SAN FRANCISCO--(BUSINESS WIRE)--Executives previously from IBM Watson, GE Digital, Infosys, and MicroStrategy announced today that they have joined forces with TPG Growth to launch Noodle Analytics, Inc. (Noodle.ai), the Enterprise Artificial Intelligence company. Enterprise AI represents a major step forward in merging human learning and machine learning, all fueled by big data. Enterprise AI solutions combine world-class expertise in human-centered design, business process engineering, and artificial intelligence technologies. Today's artificial intelligence technologies include machine learning, predictive data analytics, and data science. "Over the next three to five years, artificial intelligence technologies and big data will be the most significant competitive differentiators in business. We are excited to be a pioneer in Enterprise Artificial Intelligence, offering timely, valuable, and affordable solutions to clients. We have the right team, an optimized business model, and the right partners to create extraordinary value," says Stephen Pratt, CEO of Noodle.ai.

Former top executives from IBM Watson, GE Digital, Infosys and MicroStrategy announced last week that they have joined forces with TPG Growth to launch Noodle Analytics, Inc., the Enterprise Artificial Intelligence company. Enterprise AI represents a major step forward in merging human learning and machine learning, all fueled by big data. Enterprise AI solutions combine expertise in human-centered design, business process engineering and artificial intelligence technologies. Today's artificial intelligence technologies include machine learning, predictive data analytics and data science. "Over the next three to five years, artificial intelligence technologies and big data will be the most significant competitive differentiators in business. We are excited to be a pioneer in Enterprise Artificial Intelligence, offering timely, valuable, and affordable solutions to clients. We have the right team, an optimized business model, and the right partners to create extraordinary value," says Stephen Pratt, CEO of Noodle.ai.

Midway through the first of five recent matches between Lee Sedol, a top-ranked professional Go player, and AlphaGo, a computer program conceived by Google DeepMind, an odd thing happened: Lee's jaw dropped, hanging open for a nigh-cartoonish twenty seconds, and then he laughed. AlphaGo had just mounted an aggressive, and evidently unexpected, attack. The moment was reminiscent of a famous episode in Go history, when Honinbo Shusaku, a future legend of the game, squared off against Inoue Genan Inseki, an older and more experienced player, in 1846. The story goes that a spectator--a local doctor who knew little of Go--correctly guessed that the seventeen-year-old Shusaku was beating Inseki. Asked how he knew, the doctor responded that, after an earlier move, Inseki's ears had flushed red, a clear indication of surprise.

John Hennessy joined Stanford in 1977 right after receiving his Ph.D. from the State University of New York at Stony Brook. He soon became a leader of Reduced Instruction Set Computers. This research led to the founding of MIPS Computer Systems, which was later acquired for 320 million. There are still nearly a billion MIPS processors shipped annually, 30 years after the company was founded. Hennessy returned to Stanford to do foundational research in large-scale shared memory multiprocessors. In his spare time, he co-authored two textbooks on computer architecture, which have been continuously revised and are still popular 25 years later. This record led to numerous honors, including ACM Fellow, election to both the National Academy of Engineering and the National Academy of Sciences. Not resting on his research and teaching laurels, he quickly moved up the academic administrative ladder, going from the CS department chair to Engineering college dean to provost and finally to president in just seven years. He is Stanford's tenth president, its first from engineering, and he has governed it for an eighth of its existence. Since 2000, he doubled Stanford's endowment, including a record 6.2 billion for a single campaign. He used those funds to launch many initiatives--which often cross departmental lines--along with new buildings to house them. Undergraduate applications also doubled, for the first time making Stanford even more selective than Harvard.

This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern recognition, a better and earlier event prediction and a better understanding of complex temporal stream data through visualization of the NeuCube connectivity. The effect of the new mapping is demonstrated on three bench mark problems. The first one is early prediction of patient sleep stage event from temporal physiological data. The second one is pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set. In all cases the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared to traditional machine learning techniques or spiking neural network reservoirs with arbitrary mapping of the variables.

High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology. Often, such applications require methods that are both highly scalable, and deal with noisy data in terms of corruptions or missing values. Classical time series methods usually fall short of handling both these issues. In this paper, we propose to adapt matrix matrix completion approaches that have previously been successfully applied to large scale noisy data, but which fail to adequately model high-dimensional time series due to temporal dependencies. We present a novel temporal regularized matrix factorization (TRMF) framework which supports data-driven temporal dependency learning and enables forecasting ability to our new matrix factorization approach. TRMF is highly general, and subsumes many existing matrix factorization approaches for time series data. We make interesting connections to graph regularized matrix factorization methods in the context of learning the dependencies. Experiments on both real and synthetic data show that TRMF outperforms several existing approaches for common time series tasks.

We present Dual-Loco, a communicationefficient algorithm for distributed statistical estimation. Dual-Loco assumes that the data is distributed across workers according to the features rather than the samples. It requires only a single round of communication where low-dimensional random projections are used to approximate the dependencies between features available to different workers. We show that Dual-Loco has bounded approximation error which only depends weakly on the number of workers. We compare Dual-Loco against a state-of-theart distributed optimization method on a variety of real world datasets and show that it obtains better speedups while retaining good accuracy. In particular, Dual-Loco allows for fast cross validation as only part of the algorithm depends on the regularization parameter.

We introduce a novel approach to Compressed Path Databases, space efficient oracles used to very quickly identify the first edge on a shortest path. Our algorithm achieves query running times on the 100 nanosecond scale, being significantly faster than state-of-the-art first-move oracles from the literature. Space consumption is competitive, due to a compression approach that rearranges rows and columns in a first-move matrix and then performs run length encoding (RLE) on the contents of the matrix. One variant of our implemented system was, by a convincing margin, the fastest entry in the 2014 Grid-Based Path Planning Competition. We give a first tractability analysis for the compression scheme used by our algorithm. We study the complexity of computing a database of minimum size for general directed and undirected graphs. We find that in both cases the problem is NP-complete. We also show that, for graphs which can be decomposed along articulation points, the problem can be decomposed into independent parts, with a corresponding reduction in its level of difficulty. In particular, this leads to simple and tractable algorithms with linear running time which yield optimal compression results for trees.

Dialog is a useful way for a robotic agent performing a task to communicate with a human collaborator, as it is a rich source of information for both the agent and the human. Such task-oriented dialog provides a medium for commanding, informing, teaching, and correcting a robot. Robotic agents engaging in dialog must be able to interpret a wide variety of sentences and supplement the dialog with information from its context, history, learned knowledge, and from non-linguistic interactions. We have identified a set of nine system-level requirements for such agents that help them support more effective, efficient, and general task-oriented dialog. This set is inspired by our research in Interactive Task Learning with a robotic agent named Rosie. This paper defines each requirement and gives examples of work we have done that illustrates them.

Ensembles of climate models are commonly used to improve climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, we use an ensemble of decadal climate predictions to demonstrate the ability of sequential learning algorithms (SLAs) to reduce the forecast errors and reduce the uncertainties. Three different SLAs are considered, and their performances are compared with those of an equally weighted ensemble, a linear regression and the climatology. Predictions of four different variables--the surface temperature, the zonal and meridional wind, and pressure--are considered. The spatial distributions of the performances are presented, and the statistical significance of the improvements achieved by the SLAs is tested. Based on the performances of the SLAs, we propose one to be highly suitable for the improvement of decadal climate predictions.