Tag Archives: Artificial Intelligence

Generation AI Replacing Generation Z

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Generation AI Replacing Generation Z

by Braden Kelley

The boundary lines between different named generations are a bit fuzzy but the goal should always be to draw the boundary at an event significant enough to create substantial behavior changes in the new generation worthy of consideration in strategy formation.

I believe we have arrived at such a point and that it is time for GenZ to cede the top of strategy mountain to a new generation I call Generation AI (GenAI).

The dividing line for Generation AI falls around 2014 and the people of GenAI are characterized by being the first group of people to grow up not knowing a world without easy access to generative artificial intelligence (AI) tools that begin to transform their interactions with our institutions and each other.

We have already seen professors and teachers having to police AI-generated school essays, while the rest of us are trying to cope with frighteningly realistic deep fake audio and video. But what other impacts on people’s behavior will we see as a result of the coming ubiquity of artificial intelligence?

It is important to remember that generative artificial intelligence is not really artificial intelligence but collective intelligence informed by what we the people have contributed to the training/reference set. As such these large language models are predicting the next word or combining existing content based on whatever training set they are exposed to. They are not creating original thought.

Generative AI is being built into nearly all of our existing software and cloud tools, and GenAI will grow up only knowing a reality where every application and web site they interact with will have an AI component to it. Generation AI will not know a time where they cannot ask an AI, in the same way that GenZ relies on social search, and Gen X and Millenials assume search engines hold their answers.

Our brains are changing to focus more on processing and less on storage. These changes make us more capable, but more vulnerable too.

This new AI technology represents a double-edge sword and its effects could fall on either edge of the sword in different areas:

Option 1 – Best Case

  • Generative AI will amplify creativity by encouraging recombination of existing images, text, audio and video in new inspiring ways using the outputs of AI as inputs into human creativity

Option 2 – Worst Case

  • Generative AI will reduce creativity because people will become reliant on using artificial intelligence to create, creating an echo chamber of new content only created from existing content, leading to AI outputs becoming the only outputs and a world where people spend more time interacting with AI’s than with other people

Which of these two options on the impact of AI reliance do you see as the most likely in the areas where you focus?

How do you see Generation AI impacting the direction of societies around the world?

Are you planning to add Generation AI to your marketing strategies and strategic planning for 2024 or beyond?

Reference

For reference, here is timeline of previous American generations according to an article from NPR:

Though there is a consensus on the general time period for generations, there is not an agreement on the exact year that each generation begins and ends.

Generation Z – Born 2001-2013 (Age 10-22)

These kids were the first born with the Internet and are suspected to be the most individualistic and technology-dependent generation. Sometimes referred to as the iGeneration.

EDITOR’S NOTE: This description is erroneous, the differentiating factor of GenZ is that they experienced the rise of social media.

Millennials – Born 1980-2000 (Age 23-43)

They experienced the rise of the Internet, Sept. 11 and the wars that followed. Sometimes called Generation Y. Because of their dependence on technology, they are said to be entitled and narcissistic.

Generation X – Born 1965-1979 (Age 44-58)

They were originally called the baby busters because fertility rates fell after the boomers. As teenagers, they experienced the AIDs epidemic and the fall of the Berlin Wall. Sometimes called the MTV Generation, the “X” in their name refers to this generation’s desire not to be defined.

EDITOR’S NOTE: GenX also experienced the rise of the personal computer and this has influenced their parenting of a large portion of Millenials and GenZ

Baby Boomers – Born 1943-1964 (Age 59-80)

The boomers were born during an economic and baby boom following World War II. These hippie kids protested against the Vietnam War and participated in the civil rights movement, all with rock ‘n’ roll music blaring in the background.

Silent Generation – Born 1925-1942 (Age 81-98)

They were too young to see action in World War II and too old to participate in the fun of the Summer of Love. This label describes their conformist tendencies and belief that following the rules was a sure ticket to success.

GI Generation – Born 1901-1924 (Age 99+)

They were teenagers during the Great Depression and fought in World War II. Sometimes called the greatest generation (following a book by journalist Tom Brokaw) or the swing generation because of their jazz music.

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Eliminating 100% of Live Customer Service is a Mistake!

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Eliminating 100% of Live Customer Service is a Mistake!

GUEST POST from Shep Hyken

You need help. You call customer support. Nobody’s home!

Actually, somebody is there. They just aren’t taking support calls. Someone at home—as in a corporate office—has decided to eliminate live, human-to-human customer support, pushing the customer to a digital option such as a chatbot, frequently asked questions page, etc.

My opinion is that this could happen in the distant future, but I can’t imagine that in the next few years there will be 100% digital and AI automated customer support. And here’s why. If all you are is an automated company, you have no way to emotionally connect with your customers. That means your customers have only one way to compare you to direct competition that sells exactly what you do, and that is price.

At that point, the only way to keep your customer is to always have the lowest price, and that is typically not a viable long-term strategy.

Recently I wrote about Frontier Airlines’ decision to drop traditional live phone support, and the reviews have not been good. That said, I give them credit for a bold move that may be just a little ahead of its time—and time will tell. Maybe the reactions are from initial shock. Perhaps there have been glitches that can be fixed for a smoother experience in the near future. We’ll know in six months. If the current reactions continue, at best, some changes will be made, provided the airline wants to stay in business.

I had a chance to interview Paulo Almeida, the CEO of Clientscape, on Amazing Business Radio. We talked about the possibility of AI and automation taking over the contact center. We briefly discussed Frontier Airlines, but more importantly, Almeida articulated the perfect answer to my question:

How do you feel about complete elimination of a human-to-human customer support department?

Almeida responded, “If you’re working in an industry that chooses to automate everything, you can potentially become a commodity. If that is what a company wants to do, the only difference from one company to the next will be what they charge. If that’s the only way a customer makes a decision, the company will go bankrupt!”

“It is not a sustainable financial model. It’s the human factor that makes the difference. It’s about giving the customer the care they deserve. That’s a way to differentiate. For example, Apple may make some of the best products, but they also have some of the best support. If their reputation for support goes away, they will no longer be perceived as having the best product. They will also lose pricing power. When that disappears, they could be on the path to failure. They will lose customers, and the cost to get them back will be extravagant.”

Almeida used a powerful word to summarize a decision to eliminate a human connection, and that word is bankrupt. I can buy into this for some companies, but there will always be exceptions.

People have said, “What about Amazon?!” Yes, Amazon is a digital company, and it has great digital customer support. However, if you need to talk to someone, you can. It’s a last resort, but when you do so, it’s typically a very pleasant experience. Amazon knows how far it can go with automation before it has to say, “It’s time to talk to one of our reps.”

Many products and services are becoming automated. To Almeida’s point, 100% automation is a mistake. Without a human-to-human relationship, how can you create an emotional connection? How can you differentiate yourself from other automated companies? You can’t. You’re a commodity.

More automation and AI technology are in our future. It shouldn’t surprise you that at some point in time planes will be flown by computers, not pilots. We’re already seeing self-driving trucks moving across the country. Companies like Tesla, Google and others are investing tens of billions into autonomous self-driving vehicles (even if they are still a long way from success). Amazon and Walmart are betting on alternative delivery methods that include drones and robots. And yes, some customer service functions are being handled by automation and artificial intelligence (AI).

We can’t fight progress. I love seeing products and services get better through automation. But I’m concerned about the companies and brands that are distancing themselves from their customers by not letting them connect with customer support people, who are also brand ambassadors for the company.

If the leadership of a company thinks the customer support agents’ only role is to fix a problem, then shame on them. Your agents can do much more. If they handle a call well, they can confirm that the customer made the right decision to do business with you and give them the confidence to do even more business. So, at least for now, don’t miss that opportunity. Don’t make the mistake of 100% elimination of live customer support.

Image Credit: Pixabay

This article originally appeared on Forbes.com

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Sustaining Imagination is Hard

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by Braden Kelley

Recently I stumbled across a new Royal Institute video of Martin Reeves, a managing director and senior partner in BCG’s San Francisco office. Martin leads the BCG Henderson Institute, BCG’s vehicle for exploring ideas from beyond the world of business, which have implications for business strategy management.

I previously interviewed Martin along with his co-author Dr. Jack Fuller in a post titled ‘Building an Imagination Machine‘. In this video you’ll find him presenting content along similar themes. I think you’ll enjoy it:

Bonus points to anyone who can name this napkin sketch in the comments.

In the video Martin explores several of the frameworks introduced in his book The Imagination Machine. One of the central tenets of Martin’s video is the fact that sustaining imagination is hard. There are three core reasons why this is so:

  1. Overspecialization – As companies grow, jobs become increasingly smaller in scope and greater in specialization, leading to myopia as fewer and fewer people see the problems that the company started to solve in the first place
  2. Insularity – As companies grow, the majority of employees shift from being externally facing to being internally facing, isolating more and more employees from the customer and their evolving wants and needs
  3. Complacency – As companies become successful, predictably, the successful parts of the business receive most of the attention and investment, making it difficult for new efforts to receive the care and feeding necessary for them to grow and dare I say – replace – the currently idolized parts of the business

I do like the notion Martin presents that companies wishing to be continuously successful, continuously seek to be surprised and invest energy in rethinking, exploring and probing in areas where they find themselves surprised.

Martin also explores some of the common misconceptions about imagination, including the ideas that imagination is:

  1. A solitary endeavor
  2. It comes out of nowhere
  3. Unmanageable

And finally, Martin puts forward his ideas on how imagination can be harnessed systematically, using a simple six-step model:

  1. Seduction – Where can we find surprise?
  2. Idea – Do we embrace the messiness of the napkin sketch? Or expect perfection?
  3. Collision – Where can we collide this idea with the real world for validation or more surprise?
  4. Epidemic – How can we foster collective imagination? What behaviors are we encouraging?
  5. New Ordinary – How can we create new norms? What evolvable scripts can we create that live inbetween the 500-page manual and the one-sentence vision?
  6. Encore – How can we sustain imagination? How can we maintain a Day One mentality?

And no speech in 2023 would be complete without some analysis of what role artificial intelligence (AI) has to play. Martin’s perspective is that when it comes to the different levels of cognition, AI might be good at finding patterns of correlation, but humans have more advanced capabilities than machines when it comes to finding causation and counterfactual opportunities. There is an opportunity for all of us to think about how we can leverage AI across the six steps in the model above to accelerate or enhance our human efforts.

To close, Martin highlighted that when it comes to leading re-imagination, it is important to look outward, to self-disrupt, to establish heroic goals, utilize multiple mental models, and foster playfulness and experimentation across the organization to help keep imagination alive.

p.s. If you’re committed to learning the art and science of getting to the future first, then be sure and subscribe to my newsletter to make sure you’re one of the first to get certified in the FutureHacking™ methodology.

Image credits: Netflix

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Top 10 Human-Centered Change & Innovation Articles of April 2023

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Top 10 Human-Centered Change & Innovation Articles of April 2023Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are April’s ten most popular innovation posts:

  1. Rethinking Customer Journeys — by Geoffrey A. Moore
  2. What Have We Learned About Digital Transformation Thus Far? — by Geoffrey A. Moore
  3. Design Thinking Facilitator Guide — by Douglas Ferguson
  4. Building A Positive Team Culture — by David Burkus
  5. Questions Are More Powerful Than We Think — by Greg Satell
  6. 3 Examples of Why Innovation is a Leadership Problem — by Robyn Bolton
  7. How Has Innovation Changed Since the Pandemic? — by Robyn Bolton
  8. 5 Questions to Answer Before Spending $1 on Innovation — by Robyn Bolton
  9. Customers Care About the Destination Not the Journey — by Shep Hyken
  10. Get Ready for the Age of Acceleration — by Robert B. Tucker

BONUS – Here are five more strong articles published in March that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last three years:

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Unlocking the Power of Imagination

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How Humans and AI Can Collaborate for Innovation and Creativity

Unlocking the Power of Imagination

GUEST POST from Teresa Spangler

Imagination is not only the uniquely human capacity to envision that which is not, and, therefore, the foundation of all invention and innovation. In its arguably most transformative and revelatory capacity, it is the power that enables us to empathize with humans whose experiences we have never shared. ~J.K. Rowling

Part I of a 3 Part Series: Simple Strategies

Imagination has always been crucial in human innovation, creativity, and problem-solving. It enables us to envision possibilities beyond the present and find solutions to problems that do not exist yet. 

However, with Artificial Intelligence (AI) advancements, humans must develop their imaginative skills to stay competitive. To achieve that, it is important to understand the eight subsections of imagination – Effectuative, Intellectual or Constructive, Imaginative Fantasy, Empathy, Strategic, Emotional, Dreams, and Memory Reconstruction. Each of these subsections plays a crucial role in the human imagination, and as AI becomes more sophisticated, it will challenge humans to improve their imaginative abilities in each of these areas.

Understanding the Eight Subsections of Imagination

Effectuative Imagination: Effectuative imagination is the ability to imagine the consequences of one’s actions. It enables individuals to plan, make decisions, and predict outcomes. For example, a CEO may use effectuative imagination to anticipate the impact of a new product launch on the company’s financials.

To improve effectuative imagination, humans can:

  • Think about potential consequences before making decisions
  • Analyze data and use it to predict possible outcomes
  • Consider alternative scenarios before deciding on a course of action

Intellectual or Constructive Imagination: Intellectual or constructive imagination is the ability to imagine abstract concepts and ideas. It allows individuals to envision new inventions, theories, and ways of thinking. For example, a scientist may use constructive imagination to develop a new theory that explains a complex natural phenomenon.

To improve intellectual or constructive imagination, humans can:

  • Challenge assumptions and think outside the box
  • Experiment with new ideas and concepts
  • Combine existing ideas and concepts to create new ones

Imaginative Fantasy:  Imaginative fantasy is the ability to create and imagine fantastical worlds and stories. It enables individuals to escape from reality and explore the limits of their imaginations. For example, a writer may use imaginative fantasy to create a new universe for their story.

To improve imaginative fantasy, humans can:

  • Engage in creative activities such as writing, drawing, or painting
  • Read books or watch movies that take them to new worlds
  • Use their imagination to create new stories or scenarios

Empathy:  Empathy is the ability to imagine and understand the emotions and experiences of others. It enables individuals to connect with others more deeply and be more compassionate. For example, a therapist may use empathy to understand their client’s emotions and experiences.

To improve empathy, humans can:

  • Practice active listening and ask questions to understand others better
  • Imagine themselves in other people’s shoes
  • Practice kindness and compassion towards others

 Strategic Imagination:  Strategic imagination is the ability to imagine and plan for the future. It enables individuals to anticipate potential challenges and opportunities and to develop effective strategies. For example, a business leader may use strategic imagination to create a long-term growth plan for their company.

To improve strategic imagination, humans can:

  • Develop a long-term vision for their personal or professional life
  • Identify potential challenges and opportunities and develop strategies to address them
  • Use data and analysis to inform their decisions

Emotional Imagination:  Emotional imagination is the ability to imagine and experience a wide range of emotions. It enables individuals to empathize with others and connect with their emotions. For example, an actor may use emotional imagination to convincingly portray a character’s emotions.

To improve emotional imagination, humans can:

  • Practice mindfulness and self-awareness to understand their own emotions better
  • Engage in creative activities such as writing or acting to explore different emotions
  • Practice empathy to understand other people’s emotions better

Dreams:  Dreams are a manifestation of our subconscious mind and imagination. They enable us to explore our deepest desires, fears, and aspirations. Dreams are also a way for our brains to process and consolidate memories. For example, a person may have a dream about an experience that helps them understand and process their emotions.

To improve dreams and imagination, humans can:

  • Keep a dream journal to record and reflect on their dreams
  • Practice lucid dreaming to control and direct their dreams
  • Engage in creative activities such as writing or drawing to explore dream worlds and scenarios

Memory Reconstruction:  Memory reconstruction is remembering past events and experiences and using our imagination to fill in gaps and create a coherent narrative. It enables individuals to sense their past and create a personal identity. For example, a person may reconstruct childhood memories to understand how past experiences have shaped who they are today.

To improve memory reconstruction and imagination, humans can:

  • Reflect on past experiences and try to understand how they have influenced their life
  • Engage in creative activities such as writing or drawing to explore memories and create new narratives
  • Practice mindfulness to become more aware of their thoughts and feelings

How AI May Challenge Us to Become More Imaginative

As AI becomes more sophisticated, it will challenge humans to improve their imaginative abilities in each of the eight subsections of imagination. Working alongside AI can spark us to new levels of thinking. Consider AI as your partner in growing your own imagination, your creativity and your abilities to be more inventive.

Examples:

  • Effectuative Imagination: AI can analyze data and provide insights humans can use to make better decisions.
  • Intellectual or Constructive Imagination: AI can identify patterns and connections between data sets that humans may not have noticed, leading to new ideas and theories.
  • Imaginative Fantasy: AI can generate creative ideas and scenarios that humans may not have thought of independently. However, imaginative people can raise the bar on creativity by using these AI-generated fantasies.
  • Empathy: AI can analyze social media data and sentiment analysis to understand how people are feeling and to predict future trends.
  • Strategic Imagination: AI can analyze market data and economic indicators to identify potential challenges and opportunities for businesses and organizations.
  • Emotional Imagination: AI can analyze facial expressions and tone of voice to understand people’s emotions and to provide personalized recommendations and support.
  • Dreams: AI can use generative models to create realistic dream scenarios and to help individuals explore and process their subconscious thoughts and emotions.
  • Memory Reconstruction: AI can analyze large data sets and identify patterns and connections humans may not have noticed, leading to new insights and narratives.

Working Alongside AI to Unlock Human Imagination

AI is not a threat to human imagination but rather a tool that can help us unlock our full imaginative potential. By collaborating with AI, humans can gain new insights and perspectives that inform their imaginative processes. Here are some strategies for working alongside AI to unlock human imagination:

  • Use AI-powered tools to analyze data and identify patterns and connections that can inform imaginative processes.
  • Collaborate with AI-powered systems to generate new ideas and scenarios.
  • Use virtual and augmented reality tools to explore new environments and possibilities.
  • Leverage AI-powered chatbots and virtual assistants to provide personalized support and recommendations.
  • Participate in cross-functional teams that include both humans and AI-powered systems.

How Companies Can Support Employee Imagination

Companies have a critical role in supporting their employees’ imaginative skills. By doing so employees will be come more engaged and productive.  Here are a few strategies that companies can use to support employee imagination:

  • Encourage creativity by providing opportunities for employees to explore new ideas and experiment with new approaches.
  • Foster diversity and inclusion by promoting diverse perspectives and experiences and creating a safe and inclusive work environment.
  • Invest in technology and tools that support imaginative work, such as AI-powered systems, virtual and augmented reality tools, and collaborative platforms.
  • Provide learning and development opportunities focusing on imaginative skills, such as creativity, emotional intelligence, and strategic thinking.
  • Encourage collaboration by creating cross-functional teams and promoting a culture of collaboration.

Imagination is essential for the future of innovation and human productivity. The eight subsections of imagination provide a framework for understanding and developing imaginative abilities. As AI becomes more sophisticated, it will challenge humans to improve their imaginative skills in each of these areas. Humans can unlock new ideas and solutions that drive innovation and progress by developing imaginative skills and working alongside AI.

Moreover, individuals can develop their imaginative skills by embracing change, fostering curiosity, developing a growth mindset, practicing mindfulness, collaborating with AI, exploring new forms of art and creativity, practicing divergent thinking, and fostering the eight subsections of imagination.

Companies have a crucial role in supporting their employees’ imaginative skills. They can encourage creativity, foster diversity and inclusion, invest in technology and tools supporting imaginative work, provide learning and development opportunities focusing on imaginative skills, and encourage collaboration.

In the age of AI, humans must strive to maintain their unique skills and abilities. Imagination is a human trait that AI cannot replicate. Developing our imaginative skills and working alongside AI can unlock new opportunities and create a better future for ourselves and the world.

Image credit: Pixabay

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Top 10 Human-Centered Change & Innovation Articles of March 2023

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Top 10 Human-Centered Change & Innovation Articles of March 2023Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are March’s ten most popular innovation posts:

  1. Taking Care of Yourself is Not Impossible — by Mike Shipulski

  2. Rise of the Prompt Engineer — by Art Inteligencia

  3. A Guide to Effective Brainstorming — by Diana Porumboiu

  4. What Disruptive Innovation Really Is — by Geoffrey A. Moore

  5. The 6 Building Blocks of Great Teams — by David Burkus

  6. Take Charge of Your Mind to Reclaim Your Potential — by Janet Sernack

  7. Ten Reasons You Must Deliver Amazing Customer Experiences — by Shep Hyken

  8. Deciding You Have Enough Opens Up New Frontiers — by Mike Shipulski

  9. The AI Apocalypse is Here – 3 Reasons You Should Celebrate! — by Robyn Bolton

  10. Artificial Intelligence is Forcing Us to Answer Some Very Human Questions — by Greg Satell

BONUS – Here are five more strong articles published in February that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last three years:

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Artificial Intelligence is Forcing Us to Answer Some Very Human Questions

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Artificial Intelligence is Forcing Us to Answer Some Very Human Questions

GUEST POST from Greg Satell

Chris Dixon, who invested early in companies ranging from Warby Parker to Kickstarter, once wrote that the next big thing always starts out looking like a toy. That’s certainly true of artificial intelligence, which started out playing games like chess, go and playing humans on the game show Jeopardy!

Yet today, AI has become so pervasive we often don’t even recognize it anymore. Besides enabling us to speak to our phones and get answers back, intelligent algorithms are often working in the background, providing things like predictive maintenance for machinery and automating basic software tasks.

As the technology becomes more powerful, it’s also forcing us to ask some uncomfortable questions that were once more in the realm of science fiction or late-night dorm room discussions. When machines start doing things traditionally considered to be uniquely human, we need to reevaluate what it means to be human and what is to be a machine.

What Is Original and Creative?

There is an old literary concept called the Infinite Monkey Theorem. The basic idea is that if you had an infinite amount of monkeys pecking away an infinite amount of keyboards, they would, in time, produce the complete works of Shakespeare or Tolstoy or any other literary masterpiece.

Today, our technology is powerful enough to simulate infinite monkeys and produce something that looks a whole lot like original work. Music scholar and composer David Cope has been able to create algorithms that produce original works of music which are so good that even experts can’t tell the difference. Companies like Narrative Science are able to produce coherent documents from raw data this way.

So there’s an interesting philosophical discussion to be had about what what qualifies as true creation and what’s merely curation. If an algorithm produces War and Peace randomly, does it retain the same meaning? Or is the intent of the author a crucial component of what creativity is about? Reasonable people can disagree.

However, as AI technology becomes more common and pervasive, some very practical issues are arising. For example, Amazon’s Audible unit has created a new captions feature for audio books. Publishers sued, saying it’s a violation of copyright, but Amazon claims that because the captions are created with artificial intelligence, it is essentially a new work.

When machines can create does that qualify as an original, creative intent? Under what circumstances can a work be considered new and original? We are going to have to decide.

Bias And Transparency

We generally accept that humans have biases. In fact, Wikipedia lists over 100 documented biases that affect our judgments. Marketers and salespeople try to exploit these biases to influence our decisions. At the same time, professional training is supposed to mitigate them. To make good decisions, we need to conquer our tendency for bias.

Yet however much we strive to minimize bias, we cannot eliminate it, which is why transparency is so crucial for any system to work. When a CEO is hired to run a corporation, for example, he or she can’t just make decisions willy nilly, but is held accountable to a board of directors who represent shareholders. Records are kept and audited to ensure transparency.

Machines also have biases which are just as pervasive and difficult to root out. Amazon had to scrap an AI system that analyzed resumes because it was biased against female candidates. Google’s algorithm designed to detect hate speech was found to be racially biased. If two of the most sophisticated firms on the planet are unable to eliminate bias, what hope is there for the rest of us?

So, we need to start asking the same questions of machine-based decisions as we do of human ones. What information was used to make a decision? On what basis was a judgment made? How much oversight should be required and by whom? We all worry about who and what are influencing our children, we need to ask the same questions about our algorithms.

The Problem of Moral Agency

For centuries, philosophers have debated the issue of what constitutes a moral agent, meaning to what extent someone is able to make and be held responsible for moral judgments. For example, we generally do not consider those who are insane to be moral agents. Minors under the age of eighteen are also not fully held responsible for their actions.

Yet sometimes the issue of moral agency isn’t so clear. Consider a moral dilemma known as the trolley problem. Imagine you see a trolley barreling down the tracks that is about to run over five people. The only way to save them is to pull a lever to switch the trolley to a different set of tracks, but if you do one person standing there will be killed. What should you do?

For the most part, the trolley problem has been a subject for freshman philosophy classes and avant-garde cocktail parties, without any real bearing on actual decisions. However, with the rise of technologies like self-driving cars, decisions such as whether to protect the life of a passenger or a pedestrian will need to be explicitly encoded into the systems we create.

On a more basic level, we need to ask who is responsible for a decision an algorithm makes, especially since AI systems are increasingly capable of making judgments humans can’t understand. Who is culpable for an algorithmically driven decision gone bad? By what standard should they be evaluated?

Working Towards Human-Machine Coevolution

Before the industrial revolution, most people earned their living through physical labor. Much like today, tradesman saw mechanization as a threat — and indeed it was. There’s not much work for blacksmiths or loom weavers these days. What wasn’t clear at the time was that industrialization would create a knowledge economy and demand for higher paid cognitive work.

Today, we’re going through a similar shift, but now machines are taking over cognitive tasks. Just as the industrial revolution devalued certain skills and increased the value of others, the age of thinking machines is catalyzing a shift from cognitive skills to social skills. The future will be driven by humans collaborating with other humans to design work for machines that creates value for other humans.

Technology is, as Marshal McLuhan pointed out long ago, an extension of man. We are constantly coevolving with our creations. Value never really disappears, it just shifts to another place. So, when we use technology to automate a particular task, humans must find a way to create value elsewhere, which creates an opportunity to create new technologies.

This is how humans and machines coevolve. The dilemma that confronts us now is that when machines replace tasks that were once thought of as innately human, we must redefine ourselves and that raises thorny questions about our relationship to the moral universe. When men become gods, the only thing that remains to conquer is ourselves.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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The Coming Innovation Slowdown

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The Coming Innovation Slowdown

GUEST POST from Greg Satell

Take a moment to think about what the world must have looked like to J.P. Morgan a century ago, in 1919. He was not only an immensely powerful financier with access to the great industrialists of the day, but also an early adopter of new technologies. One of the first electric generators was installed at his home.

The disruptive technologies of the day, electricity and internal combustion, were already almost 40 years old, but had little measurable economic impact. Life largely went on as it always had. That would quickly change over the next decade when those technologies would drive a 50-year boom in productivity unlike anything the world had ever seen before.

It is very likely that we are at a similar point now. Despite significant advances in technology, productivity growth has been depressed for most of the last 50 years. Over the next ten years, however, we’re likely to see that change as nascent technologies hit their stride and create completely new industries. Here’s what you’ll need to know to compete in the new era.

1. Value Will Shift from Bits to Atoms

Over the past few decades, innovation has become almost synonymous with digital technology. Every 18 months or so, semiconductor manufacturers would bring out a new generation of processors that were twice as powerful as what came before. These, in turn, would allow entrepreneurs to imagine completely new possibilities.

However, while the digital revolution has given us snazzy new gadgets, the impact has been muted. Sure, we have hundreds of TV channels and we’re able to talk to our machines and get coherent answers back, but even at this late stage, information and communication technologies make up only about 6% of GDP in advanced countries.

At first, that sounds improbable. How could so much change produce so little effect? But think about going to a typical household in 1960, before the digital revolution took hold. You would likely see a TV, a phone, household appliances and a car in the garage. Now think of a typical household in 1910, with no electricity or running water. Even simple chores like cooking and cleaning took hours of backbreaking labor.

The truth is that much of our economy is still based on what we eat, wear and live in, which is why it’s important that the nascent technologies of today, such as synthetic biology and materials science, are rooted in the physical world. Over the next generation, we can expect innovation to shift from bits back to atoms.

2. Innovation Will Slow Down

We’ve come to take it for granted that things always accelerate because that’s what has happened for the past 30 years or so. So we’ve learned to deliberate less, to rapidly prototype and iterate and to “move fast and break things” because, during the digital revolution, that’s what you needed to do to compete effectively.

Yet microchips are a very old technology that we’ve come to understand very, very well. When a new generation of chips came off the line, they were faster and better, but worked the same way as earlier versions. That won’t be true with new computing architectures such as quantum and neuromorphic computing. We’ll have to learn how to use them first.

In other cases, such as genomics and artificial intelligence, there are serious ethical issues to consider. Under what conditions is it okay to permanently alter the germ line of a species. Who is accountable for the decisions and algorithm makes? On what basis should those decisions be made? To what extent do they need to be explainable and auditable?

Innovation is a process of discovery, engineering and transformation. At the moment, we find ourselves at the end of one transformational phase and about to enter a new one. It will take a decade or so to understand these new technologies enough to begin to accelerate again. We need to do so carefully. As we have seen over the past few years, when you move fast and break things, you run the risk of breaking something important.

3. Ecosystems Will Drive Technology

Let’s return to J.P. Morgan in 1919 and ask ourselves why electricity and internal combustion had so little impact up to that point. Automobiles and electric lights had been around a long time, but adoption takes time. It takes a while to build roads, to string wires and to train technicians to service new inventions reliably.

As economist Paul David pointed out in his classic paper, The Dynamo and the Computer, it takes time for people to learn how to use new technologies. Habits and routines need to change to take full advantage of new technologies. For example, in factories, the biggest benefit electricity provided was through enabling changes in workflow.

The biggest impacts come from secondary and tertiary technologies, such as home appliances in the case of electricity. Automobiles did more than provide transportation, but enables a shift from corner stores to supermarkets and, eventually, shopping malls. Refrigerated railroad cars revolutionized food distribution. Supply chains were transformed. Radios, and later TV, reshaped entertainment.

Nobody, not even someone like J.P. Morgan could have predicted all that in 1919, because it’s ecosystems, not inventions, that drive transformation and ecosystems are non-linear. We can’t simply extrapolate out from the present and get a clear future of what the future is going to look like.

4. You Need to Start Now

The changes that will take place over the next decade or so are likely to be just as transformative—and possibly even more so—than those that happened in the 1920s and 30s. We are on the brink of a new era of innovation that will see the creation of entirely new industries and business models.

Yet the technologies that will drive the 21st century are still mostly in the discovery and engineering phases, so they’re easy to miss. Once the transformation begins in earnest, however, it will likely be too late to adapt. In areas like genomics, materials science, quantum computing and artificial intelligence, if you get a few years behind, you may never catch up.

So the time to start exploring these new technologies is now and there are ample opportunities to do so. The Manufacturing USA Institutes are driving advancement in areas as diverse as bio-fabrication, additive manufacturing and composite materials. IBM has created its Q Network to help companies get up to speed on quantum computing and the Internet of Things Consortium is doing the same thing in that space.

Make no mistake, if you don’t explore, you won’t discover. If you don’t discover you won’t invent. And if you don’t invent, you will be disrupted eventually, it’s just a matter of time. It’s always better to prepare than to adapt and the time to start doing that is now.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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Rise of the Prompt Engineer

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Rise of the Prompt Engineer

GUEST POST from Art Inteligencia

The world of tech is ever-evolving, and the rise of the prompt engineer is just the latest development. Prompt engineers are software developers who specialize in building natural language processing (NLP) systems, like voice assistants and chatbots, to enable users to interact with computer systems using spoken or written language. This burgeoning field is quickly becoming essential for businesses of all sizes, from startups to large enterprises, to remain competitive.

Five Skills to Look for When Hiring a Prompt Engineer

But with the rapid growth of the prompt engineer field, it can be difficult to hire the right candidate. To ensure you’re getting the best engineer for your project, there are a few key skills you should look for:

1. Technical Knowledge: A competent prompt engineer should have a deep understanding of the underlying technologies used to create NLP systems, such as machine learning, natural language processing, and speech recognition. They should also have experience developing complex algorithms and working with big data.

2. Problem-Solving: Prompt engineering is a highly creative field, so the ideal candidate should have the ability to think outside the box and come up with innovative solutions to problems.

3. Communication: A prompt engineer should be able to effectively communicate their ideas to both technical and non-technical audiences in both written and verbal formats.

4. Flexibility: With the ever-changing landscape of the tech world, prompt engineers should be comfortable working in an environment of constant change and innovation.

5. Time Management: Prompt engineers are often involved in multiple projects at once, so they should be able to manage their own time efficiently.

These are just a few of the skills to look for when hiring a prompt engineer. The right candidate will be able to combine these skills to create effective and user-friendly natural language processing systems that will help your business stay ahead of the competition.

But what if you want or need to build your own artificial intelligence queries without the assistance of a professional prompt engineer?

Four Secrets of Writing a Good AI Prompt

As AI technology continues to advance, it is important to understand how to write a good prompt for AI to ensure that it produces accurate and meaningful results. Here are some of the secrets to writing a good prompt for AI.

1. Start with a clear goal: Before you begin writing a prompt for AI, it is important to have a clear goal in mind. What are you trying to accomplish with the AI? What kind of outcome do you hope to achieve? Knowing the answers to these questions will help you write a prompt that is focused and effective.

2. Keep it simple: AI prompts should be as straightforward and simple as possible. Avoid using jargon or complicated language that could confuse the AI. Also, try to keep the prompt as short as possible so that it is easier for the AI to understand.

3. Be specific: To get the most accurate results from your AI, you should provide a specific prompt that clearly outlines what you are asking. You should also provide any relevant information, such as the data or information that the AI needs to work with.

4. Test your prompt: Before you use your AI prompt in a real-world situation, it is important to test it to make sure that it produces the results that you are expecting. This will help you identify any issues with the prompt or the AI itself and make the necessary adjustments.

By following these tips, you can ensure that your AI prompt is effective and produces the results that you are looking for. Writing a good prompt for AI is a skill that takes practice, but by following these secrets you can improve your results.

So, whether you look to write your own AI prompts or feel the need to hire a professional prompt engineer, now you are equipped to be successful either way!

Image credit: Pexels

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AI is a Powerful New Tool for Entrepreneurs

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AI is a Powerful New Tool for Entrepreneurs

by Braden Kelley

In today’s digital, always connected world, Google too often stands as a gatekeeper between entrepreneurs and small businesses and financial success. Ranking well in the search engines requires time and expertise that many entrepreneurs and small business owners don’t have, because their focus must be on fine tuning the value proposition and operations of their business.

The day after Google was invented, the search engine marketing firm was probably created to make money off of hard working entrepreneurs and small businesses owners trying to make the most of their investment in a web site through search engine optimization (SEO), keyword advertising, and social media strategies.

According to IBISWorld the market size of the SEO & Internet Marketing Consulting industry is $75.0 Billion. Yes, that’s billion with a ‘b’.

Creating content for web sites is an even bigger market. According to Technavio the global content marketing size is estimated to INCREASE by $584.0 Billion between 2022 and 2027. This is the growth number. The market itself is MUCH larger.

The introduction of ChatGPT threatens to upend these markets, to the detriment of this group of businesses, but to the benefit to the nearly 200,000 dentists in the United States, more than 100,000 plumbers, million and a half real estate agents, and numerous other categories of small businesses.

Many of these content marketing businesses create a number of different types of content for the tens of millions of small businesses in the United States, from blog articles to tweets to Facebook pages and everything in-between. The content marketing agencies that small businesses hire recent college graduates or offshore resources in places like the Philippines, India, Pakistan, Ecuador, Romania, and lots of other locations around the world and bill their work to their clients at a much higher rate.

Outsourcing content creation has been a great way for small businesses to leverage external resources so they can focus on the business, but now may be the time to bring some of this content creation work back in house. Particularly where the content is pretty straightforward and informational for an average visitor to the web site.

With ChatGPT you can ask it to “write me an article on how to brush your teeth” or “write me ten tweets on teethbrushing” or “write me a facebook post on the most common reasons a toilet won’t flush.”

I asked it to do the last one for me and here is what it came up with:

Continue reading the rest of this article on CustomerThink (including the ChatGPT results)

Image credits: Pixabay

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