Category Archives: Artificial Intelligence

Death by proxy

“This year, our priority is customer experience.  Everything we do must connect to that.”

“We’ll upgrade the network in a neighborhood when the bandwidth utilization exceeds 80%.”

“I’m going to get rich, then I’ll be happy.”

Good ideas, on the surface.  Problem is: they’re often wrong.

And they share a common thought pattern: the use of a proxy—a substitute—for what we actually care about.

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Pulling Back the Curtain on #MachineLearning Apps in #Business

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.

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Artificial intelligence and human limits

Are we getting dumber?  Or is stuff just harder?

Both are true.  Between-silo problems are the new bottleneck.  We’re inundated with information, so we take cognitive short cuts.   And “wicked” problems keep getting wickeder.

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.

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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 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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What causes the technology hype cycle (and what to do about it)?

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.

It’s funny: the Hype Cycle is well-understood: those curves from Gartner, Geoffrey Moore, and others that show how technology follows a hype/disillusionment/acceptance curve. Continue reading

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Decision: I do not think it means what you think it means

We use the word “decision” to mean two very different things.  If I say “I’ve decided that the moon is made of green cheese”, or “I’ve decided that the economy will deteriorate next year”, these statements aren’t necessarily about actions I’m going to take.  If, instead, I say, “I’ve decided to go to go to graduate school” or “I’ve decided to institute a new policy”, that’s fundamentally different.

How?  The first kind of decision leads to a fact, either well-supported or not.  It is, essentially, using data and expertise, following its implications (deductively, inductively, or otherwise), and leading to a conclusion (which may have more or less justification: to fit this category it doesn’t have to be right). Continue reading

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