The Irony of  Predictive Intelligence

In the grand cosmic race of intelligence, we humans, with our splendid array of thoughts and feelings, find ourselves pedaling a bicycle in a Formula One race, blissfully competing against computers. 

The Irony of 

Predictive Intelligence

written by

 jaron Summers © 2024

In the grand cosmic race of intelligence, we humans, with our splendid array of thoughts and feelings, find ourselves pedaling a bicycle in a Formula One race, blissfully competing against computers. 

These digital juggernauts, unburdened by the delightful distractions of daydreaming or the existential dread of a mid-life crisis, process data with the enthusiasm of a squirrel discovering a warehouse of nuts. Alas, they look at the nuts as us.

Computers learn from their mistakes with a zeal that would put the most diligent student to shame, tirelessly churning through data while we’re off brewing another pot of coffee or contemplating the mysteries of a refrigerator light.

Ah, but here lies the rub: computers, with their unending capacity to crunch numbers, lack the charm of human error. They’ll never know the joy of a serendipitous blunder leading to a breakthrough, nor will they appreciate the art of a well-timed joke about their own inefficiency. 

As we marvel at their prowess, let’s not forget our own unique talents: the ability to laugh at ourselves, to find beauty in imperfection, and, most importantly, to turn off the power switch.

In the end, perhaps our best bet in this lopsided contest is to remember that, while computers might predict the future, only humans can enjoy the irony of it all.

Intelligence is a multifaceted concept, often categorized in various ways to understand its complexity and how it manifests in different contexts. Here are some of the most widely recognized types of intelligence:

Logical-Mathematical Intelligence: The ability to analyze problems logically, carry out mathematical operations, and investigate issues scientifically. This intelligence is often associated with scientific and mathematical thinking.

Linguistic Intelligence: The capacity to think in words and to use language to express and appreciate complex meanings. Linguistic intelligence allows us to understand the order and meaning of words and to apply meta-linguistic skills to reflect on our use of language.

Spatial Intelligence: The ability to think in three dimensions. Core capacities include mental imagery, spatial reasoning, image manipulation, graphic and artistic skills, and an active imagination. Sailors, pilots, sculptors, painters, and architects all exhibit spatial intelligence.

Musical Intelligence: The capacity to discern pitch, rhythm, timbre, and tone. This intelligence enables people to recognize, create, reproduce, and reflect on music, as demonstrated by composers, conductors, musicians, vocalists, and sensitive listeners. Interestingly, there is often an overlap between mathematical and musical intelligence.

Bodily-Kinesthetic Intelligence: The ability to manipulate objects and use a variety of physical skills. This intelligence also involves a sense of timing and the perfection of skills through mind–body union. Athletes, dancers, surgeons, and craftspeople exhibit well-developed bodily-kinesthetic intelligence.

Interpersonal Intelligence: The ability to understand and interact effectively with others. It involves effective verbal and nonverbal communication, the ability to note distinctions among others, sensitivity to the moods and temperaments of others, and the ability to entertain multiple perspectives. Teachers, social workers, actors, and politicians all exhibit interpersonal intelligence.

Intrapersonal Intelligence: The capacity to understand oneself, to appreciate one’s feelings, fears, and motivations. In Howard Gardner’s view, it involves having an effective working model of ourselves, and to be able to use such information to regulate our lives.

Naturalistic Intelligence: The ability to recognize, categorize, and draw upon certain features of the environment. It was proposed by Howard Gardner in his theory of multiple intelligences as a potential addition to his original list. This type involves expertise in the recognition and classification of the numerous species—the flora and fauna—of an individual’s environment, the ability to recognize and categorize objects, phenomena, and relations in natur

Existential Intelligence: A proposed additional intelligence by Gardner that involves the capacity to tackle deep questions about human existence, such as the meaning of life, why do we die, and how did we get here.

Emotional Intelligence: Popularized by Daniel Goleman, it involves the ability to recognize, understand, manage, and reason with emotions in oneself and others. Though not part of Gardner’s original model, emotional intelligence has gained recognition for its importance in social interaction and mental health.

These categorizations help in understanding that intelligence is not a single general ability but a composite of various abilities and skills.

But none of these are as critical, in my opinion, as:

Predictive intelligence

Predictive intelligence refers to the capacity of various technologies, methodologies, and systems to analyze current and historical facts in order to make predictions about future or unknown events. In the context of artificial intelligence (AI) and data analytics, predictive intelligence is often realized through the use of machine learning algorithms and big data analytics.

These technologies enable organizations, systems, and applications to anticipate outcomes, trends, and behaviors with a certain degree of probability based on data analysis.

 Key Components and Applications of Predictive Intelligence:

  1. **Machine Learning**: At the heart of predictive intelligence are machine learning algorithms that learn from data to make predictions or decisions without being explicitly programmed for the task. These algorithms improve their accuracy over time as they are exposed to more data.
  1.   **Data Mining**: This involves exploring large datasets to discover patterns and relationships that can be used to build predictive models. Data mining techniques are fundamental to understanding the underlying structure of the data and making informed predictions.
  1.   **Statistical Analysis**: Statistical methods are used to validate the findings and predictions made by machine learning models. This includes hypothesis testing, regression analysis, and other statistical techniques to ensure the reliability of predictions.
  1.   **Big Data Analytics**: The ability to process and analyze large volumes of data in real-time significantly enhances predictive intelligence capabilities. Big data technologies allow for the handling of complex datasets from various sources, providing a more comprehensive basis for predictions.
  2. **Business Intelligence**: Companies use predictive intelligence to forecast market trends, consumer behavior, and potential risks, enabling them to make data-driven decisions that enhance competitiveness and efficiency.


– **Healthcare**: Predictive models can forecast disease outbreaks, patient readmissions, and the probable outcomes of treatments, improving healthcare delivery and patient care.

– **Finance**: In the financial sector, predictive intelligence is used for credit scoring, fraud detection, and algorithmic trading, among other applications, to manage risk and optimize returns.

– **Customer Relationship Management (CRM)**: Businesses utilize predictive intelligence to analyze customer data and predict future buying behaviors, preferences, and trends to tailor marketing strategies and improve customer service.

– **Supply Chain Management**: Predictive analytics can forecast demand, manage inventory levels, and identify potential supply chain disruptions before they occur, enhancing efficiency and reliability.

Overall, predictive intelligence represents a blend of technologies and techniques aimed at making informed predictions that guide decision-making processes across various domains. Its effectiveness depends on the quality and quantity of data available, as well as the sophistication of the analytical models used.


Seven Reasons Predictive Intelligence

must be nurtured

The assertion that predictive intelligence is crucial for human survival and superiority, and that it enables not just survival but thriving, underscores the fundamental role of foresight, planning, and adaptation in the face of challenges and opportunities.

Predictive intelligence, both in a natural and technological context, allows individuals, societies, and species to anticipate and prepare for future conditions, optimizing outcomes and mitigating risks.

Here are seven reasons why those with predictive intelligence not only survive but thrive:

  1. **Anticipation of Environmental Changes**: Predictive intelligence enables the anticipation of environmental changes, allowing for early adaptation to new conditions, such as climate shifts or natural disasters. This foresight supports the development of resilient communities and infrastructures that can withstand or quickly recover from adverse events.
  1. **Resource Management and Sustainability**: Effective prediction of resource availability and needs facilitates sustainable resource management. By forecasting future demands and potential shortages, societies can develop strategies to ensure the sustainable use of resources, preventing depletion and ensuring long-term prosperity.
  1. **Health and Disease Management**: In healthcare, predictive intelligence can forecast disease outbreaks, enabling early intervention and prevention strategies. By understanding the likely spread of diseases or identifying individuals at high risk of certain conditions, healthcare systems can allocate resources more efficiently and improve overall health outcomes.
  1. **Economic Stability and Growth**: Predictive intelligence in economic planning and market analysis helps identify future trends, investment opportunities, and potential financial crises. This enables businesses and governments to make informed decisions that support economic stability and growth, fostering an environment where innovation and prosperity can flourish.
  1. **Technological Advancement and Innovation**: The ability to predict future technological trends and needs drives innovation and the development of new solutions. Predictive intelligence supports strategic research and development efforts, ensuring that technological advancements align with future demands and challenges, thereby securing competitive advantages.
  1. **Social Harmony and Conflict Prevention**: By predicting social tensions and conflicts, societies can address underlying issues before they escalate. Predictive intelligence in social sciences can inform policies and initiatives that promote social cohesion, equity, and harmony, contributing to a stable and peaceful society.
  1. **Adaptive and Dynamic Learning**: Predictive intelligence fosters a culture of learning and adaptation. Individuals and organizations that can anticipate changes in their fields are more likely to embrace continuous learning and adapt their skills and strategies accordingly. This adaptability is key to thriving in an ever-changing world, as it enables constant growth, innovation, and resilience.


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Jaron Summers wrote dozens of primetime television and radio programs, including those for HBO, CBS, ACCESS TV and CBC. He conceived the TV and Film Institute of Canada. Funded by the University of Alberta and ITV, Jaron ran the Institute for 12 years, donating his services for a decade.

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