Celaton today announced the release of inSTREAM version 6, its intelligent automation platform that applies sophisticated algorithms, including artificial intelligence and cognitive learning, to streamline and automate the processing of semi-structured and unstructured content. Unstructured and semi-structured unpredictable content flows into organisations every day by email, post, paper, fax, social media, web feeds and other electronic data streams and creates challenges for customers due to the cost and need for experienced staff to process it. Unique to inSTREAM is its ability to learn the pattern of content through the natural consequence of processing and monitoring human intervention. Confidence is improved through accelerated learning. Efficiency is improved through accelerated learning.

Uplift modeling, also known as incremental modeling, true lift modeling, or net-lift modeling is a predictive modeling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behavior. Uplift modeling has applications in customer relationship management for up-sell, cross-sell and retention modeling. It has also been applied to personalized medicine. Unlike the related Differential Prediction concept in psychology, Uplift modeling assumes an active agent. All of your marketing effort are about Return on Investment (ROI), ultimately, unless you are a non-profit.

Two quantitative analysts using artificial intelligence in an online data science competition showed they could diagnose heart disease about as accurately as doctors. Qi Liu and Tencia Lee, hedge fund analysts and self-described "quants," built the winning algorithm in the competition, which could find indicators of heart disease. The online data contest challenged participants to develop machine algorithms that could measure cardiac volumes from MRIs provided by the National Heart, Lung and Blood Institute. Mr. Liu and Ms. Lee didn't know each other before they won the competition, beating out more than 1,390 algorithms. They met each other in a forum on the Kaggle site, where the competition was hosted over a three-month period.

Industry associations and analysts are a special kind of nuisance. They write research papers with titles like: "A World Without Work;" "Anticipating a Luddite Revival;" "Our Work Here is Done;" "Who Owns the Robots Owns the World;" "Robots not Immigrants Could Take Half of Jobs;" and "AI and Robots Threaten to Unleash Mass Unemployment." These alarmist titles cause news editors to rely on the research papers like fortune-tellers rely on tealeaves. Despite the good intentions of the authors and their predictions the research evidence is limited and primarily correlational in nature, few studies have demonstrated a causal relationship between the impacts of robots on jobs. In the minds of many analysts and researchers, humanity is nearing a robot apocalypse where Robots will have taken over most jobs within 30 years leaving humanity facing its'biggest challenge ever.'

Businesses increasingly use machine-learning models to recognize patterns in big data and to implement data-driven decision-making. In this webinar, you will learn how Domino serves as a platform for experimentation and collaboration, and facilitates the creation and distribution of machine-learning models. We will give you an introduction on how to use Plotly--an interactive data visualization tool--to share the results from your models more effectively. We will also show you how to use Plotly's API libraries in Domino Data Lab to build insightful graphs, charts and data visualizations in Python and in R. Chelsea is a core developer of Plotly, the data visualization platform, and plotly.js, the open-source Javascript library. She is also responsible for maintaining Plotly's interactive, browser-based charting libraries and documentation for R and Python.

I'm really hoping for some feedback or improvements to what I've written as I'm trying to make a complete tutorial series that will introduce a novice to machine learning and set them up with the skills needed to produce their own Neural Networks I've never tried writing anything like this before, so any constructive feedback that I can apply to the next entry in the series would be much appreciated

Solutions architects are the experts on our team at understanding and implementing Watson technology. They have developed this expertise by providing technical support to our partners through multiple mediums. Through their work, they have a deep understanding and point of view about the Watson APIs, but also the cognitive landscape at large. I interviewed solutions architect, Chris Ackerson on his thoughts on Watson and cognitive computing, as well as his specific tips and resources. Where do you see the Watson APIs growing in 2016 and beyond?

DEDHAM, MA--(Marketwired - Mar 30, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced new deployments of its traceable "deep learning" technology known as Rage AI across several global financial services, consumer products and manufacturing firms. The challenges these organizations faced required the understanding and interpretation of complex documents and integration of other transaction data from enterprise resource planning (ERP) systems to identify significant cost efficiencies and compliance conformance. RAGE AI incorporates deep linguistic parsing and proprietary linguistics-based innovations to understand the real meaning of documents and interpret them as a human would, and can operate completely unsupervised or with assistance by human experts. With its traceable, deep learning technology, RAGE AI significantly extends the frontier of deep learning and machine intelligence from "natural language processing" to "natural language understanding." The platform reads and interprets documents within its context, and as a totally transparent solution, RAGE AI enables knowledge workers to move forward confidently knowing the reasoning behind the platform's insights is completely auditable.

Recently, MaxSAT reasoning is shown very effective in computing a tight upper bound for a Maximum Clique (MC) of a (unweighted) graph. In this paper, we apply MaxSAT reasoning to compute a tight upper bound for a Maximum Weight Clique (MWC) of a wighted graph. We first study three usual encodings of MWC into weighted partial MaxSAT dealing with hard clauses, which must be satisfied in all solutions, and soft clauses, which are weighted and can be falsified. The drawbacks of these encodings motivate us to propose an encoding of MWC into a special weighted partial MaxSAT formalism, called LW (Literal-Weighted) encoding and dedicated for upper bounding an MWC, in which both soft clauses and literals in soft clauses are weighted. An optimal solution of the LW MaxSAT instance gives an upper bound for an MWC, instead of an optimal solution for MWC. We then introduce two notions called the Top-k literal failed clause and the Top-k empty clause to extend classical MaxSAT reasoning techniques, as well as two sound transformation rules to transform an LW MaxSAT instance. Successive transformations of an LW MaxSAT instance driven by MaxSAT reasoning give a tight upper bound for the encoded MWC. The approach is implemented in a branch-and-bound algorithm called MWCLQ. Experimental evaluations on the broadly used DIMACS benchmark, BHOSLIB benchmark, random graphs and the benchmark from the winner determination problem show that our approach allows MWCLQ to reduce the search space significantly and to solve MWC instances effectively. Consequently, MWCLQ outperforms state-of-the-art exact algorithms on the vast majority of instances. Moreover, it is surprisingly effective in solving hard and dense instances.

We introduce a data-based approach to estimating key quantities which arise in the study of nonlinear control systems and random nonlinear dynamical systems. Our approach hinges on the observation that much of the existing linear theory may be readily extended to nonlinear systems - with a reasonable expectation of success - once the nonlinear system has been mapped into a high or infinite dimensional feature space. In particular, we develop computable, non-parametric estimators approximating controllability and observability energy functions for nonlinear systems, and study the ellipsoids they induce. In all cases the relevant quantities are estimated from simulated or observed data. It is then shown that the controllability energy estimator provides a key means for approximating the invariant measure of an ergodic, stochastically forced nonlinear system.