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Shaping the Future of A.I.

One of the biggest news subjects in the past few years has been artificial intelligence. We have read about how Google’s DeepMind beat the world’s best player at Go, which is thought of as the most complex game humans have created; witnessed how IBM’s Watson beat humans in a debate; and taken part in a wide-ranging discussion of how A.I. applications will replace most of today’s human jobs in the years ahead.

Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come. Early rule-based A.I. applications were used by financial institutions for loan applications, but once the exponential growth of processing power reached an A.I. tipping point, and we all started using the Internet and social media, A.I. had enough power and data (the fuel of A.I.) to enable smartphones, chatbots, autonomous vehicles and far more.

As I advise the leadership of many leading companies, governments and institutions around the world, I have found we all have different definitions of and understandings about A.I., machine learning and other related topics. If we don’t have common definitions for and understanding of what we are talking about, it’s likely we will create an increasing number of problems going forward. With that in mind, I will try to add some clarity to this complex subject.

Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.

Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.

Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learning and/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.

Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement.  For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.

Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.

A.I. advances represent a Hard Trend that will happen and continue to unfold in the years ahead. The benefits of A.I. are too big to ignore and include:

  1. Increasing speed
  2. Increasing accuracy
  3. 24/7 functionality
  4. High economic benefit
  5. Ability to be applied to a large and growing number of tasks
  6. Ability to make invisible patterns and opportunities visible

Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans. Yes, A.I. — like all technology — will take the place of many current jobs. But A.I. will also create many jobs if we are willing to learn new things. There is an old saying “You can’t teach an old dog new tricks.” With that said, it’s a good thing we aren’t dogs!

Start off The New Year by Anticipating disruption and change by reading my latest book The Anticipatory Organization. Click here to claim your copy!

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Four Big Brands Blindsided by Accelerated Change

It’s one of life’s universal lessons: Look both ways before crossing the street. Parents have been impressing its importance on every generation since Henry Ford tinkered with the internal combustion engine. However, many of us forgot that good advice, or assumed it didn’t apply, when crossing from one decade of business into the next.

From the 1970s into the 1980s, 1990s and 2000s, the prevailing assumption was that the future would be relatively similar to the past, and that major changes only took place over long stretches of time, which provided plenty of leeway to adjust.

We stepped off the curb, looking straight ahead—and wham! Individuals and organizations were blindsided by massive changes. It happened to big companies like IBM, Motorola, Research In Motion, Sears and countless others.

Four Big Brands That Were Blindsided

IBM. The original computer giant was late to act on the Hard Trends shaping the future of computing and missed the huge need for personal computers, entering the market late. Then in 2005, IBM sold its personal computer portfolio of products, including the popular ThinkPad brand, to Lenovo, which is now the world’s largest personal computing vendor. IBM was also late to embrace the Hard Trends of increasing use of mobility and the cloud.

Motorola. Similarly, the historic telecommunications company failed to anticipate exponential changes of the early 21st century, though it had many telecom firsts—first car radio, first handheld mobile phones in the early 1970s and the first smartphone using the Google Android OS. Unfortunately, the Motorola Mobility branch relied on being Agile, reacting after a disruption occurs, while leading companies were Anticipatory, using Hard Trends to see the future first and jump ahead and stay there.

Research In Motion. The company’s BlackBerry was the undisputed leader in business mobility, with a highly usable mini keyboard and tight integration of mobile email and calendar functionality. When Apple released the first iPhone, Research In Motion’s leadership failed to see the new future Apple had enabled and focused instead on making improvements instead of embracing the Hard Trends that were shaping the future of mobility and taking its loyal user base into the smartphone future.

Sears. Widely considered the first “everything” store, Sears had a winning business strategy: a notoriously large selection of goods in a catalog that was mailed to just about everyone. Products that were ordered were delivered right to the customer’s home. Like many big brands blindsided by game-changing Hard Trends followed by disruptive innovation, Sears didn’t see how serious competition had become—for both brick and mortars like Walmart and online-only retailer Amazon. Their past success and organizational ego limited their view of the future.

Based on these and other painful experiences, the prevailing assumption was dramatically adjusted: Change is speeding up—get used to it. But then with each passing decade, crossing the street of change became an exercise in advanced risk analysis. Dodging oncoming traffic was the name of the game.

Seeing Change Is Only Part of the Solution

Spotting technology-driven change provides only part of the solution, however. Literally thousands of important high-tech breakthroughs are zooming at us from left and right. Not only do we need to carefully look both ways, it is essential to actually see and understand the ramifications of what’s coming.

Hopping out of the way in a panic or jumping onboard the next new thing isn’t the answer; nor is taking a wait-and-see attitude. By reinventing how welookat technology-driven change, it is possible to reinvent the way we thinkabout change. Once that happens, the reinvention of how we actin response to change takes place.

Look. Think. Act. These distinct steps are the key to both finding and profiting from the many new opportunities that are headed our way.

Look at the Hard Trends that willhappen and the game-changing opportunities they represent. Look at the Soft Trends that might happen and the opportunities to influence them.

Think about your list of opportunities and refine them into a few Must-Do actions.

Pick at least one opportunity and act on it now, because if you don’t do it, someone else will!

Today, agility—reacting quickly after a problem occurs or after a disruption disrupts, is not good enough. It’s time to learn how to become Anticipatory, using Hard Trends to anticipate disruptions beforethey happen, turning disruption and change into a choice.

If you would like to learn more, check out my latest bestseller, The Anticipatory Organization: How to Turn Change and Disruption Into Opportunity and Advantage.

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