principal Component Analysis (PCA) is eventually applied for the dimensionality reduction of the predictor set and the meta-parameters of the methods are optimized on the validation sample.

Artificial Intelligence has become a household word. It has also become a manipulator of all households. The unchecked explosion in AI across all businesses and business models has been a phenomenal driver of growth, but it raises questions that need to be answered. Data Scientists and AI Researchers will increasingly drive human behaviour, impact how businesses make decisions, and even steer government. Furthermore, those models will increasingly move from traditional human-understandable designs to complex Deep-Learning models that involve an incredible amount of complexity.

For millennia, humans have pondered the idea of building intelligent machines. Ever since, artificial intelligence (AI) has had highs and lows, demonstrated successes and unfulfilled potential. Today, the news is filled with the application of machine learning algorithms to new problems. From cancer detection and prediction to image understanding and summarization and natural language processing, AI is empowering people and changing our world. The history of modern AI has all the elements of a great drama. Beginning in the 1950s with a focus on thinking machines and interesting characters like Alan Turing and John von Neumann, AI began its first rise. Decades of booms and busts and impossibly high expectations followed, but AI and its pioneers pushed forward.

Financial markets have been one of the earliest adopters of machine learning (ML). People have been using ML to spot patterns in the markets since 1980s. Even though ML has had enormous successes in predicting the market outcomes in the past, the recent advances in deep learning haven't helped financial market predictions much. While deep learning and other ML techniques have finally made it possible for Alexa, Google Assistant and Google Photos to work, there hasn't been much progress when it comes to stock markets. I am not a researcher.

Bangalore: Zendesk has developed and trained deep-learning applications on AWS to deliver new capabilities to customer service organizations and scale it to support current and future application demands. In doing so, it relied on AWS GPU instances and the TensorFlow deep-learning framework to create new applications for customers. It needed to respond to a growing trend: customers wanting to quickly find answers to questions on their own, without having to talk to a support agent. "We wanted to give customers more relevant answers as fast as possible, and we wanted to drive a self-service customer support model," said Soon-Ee Cheah, a data scientist at Zendesk. Zendesk met this challenge by leveraging deep learning – a popular branch of artificial intelligence (AI).

A non-profit AI research company, OpenAI, basically, is now, to its list is releasing a new batch of seven unsolved problems which have come up in the course of their research at OpenAI. Very similar to their original Requests for Research which resulted in the upbringing of several papers, the company expects these problems for new people to enter the field to be a fun and a meaningful way to do the same, as well as to hone the skills for practitioners. Not to forget that is also is a great way to get a job at OpenAI that aims at enacting and discovering the path to safe general artificial intelligence. Also, If one is not sure where to begin, they also have some solved starter problems. Environment: Start with two snakes, and scale from there and then with multiple snakes have a reasonably large field; snakes grow when eating randomly-appearing fruit; a snake dies when colliding with another snake, itself, or the wall; and the game ends when all snakes die.

End of life care might be improved with Deep Learning. An AI program in a successful pilot study predicted how long people will live. George Dvorsky in Gizmodo and others reported on their work. The Stanford University team is using an algorithm to predict mortality, and their goal is to improve timing of end-of-life care for critically ill patients. While 80 percent of Americans prefer to spend their final days in their home, only 20 percent do just that.

Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data.