Sparkling Logic is focused on enabling business and data analysts to manage automate decisions better and faster – what they call Analytics driven Decision Management. Sparkling Logic was founded in 2010 and I have blogged a few times about their decisioning platform (most recently here). Customers include Equifax, Paypal, FirstRate, Accela, Northrop Grumman and others across a wide range of solution areas with a strong focus on enterprise customers. These enterprise customers are very focused on multiple projects, enterprise integration and supporting both on-premise/cloud deployments. The Product Portfolio now includes Pencil, a decision modeling and requirements tool, and SMARTS, their full lifecyle decision management platform supporting predictive models, data analysis, and expertise / business rules. SMARTS is available on premise, on cloud or embedded in another product and runs across Java and .NET deployments.

Win-Vector LLC's Dr. Nina Zumel has just started a two part series on Principal Components Regression that we think is well worth your time. You can read her article here. Principal Components Regression (PCR) is the use of Principal Components Analysis (PCA) as a dimension reduction step prior to linear regression. It is one of the best known dimensionality reduction techniques and a staple procedure in many scientific fields. We often find ourselves having to often remind readers that this last reason is not actually positive.

Do you understand the relative impact that machine learning and predictive analytics can have over patient care? If you work in the healthcare industry, I'd suggest that you familiarize yourself with these technologies because working in this environment will soon be an unavoidable reality. This way of treatment with machine learning and predictive analytics is becoming more accepted in today's healthcare industry, but people are still more than 80% under water when it comes to a full-fledged understanding of these cutting edge solutions. There are thousands of healthcare IT companies all over the United States that insource and outsource the development of software and mobile ready applications to solve issues being faced in the technological age of EMRs. It's tough to turn a blind eye to the big named corporations like IBM, rolling out Watson, the machine learning powerhouse that is being implemented across multiple industries, including healthcare.

The growing importance of business intelligence and data analytics applications in driving business decision making has made data integration's vital role in the enterprise crystal clear. From gathering data, transforming it into useful information and delivering it to the business users or processes that need it, data integration routines provide the crucial link between a variety of source and target systems. As the first article in this series examined, several types of packaged software have emerged to meet the challenges of data integration. The current generation of data integration tools consists of full-fledged suites that support extract, transform and load (ETL) processes, application integration, cloud-based and real-time integration, data virtualization, data cleansing and data profiling. How can you determine if your organization should invest in a data integration tool?

For consumer product companies to leverage sales and increase overall growth, professional development is a top priority for the VP of sales. But it can be challenging to determine where to allocate resources and training budgets. The VP of sales needs to pay careful attention to return on investment (ROI) and ensure that educational spending is improving sales team performance. Here are three data-driven approaches that executives can use to tackle team education and enablement challenges for consumer product sales. At a CPG company, the VP of sales is responsible for developing and driving go-to-market strategies while also ensuring a unified value proposition.

As usual, our goal is to quickly learn a policy which receives a high reward per episode. We can apply a traditional RL algorithm to the semi-supervised setting by simply ignoring all of the unlabelled episodes. This will generally result in very slow learning. The interesting challenge is to learn efficiently from the unlabelled episodes. I think that semi-supervised RL is a valuable ingredient for AI control, as well as an interesting research problem in reinforcement learning.

Researchers from a joint Australian National University (ANU), University of Adelaide and UNSW ADFA project were surprised to find artificial intelligence could be used to replicate the 2001 Nobel Prize winning experiment that trapped extremely cold gas in a laser beam, known as the Bose-Einstein condensate. Physicists put themselves out of job use artificial intelligence to run complex experiment https://t.co/FKLwQQ7ZX0 "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said ANU co-lead researcher Paul Wigley. "A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the universe and their extreme sensitivity can be used for mineral exploration or navigation.

According to a new survey (PDF) from the CFA Institute, Wall Street is getting a bit worried about fintech replacing its jobs. The majority of respondents, which included more than 3,000 chartered financial analysts around the world, view asset management as the industry most at risk from disruption by financial technology. Fifty-four percent of respondents said the sector would feel the biggest changes, followed by banking, securities, and insurance. Robo-advisers, a low cost alternative to traditional financial advice, has garnered headlines recently as fees have come under increased scrutiny. According to remarks this week by Wall Street executives attending the Milken Institute Global Conference, the entire world of finance should fear job replacement.

WHAT: SoftServe's "Will Artificial Intelligence Change Healthcare?" on-demand webinar will deep dive into the most talked about topic in Healthcare – Artificial Intelligence. WHO: During the webinar SoftServe's Eugene Borukhovich, Senior Vice President and Healthcare Global Vertical Practice Leader, will provide insights on how AI can add value in Healthcare by: WHERE: Register to access a recording of the "Will Artificial Intelligence Change Healthcare?" webinar. Eugene Borukhovich is an international expert on healthcare information technology innovation. He is also a member of HIMSS EU Industry Advisory Committee, convened in September 2014 to discuss and collaborate on key Healthcare IT topics. Eugene is a frequent speaker at various healthcare conferences and events, including HealthXL, mHealth Summit, Health 2.0, Week of Health and INNovation, etc. Eugene's articles and blogs have been published in numerous healthcare resources including SoftServe United, HealthWorksCollective, Medical Design Technology, intrepidNOW, and many more.

For a while now, I've been intrigued by the potential of artificial intelligence (AI) on manufacturing and decision-support systems. Sure, we are beginning to see the glimmers of opportunity in predictive and prescriptive analytics, but most of this applies to high-value assets like massive turbines, and has not yet reached everyday manufacturing. The reality is most manufacturers are still running their operations with problems in basic process visibility, working with islands of information and using many of the same tools they've used for decades to run their business. Some have implemented process automation or even manufacturing execution systems, but every environment is somewhat unique and in large part disconnected. The fact that equipment runs for decades means "if it ain't broke" still applies, and the demand for crazy new technologies like cloud, hadoop, or artificial intelligence brings mostly bored looks or even outright scorn from plant managers incentivized to reduce risk and maintain output on the brownfield equipment they have.