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.
Please enjoy this interview broadcast today with me and Daniel G. Faggella of TechEmergence. I touch on intelligence augmentation (IA), machine learning in vision, text, and other domains, the emerging decision intelligence ecosystem, the limits of data, and how to hire a machine learning consultant.
Are we getting dumber? Or is stuff just harder?
Take this “invisible art” artist. She sold a few.*
Real decisions are made in the heart, the gut, based on a good story. So we’re vulnerable to master wizards: good story tellers. And, often, we’ll do what they say.
It’s impossible to assemble hundreds of graphs and data visualizations in our heads to make good decisions. It’s a fiction that we can. So we’re overwhelmed, take short-cuts, but it’s hard to admit.
The good news: we have new superpowers.
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.
Since thinking so much about levers in the last two posts, I’ve also been pondering the variety of levers I’ve built and seen, and the different purposes they serve in a decision model. In particular, given that our goal is for models to be as easy to understand as possible to facilitate collaborative team alignment, I think that some principles are emerging. Here are a few ideas. Continue reading
Once upon a time there were programmers, but not software engineers. As businesses and other organizations learned the value of this new technology, software engineering emerged as a discipline to derive maximum business value from it.
There is a similar need emerging in data science today. This means that machine learning is underutilized compared to its potential in solving business problems. So the question is, how to bridge from the business to data and machine learning to drive maximum value?
Quantellia co-founder Mark Zangari presented a talk on this topic, called “Agency”, in Seattle on May 1 at MLConf 2015.
Earlier this year, the newly appointed White House CTO Megan Smith told Wired Magazine that the tech industry needs to “show up” in DC. It’s starting to happen: award-winning teams from around the country flew to Washington earlier this week to attend the #hack4congress finalist presentation, which you can watch below.
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.
At Quantellia, we’ve been delivering enterprise-scale, desktop- and PC-based decision intelligence models to our clients for a few years now, using our World Modeler™ software. In the last few months, every single one of our clients has asked for our work to be delivered through a web interface, so we’ve been heads-down in delivery and development to meet their needs. These are not available to be viewed by the general public, however, so I’ve spent the last few days building a demonstration to show you what we do, and as part of my answer to a recent Quora question on Agency theory as well as Mark’s upcoming talk on this topic at MLConf Seattle.
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” government, Anne 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.