A toolkit used across Microsoft to achieve breakthroughs in artificial intelligence is generally available to the public via an open-source license, a team of researchers and software engineers announced today. "The 2.0 version of the toolkit is now in full release," said Chris Basoglu, a partner engineering manager at Microsoft. He has played a key role in developing Microsoft Cognitive Toolkit (previously known as CNTK). The full release of Microsoft Cognitive Toolkit 2.0 for use in production-grade and enterprise-grade deep learning workloads includes hundreds of new features incorporated since the beta to streamline the process of deep learning and to ensure the toolkit's seamless integration throughout the wider AI ecosystem. New with the full release today is support for Keras, a user-friendly open-source neural network library that is popular with developers working on deep learning applications.

Machine learning – a piece of the artificial intelligence constellation – holds a lot of promise for enterprises, enabling programs and algorithms to become ever more intelligent. However, there's one problem: even the best-educated humans need more learning before they can understand machine learning. Bob Hayes, a professional data scientist and keen observer of all things data, picked up on a survey by Kaggle that finds that even data scientists still have a grasp on machine learning. The survey "revealed that a limited number of data professionals possess competency in advanced machine learning skills," says Hayes. "About half of data professionals said they were competent in supervised machine learning (49%) and logistic regression (53%). Deep learning techniques were among the ML skills with the lowest competency rates."

In 2008, the National Academy of Engineering presented 14 Grand Challenges that, if solved, had the potential to radically improve the world. Thanks to recent breakthroughs in artificial intelligence – specifically, the advent of deep neural networks -- we're on pace to solve some of them, Google Senior Fellow Jeff Dean said last week at the Strata Data Conference. The Academy certainly didn't lack for ambition 10 years ago when it drew up the 14 Grand Challenges. Delivering a solution for any one of them – such as providing energy from nuclear fusion or finding out how to sequester carbon – could have a dramatic impact on billions of people's lives. As a result of advances in deep learning techniques, the presence of enormous data collections, and the availability of massive server clusters, we will be able to compute our way toward solving them, Dean told a packed room of attendees during his presentation Thursday afternoon at the San Jose McEnery Convention Center.

Microsoft researchers have already created technology that can do two difficult tasks about as well as a person: identify images and recognize words in a conversation. Now, the company's leading AI experts are working on systems that can do something even more complex: Read passages of text and answer questions about them. "We're trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it's written or orally," said Kaheer Suleman, the co-founder of Maluuba, a Quebec-based deep learning startup that Microsoft acquired earlier this year. The Maluuba team is one of several groups at Microsoft that are tackling the challenge of machine reading. Two other research teams, one at the company's Redmond, Washington, headquarters and the other in its Beijing, China, research lab, are currently leading a competition run by Stanford University that uses information from Wikipedia to test how well AI systems can answer questions about text passages.

Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of hyperbolic tangent and the sigmoid action functions results in gradient decay over layers. Consequently, construction of an efficiently trainable deep network is challenging. In addition, all the neurons in an RNN layer are entangled together and their behaviour is hard to interpret.

MEConferences team cordially invites all the participants from all over the world to attend World Machine Learning and Deep Learning Congress during August 30 - 31, 2018 in Dubai, UAE. This includes prompt keynote presentations, Oral talks, Poster presentations and Exhibitions. Machine Learning is a subset of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed and to make intelligent decisions. It also enables machines to grow and improve with experiences. It has various applications in science, engineering, finance, healthcare and medicine.

It's impossible to read about the future of healthcare without encountering two pixilated vowels that, together, represent the hopes and fears of an industry seeking more intelligent solutions. Though the field of artificial intelligence (AI) has been around since 1956, it has made precious few contributions to medical practice. Only recently has the hype of machine-based learning begun to merge with reality. What Is Artificial Intelligence, Really? Confusion surrounding AI – its applications in healthcare and even its definition – remains widespread in popular media.

The era of artificial intelligence (AI) is officially here. The AI market is expected to grow from $21.46 billion in 2018 to $190.61 billion by 2025, at a CAGR of 36.62% between 2018 and 2025, according to a recent report. AI's phenomenal growth across different industries is being fueled by unprecedented computing power, ever-increasing amounts of data--billions of gigabytes every day--and sophisticated deep-learning algorithms. According to the AI Index report, the number of active U.S. startups developing AI systems has increased 14 times whereas the annual VC investment into such startups has increased only 6 times since 2000. Moreover, the share of jobs requiring AI skills in the U.S. has grown 4.5 times since 2013.

Aw come on now, if you're going to implement this in a database: use the database. Store the weights and biases in a table, and use JOIN and GROUP BY operations to form the dot products. If you reformulate the inputs as a design matrix, you can store the weights and biases for each layer in a single table. In addition to making your code readable, this has the added benefit that the update operation can be implemented as a literal update (i.e. on the weights table) as opposed to running a pass through the network to output new weights which then need to be passed in directly to a new select statement. The way the author implemented it, it would be extremely expensive just on the client's bandwidth to run either a forward or backwards pass on a large model since you'd need to pass all of the parameters over the connection twice for a single update. The database should store and manage all the parameters.

Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be dissolved on the tongue within 3min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by Artificial Neural Network (ANN) and Deep Neural Network (DNN) techniques. 145 formulation data were extracted from Web of Science. All data sets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model has reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model was 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product.