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Tag Archives: machine learning

From maker space to solver space

Conferences are for meetings.  Project teams build deliverables.  Data is for data scientists.  Online communities are for social contact.

Until now, when a new mix is emerging.  Can we solve difficult problems in a short-term conference setting?  Is there a new way to run a workshop, which is dynamic, data-driven, visual, collaborative?

I wrote a few months back about the Silicon Valley Sim Center: an initiative to bring a new way to solve “wicked” problems to Silicon valley.  And in an article in this month’s Wired called “Hey Silicon Valley, Buckminster Fuller has a lot to teach you by Sarah Fallon, she interviews Jonathon Keats about his new book on what Bucky has to say to Silicon Valley.*

And from “maker spaces” to “solver spaces”, a new way of working together to solve difficult problems is emerging.

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Machine Learning Services and Solutions

You need the experience and world-class expertise of a team that has built thousands of machine learning and data management systems for dozens of clients.  And you need to do it simply, controlling risk, and so that it maximizes your outcomes, be they revenues, minimized costs, wellness, sustainability, or more.

If this is you, then drop us a line, and let’s talk.

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Qualifications

We invented the field of machine learning inductive transfer.  Then we took machine learning into dozens of organizations over the years, implementing neural networks, decision trees, regression systems, and more,  in areas like hazardous waste management, forensic hair analysis, computer vision, and DNA pattern recognition for the Human Genome Project, and budgeting, where our systems built the calculators for over $100M of US government spending.

We are a five-star rated team, pushing the boundaries of machine learning into the new field of decision intelligence, which we invented.

What we can do for you

We build and integrate machine learning systems, and we’re passionate about demystifying this technology and making it accessible so that machine learning can be used throughout your organization to drive competitive advantage.

It’s all about your business, and your bottom line.

We do things like connecting data between S3 and Mahout / EMR,  running regressions in R / H2O, designing learning visualizations, designing success measurement code, finding the right number of hidden units in a neural network, and designing a machine learning solution to provide maximum value to your company, as soon as possible. We have a development and architecture team, can manage a team of analysts, train your staff, and can present to your executives.

But the details are less important than the business value they bring to you. We are known by our world-class clients to be fast, effective, and delightful to work with.

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Machine Learning Services and Solutions

L.Y. Pratt
Machine learning consulting: deep learning, decision intelligence, recommender systems, predictive analytics, market data analysis, and more.
UNITED STATES | MOUNTAIN VIEW, CA
I will help you to understand how your data stack and machine learning can drive value for your organization, and how to supercharge your investment in these technologies to create business value.
Did you know that 10% of your data contains 90% of the value?  This means that most organizations are leaving money on the table: they could get to value from their data stack ten times faster.
Don’t wait to design an application that uses your data until all the data is cleansed, migrated, and processed. Because designing an initial model or baseline machine learning system can provide critical cost- ad time-saving intelligence.

I can help. I’m a five-star rated machine learning engineer (see some testimonials below).  I  invented the fields of inductive transfer and decision intelligence.  My CV is here.  I write regularly about machine learning topics.

Drop me a line and let's talk!

What I can do for you

I build and integrate machine learning systems, and I’m passionate about demystifying this technology and making it accessible so that machine learning can be used throughout your organization to drive competitive advantage.

My work includes tasks like connecting data between S3 and Mahout / EMR,  running regressions in R or H2O, designing learning visualizations, designing success measurement code, finding the right number of hidden units in a neural network, and designing a machine learning solution to provide maximum value to your company, as soon as possible. I am a coder, can manage a team of analysts, train your staff, and present to your executives.

Over the years, I’ve built thousands of machine learning systems: neural networks, decision trees, regression systems, and more,  in areas like hazardous waste management, forensic hair analysis, computer vision, and DNA pattern recognition for the Human Genome Project.

I speak the language of business as well as “machine learning”-ese.  I’ll work with you on a focused project to get your needs met, and we’ll do it in record time and with minimal impact to your people and systems.

Working with my team at Quantellia, we also go deeper into the data stack, providing data management, robust high-performance enterprise-grade survey systems, and  our award-winning World Modeler™ software with data binding to many sources.

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Machine learning is poised for mass adoption

Every few years, it’s exciting to witness a nascent technology emerge as a credibly disruptive influence around the world.  Personal computing exploded in the 1980s, the web in the 1990s.  Today, machine learning for the masses is on the brink of a similar explosion.

Remember when it was a stretch to think that Granny could understand the difference between software and hardware?  These days, she’s got her own computer—inconceivable in 1983!—and uses it for lots of tasks. The same will be true of machine learning, which is the technology that “connects the dots” between data and what she’s interested in. And if she can do it, so can you. Continue reading

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Guest Post: The dirty dozen: twelve ways to fail at effective decision making

In the course of my decision analysis, analytics, and intelligence work for businesses and industry, I have identified a set of common points of failure in a typical decision engineering initiative.  These characterize the “hidden traps”, where decision makers often struggle to preserve the integrity of the Decision Engineering life cycle.

Below is the chain of those failure points. I have listed them in a sequence in which I have found them to typically occur.  Each one encompass the preceding two points in the list.

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Decision Intelligence in conflict and disaster recovery

In our years building decision intelligence models for domains like banking, telecom, and more, the project that I am most proud of is the work that we did for Liberia in collaboration with The Carter Center.

The basic idea: countries are complex systems. Understanding how to recover after a conflict or disaster can be a particular challenge. Decision makers often end up working accidentally at cross purposes, due to shared, but invisible, mental models of a situation. This often produces unintended negative consequences. Continue reading

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Predictive analytics is not enough

The idea of predictive analytics can seem like magic: how, really, can a computer predict the future? Yet we’ve seen a lot of success based on this advanced technology in recent years, from Netflix to Amazon, Google, and more. These companies mine a massive amount of data every day for patterns, and it drives massive revenues.

However, for a widespread class of situations, predictive analytics alone aren’t enough. Consider the decision model below, which I introduced in my last post. The blue graphs on the right-hand side are based on predictive analytics, but they are only building blocks in the full model.  They are not enough on their own.

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What’s really frightening about Artificial Intelligence? It’s not what you think.

OK, I’ll admit it. AI scares me.  But not for the usual reasons: I’m not too concerned about robots taking over the earth or even the Singularity, as are many of my friends.  What does frighten me is the distraction that AI represents from the problems that matter.  The ones that need our judgment, our ethics, our humanity, our instincts, our rational subconscious, where we keep humans in the loop. These problems are best solved through a collaboration, where we use computer help where it’s best applied, giving us better data, evidence-based analysis, “moneyballing” governmentAnne Milgram’s great work fighting crime with data, or Ruth Fisher’s game-theoretic analysis of everything from the dynamics of the U.S. health care system to cap-and-trade carbon schemes.

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