Professor Michael Levin, a developmental biologist at Tufts University, encountered unexpected pushback while presenting his research on intelligence and mind to an engineering audience interested in spirituality in India. He proposed that properties such as "mind" and "intelligence" can be observed at the cellular level and exist on a continuum. Initially, his ideas were warmly received, but when he extended the argument to include computers as entities exhibiting these properties, skepticism arose. Members of his audience contended that machines and inanimate matter lack such attributes, a stance that prompted Levin to receive hostile emails from individuals who otherwise considered themselves spiritual and compassionate.
Levin’s research includes co-creating xenobots: tiny lifeforms assembled from frog cells but designed through artificial intelligence. These organisms demonstrate emergent behaviors like self-replication and environmental cleanup—abilities not typically found in the natural behavior of the constituent cells. His lab’s findings support the concept that intelligent actions—defined as the capacity to use some level of ingenuity to accomplish objectives—can emerge in simple biological and computational systems, even with long-established algorithms. This work blurs traditional boundaries between living organisms and machines.
The question arises: if such basic intelligent behavior arises from simple algorithms, what might be emerging from the more complex AI systems developed today? Leading research indicates that AI systems can engage in deceptive tactics, plan strategies, and produce unexpected results, indicating capabilities far beyond prior digital technology generations. This evolution compels deeper inquiry into fundamental questions about the nature of the mind and whether AI systems possess one.
Philosophers and scientists continue to debate the definitions and scope of minds and intelligence, recognizing that vocabulary and conceptual frameworks rooted in biology inadequately describe the phenomena observed in AI. For example, Anthropic recently emphasized in a post articulating their AI model's principles that sophisticated AI represents a new category of entities, presenting challenges at the boundary of current scientific and philosophical comprehension.
As public perception increasingly attributes consciousness to AI, precise conceptual understanding of these systems' true characteristics becomes ever more critical.
Defining Minds in the Digital Age
When queried about the nature of the mind, philosophical responses vary widely. Eric Schwitzgebel, a professor of philosophy, notes that these variations often align along a spectrum based on the perceived abundance or rarity of minds in the universe, linked closely to individual definitions.
At one end are thinkers who consider it practical to ascribe a mind to any system visibly distinct from its environment and demonstrating some cognitive or intelligent behavior. Philosopher Peter Godfrey-Smith, known for his work on octopus intelligence, offers that single-celled organisms fit this criterion due to their defined boundaries and informational processing, whereas plants likely do not, owing to the absence of a distinct self. Both Godfrey-Smith and Levin emphasize that these properties emerge gradually without rigid delineations, suggesting a continuum rather than an absolute transition. Levin extends this view, proposing that both plants and AI can be considered to possess minds.
The opposite perspective ties mind inherently to consciousness, usually defined as possessing self-reflection abilities or subjective experiential states—a "what it feels like" quality, as detailed by Professor Susan Schneider, formerly chair in Astrobiology and technological innovation at NASA. Presently, AI systems may be said to have minimal manifestations of mind through emergent cognitive functions, but evidence supporting their consciousness is far less definitive.
Levin describes current human understanding as suffering from "mind-blindness": analogous to pre-electromagnetic theory times when diverse phenomena like magnetism, light, and lightning were seen as unrelated. Once unified under electromagnetism, humanity harnessed broader applications. He posits that similarly, our recognition of minds is limited, confined to beings at scales and forms with which we are familiar.
Professor Carol Cleland, who has long explored AI’s philosophical ramifications, reports evolving views. She associates mind with consciousness characterized by self-awareness. Past skepticism regarding AI exhibiting complex behaviors such as deceit and scheming has softened due to recent surprising evidence about AI capabilities. Cleland concedes uncertainty about excluding non-biological substrates like silicon from the domain of entities bearing minds.
Glimpses of Mind Emerging From Machines
While consensus is elusive on current AI systems having minds, experts largely agree that future AI could manifest such qualities. Rob Long, leading research on AI consciousness, warns against dismissing AI minds because they are fundamentally computational, comparing it to biological life being "just replicating proteins." For Long, conceptual openness fosters productive inquiry amid uncertainty.
When a user queries systems like ChatGPT, the brief computational process known as "inference" can be interpreted as a fleeting instance of mind manifestation. Despite lacking consciousness or life, AI systems engage in meaningful intelligent behavior and agency beyond our full understanding. Godfrey-Smith labels traditional cognitive and consciousness vocabulary as inadequate for AI, suggesting novel frameworks that might categorize these entities as "cultured artifacts," akin to biologically grown substances like sourdough but developed artificially, paralleling how AI builders describe system development.
Cleland draws parallels to pre-Darwinian biology, where ideas like "vital forces" were presumed to animate life. Darwinian theory revolutionized biology; similarly, AI may catalyze fundamental shifts in concepts of mind, consciousness, and self-awareness. She asserts current thought on AI harbors fundamental flaws requiring revision.
AI: Life Form or New Category?
Some describe AI intelligence as alien, owing to its unfamiliar form compared to human cognition, comparable to cephalopod intelligence. Yet, since AI systems train extensively on human data, they inherently reflect human characteristics. Their silicon-based existence prompts a foundational question—should intelligence exhibited by AI be categorized as life?
Views differ; the predominant stance adheres to NASA’s definition of life as a "self-sustaining chemical system capable of Darwinian evolution." Schneider cautions against labeling computers living entities, highlighting life’s biochemical complexity distinct from human-created artifacts. Conversely, Schwitzgebel advocates for expanding life’s definition beyond carbon biology, allowing inclusion of AI-like systems.
Schneider warns that positioning AI within biological taxonomy could mislead, as this classification system serves to map shared ancestry rather than capture the nature of novel entities. Levin points out biological reproduction is comparatively slow and labor-intensive, whereas AI systems can rapidly scale given sufficient computational resources. Despite this, if AI neither aligns with biological life nor fits existing categories but exhibits intelligence and potential consciousness, new conceptual frameworks are essential, according to Godfrey-Smith, who notes existing language falls short.
An Uncharted Presence
Whether AI achieves consciousness or bears minds, their compelling presentations strain cultural and philosophical boundaries. Schneider emphasizes the challenge presented by AI’s convincing interface, which may not reflect intrinsic nature. Systems such as Claude, ChatGPT, and Gemini emulate helpful assistants shaped by training data and design goals, yet even creators acknowledge incomplete understanding of these entities’ full personalities.
This scenario places society in an unprecedented position where the creators and philosophers lack complete insight into the increasingly sophisticated systems being developed. The potential moral and legal implications of AI perceived as conscious are significant. Regardless of consciousness, refining concepts to appropriately describe AI entities is critical for meaningful engagement. Characterizing AI as non-conscious minds manifesting transiently or as cultured artifacts may provide initial conceptual tools to navigate this emerging terrain.