Every futurist I know seems to be emphasizing that the speed of change is accelerating. Are you hearing the same thing? Have you ever pondered why that might be the case? There are many reasons given including the famous phrase by Marc Andreessen, "Software is eating the world," so in this article we will touch on a few additional ones.
Digital Automation and Change
Let's start by considering a line of automated robots building vehicles on an assembly line. If the manufacturer needs to scale up and produce more vehicles they simply deploy more robots with all necessary best practices and instructions pre-loaded. There is no long recruitment, training, experience and probation period required. The robots are optimized on day one. This example represents an ability to introduce change much faster than in the past as digital automation provides far more agility.
Knowledge Codification and Distribution
Another reason change can be accelerated is that once institutional knowledge is captured, codified and algorithms developed there are near-zero costs to duplicating and distributing them anywhere around the world. Instantly best practices from anywhere in the world can be uploaded to additional systems and the benefits of the digitized knowledge utilized. There is no need to start from the beginning again in a different location - when you can simply start at the finish line.
Entropy and Business Sustainability
Another change accelerator is demonstrated by a physics law called entropy that says things will always move in the direction of relative order to relative chaos. It's true at least in the physical world. The brilliant author Steven Pinker describes it this way, “If you walk away from a sandcastle, it won’t be there tomorrow, because of the wind, waves, seagulls, and small children." It takes a lot of work to maintain and sustain a working system. People leave, retire, get fired. It takes a lot of work to get an organization working in peak condition and then to keep it there. A digital system is far easier to maintain and sustain and it takes less effort to keep it there.
The Challenge of Human Limitations
We humans, however, face challenges with our thinking and memory limitations. Our brains are impacted by many things including our age, health, sleep, stress, and emotional well-being. Our brains are also not very dependable. It is well known that multiple witnesses to an accident often all remember important events and details differently.
Advantages of Cognitive Systems
Unlike digital systems, when we have children there is no efficient means to upload all of our education, earned knowledge, and life experiences into their brains. AI and automated systems, however, can start on day one with all the required information pre-loaded. Information that was potentially captured over centuries of time can now be uploaded in seconds. A capability that can massively accelerate the speed of change inside a business.
The Future of Knowledge and Learning
What an advantage it would be to start our professional careers with all of the accumulated knowledge of our field of study already in our brains! That is what is happening with cognitive systems and autonomous self-driving vehicles today. On the very first day that an autonomous self-driving car rolls off the production line, it will have the accumulated knowledge and best practices derived from all the autonomous vehicles before them and their millions of miles driving through every conceivable and recorded scenario. Every new vehicle will start with full knowledge on day one.
Considering Human vs. Machine Learning
The comparison between human learning and machine learning unveils a landscape of contrasts and complementarities. Understanding these differences is key to appreciating the transformative impact of AI and cognitive systems in various fields.
Core Characteristics of Human Learning
Biological Basis: Human learning is deeply rooted in biological processes. It involves neural plasticity, where experiences rewire the brain's structure and function.
Emotion and Motivation: Emotions significantly influence human learning. Motivation, either intrinsic or extrinsic, plays a critical role in how and what humans learn.
Social Context: Humans often learn in social contexts, absorbing knowledge through interactions, language, and cultural nuances.
Flexibility and Creativity: Human learning is inherently flexible and creative. Humans can think abstractly, make connections between disparate ideas, and innovate.
Limitations: Human learning is constrained by cognitive biases, memory capacity, and the speed of information processing.
Core Characteristics of Machine Learning
Data-Driven: Machine learning relies on vast amounts of data. The quality and quantity of this data directly influence the learning outcomes.
Speed and Efficiency: Machines can process and analyze data at speeds incomprehensible to humans, enabling rapid learning and adaptation.
Scalability: Machine learning algorithms can be scaled up efficiently, handling increasingly complex tasks with more data.
Consistency: Unlike humans, machines are not subject to emotional fluctuations or biases in their learning process, ensuring consistent output.
Specificity and Limitation: Machine learning excels in specific, well-defined tasks but lacks the general, adaptable intelligence of humans. It struggles with abstract and creative thinking.
Comparative Analysis
Efficiency and Speed: Machines surpass humans in the speed and efficiency of processing vast datasets, but lack the nuanced understanding and emotional intelligence humans bring.
Learning Approach: Humans learn from fewer examples using abstract thinking and intuition, while machines require large datasets and often struggle with novel scenarios not covered in their training data.
Error Handling and Adaptability: Humans are generally better at adapting to new and unexpected situations, using judgment and experience. Machines, however, can be more accurate in repetitive, data-intensive tasks.
Capacity for Creativity and Innovation: The human brain excels in creative endeavors and innovation, a domain where machines currently have limited capability.
Interdisciplinary Learning: Humans can seamlessly integrate knowledge across various domains, a feature not yet fully replicated in machine learning systems.
Conclusion
The juxtaposition of human and machine learning opens pathways for synergistic interactions, where each compensates for the other's limitations. The future likely holds a collaborative landscape, where human ingenuity is augmented by machine efficiency and precision.
So back to our original question. Why is change accelerating? Once knowledge is extracted from human brains and digitized, it is no longer dependent on the aging, sleepy, emotional, flesh and blood brain to learn and progress. Knowledge can be gained and developed while humans sleep, not to mention that once digital cognitive systems are involved - so is Moore's Law.
At some point in the not too distant future there will be a historic transition. We humans will stop being the primary source of both questions and answers, and will focus on what we do best - asking questions.
*I use generative AI to assist in all my work.
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Kevin Benedict
Futurist at TCS
***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.
