Innovation and Technology
Make Better Decisions with Slow-Developing Technology

Evolution of Self-Driving Automobiles: A Century-Long Journey
A Century of Progress
Self-driving automobiles may seem like a cutting-edge 21st-century technology — a challenge still facing obstacles before widespread adoption. But in fact, autonomous driving has been evolving in fits and starts for a full century. Its evolution can teach managers how to deal with innovations that depend on multiple slow-developing technologies that come together at different speeds and costs.
Early Experimentation
The first self-driving vehicle was invented in the 1920s by a American inventor, Norman Polmar. His invention, known as the “Auto-Pilot,” used a radio transmitter to control the vehicle’s movements. Although it was not practical for widespread use, it marked the beginning of the development of autonomous vehicles.
The 1950s and 60s: The First Autonomous Vehicles
In the 1950s and 60s, the development of autonomous vehicles continued with the introduction of the “Autonomous Vehicle” (AV) concept. AVs were designed to be used in military and industrial settings, but they were not commercially viable.
The 1980s and 90s: The Rise of Robotics and Computer Vision
The 1980s and 90s saw significant advancements in robotics and computer vision, which enabled the development of more sophisticated autonomous vehicles. Companies like Stanford Research Institute (SRI) and Carnegie Mellon University began working on autonomous vehicle projects, leading to the creation of the first autonomous vehicle prototype in 1995.
The 2000s and 2010s: Modern Autonomous Vehicles
The 2000s and 2010s saw the rise of modern autonomous vehicles, with companies like Google, Tesla, and Uber investing heavily in the technology. The development of advanced sensors, GPS, and machine learning algorithms enabled the creation of more sophisticated autonomous vehicles.
Challenges and Obstacles
Despite progress, autonomous vehicles still face significant challenges and obstacles, including regulatory hurdles, public acceptance, and technical issues. However, the evolution of autonomous vehicles can teach managers how to deal with innovations that depend on multiple slow-developing technologies that come together at different speeds and costs.
Conclusion
The evolution of self-driving automobiles is a century-long journey that has seen significant advancements and setbacks. By understanding the history of autonomous vehicles, managers can gain valuable insights into how to navigate the challenges and opportunities presented by other complex innovations.
FAQs
* Q: What was the first self-driving vehicle invented?
A: The first self-driving vehicle was invented in the 1920s by American inventor Norman Polmar.
* Q: What was the purpose of the Autonomous Vehicle (AV) concept?
A: The Autonomous Vehicle (AV) concept was designed for use in military and industrial settings, but it was not commercially viable.
* Q: What advancements were made in the 1980s and 90s?
A: The 1980s and 90s saw significant advancements in robotics and computer vision, enabling the development of more sophisticated autonomous vehicles.
Innovation and Technology
Growing with Users

Planned Obsolescence: A Strategy to Boost Sales
The Origins of Planned Obsolescence
In the past, companies have used “planned obsolescence,” deliberately designing products with limited lifespans so that customers would have to buy more. This strategy, often used in the 1950s and 1960s, was meant to increase sales and boost profits.
The Evolution of Planned Obsolescence
Over time, companies have refined their approach to planned obsolescence. Instead of simply designing products to break, companies have focused on creating products that are designed to slow down, become less functional, or become outdated. This can take many forms, from light bulbs engineered to burn out after a specific number of hours to smartphones that slow down with new software updates.
Examples of Planned Obsolescence
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Light Bulbs
Light bulbs have been designed with a limited lifespan, often burning out after a certain number of hours. This forces consumers to replace them, increasing sales and profits for the manufacturers.
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Smartphones
Smartphones are another example of planned obsolescence. Software updates can slow down the device, making it less functional and more likely to be replaced. This creates a continuous cycle of sales and revenue for the manufacturers.
The Impact of Planned Obsolescence
Planned obsolescence has several negative consequences:
*
Waste and Environmental Impact
The constant need to replace products contributes to waste and environmental degradation.
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Increased Consumer Spend
Consumers are forced to spend more money on new products, taking a significant toll on their budgets.
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Lack of Innovation
The focus on planned obsolescence can stifle innovation, as companies prioritize short-term profits over long-term sustainability and development.
Conclusion
Planned obsolescence is a widespread practice that has been used by companies to increase sales and profits. However, it has significant negative consequences for consumers, the environment, and innovation. As consumers become more aware of this practice, it is essential to promote sustainable and responsible business practices that prioritize long-term value over short-term gains.
FAQs
*
What is planned obsolescence?
Planned obsolescence is a business strategy where companies design products with limited lifespans to encourage frequent replacements.
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How does planned obsolescence affect consumers?
Planned obsolescence can lead to increased consumer spending, reduced product lifespan, and a negative impact on the environment.
*
Can planned obsolescence be avoided?
Yes, consumers can make informed purchasing decisions, prioritize sustainable products, and support companies that prioritize long-term value over short-term profits.
Innovation and Technology
The Future of Intelligence

Why “Living Intelligence” Is the Next Big Thing
The Emergence of AI-Powered Ecosystems
In today’s fast-paced digital landscape, the term "Artificial Intelligence" (AI) has become synonymous with innovation and progress. However, as we continue to explore the possibilities of AI, a new concept is emerging – "Living Intelligence." This concept goes beyond traditional AI and AI-powered systems, and instead, focuses on creating intelligent ecosystems that learn, adapt, and evolve over time.
The Key Characteristics of Living Intelligence
So, what sets living intelligence apart from traditional AI? Here are some key characteristics that define this new era of intelligence:
- Self-Awareness: Living intelligence systems have the ability to understand their own strengths, weaknesses, and biases, allowing them to make more informed decisions and adapt to new situations.
- Autonomy: These systems are designed to operate independently, making decisions and taking actions without human intervention, but within predetermined parameters.
- Contextual Understanding: Living intelligence systems can understand the nuances of human language, emotions, and behavior, enabling them to provide more personalized and empathetic responses.
- Continuous Learning: These systems learn from their experiences, incorporating new data and insights to refine their performance and adapt to changing circumstances.
The Benefits of Living Intelligence
The advantages of living intelligence are numerous, including:
- Improved Decision-Making: With the ability to understand complex situations and make informed decisions, living intelligence systems can significantly reduce errors and improve outcomes.
- Enhanced Customer Experience: By understanding human emotions and behavior, living intelligence systems can provide personalized and empathetic support, leading to increased customer satisfaction and loyalty.
- Increased Efficiency: Autonomous systems can automate routine tasks, freeing up human resources for more strategic and creative work.
Real-World Applications of Living Intelligence
Living intelligence is already being applied in various industries, including:
- Healthcare: AI-powered chatbots and virtual assistants are being used to provide personalized patient care and support.
- Finance: Intelligent trading platforms and predictive analytics are helping investors make more informed decisions.
- Education: Adaptive learning systems are revolutionizing the way we learn, providing personalized instruction and feedback.
Challenges and Concerns
While living intelligence holds tremendous potential, there are also concerns surrounding its development and implementation, including:
- Ethical Considerations: As with any AI system, there are ethical implications to consider, such as bias, privacy, and accountability.
- Regulatory Frameworks: Governments and organizations will need to establish guidelines and regulations to ensure the responsible development and deployment of living intelligence.
Conclusion
In conclusion, living intelligence is the next big thing in the world of AI. This new era of intelligence has the potential to transform industries and revolutionize the way we live and work. As we move forward, it’s essential to consider the challenges and concerns surrounding living intelligence, while also embracing its vast potential to improve our lives and create a better future.
FAQs
Q: What is the difference between AI and living intelligence?
A: AI is a type of machine learning that can perform specific tasks, whereas living intelligence is a more comprehensive and adaptive system that learns, adapts, and evolves over time.
Q: Is living intelligence the same as artificial general intelligence (AGI)?
A: No, living intelligence is a distinct concept that focuses on creating intelligent ecosystems that learn and adapt, whereas AGI is a hypothetical AI system that possesses human-like intelligence and can perform any intellectual task.
Q: Can living intelligence be used for malicious purposes?
A: Like any technology, living intelligence can be used for good or ill. It’s essential to consider the ethical implications and implement safeguards to prevent its misuse.
Innovation and Technology
Redefining Work

The Missing Middle Of Human-Machine Collaboration
Jim Wilson, Global Managing Director at Accenture, reveals why the future belongs to those who can master human-machine collaboration and why this partnership could transform 40% of working hours across industries.
In a world where groundbreaking AI advancements seem to be delivered each month, Wilson offers a refreshingly optimistic perspective that cuts through the noise. Rather than viewing AI as a job-stealing threat, he presents compelling evidence for a future built on collaborative intelligence.
Transforming Business Functions And The Economy
The implications of this collaborative approach extend far beyond scientific research. According to Accenture’s research, generative AI will transform more than 40% of working hours across industries, with six business functions seeing over half of their work hours reshaped through automation, augmentation, and collaboration.
Redesigning Jobs For The AI Era
As AI adoption accelerates, how should leaders reimagine roles and job descriptions? Wilson believes most companies are still missing the mark.
There’s an emerging kind of collaborative intelligence that companies are going to need now to compete and innovate. It’s really about thoughtfully and rigorously creating that combined effect where human ingenuity, human innovation, plus AI systems outperform what either one could do alone.
A Framework For AI Transformation
For business leaders looking to implement AI effectively, Wilson offers a structured approach called MELDS – Mindset, Experimentation, Leadership, Digital Core, and Skills.
The New Fusion Skills For The AI Age
With 95% of workers seeing potential value in working with generative AI and 94% ready to learn new skills, the critical question becomes: what competencies do we need to develop?
The Ultimate Currency: Trust
As AI systems become more capable and autonomous, Wilson emphasizes that trust will be the limiting factor in realizing AI’s potential benefits.
Conclusion
In this new era of collaborative intelligence, the future belongs to organizations that can successfully blend human creativity with AI capabilities, build trust through explainable systems, and develop the fusion skills needed for effective human-machine partnerships. Those who master this balance won’t just survive the AI revolution – they’ll thrive in it.
FAQs
Q: How can organizations effectively implement AI?
A: Wilson offers a structured approach called MELDS – Mindset, Experimentation, Leadership, Digital Core, and Skills.
Q: What are the essential job categories for AI era?
A: Wilson identifies six major groups: trainers, explainers, sustainers, amplifiers, interactors, and embodiment workers.
Q: How can organizations develop the necessary skills for AI?
A: Wilson suggests that organizations invest in skills development, focusing on “fusion skills” such as judgment integration, explainability, and human-AI collaboration.
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