Decision Intelligence News

Mark your calendars for Dr. Lorien Pratt’s Institute for the Future talk (presented last week) being broadcast on C-SPAN2 Sunday, February 9 at 8pm PT.

Dr. Pratt introduces her book Link (www.linkthebook.com) which describes the new field of Decision Intelligence (DI). Many people think that DI is the next generation of AI, but it also goes considerably beyond that context. She explains how DI is the mechanism by which humans will work hand-in-hand with technology to solve a new class of the most complex problems we face, like climate change and inequality. Pratt shows how decisions can be supported by AI, understood more effectively, and how the use of social messages fits into effective decision making. The broadcast consists of 1/2 Pratt's  presentation and the second half hour of Q&A hosted by Joy Mountford, of Ford’s Research Innovation Center, Palo Alto (RICPA) and the NextNow network.  During the Q&A, questions from a senior technologist audience, many of whom are also involved in DI-related initiatives, create a fascinating interchange.

The complex, adaptive system…can’t be understood by reducing it to smaller parts, it must be viewed holistically. 

The Power of Decision Intelligence  

Finally, an AI platform that takes decision making to a completely new level  

Periculum Labs has pioneered advanced artificial intelligence technologies which enable organizations to optimize complex and high-risk decision-making. Our proprietary AI platform injects real-time intelligence into decisions – at scale. Autonomous transportation organizations can increase operational capacity, safely and sustainably. 

Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle. AI Marketplaces, Reinforcement Learning, Decision Intelligence, AI Cloud Services, Data Labeling, and Annotation Services, and Knowledge Graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven.

"AI, BI, CI. Artificial Intelligence, Business Intelligence, Collaborative/Collective Intelligence. What’s next? As an AI/DI intern at Quantellia this past summer, I have learned that Decision Intelligence (DI) spans all of the above, and seeks to crystallize a new field of responsible use of AI systems.

"But what is Decision Intelligence?

"Throughout my experiences contributing to different projects and interviewing Lorien Pratt, Quantellia’s Co-founder and Chief Scientist, I have come to understand that DI, put simply, helps people make better decisions, by bridging AI and ML with process methodology, visual mapping, and design thinking."

"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."

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.

"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."

"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."

The human-computer partnership is the future. Decision Intelligence (DI) gives us a formal way to integrate AI into existing decision making. Just having a structured way of thinking through complex decisions can really move the needle, and especially on big problems, even five percent can save lives or save money.

Since 1976, the Association of American Publishers’ awards for Professional and Scholarly Excellence (PROSE Awards) have recognized publishers who produce books, journals, and digital products of extraordinary merit that make a significant contribution to a field of study in the humanities, biological and physical sciences, reference and social sciences.

You advocate using decision intelligence to take AI to the “next level” to help improve conditions in the world. Can you provide some examples of what you envision here? 

• Instead of just using ML to identify where a climate-change-driven flood is likely to impact a country, use DI to choose between several prevention / remediation measures to minimize the cost of property and lives lost. 

• Instead of just using ML to analyze the movement of asteroids, surround the ML model with a DI model to decide what to do to deflect one that is aiming for Earth (this is the nasa.ai's "deflector selector").

Why does HR Need Decision Intelligence? 

HR is a dynamic and strategic function in today’s business landscape. The decisions in HR significantly influence the future of business organizations, implying the importance of thoughtful decision-making. With the right support from decision intelligence, HR decisions can increase operational efficiencies for organizations on multiple levels. 

"New graduates in the business development field have a wealth of career paths available to them. Some industries, like tech, are always booming, and job seekers may naturally gravitate toward those areas. However, there are plenty of other industries that can provide incredible work opportunities and growth potential for recent grads.

"We asked members of Forbes Business Development Council about the hottest industries for new business development professionals to explore right now."

". . . [B]uilding a true AI is likely to require investing years of work into machines that won’t solve a single business problem. Search engines and image recognizers make a lot of money but don’t think like people. Any true AI is likely to spend years stumbling around and making mistakes before it does anything useful, just like we do. Remember, training a human intelligence (the best kind we know about) takes about twenty years per installation, and that’s with millions of years of evolutionary advantage baked in. This means that for the machine learning problems that really count, money is likely to stay tight.

"So, you might wonder, if all this is true, why get excited about the upcoming AI winter? Simple. This is when all that expertise we’ve built up during the current boom will start filtering out into other fields. The dirty secret of the current AI boom is that not everyone is trying to build a self-driving car. In fact, for most businesses, the problems they need to solve are far more pragmatic. Often, companies need to make sense of the data they’re already sitting on and those datasets are usually too small and too messy for deep learning to be much use. Or they need to figure out how to take dodgy intermittent data from new sources like IoT sensors and make it reliable."

"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."

"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?"