In a compelling critique of the pursuit of artificial general intelligence (AGI), renowned computer scientist Peter J. Denning asserts that the journey for human-like AI may be fundamentally flawed. His recent work, "Turing’s Mistake: Escaping the Yoke of Unintelligent Machines," argues that a key proposal by Alan Turing, the father of theoretical computer science, paved a misguided path for AI development over the past 75 years.
Denning highlights that Turing's 1950 stance—that human intelligence could exist independently of a physical body and thus be simulated in software—has significantly shaped AI research. He takes aim at the longstanding belief that artificial intelligence can be effectively measured through the Turing test, a method designed to evaluate a machine's ability to exhibit behavior indistinguishable from that of a human.
According to Denning, the assumptions made about human cognition and machine capabilities have contributed to the current state of AI confusion and limitations. He warns that the future of AI development is unlikely to yield true human-level intelligence and instead may pose serious risks to society.
Central to Denning’s argument is the concept of tacit knowledge—the intricate body of understanding that humans possess but often cannot articulate or represent in a way that machines can comprehend. He identifies five key areas of tacit knowledge that resist machine learning: common sense knowledge, interpersonal interactions, emotional understanding, performance skills, and cultural context.
Historically, attempts to encapsulate common sense knowledge, such as Douglas Lenat's Cyc project, have yielded significant but inadequate results. Despite accumulating 25 million entries over four decades, this repository still lacks the depth of understanding needed to enable machines to think like experts.
Further complicating this landscape is the challenge of performance skills. As Denning points out, "know how" cannot be easily communicated to machines. For instance, while a skilled violinist can produce beautiful music, they often struggle to convey exactly how they achieve it. Even a robot, however adept, would be hindered by the inability to experience and interpret the emotions involved in both playing and listening.
A critical obstacle in achieving AGI is what Denning refers to as "the representation problem." For computational systems to function, they require data and instructions to be encoded in formats they can process, yet tacit knowledge inherently resists such encoding. Denning explains that the meaning behind words is rooted in deep reserves of tacit knowledge that machines simply cannot access. Current large language models, such as ChatGPT and others, operate by manipulating words without genuinely grasping their meanings.
He also emphasizes the importance of context, which adds layers of meaning to our communications and actions. This contextual richness allows humans to discern nuances like sarcasm versus sincerity or to choose between humor and seriousness based on the situation. Denning argues that discussions about context are intertwined with prior conversations, creating a complex web that machines cannot untangle.
Cultural knowledge presents yet another layer of complexity. Our beliefs, values, histories, and social dynamics shape communication in profound ways. Denning asserts that simply enhancing the size of neural networks will not enable these models to assimilate the embodied knowledge encapsulated in human culture, nor will it help them pass the Turing test.
Ultimately, Denning suggests a significant disconnect exists between human understanding and machine intelligence. As machine intelligence evolves, it may develop forms of tacit knowledge that remain opaque to humans. This divergence poses serious implications for AI safety; since machines cannot interpret the unarticulated nuances of human intent, aligning their actions with our interests could prove impossible.
Denning warns that as AI automation progresses, we may witness the emergence of machine intelligences that, while not fully achieving human-like capabilities, can still present substantial challenges to society. These machines may possess motivations that differ markedly from our own, raising concerns about their potential to create serious issues for humanity.
To navigate this uncertain future safely, Denning advocates for a conscious effort to reaffirm our humanity amid the rise of intelligent machines. He emphasizes the importance of recognizing and celebrating the differences that define human experience, resisting the notion of being subservient to machine intelligence, and acknowledging that an emerging AI landscape will require significant adjustments in our understanding and interactions with these technologies.
