Must We be Good to Have a Good Future?

The renowned Futurist Gerd Leonhard, in this short and impactful video, says in order to create a good future - we must be good.  He suggests four focus areas for the future: people, prosperity, purpose and planet.  If you agree with Gerd, then the first question is "What is good?  Secondly, "How do we become good?"  And, thirdly, "How do we use that good to create the future we all want?"

A satisfying definition of "good" for me is something that promotes happiness, community well-being, is loving, pleasing, admirable, kind, desirable and virtuous.  Once we figure out how to become these things ourselves, we must embed them in our technology in the form of AI, to help us shape a "good" future.

As more of our daily activities and interactions involve artificial intelligence, we will want our interfaces and communications with AI (digital assistants, chatbots and robots) to feel and be "good."  We will want AI to make accurate, consistent and "good" decisions, and then to execute "good" actions.  Training AI to be good and act good is a real challenge.  These kinds of philosophical, moral and inspired traits and actions are not AI's strong suite.  Now that I am writing this I realize they aren't particularly the strong suite of humans either.

I can imagine a scenario where an algorithm processes data that suggests three equally logical actions.  The final choice, however, is determined by which option is most heavily weighted to the "good."  Which one of us is going to determine the "good" weight?

The obvious problem with this scenario is us humans can't agree on what is good, or to what degree it is good.  Feeding and sheltering homeless families and giving them medical assistance is considered good by some, and bad by others.  Saving millions of lives by vaccinating people is considered good by some and bad by others.

Our artificial intelligence powered digital assistants, chatbot and robots are all awaiting their instructions about how they can help create a "good" future.  What should I tell them?  

Watch the latest on Oracle's digital assistants, chatbots and artificial intelligence here in my interview with Oracle expert Suhas Uliyar.

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Kevin Benedict

Partner | Futurist at TCS

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***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I work with and have worked with many of the companies mentioned in my articles.

Will You Trust a Robot?

Jeff Bezos and team launched 66.5 miles into suborbital space on a rocket ship with no pilot this week.  The rocket ship operated autonomously using sensors and artificial intelligence.  That takes trust. They had to believe in the science and that the AI system would get them safely there and back.  They had to have trust in the scientists, programmers, engineers, physicists and chemists.  They had to have trust in the math and physics.  They had to trust the coding and formulas used in the algorithms.  They had to trust in the data coming from the sensors.  Although there were likely many failures along the way, they trusted the process - the scientific method.

The Future of Managers

Managers must explain a lot of things.  Early in my career I managed a team of six IT experts responsible for EDI and other forms of business-to-business data exchanges with suppliers.  Our data, from planning and manufacturing systems, was shared with our suppliers' to support the just-in-time manufacturing of electronics.  Our senior leadership would often ask us to defend our data, yet we often struggled to explain where it originated from or how the numbers were generated.  The data we were using came from a figurative "black box."  We received it without explanation.  That of course was an untenable position for a manager.

In the near future managers will increasingly depend on artificial intelligence for assistance, and hopefully it will be explainable AI to avoid the challenges I faced.  Explainable AI (XAI) is artificial intelligence in which the results, and the logic and data used, can be understood by humans.  Understanding how the system works is critical to establishing trust.  

As AI becomes integrated into more and more businesses and IT systems, the role it plays will become ever more critical.  Any questions about why an AI system made a particular decision or took an action must be able to be quickly deciphered, explained and adjusted if necessary.  Having trust in the AI system is a critical first step for managers to open up and use it for an expanding list of tasks.

AI systems, implemented correctly can be a manager's right hand.  Common benefits of AI are:

• Reduced human errors
• Is available 24x7x365
• Can complete mundane, repetitive and routine administrative work without distraction
• Can scale
• Can make faster decisions, and take faster actions based upon established business processes 
• Can find solutions and innovations faster by analyzing patterns within oceans of data

In a report by HBR, it was found that 54% of a manager's time is usually spent on administrative tasks - tasks well suited for AI assistance.  If AI can take over these tasks it could free up managers to spend more time on the things humans are best at including applying their knowledge of organizational history and culture, empathy, ethical reflection, judgement, creativity, experiments, innovation, strategy, discretion, experience and improvisation. 

In the HBR report there are five pieces of advice for managers of the future:

1. Leave administration to AI
2. Focus on judgement work
3. Treat intelligent machines/agents as colleagues
4. Work like a designer
5. Develop your social skills and networks

I can imagine a scenario in the near future where there will be an organizational chart of robots, robot workers, managers and robot executives.  Each using XAI to explain how they are managing the robots, tasks and operations under their responsibility.  I guess that means we humans will need to figure that out first.

Read more on AI here:

• Redemptive AI, Biases and the American Dream

• The Future of AI Starts Yesterday

Kevin Benedict

Partner | Futurist at TCS

View my profile on LinkedIn

Follow me on Twitter @krbenedict

Join the Linkedin Group Digital Intelligence

***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I work with and have worked with many of the companies mentioned in my articles.

Redemptive AI, Biases and the American Dream

The American Dream is the national ethos of the United States - a set of ideals which includes the opportunity for prosperity, success and access to upward social mobility for individuals and their families.  The last thing any of us want is to invent and deploy technologies that are barriers to this dream.

Artificial intelligence (AI), configured wrongly, can become a barrier.  Many companies today are now using AI to interview candidates, interpret their potential, and rank them from best to worst.  How emotive a person's face muscles are, their use of the english language, and the sophistication of their vocabulary are now all being used to select or reject job candidates.

One can only imagine how difficult AI interviews are for immigrants and refugees looking for their first big opportunity.  Facial expressions are often influenced by culture.  English being a second, third or fourth language could present all kinds of barriers to getting past the AI gatekeepers and into the land of opportunity. 

The Future of AI Starts Yesterday

"The best time to start implementing artificial intelligence in the future was yesterday." 

                ~Kevin Benedict

Artificial intelligence (narrow AI) today is beyond its proof-of-concept phase - as it is already proven and delivering tactical value in many well documented areas: 

• Reduction in human error
• Available 24x7x365
• Improved quality
• Improved productivity
• Improved efficiencies
• Able to dependably complete mundane, repetitive and routine jobs
• Makes faster decisions and taking quicker actions

Artificial intelligence, although still in its infancy, is already delivering impressive results and competitive advantages for those prepared.  The preparation, however, is not insignificant and requires much work including: