Decision Intelligence
16.0K views | +0 today
Follow
Decision Intelligence
Decision intelligence (DI) is an interdisciplinary field that solves the world's most complex problems.   DI connects human decision makers to technologies like machine learning, AI, deep learning, visual decision modeling, complex systems modeling, big data, predictive analytics, UX design, statistical analysis, business intelligence, business process management, causal reasoning, evidence-based analysis, and more. For an overview, see the webinar at http://youtu.be/XRTJt3bVCaE, and more videos at http://www.youtube.com/quantellia. Many of these topics are vigorously discussed in the Decision Intelligence LinkedIn group: http://www.linkedin.com/groups?gid=205078.  See http://www.lorienpratt.com/the-decision-intelligence-and-decision-engineering-ecosystem/ to learn about the emerging ecosystem of decision intelligence companies, influencers, and problems solved. Also, I offer DI and machine learning consulting services.  See http://bit.ly/1X8O2zF to learn more. Would you like to receive the DI News in email? Click here to sign up: http://bit.ly/29wYd1u
Curated by Lorien Pratt
Your new post is loading...
Your new post is loading...
Scooped by Lorien Pratt
Scoop.it!

Innovation: it's not what you think.

Technology companies invest billions in developing new gadgets; business leaders see innovation as the key to a competitive edge; policymakers craft regulations to foster a climate of innovation. And yet businesses report a success rate of only four percent for innovation initiatives. Can we significantly increase our odds of success? 

Lorien Pratt's insight:

I'm about halfway through this book, and it's chock-full of understanding for how we need to innovate DI.  Probably the most important bit so far: innovations are emergent from societies of practices, and innovators cultivate, not create from scratch.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

How to hire a machine learning consultant

How to hire a machine learning consultant | Decision Intelligence | Scoop.it
More and more organizations are realizing the tremendous benefit of machine learning to their bottom line, yet many are not ready to hire a full-time machine learning expert. So a machine learning contractor/consultant/freelancer makes sense. For instance, your business might depend upon "just in time" delivery of some goods, and you need to predict when…
Lorien Pratt's insight:

Nuts and bolts for how to get started.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Use Big Data Strategy To Get Connected To Your Customers

Use Big Data Strategy To Get Connected To Your Customers | Decision Intelligence | Scoop.it

"With the increasing number of social media platforms, there is great value in investing in Enhanced Social Listening (ESL). ESL can provide real time data on what people do; and say about your brand. If conventional paradigms are to be believed, this will have a very real impact on your brand image. If, say, Net Promotor Score (NPS) is the measure of your choice, then ESL can be leveraged to predict NPS ahead of time - through a healthy mix of text mining and advanced analytics. "

Lorien Pratt's insight:

A good overview of a number of ways that big data and analytics combine to improve a company's ability to understand its customers.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Machine learning is poised for mass adoption

Machine learning is poised for mass adoption | Decision Intelligence | Scoop.it

Looking backwards, we’ve already been through the AI/ML hype cycle (remember The Fifth Generation of the 1980s?).  This second time around, it’s serious.  From this point of view, I disagree with where Gartner places Machine Learning on its 2015 emerging technology curve, as shown in the graphic above.  We’re not just past the “Peak of Inflated Expectation”: to the contrary, we’re well on the way towards the “Plateau of Productivity”

Lorien Pratt's insight:

An alternative to Gartner's assessment of where machine learning sits on the adoption curve.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Why we must model.

Slides for my presentation on civic modeling for the Oceanus Conference October 2015 in the Sacramento Delta aboard the Aurora.
Lorien Pratt's insight:

One of the best decks I've seen explaining the imperative for modeling for the planet and all wicked problems.  From @anselm hook at the @oceanus conference last week. 

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

What drives the hype cycle? (and how to survive it)

What drives the hype cycle?  (and how to survive it) | Decision Intelligence | Scoop.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?

Lorien Pratt's insight:

Journalists and analysts hear a lot more from the sell-side than from buy-side decision makers.  This leads to a biased view of the world, which drives hype that goes beyond reality.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Guest post: Can technology help fix us? | Grok

Guest post: Can technology help fix us? | Grok | Decision Intelligence | Scoop.it

We have Smith using videos and web sites to support his grass roots approach to reforming our systems and we have Pratt’s big systems approach to analyzing the big problems that split the political spectrum. These approaches and, possibly others like them, give me hope that solutions are out there waiting to be found and implemented.

Lorien Pratt's insight:

What is the role of technology in fixing wicked problems, in US national politics and beyond?

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

The antifragility of the open source bazaar

The antifragility of the open source bazaar | Decision Intelligence | Scoop.it

"...we were careful and diligent, writing thorough specifications documents, then carefully crafted designs, and only then could code begin.


All good software engineers know this is the way to go.   Or at least that’s what we always thought.


But it turns out we were wrong.  And not in a small way."

Lorien Pratt's insight:

A shift in reality as we know it.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Society for Judgment and Decision Making annual meeting to feature Danny Kahneman, Women in SJDM, more

Society for Judgment and Decision Making annual meeting to feature Danny Kahneman, Women in SJDM, more | Decision Intelligence | Scoop.it
The 2015 SJDM (Society for Judgment and Decision Making) preliminary conference program is now online
Lorien Pratt's insight:

Looks like a good show.  Anyone going?

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

In-depth interview with Decision Intelligence pioneer Lorien Pratt on Johnmac Radio

In-depth interview with Decision Intelligence pioneer Lorien Pratt on Johnmac Radio | Decision Intelligence | Scoop.it

"My guest this week on the johnmac Radio Show is Lorien Pratt, Phd, founder and Chief Scientist at Quantellia, which offers data, machine learning, and decision intelligence software and services worldwide."

Lorien Pratt's insight:

This pretty much traverses my whole resume, including learning to code in high school, Dartmouth, my time at IBM, and more.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

World Makers: Simulation and models will save the world

World Makers: Simulation and models will save the world | Decision Intelligence | Scoop.it

"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.

 

Simulations are a powerful holistic way of understanding and communicating the complexity of the challenges we are facing. The fact is that we are all stakeholders in a complex natural planetary ecosystem. It defines us, our lives, our values. Without nature humanity as we know it will quickly cease to exist.

 

At the same time we are making poor decisions that are undermining our diverse heritage. We risk runaway effects that may be beyond our ability to control. We are playing a high stakes game of planetary Jenga.

 

This is a crisis of organization. We routinely spend trillions of dollars of value to make millions of dollars in profit. Consider even just dredging the Louisiana swamplands or cutting the mangrove trees and the impact on New Orleans. Nature simply doesn’t care about our rhetoric or our aspirations. We cannot predict or model outcomes in such a way as to reach consensus. Almost universally the side-effects of our actions dominate over intended consequences. Private interests can pursue short term gains and externalize true costs onto the rest of us. In sum the world has become too complex to understand with current tools.

 

We simply do not know the outcomes of our decisions. We don’t even have trustworthy predictions of those outcomes. Will the world as we know it even exist after 2100? Or will we see massive ecosystem collapse, human migration, war and other disruptive effects? Or will everything be fine? There is no consensus.

 

The one phenomena moving as fast as the environmental crisis is the Internet and computing power. The hybrid synthesis of human intuition and brute force computation lets us explore more complex situations in more depth. The simulations we are starting to build will eventually allow the most vulnerable and concerned stakeholders predict outcomes at the intersection of policy, law and land-use in a rigorous manner. By doing so we’re shifting the civic debate from “rhetoric” to “model based” reasoning.

 

In the future there will be nobody who is not an environmentalist. There will be nobody who isn’t a direct participant in helping save this planet. We will find ourselves needing to get the participation and buy in from millions of voices that are currently disempowered. We will need to find a way to foster diversity. We need to find a way to stop private interests from eating the public good.

 

Ultimately we’re going to need new models of governance and decision making. It’s clear we’re going to have to hack the systems around us. The structures that got us into this mess are not the same as the structures that are going to get us out. This is how we’re going to save the world."

Lorien Pratt's insight:

This is so important that I'm reproducing it in its entirety here.  Please read and share.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Beyond Data (part 1): J. K. Rowling, Shakespeare, and the Sorcerer’s Decision

Beyond Data (part 1):  J. K. Rowling, Shakespeare, and the Sorcerer’s Decision | Decision Intelligence | Scoop.it

'The decision is only as good as the data that supports it'.  It was bound to be said"

Lorien Pratt's insight:

Why data matters less than you think.  Me on one of my soapboxes :-)

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Decision lever visualization: an animated review

Decision lever visualization: an animated review | Decision Intelligence | Scoop.it
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…
Lorien Pratt's insight:

Enjoy this tour of decision lever animations.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

2015 in ML: Miracles and wonders: deep dreams, label machines, and my new super-super-super computer

2015 in ML: Miracles and wonders: deep dreams, label machines, and my new super-super-super computer | Decision Intelligence | Scoop.it

2015 was a turning point year for machine learning.  Check it out: https://www.youtube.com/watch?v=8BFzu9m52sc Based on Andrej Karpathy's "NeuralTalk2" code, as modified by Kyle McDonald: a Brooklyn-based artist who "works in code".  Neuraltalk uses GPUs - another breakthrough for 2015, where my (and probably your!) laptop took a big leap in performance.

Lorien Pratt's insight:

This is where it gets interesting...

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Machine learning tools of the trade

Machine learning tools of the trade | Decision Intelligence | Scoop.it

Like any maturing discipline, machine learning is splitting into specialties.   And just as a surgeon uses a scalpel, and a general practitioner prefers a stethoscope, different tools are appropriate for different use cases within these subfields. In the last few months, I've run projects that have used tools in at least five categories.    Here's a survey"

Lorien Pratt's insight:

It's exciting to see an explosion of new machine learning tools.  But the choices can be overwhelming.  This article helps to sort things out.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

What is machine learning, and why you should care (in 500 words)

What is machine learning, and why you should care (in 500 words) | Decision Intelligence | Scoop.it

It's critical that you understand machine learning, even if just a little bit. Why? Machine learning is at the heart of the most common artificial intelligence systems today. It's an important new technology that's moved beyond hype to the brink of an exponential explosion..."

Lorien Pratt's insight:

For my friends who would like to know what this Machine Learning thing is all about.  It's a very sophisticated technology, but surprisingly it's not at all hard to understand the essence of what's happening.  Since ML is how we bridge from data to value in many situations (it gives us some cause-and-effect links in most decision models), ML is also essential.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Artificial intelligence will replace traditional software approaches, Alphabet's Eric Schmidt says

For some enterprise users, it's difficult to separate real artificial intelligence from the sci-fi depictions we've seen. But according to Eric Schmidt, executive chairman at Alphabet, the next big evolution for software is machine learning, and the business world will follow suit – think less R2-D2 and more ROI.
Lorien Pratt's insight:

We'll look back at 2015 as the transition year: between when AI / ML was a cool, edgy thing that only the likes of Facebook and Google used, to where it started to be ubiquitous.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

10% of the data holds 90% of the value: four reasons why

10% of the data holds 90% of the value: four reasons why | Decision Intelligence | Scoop.it
Lorien Pratt's insight:

There's a tremendous amount of resource waste in "gratuitous data management": of fields and tables that matter less than others.  All data is not created equal when it comes to solving business problems.

more...
Sandy Gilchrist's curator insight, February 11, 2016 9:45 AM

1% of the world's data is compatible with another 1% of the rest of the 99% available data.  This is due to the abundance of data standards, and importantly the accessibility of that data.  For example, there are many proprietary systems, build as proprietary systems so that they would have competitive value.  We see this in the way that institutions have developed or bought software over the past 50 years from banks to governments to telcos, to retailers to mines to manufacturers, er, etc.  Then the systems were upgraded, often without linkage to old legacy data.  Then someone made an attempt to make one system 'interoperate' with another system, creating a whole new data set, with a new data schema, data library, data definitions, etc - all of the, of course, complete incompatible with any other system of it's kind.  Standards like ODBC appeared, which helped 1%, so thanks for that.  The point is that data these days includes multimedia data like video and voice, not to mention all sorts of sensor data... less than 1%  of it compatible with anything else.  So, the big data industry is growing and that means loads of money being thrown at it.  Reality check - be careful what you expect.  On the other hand, we could give everybody their own data, on an open standard, for example, on a platform like LifeBank and make it really easy for LifeBank to interoperate with any other system... I say "we could", but I should've said, "we already are."  Enough said

Suggested by John
Scoop.it!

Machine Learning basics for a newbie

Machine Learning basics for a newbie | Decision Intelligence | Scoop.it
Machine learning involved supervised, unsupervised & reinforcement learning. This article tells basics of ML & its difference from data mining
Lorien Pratt's insight:

A great little article for everyone wondering "what is this machine learning thing all about, anyway?"

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Data from the future in the presidential race

Data from the future in the presidential race | Decision Intelligence | Scoop.it

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.

Lorien Pratt's insight:

The role of forward systems models  - which can go beyond data - in presidential elections.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Guest Post: A knowledge management system capable of blinking red

Guest Post: A knowledge management system capable of blinking red | Decision Intelligence | Scoop.it
Inattention to critical knowledge is an old problem. Lessons are forgotten, near misses are ignored, caution is dismissed, disasters result. Titanic. Bhopal. AIG. Katrina. Fukushima. And on and on. Knowledge Management (KM) is supposed to make the right information available to the right people at the right time in the right form—and to the best…
Lorien Pratt's insight:

The connection between risk,  knowledge management, and decision intelligence.  Thanks @lindalarsonkemp!

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Decision modeling to understand energy transformation decisions

Decision modeling to understand energy transformation decisions | Decision Intelligence | Scoop.it
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…
Lorien Pratt's insight:

A demonstration of Quantellia's World Modeler tool for a complex corporate decision.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Decision engineering a subdiscipline of engineering management

Engineering management - Wikipedia, the free encyclopedia

Outside the USA, in Germany the first department concentrating on Engineering Management was established 1927 in Berlin. In Turkey the Istanbul Technical University has a Management Engineering Department established in 1982, offering a number of graduate and undergraduate programs in Management Engineering.

Decision engineering

Decision engineering seeks to use engineering principles in the creation of a decision, which it views as an engineering artifact in its own right. From this point of view, the creation of a decision includes agreeing to objectives, developing a detailed specification, and then creating a decision model, which captures the key cause-and-effect elements of the decision environment (a systems thinking approach) with a focus on the particular decision, instead of the entire system (which can be otherwise intractable). Like other engineered artifacts, a decision model can be subject to Quality assurance review, and-since it is documented-is amenable to Process improvement over time. Decision engineering models draw from the information technologies described above for data supporting the decision, but are distinguished from IT in that they model the decision, not just the data supporting it.

Lorien Pratt's insight:

It's great to see decision engineering recognized as a subdiscipline of engineering management.

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Dance Dance Brainy: Can we get smarter through whole-body interfaces instead of just sitting all the time?

"I'd rather come home with a sore shoulder from having to move all that data around than with Diabetes [from sitting too much]"

Lorien Pratt's insight:

I love this prescient talk from my friend Anselm about whole-body gestural interfaces to computers.  We'll be smarter if we do this, too (http://bit.ly/1LevmNg).

more...
No comment yet.
Scooped by Lorien Pratt
Scoop.it!

Why machine learning needs humans

Why machine learning needs humans | Decision Intelligence | Scoop.it

As soon as the situation becomes dynamic, when something unexpected happens and basic assumptions change, the machine-only approach fails mightily

Lorien Pratt's insight:

What a terrific argument for the importance of human-in-the-loop machine learning!  Great job Forbes, Dan, and  Arnab!

more...
No comment yet.