Jacques Ludik is sharing his insights on human intelligence and machine intelligence which is a crucial topic of his recently published book.


Copyright: Linkedin – Jacques Ludik – “Human Intelligence versus Machine Intelligence”


Dr. Geoffrey Hinton, one of the most influential Machine Learning researchers of the past few decades, recently left Google to, amongst others, speak out about the potential risks of AI such as the significant job losses, flood of misinformation, as well as concerns about AI or machine intelligence surpassing human intelligence and posing a potential existential risk for humanity. In order to make sense of the AI debates and specifically this viewpoint, I have a chapter in my book “Democratizing Artificial Intelligence to Benefit Everyone: Shaping a Better Future in the Smart Technology Era” titled “The Debates, Progress and Likely Future Paths of AI” that is dedicated to exploring the debates, progress and likely future paths of AI. This can assist us in developing a more realistic, practical, and thoughtful understanding of AI’s progress and likely future paths, and in turn be used as input to help shape a beneficial human-centric future in the Smart Technology Era (see also my article The Debates, Progress and Likely Future Paths of Artificial Intelligence). As the focus of this article is specifically on an introductory exploration of the topic of human intelligence versus machine intelligence, I’m sharing an extract of Chapter 9 of my book that specifically deals with this topic.

Multifaceted perspectives on human intelligence versus machine intelligence

Before I share the extract, I would like to briefly mention a subset of researchers and thought leaders across a wide spectrum of disciplines that influence my current thinking and sense-making on reality and also a multifaceted perspective on human intelligence versus machine intelligence.

David Deutsch: Deutsch’s work, particularly in “The Fabric of Reality” and “The Beginning of Infinity,” emphasizes the power of human creativity and problem-solving. He argues that humans have the unique capacity for creating explanatory knowledge, which allows them to understand and manipulate the world. Deutsch’s perspective on machine intelligence is that it can complement human creativity, but it cannot replace it. In the context of human vs. machine intelligence, Deutsch’s views suggest that both forms of intelligence have their unique strengths, and that human creativity and problem-solving abilities are irreplaceable. This is all part of a world view that incorporates four strands of reality which is best described by combining four key theories: quantum physics, the theory of evolution, the theory of computation, and the theory of knowledge (epistemology).

Joscha Bach: Bach’s research focuses on artificial general intelligence (AGI) and cognitive architectures. His work aims to understand the principles underlying human intelligence and replicate them in machines. Bach posits that there are universal principles of intelligence that can be applied to both humans and machines, with the goal of creating AGI that can approach or even surpass human-level intelligence. In his view, machine intelligence has the potential to reach the same level of adaptability and cognitive prowess as human intelligence, provided we can discover and implement the right cognitive architectures. Joscha also recently tweeted “It’s not about Artificial General Intelligence but General Agency. Intelligence is always a means, not an end.” and “Ethics requires love and truth. AI research needs to understand love, ethicists need to commit to truthfulness.”

John Vervaeke: Vervaeke’s work revolves around the study of cognitive science, wisdom, and meaning. His ideas emphasize the importance of cultivating cognitive flexibility, insight, and wisdom in both humans and AI systems. Vervaeke argues that human intelligence is grounded in our ability to make sense of the world and establish meaningful connections. In the context of human vs. machine intelligence, he highlights the importance of developing AI systems that not only possess computational power but also understand the context and meaning behind the information they process. See also “AI: The Coming Thresholds and The Path We Must Take“.

Karl Friston: Friston is known for his work in theoretical neuroscience and the development of the Free Energy Principle, which provides a unifying framework for understanding the brain’s function. He suggests that both human and machine intelligence can be described as the minimization of surprise or prediction error. In his view, the key difference between human and machine intelligence lies in the nature of their generative models and the way they learn from their environments. Friston’s work has implications for building AI systems that can learn and adapt in a more human-like way. See Designing Ecosystems of Intelligence from First Principles.[…]

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