Austin startup CognitiveScale has raised 21.8 million to ramp up sales and marketing of its artificial intelligence technology. The company, founded by members of the IBM Watson team, received the funding from Norwest Venture Partners and Intel Capital. It has raised a total of 32 million since inception. CognitiveScale has created a cloud platform that can mine huge databases and use natural language to present data in an easy to understand format. The platform can be plugged into a company's website and extracts insights from the company's existing data.

The Tate Britain art museum has just launched a new artificial intelligence exhibit that applies machine learning technology to images in some pretty unique ways. Called "Recognition," the exhibit is the winner of Tate's annual IK Prize, which was created in association with Microsoft and awards "digital innovation." It compares images from Tate's enormous archive of artwork with up-to-the-minute Reuters news photography, based on various pattern-recognition tools. These include object recognition, facial recognition, composition analysis, and even natural language processing for looking at captions and text. "From the moment it launched, it's continually scanning both databases and comparing images, trying to find works which are comparable -- whether that be visually or thematically -- and then publishing them online in a virtual gallery," Tony Guillan, producer of the IK Prize, told Digital Trends.

Gov. John Kasich says manufacturing is still important to Ohio, but high-tech is the future. Kasich was the keynote speaker at tonight's annual meeting of the Greater Cleveland Partnership, the area's chamber of commerce. Gov. Kasich started off his address by praising Cleveland for recent successes like the Republican National Convention, the Cavalier's championship and the city's growth as a tourist and convention destination. He moved on to talking about the entire state. He says he's working with artificial-intelligence and information- technology experts on ways to help Ohio advance.

Osprey is a tool for hyperparameter optimization of machine learning algorithms in Python. Hyperparameter optimization can often be an onerous process for researchers, due to time-consuming experimental replicates, non-convex objective functions, and constant tension between exploration of global parameter space and local optimization (Jones, Schonlau, and Welch 1998). We've designed Osprey to provide scientists with a practical, easy-to-use way of finding optimal model parameters. As hyperparameter optimization is an embarrassingly parallel problem, Osprey can easily scale to hundreds of concurrent processes by executing a simple command-line program multiple times.

Osprey is a tool for hyperparameter optimization of machine learning algorithms in Python. Hyperparameter optimization can often be an onerous process for researchers, due to time-consuming experimental replicates, non-convex objective functions, and constant tension between exploration of global parameter space and local optimization (Jones, Schonlau, and Welch 1998). We've designed Osprey to provide scientists with a practical, easy-to-use way of finding optimal model parameters. The software works seamlessly with scikit-learn estimators (Pedregosa et al. 2011) and supports many different search strategies for choosing the next set of parameters with which to evaluate a given model, including gaussian processes (GPy 2012), tree-structured Parzen estimators (Yamins, Tax, and Bergstra 2013), as well as random and grid search. As hyperparameter optimization is an embarrassingly parallel problem, Osprey can easily scale to hundreds of concurrent processes by executing a simple command-line program multiple times.

Geoffrey Hinton is a pioneer in the field of artificial neural networks and co-published the first paper on the backpropagation algorithm for training multilayer perceptron networks. He may have started the introduction of the phrasing "deep" to describe the development of large artificial neural networks. He co-authored a paper in 2006 titled "A Fast Learning Algorithm for Deep Belief Nets" in which they describe an approach to training "deep" (as in a many layered network) of restricted Boltzmann machines. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. This paper and the related paper Geoff co-authored titled "Deep Boltzmann Machines" on an undirected deep network were well received by the community (now cited many hundreds of times) because they were successful examples of greedy layer-wise training of networks, allowing many more layers in feedforward networks.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Lately, it seems that every time you open your browser or casually scroll through a news feed, someone is writing about machine learning and its impact on both humans and the advancement of artificial intelligence. Machine learning has been highlighted in articles covering everything from Virtual Assistant solutions to self-driving cars and robots that can perform the same tasks as humans. A number of large companies are defining machine learning as'the future' – but what does that really mean? Machine learning is nothing new. The history, in fact, dates back over sixty years to when Alan Turing created the'Turing test' to determine whether a computer had real intelligence.

DEDHAM, MA--(Marketwired - Sep 7, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced that a leading multinational financial services firm has selected its Artificial Intelligence platform (RAGE AI) to drive improved results for its investment customers by using artificial intelligence to discover signals captured in a wide variety of data sources with Rage's innovative deep learning capabilities. RAGE AI significantly extends the frontier of deep learning and machine intelligence technology as it incorporates proprietary linguistics-based machine learning innovations to understand market developments in the context of individual companies and interpret those signals as a human would. After demonstrating via historical back-testing that the Rage AI platform repeatedly delivered returns in excess of what the firm's quantitative team was able to produce, Rage's solution was integrated in order to drive significant lift in the returns generated for the firm's clients. In fact, Rage has repeatedly shown that its deep background in computational linguistics and Natural Language Understanding can systematically discover Alpha by forming assessments of a company's financial projections that effectively predict future performance for businesses such as Wal-Mart (attached), where Rage AI predicted an upward trend in stock price months in advance. The RAGE AI platform does this by continuously interpreting unstructured content from over 100,000 sources and translating it into valuable intelligence.

After crossing 1.5M downloads, the free music-syncing app AmpMe now allows users to party without an internet connection using a new machine learning-powered predictive syncing technology. AmpMe streams music to multiple devices in perfect sync so friends can enjoy music together anywhere, without setting up a sound system. In the latest version, the new Predictive Sync" feature allows AmpMe to synchronize directly with other devices using a local Wi-Fi hotspot created by the phone that is hosting the party. "We think of it as a portable Sonos," AmpMe's founder and CEO Martin-Luc Archambault told The New York Post. The "Predictive Sync" capability was created by training their neural network models using the NVIDIA CUDA Deep Neural Network library (cuDNN) to analyze thousands of devices and hardware configurations.