As we look forward into 2017, intelligence augmentation (IA) will begin to take its rightful place alongside artificial intelligence (AI).
Here’s one of my talks about this. Summary below.
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.
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.
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 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.
Last month I received an intriguing email inviting me to an event at Kimberly Wiefling’s house. I’d met Kimberly before through Jonathan Trent, as part of the work I’ve been doing to help out the Omega Global Initiative. I knew she was an international consultant, but it was great to also learn that she was passionate about systems thinking and visualization. Jonathan and I drove up to Kimberly’s house together, where she and Peter Meisen explained their initiative to bring a Buckminster Fuller-inspired Sim Center, based on a similar center in San Diego, to Silicon Valley.
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.
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 solutions built with our award-winning World Modeler software, with data binding to many sources.
Many companies are facing the prospect of steep increases in the cost of energy in the coming years. In response, many are looking at alternative energy sources. However, navigating the transition to this new world contains hidden dangers, so an evidence-based modeling approach can make a big difference. This article looks at this decision-making process through the lens of US cable operators, to understand the specific decisions they face.
The goal of World Makers is to encourage people to build computer simulations of the world. This includes simulating water, weather, crops, land use policy or anything else. Models can be regional or global, simple sketches or full blown simulations.
The classic game ‘Sim City’ by Will Wright is perhaps the best known example of a computer simulation. It lets people build their own imaginary city from the ground up, placing roads, homes and services and measuring their success against the happiness of the population. The goal here is similar – but real – with real data, real stakeholders and real outcomes.