AI vs HI (Human Intelligence): Have we been asking the wrong questions?

We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run

Amara’s Law

Over the last while I have noticed a lot of discussions about AI not generating the real world benefits and is it all hype.

The truth is it always takes time for technology to find the right uses, reliability and "profitability".

Let's look at the differences between AI and HI.

Development and Training of AI

Advantages:

1. Scalability: Once an AI model is developed, it can be replicated and deployed on numerous machines simultaneously.
2. Speed: Training AI models can be relatively fast with sufficient computational resources.
3. Consistency: AI can be trained to perform specific tasks with high consistency, without fatigue or deviation.
4. Accessibility to Data: AI can be trained on vast datasets that can be continuously updated and refined.

Challenges:

1. Initial Complexity: Developing an AI requires substantial expertise in machine learning, data science, and domain-specific knowledge.
2. Resource Intensive: Training sophisticated AI models can require significant computational power and time.
3. Maintenance: AI models need regular updates and maintenance to stay relevant and accurate.
4. Ethical Considerations: Developing AI involves ethical issues such as bias, privacy, and accountability.

Development and Training of a Human Mind

Advantages:

1. Natural Learning: Humans have an innate ability to learn from a wide variety of experiences and adapt to new situations.
2. Creativity and Intuition: Humans can think creatively and intuitively, going beyond data to develop novel ideas and solutions.
3. Social and Emotional Intelligence: Humans can understand and navigate complex social situations and emotions.

Challenges:

1. Time-Consuming: Human development is a long-term process, requiring years of education and personal growth.
2. Variability: Human learning and development vary significantly from person to person, influenced by genetic, social, and environmental factors.
3. Resource-Intensive: Educating and training a human involves significant resources, including time, money, and effort from educators, mentors, and the individuals themselves.
4. Susceptibility to Error: Humans can make mistakes, be influenced by biases, and suffer from fatigue and other limitations.

Conclusion

• Easier to Develop: From a purely technical standpoint, it might be argued that once the initial setup is in place, scaling and training AI could be easier and faster due to automation and computational power.
• More Nuanced Learning: On the other hand, human minds possess a depth of learning, creativity, and adaptability that current AI technologies cannot fully replicate.

Ultimately, the "easiness" depends on the specific context and what aspects of development and training are prioritized. For repetitive, well-defined tasks, AI may be easier to develop and train. For tasks requiring creativity, emotional intelligence, and complex problem-solving, human minds are currently superior.

Areas Where AI is Easier and Better to Train, Use, and Deploy

1. Data Analysis and Pattern Recognition:
2. Automation of Repetitive Tasks:
3. High-Speed and High-Volume Processing:
4. Precision Tasks:
5. 24/7 Operation:
6. Personalization at Scale:

Areas Where Human Intelligence is Better

1. Creativity and Innovation:
2. Emotional and Social Intelligence:
3. Complex Problem-Solving:
4. Adaptability and Learning from Unstructured Data:
5. Ethical and Moral Decision-Making:
6. Interpersonal Relationships and Empathy:

Summary

• AI is better suited for tasks that involve large-scale data processing, repetitive activities, precision, and continuous operation.
• Human intelligence excels in areas requiring creativity, emotional intelligence, complex problem-solving, ethical judgment, and interpersonal relationships.

By leveraging the strengths of both AI and human intelligence, organizations can optimize performance and achieve a balanced, effective approach to various tasks and challenges.

As much as there is an arms race, expecting AI to generate quick wins and profits is fools gold.

Returns, reliability etc all take time and it is often takes trial and error to improve something to become a reliable everyday tool.

We we take something as ubiquitous as the car today, we often forget the technologies we use today have actually been a long time in evolving to their current level of sophistication.

The car has undergone continuous development over the past 130+ years, evolving from simple motorized carriages to complex, highly sophisticated machines. This evolution has been driven by technological advancements, regulatory changes, and consumer demands, resulting in the reliable and advanced vehicles we have today.

Key Aspects of Current Sophistication and Reliability

1. Safety Features: Modern cars are equipped with advanced safety systems, including multiple airbags, collision avoidance systems, lane-keeping assist, and adaptive cruise control.
2. Fuel Efficiency and Emissions: Significant improvements in fuel efficiency and reduced emissions due to better engine technology, hybrid systems, and electric vehicles.
3. Autonomous Driving: Development of self-driving technology, with features like automatic parking, highway autopilot, and advanced sensor systems.
4. Connectivity: Integration of infotainment systems, navigation, and connectivity features like Bluetooth, Wi-Fi, and smartphone integration.
5. Material and Manufacturing Advances: Use of lightweight materials like aluminum and carbon fiber, and advancements in manufacturing techniques leading to more reliable and durable vehicles.

Trying to figure out who will be the winners and losers is not so certain and lets not assume that all the current tech giants will be here still and which of the new tech darlings will survive.

For example will Nvidia still be around?

here is some food for thought is we were to look at the Car Industry.

Since the birth of the car about 130 years ago, there have been thousands of car companies worldwide, although the exact number is difficult to pin down due to the ever-changing nature of the industry. Many early car manufacturers have come and gone, with significant consolidation over time.

Early Pioneers and Expansion

• In the late 19th and early 20th centuries, numerous companies were established as the automobile industry began to take shape. Early pioneers like Karl Benz, who created the first automobile in 1886, were soon followed by others across Europe and the United States.
• By the 1920s, the automobile industry saw a rapid increase in the number of manufacturers, with hundreds of companies in the U.S. alone producing cars. This period was marked by significant experimentation and innovation.

Early Development (Late 19th Century - Early 20th Century)

• 1886: Karl Benz is credited with creating the first true automobile, the Benz Patent-Motorwagen.
• 1908: The Ford Model T, introduced by Henry Ford, revolutionized the automobile industry with mass production techniques.

So who will be the winners and losers? Would you make a long term bet (ie 10-20 year bet.

Google was not the first search engine, IBM launched the PC but exited.

Love your thoughts on AI and where you think it will be 5, 10 and 20 years from now.