Decision Intelligence News
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Decision Intelligence News
AI/BI/CI/DI: Decision intelligence (DI) solves the world's most complex problems.   It 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. See https://en.wikipedia.org/wiki/Decision_Intelligence. For an overview, see the webinar at http://youtu.be/XRTJt3bVCaE, http://www.lorienpratt.com, and the Decision Intelligence group: http://www.linkedin.com/groups?gid=205078.   Also, my company offers DI and machine learning consulting services.  See http://bit.ly/1X8O2zF to learn more.  Don't miss the latest DI news!  Click here to sign up: http://bit.ly/29wYd1u.
Curated by Lorien Pratt
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Responsible AI/DI Ecosystem website redesign, including videos from the 2018 Summit

Responsible AI/DI Ecosystem website redesign, including videos from the 2018 Summit | Decision Intelligence News | Scoop.it

"Artificial Intelligence (AI) and Decision Intelligence (DI) are powerful technologies with potential for both great good and great harm. Responsible AI/DI (RAIDI) Ecosystem members consist of stakeholders and thought leaders working together to maximize the positive impact of these technologies worldwide.

 

Our first RAIDI Summit was held in September 2018. The second will be in September 2019 in Washington DC.

 

Please join us in using these revolutionary technologies for the common good."

Lorien Pratt's insight:

Please join me in congratulating the Responsible AI / DI ecosystem team for their new web design, including videos from the 2018 Responsible AI/DI Summit.

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Risk Roundup Webcast: Algorithmic Decision Making

Risk Roundup Webcast: Algorithmic Decision Making | Decision Intelligence News | Scoop.it

"Our whole infrastructure needs to be tuned up with security and risk management in mind.  Fortunately I think AI will help with that...we need a human/AI cooperation / collaboration...the challenges of data [will only increase]...you need the compute infrastructure that can handle all of that data...data becomes part of the value and the currency that a company or a society has that helps enable it to make better decisions but it becomes a burden in and of itself."

Lorien Pratt's insight:

Steve talks about how AI supports decision making, with a focus on provable algorithms and the necessity of handling vast amounts of data.

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Steve Brant at the United Nations: Has Science Made Fighting Wars Obsolete? (Full Question)

Steve Brant at the United Nations: Has Science Made Fighting Wars Obsolete? (Full Question) | Decision Intelligence News | Scoop.it
I asked how we can start a cultural conversation re: Has Scientific Advancement Made Fighting Wars Obsolete? This is my full question at the UNA-USA'
Lorien Pratt's insight:
Cultural shift ideas, inspired by Buckminster Fuller, from DI ecosystem member Steve Brant
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The AI^2 initiative integrates AI with consciousness, biology, culture, and systems

The AI^2 initiative integrates AI with consciousness, biology, culture, and systems | Decision Intelligence News | Scoop.it

"Instead of fearing that AI will be used to create some sort of corporate totalitarian state, let’s instead use AI in a way that puts human values, people, the planet, plants and animals at the core, and all technology and economic and political systems are in service to that. This will change everything. Instead of the hype around the singularity where humans and robots merge we will use AI in service to meeting all people’s basic needs around the world and healing the earth."

Lorien Pratt's insight:

About the AI^2 initiative at www.worldsummit.global, which is creating a vision for 'humanity's phase shift'.  Integrating consciousness, biology, culture, and systems, this initiative seems to be on the right track.

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The road to accessible health information interoperability

The road to accessible health information interoperability | Decision Intelligence News | Scoop.it

"To date, no health system has been able to overcome the obstacles and challenges to cross-border, comprehensive health information interoperability. Despite enormous investments and advances in technology, the health industry is still unable to consistently identify patients; give them access to their information; analyse and measure trends, risks, outcomes and costs; or agree on common standards to safely send, receive and manage data across different records systems.

 

"When we have so much to gain by working together to reduce illness and improve health outcomes, why are healthcare providers, practitioners, academics and health research institutions still unable to access, aggregate and share health information?"

Lorien Pratt's insight:

By binding criteria and credentials to the smallest attribute levels from everything gathered about and from an individual, the EP3 Foundation enables Decision Intelligence on people and their health. This capability is based on numerous criteria, and serves to protect the privacy of patients. This is a major leap forward in providing health information interoperability.

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The Salt in the Broth, Warm Data and Systems Change

The Salt in the Broth, Warm Data and Systems Change | Decision Intelligence News | Scoop.it

"I learned recently of a project by SEED to “End poverty one person at a time”—and thought, oh my… why did they ever think that poverty could be anything less than communal, cross-cultural, international, trans-contextual? Millions went into that project, and it was a disaster, of course.

 

"None of what we are faced with can be tackled in isolation. I am learning from the work I have been doing with Warm Data about the nature of interdependency. I have found that culture, society, economy industry are inextricably saturated into one another. Working with parts and wholes is not so useful in this case. Zooming in and zooming out is insufficient, we have to do both at once. I think of the systemic combining as a kind of broth, instead of linked pieces. The difference is akin to the contrast between that which is interconnected, and that which is interdependent. Interconnected things can be taken apart and fiddled with, fixed and replaced. Interdependency requires another sort of approach. You cannot take the salt out of the broth, or the grapes out of the wine."

Lorien Pratt's insight:

Despite her protestations to the contrary, Nora Bateson quite poetically discusses the pressing need to understand interdependency when addressing the most difficult and complex problems faced by humanity. She quotes a proverb from Haida Gawaii, "Everything depends on everything else," which is reminiscent of what Carl Sagan had to say about our world and its systems, "If you want to make an apple pie from scratch, you must first create the universe." In a very real sense, we are all one.

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This is how AI bias really happens—and why it’s so hard to fix

This is how AI bias really happens—and why it’s so hard to fix | Decision Intelligence News | Scoop.it

"We often shorthand our explanation of AI bias by blaming it on biased training data. The reality is more nuanced: bias can creep in long before the data is collected as well as at many other stages of the deep-learning process."

Lorien Pratt's insight:

Bias in AI efforts seems to be introduced primarily by human constructs and our limited understanding of context and nuance. Whether it's in how we frame the problem we're trying to solve, or how we collect and prepare the data we're using without regard to how it might be implicitly biased, we need to recognize our role in injecting our, or our society's, biases into our efforts.

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Enterprise AI/DI Risk Management Challenge (Part 2)

Enterprise AI/DI Risk Management Challenge (Part 2) | Decision Intelligence News | Scoop.it

"If we wait for the hidden biases and other flaws hiding in our data to emerge out of AI-based decision systems, it will be too late.

 

"We are moving artificial intelligence (AI) systems from the lab, where we can control for a limited set of variables, to potentially massive-scale implementations, where variables will propagate and multiply. We must have an AI engineering discipline that can help predict and adjust for those variables.

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How Big Data Assists in Making Life-Saving Decisions

How Big Data Assists in Making Life-Saving Decisions | Decision Intelligence News | Scoop.it

Over the last two decades, remarkable innovations such as the Internet of things (IoT) have entered the mainstream. While the intensity of natural disasters is increasing, advances in communications because of this technology has greatly reduced casualties and injuries. For instance, agencies such as NASA and the National Oceanic and Atmospheric Administration (NOAA) leveraged big data technology to predict hurricane Harvey’s landfall and coordinate emergency response personnel.

 

The technology helped the agencies choose ideal disaster response staging locations and evacuation routes as well as pinpoint likely flooding areas and prepare accordingly. Additionally, agencies throughout the storm impact area used machine learning algorithms to dictate the trajectory of the storm and its potential damage.

Lorien Pratt's insight:

When lives are hanging in the balance, first responders and disaster relief workers need lots of data and information to decide on where best to place and utilize limited assets. Big data provides the raw material with which DI can help make better decisions, thereby saving lives and ameliorating property damage.

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Link | The Secret Inside the Solutions Renaissance

Link | The Secret Inside the Solutions Renaissance | Decision Intelligence News | Scoop.it

"Why aren’t the most powerful technologies and academic disciplines being used to solve the most important problems: hunger, poverty, conflict, employment, democracy, disease, and new complexities faced by businesses? What’s missing?"

Lorien Pratt's insight:

Link is the breakthrough "big think" book of 2019, connecting a visionary future to practical steps you can take today to contribute to solutions to the hardest problems.  Sign up here for _Link_ community perks, early publication notice, excerpts, opportunities to contribute, and more.

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Decision Intelligence Is the Near Future of Decision Making: A Gartner Trend Insight Report

Decision Intelligence Is the Near Future of Decision Making: A Gartner Trend Insight Report | Decision Intelligence News | Scoop.it
Making ever-faster decisions leveraging a maelstrom of data in ecosystems that are in constant motion, requires an assembly of increasingly complex techniques. Data and analytics leaders should use decision intelligence as a practical discipline framing a wide range of decision-making techniques.
Lorien Pratt's insight:

It's good to see #decisionintelligence on Gartner's radar

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Jeff Bridges, Cecil B. deMille Award, and Buckminster Fuller at the Golden Globes

"...And Bucky made the analogy that that trim tab is an example of how the individual is connected to society and how we affect society.
And I think -- I like to think of myself as a trim tab.
And all of us are trim tabs.
We may seem like we're not up to the task, but we are, man.
We're alive. We can really --
We can make a difference. [ Cheers and applause ]
We can turn this -- We can turn this ship
in the way we want to go, man!
Towards love, creating a healthy planet for all of us."

Lorien Pratt's insight:

well, YEAH! :-)

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Why build a decision model?

Why build a decision model? | Decision Intelligence News | Scoop.it
Lorien Pratt's insight:
Why build a decision model?  To identify the hard-to-find set of choices that meet the needs of multiple stakeholders, previously in conflict. To find the "trimtab" leverage points, where a small investment has a big impact.  To understand virtuous and vicious cycles that can lead to monstrous growth or failure. To find unintended consequences.
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The future of data science and AI (and DI) brings women back into the tech workforce

"Women are going to lead this
next phase just like they led the early development of
software engineering"
Lorien Pratt's insight:

Thank you to #wids2019, @sap, and our #diversity allies for the opportunity to pay it forward to the next generation of brilliant woman data scientists.

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Linear versus nonlinear thinking in recent events

In this short video, Kedge's Frank Spencer talks about the need to adopt a new mental model if we are going to navigate the future.

Lorien Pratt's insight:

We must shift our mindsets from reduction to synthesis, from linear to nonlinear thinking, and more. Frank Spencer reflects on recent events which highlight this challenge.

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1/2-hour keynote in Palo Alto by Lorien Pratt: Decision Intelligence: How AI and ML are Evolving to the Next Level

1/2-hour keynote in Palo Alto by Lorien Pratt: Decision Intelligence: How AI and ML are Evolving to the Next Level | Decision Intelligence News | Scoop.it
Lorien Pratt's insight:

I'll be presenting a half-hour keynote as part of the SAP Women in Data Science Silicon Valley 2019 event on March 5. 

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Retail AI: Death By A Thousand Cuts

Retail AI: Death By A Thousand Cuts | Decision Intelligence News | Scoop.it

"When people say 'AI software', what do they really mean? This is a critically important question to understanding the impact that AI can have on the retail industry (on any industry, really), and a significant source of the gap that exists between AI hype and reality.

 

"While McKinsey focuses on AI types of classification, prediction, and generation, I’ve found it more useful to look at natural language processing, computer vision, and prediction. But each of these are umbrella terms for lots of what amount to 'micro-capabilities', which is an important limitation when thinking about AI. Artificial General Intelligence (AGI, but sometimes referred to as Human-Level Artificial Intelligence) is at least 20 years off, according to experts in the field, and possibly 50 years. And both ends of the estimate acknowledge that some kind of breakthrough that we have not currently achieved would have to happen before we can get there – there is no current known path to achieving AGI."

Lorien Pratt's insight:

AI systems can be applied to human interactions, but the key to teaching machines how to intuitively read human behavioral cues still remains unknown. After all, how can an algorithm accurately interpret variables as subjective as speech inflection? With this in mind, how can 'Human-Level Artificial Intelligence' be viable within our lifetimes if we don't know how to get there yet? The capabilities of AI technologies are expanding at an incredible pace, but there is still so much we need to discover and explore before our machines will be able to know how we feel by the sound of our voice.

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10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel | Decision Intelligence News | Scoop.it

"For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

 

"89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example."

Lorien Pratt's insight:

Omnichannel is a strategy that uses a variant of systems thinking to understand the desires and needs of customers in various retail settings, as well as in many B2B environments. Machine learning and AI are providing greater understanding of these desires and needs and assisting in the decision-making necessary to provide customers with an optimal experience, thereby increasing satisfaction and sales while reducing unpleasant experiences and customer churn.

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The ML Surprise

The ML Surprise | Decision Intelligence News | Scoop.it
When I talk to practitioners that have had a lot of experience building and scaling these ML systems inside Google, I hear a very different story. Based on these conversations, optimizing an ML algorithm takes much less relative effort, but collecting data, building infrastructure, and integration each take much more work. The differences between expectations and reality are profound.
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Christophe Acher's curator insight, February 10, 6:04 AM
Fair comment from Lorien. That's one of the reason we designed VANTIQ: to orchestrate the flow of data (most of the time realtime or stream) and ML into event driven apps independent from the infrastructure.
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Your Deep-Learning-Tools-for-Enterprises Startup Will Fail

Your Deep-Learning-Tools-for-Enterprises Startup Will Fail | Decision Intelligence News | Scoop.it

"Given that companies have broader needs that go beyond the application area of deep learning, you have to consider the technical and organizational complexity being imposed on your enterprise users by having to adopt yet another set of tools."

Lorien Pratt's insight:

Figure 3, shown here, speaks primarily to the technical aspects of providing deep-learning tools for the enterprise. What's missing upstream of the applications is the business analysis and understanding that must be brought to bear on the issue(s) for which you're seeking resolution, e.g. "what are we trying to accomplish, why do we need it, how best can we implement it?

 

As well, there's a huge downstream component missing, and that's the decision analysis that requires more wisdom than mere deep learning can provide. Make sure you're contemplating not merely the software stack, but also the entire workflow.

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The Enterprise AI/DI Risk Management Challenge (Part 1)

The Enterprise AI/DI Risk Management Challenge (Part 1) | Decision Intelligence News | Scoop.it
As enterprise companies embrace AI, the potential for harm is greater than it is for consumer applications. Here are some examples and potential implications.
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No, the robots aren't coming to kill you

"I think as long as people view it as entertainment, and say that an artificial [general] intelligence is no more real than a vampire or a zombie, it's good clean fun.  ... [Regarding practical, task-focused AI:] There's so much more good than bad, and that's the vision for AI that I see moving forward."

Lorien Pratt's insight:

THANK YOU, Kristin Lennox deconstructs the reality of tech in one AI movie after another: 2001, HER, Ex Machina, War Games.  Please friends, distinguish science fiction from science fact. This is great technology, which lends itself to scifi like no other, but it's just a machine, and a much less advanced machine than movies will have you believe. 

 

 

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Where will the greatest AI value be in the next 3-5 years?

"...I'm seeing us hitting a point of diminishing returns from the current AI use cases...why are we predicting things with AI ...if AI doesn't lead to some action in the real world then what's the point after all?  And what happens before that action is some decision we make, and so almost every AI system that we look at sits within a larger decision context"
 
 
Lorien Pratt's insight:

Where will the greatest #artificialintelligence value be in the next 3-5 years? #futureinreivew2018 #decisionintelligence @daggatt Tom Conte Joseph Smarr

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Machine Learning Software Engineering: Top Five Best Practices

Machine Learning Software Engineering: Top Five Best Practices | Decision Intelligence News | Scoop.it

"Machine learning project success depends on ML-specific software engineering best practices"

Lorien Pratt's insight:

Fails to avoid, at all costs

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Machine Learning Classic Mistakes, Best Practices, and Epic Fails

Machine Learning Classic Mistakes, Best Practices, and Epic Fails | Decision Intelligence News | Scoop.it

"These mistakes form patterns, which if avoided can save years from a project and millions of dollars of unnecessary cost. "

Lorien Pratt's insight:

Machine Learning Classic Mistakes, Best Practices, and Epic Fails (and how to avoid them): the first in a series.

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