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.
What does cold dead fish have to do with random forests? If you were to open a restaurant that served cold dead fish, you would not stay open for very long. However, if you used the concept of framing and instead sold a delicacy called Sushi, you would have much better chance of staying in business. Framing is a concept in the emerging field of behavioral economics that attempts to understand how people make decisions as well as how to influence those decisions. Other examples include the use of a default preference in order to encourage more people to become organ donors and rearranging the layout of food in a school cafeteria to get kids to eat more healthy fruits and vegetables. Continue reading
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
Often 10% of the data contains 90% of its value in solving the problems we need solved. This means that we can waste enormous resources cleansing, migrating, and managing that 90%, with little to show for it. What’s worse, we can miss the 10%: a big disconnect to achieving our objectives. Continue reading
The Internet of Things…Machine Learning…Self-Driving Cars…Artificial Intelligence…Big Data…Smart Cities…Decision Intelligence…my friends talk to me about their excitement about a whole lot of trends. But which ones are real, and which will fizzle?
Before founding Quantellia, I spent six years as a technology analyst, where I was privileged to have an inside look at how tech trends boom and bust. I learned a few important lessons.
Looking back on the presidential election of 2012, one view of the Obama win is to attribute it to his team’s understanding of a phase shift in electoral dynamics: Democrats looked at historical turnout numbers and perceived a systemic change; in contrast many believed that Republican certainty in a Romney win was based on a reasonably expected regression to the mean. This is the essential idea behind “data from the future“. We ignore these principles in this system, as in many others, at our peril.
In light of this history, it’s worth asking if the fundamental dynamics of how elections are won is shifting this year again. Continue reading
As computer scientists, one of our first lessons is about “big O” complexity of software. It’s used to understand the expected time for a program to run. “Big O” theory tells us that it’s the order of magnitude of a system—like O(n^2) or O(n)— that matters, much more than smaller factors. To understand the parts of a computer program that dominate the time required to run it, we know not to focus on the tiny parts of the system that irrelevant to the overall behavior. Measuring the performance of a line of code to the fourth decimal place, when it only runs a ten-thousandth of the time that other code does, is wasted time.
As introduced in Beyond Data Part 1, we need to apply the same kind of insight to analyzing data, whether it be for business intelligence, decision support, dashboards, or other systems.
Think about your company as a snowball rolling downhill. If you start five balls down a slope, they’ll all roll a little while, but some will happen to hit stickier snow, and get a little bit bigger than the others. Those heavier snowballs will have just a bit more momentum, so they’ll roll a bit further, and use up all the snow, gaining a bit more size in the process. Which makes them heavier, which makes them roll faster. You get the idea: in general the fortunate few snowballs will end up much bigger, leaving the smaller ones stuck at the top of the hill and others much bigger and much faster. Continue reading
I rode the train on a beautiful spring day through New England. Arriving at the station, a few guys from our partner company greeted me happily; we were going to win this one. We chatted about the customer on the drive across town: “They’re really turning over a new leaf”…”Very innovative”…”Hungry for new ideas”.
When we arrived, it was a big meeting: folks from several departments, some who hadn’t met each other before. The IT guys carried stacks of paper: the data model…the spreadsheets. As usual, the first part of the meeting was a data dump, syncing up. And, as usually happens, it was mostly about data. Our customer took us through a great database schema on the whiteboard…his colleague handed out reports. Great!'The decision is only as good as the data that supports it.' It was bound to be said. Click To Tweet
Because every organization we work with does sooner or later. But it’s not always true. Continue reading
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.