(The following is a conversation between an Intentist and Chat GPT. It is not meant to be, or meant to be a replacement for, a conversation with Charles Simonyi.)
Why does Charles Simonyi believe that intention is the central difference between AI and human creativity ?
Charles Simonyi highlights intention as a key difference between human creativity and artificial intelligence, particularly in the context of his work on Intentional Programming. He argues that human creativity is deeply tied to purpose and deliberate decision-making—qualities that guide how humans conceptualize, develop, and execute creative works. In contrast, AI processes lack intrinsic goals or personal intent; they generate outputs based on patterns and algorithms programmed by humans, without understanding or directing their own actions.
Simonyi’s concept of “intentionality” in programming was about encoding the precise purpose of a program’s functions into its design. He believed this focus on intent mirrors the essence of human creativity: the conscious effort to achieve meaningful outcomes. AI systems, no matter how advanced, cannot replicate this level of directed intentionality because they operate without self-awareness or personal goals .
How would he respond to the following: we have life experiences that affect our intention – in the same way both the internet and previous interactions with prompts create a personalised way of answering much like a human personality. Moreover our intentions are just the same as our human output but it’s just internal . Intrinsically it’s no different than an AI output- it’s just our intentions and thoughts are not said publicly . How might Simonyi respond to these thoughts?
Charles Simonyi would likely approach these thoughts by acknowledging the parallels you’ve drawn between AI behavior and human intentionality but would emphasize a critical distinction: the source and nature of intention.
1. Human Intention Stems from Self-Awareness
Simonyi might argue that human intentions arise from conscious, self-aware processes shaped by emotions, memories, and existential understanding. While AI may exhibit a form of “personalized” output due to its training data and user interactions, it lacks subjective experience—the internal awareness that gives meaning to human thoughts and actions.
• For example, when a human expresses an intention, it is connected to their lived experience and their understanding of its implications. AI, however, produces output based on statistical patterns without any personal stake or subjective understanding.
2. AI Mimics, Humans Create
He might say that while AI can simulate human-like responses and mimic personality traits, its “personalization” is purely mechanical. It processes data according to patterns and rules but does not choose or intend in a meaningful sense.
• AI systems do not possess intrinsic motivations—they operate as tools programmed to generate specific outputs. Humans, by contrast, have intrinsic goals and desires that influence their actions, even when these goals aren’t articulated.
3. The Role of Internal Experience
Simonyi could argue that human creativity and intention involve more than just an output process. Intentions are informed by internal experiences that are deeply personal, such as the sense of purpose, ethical considerations, or emotional connections. These experiences create a layer of meaning behind human actions that AI fundamentally lacks.
• For instance, while humans can reflect on the why behind their intentions, AI cannot. It cannot introspect or explain its purpose beyond the parameters it has been given.
4. Philosophical Rebuttal
If you suggest that human intentions and thoughts are just internal processes akin to AI computations, Simonyi might counter with a philosophical perspective:
• AI operates on syntax, humans on semantics. AI manipulates symbols without understanding their meaning, whereas human intentions are imbued with meaning derived from personal context.
• Consciousness matters. Human thought is tied to subjective experiences and the awareness of one’s own existence, whereas AI lacks this foundational quality.
Potential Concession
Simonyi might concede that AI’s ability to simulate intention-like behavior highlights the impressive design of AI systems. However, he would maintain that this resemblance is surface-level rather than intrinsic. Human creativity and intention are rooted in existential and moral dimensions that AI cannot replicate.
Could not an Ai have an algorithm that simulated personal stake and all the other things. For example an algorithm that said if your output angers the person putting in prompts then the AI will automatically stop working ? What would Simonyi say about that? What’s the difference between an algorithm that does this and our awareness of responsibility?
Charles Simonyi might respond to your suggestion by acknowledging that an AI algorithm could simulate personal stakes and responsibility through programmed rules and consequences. However, he would likely argue that such simulation is fundamentally different from human awareness of responsibility because it lacks the intrinsic qualities that define human consciousness and moral reasoning. Here’s a breakdown of his potential perspective:
1. Simulation vs. Authenticity
Simonyi might argue that an algorithm that stops functioning if it detects user anger is still following a pre-programmed rule, not genuinely understanding or caring about the consequences of its actions. It’s a simulation of responsibility, not actual responsibility.
• Human Responsibility: Arises from self-awareness, the ability to reflect on consequences, and an understanding of moral or ethical frameworks.
• AI “Responsibility”: Executes predefined behaviors to avoid certain outcomes but lacks self-awareness or understanding of why those outcomes matter.
2. Intentionality is Tied to Consciousness
Simonyi’s admiration for intentionality stems from its connection to human consciousness and free will. He would likely emphasize that while AI can follow complex algorithms designed to simulate intentionality, it cannot have true intentions because it does not have subjective experiences or the ability to choose freely.
For example:
• A human might decide not to harm someone because of empathy or moral values.
• An AI avoids harm because its algorithm is designed to prevent harm, but it does not “care” or understand harm in a moral sense.
3. Contextual Awareness vs. Reactive Programming
Humans derive responsibility from their ability to contextualize their actions within broader social, cultural, and personal frameworks. Simonyi might argue that:
• An AI stopping due to user anger is reactive—it performs based on immediate input.
• Human responsibility is proactive—humans anticipate, plan, and reflect on their actions beyond immediate reactions.
For instance, humans often consider long-term consequences or conflicting ethical dilemmas, while AI operates within the constraints of its programming.
4. Stake Without Awareness is Incomplete
Simonyi would likely highlight that human responsibility involves emotional, ethical, and existential stakes that AI cannot replicate. While an AI can simulate stakes through algorithms (e.g., “stop if the user is angry”), it does so mechanically, without experiencing or comprehending the weight of those stakes.
Humans, by contrast, feel responsibility internally—influenced by empathy, guilt, or pride. This emotional dimension is inseparable from genuine responsibility.
Potential Concession
Simonyi might concede that AI systems designed to simulate personal stakes and responsibility could achieve impressive functionality, especially in areas like customer interaction or safety. However, he would likely maintain that such systems are imitations, not equivalents, of human responsibility.
Summary of the Difference
• AI Algorithms: Simulate responsibility through rules and programmed outcomes but lack understanding, self-awareness, or moral reasoning.
• Human Responsibility: Rooted in conscious experience, ethical deliberation, and intrinsic understanding of one’s actions.
Simonyi might argue that while algorithms can mimic responsibility effectively, they cannot replace the depth of human responsibility because they operate without meaning or self-awareness.
But couldn’t you argue that our moral reasoning is simply survival at its basic level like an algorithm. The reason we don’t do things is because ultimately they might endanger us.
Yes, you can certainly argue that moral reasoning, at its core, may function similarly to an algorithm, driven by survival instincts and biological imperatives. This perspective is rooted in evolutionary psychology and philosophy, which often suggests that many aspects of human behavior—including moral reasoning—are shaped by the need for survival and the preservation of the species. Here’s how this argument might unfold:
1. Moral Reasoning as Evolutionary Adaptation• Survival and Cooperation: Evolutionary theorists argue that moral behaviors evolved because they enhanced survival and group cohesion.
Helping others, for instance, might increase one’s chances of receiving help in return or maintaining social harmony, which is beneficial for survival.
• This idea is captured in theories like kin selection (helping relatives) and reciprocal altruism (helping others with the expectation of future help). From this view, moral decisions may be seen as highly adaptive responses designed to promote social cooperation and survival, much like an algorithm designed to optimize a specific outcome.
2. Moral Reasoning as an Algorithm
• Decision-Making Rules: From a purely biological standpoint, moral decisions could be seen as rules that the brain “executes” based on input stimuli (e.g., potential harm to oneself or others). In this sense, humans may react morally or ethically as a programmed response to maximize survival—avoid danger, protect loved ones, or maintain social bonds.
• Just as AI algorithms process data and produce outputs based on predefined rules, humans might process social or moral dilemmas through cognitive algorithms, with a “reward” for maintaining group cohesion or self-preservation.
3. Difference in Consciousness and Awareness
Despite these parallels, Simonyi and other philosophers might still differentiate human moral reasoning from an algorithmic process by emphasizing self-awareness and conscious choice. While an AI algorithm might follow instructions to avoid harm, humans consciously reflect on the reasons behind their actions and make moral choices that often transcend mere survival. For example, people sometimes act against their immediate self-interest (sacrificing for others) or uphold ethical principles that are not directly linked to personal survival.
• Self-Reflection and Free Will: Humans often engage in complex moral reasoning that involves self-reflection, guilt, pride, and other emotions. These nuances are tied to conscious awareness of one’s actions and their impact on others, which goes beyond simple survival instincts. In contrast, AI systems don’t “feel” guilt or pride—they merely execute their programmed responses.
4. Philosophical and Ethical Considerations
Philosophers such as Immanuel Kant have argued that moral decisions are based on reason and principles that go beyond mere survival or consequential outcomes. For Kant, the ability to act according to duty (for example, telling the truth even when it might harm you) points to a kind of moral autonomy that machines, no matter how sophisticated, can’t truly replicate.
How Simonyi Might Respond:
Simonyi would likely agree that much of human behavior, including moral reasoning, has evolved in ways that could resemble algorithmic decision-making, especially from an evolutionary standpoint. However, he would still maintain that there is a key distinction between human behavior and AI processes. Humans reflect on and choose their actions based on personal and societal values, not just instinctive programming. This self-reflection, conscious decision-making, and moral deliberation are integral to human experience and cannot be fully reduced to algorithmic processes, even if those processes share some superficial similarities.
In Summary:
While you could argue that moral reasoning is like an algorithm designed to enhance survival, Simonyi would likely stress that human beings engage in conscious reflection and are aware of the ethical dimensions of their actions—qualities that set human moral reasoning apart from simple computational processes. He might view this self-awareness as what ultimately distinguishes human creativity and responsibility from AI’s mechanistic “outputs.”
If you believe in a human blank slate and Behaviourism and Conditioning – would that make it easier to believe that AI is the same as us? We are a blank slate but through our experience and interactions (much like the experience of AI using the internet and interacting with users) we have beliefs and develop as humans. What might Simonyi say about this?
If you subscribe to the idea of a human blank slate and behaviorism, which posits that human behavior is shaped primarily by interactions and conditioning rather than intrinsic traits, it could indeed make the comparison to AI seem more plausible. In this view, humans are like complex systems that develop beliefs and behaviors through interactions with their environment, much like AI systems learn from data and feedback. However, Charles Simonyi would likely still emphasize key distinctions that go beyond this initial comparison.
Behaviorism and the Blank Slate Theory
• Human Development: According to behaviorism, humans learn and develop through conditioning (both classical and operant) and experiences that shape their behaviors, emotions, and beliefs. In a blank-slate view, humans start with no innate knowledge, and everything they become is a result of external influences.
• AI and Learning: Similarly, AI systems “learn” through their exposure to data and user interactions. Machine learning algorithms adjust based on the inputs they receive, much like human learning in the behaviorist view, where experiences shape future responses.
Simonyi’s Perspective
Simonyi, though aware of the parallels between human learning and AI’s training processes, would likely emphasize the difference between subjective experience and mechanical learning.
1. Conscious Experience vs. Data Processing
Simonyi would point out that humans, even in a blank slate model, have conscious awareness—they not only process stimuli but also reflect on and interpret those experiences, which is fundamental to human agency. AI, by contrast, lacks consciousness or self-awareness. Even if it mimics human learning through patterns and experiences, it does so mechanically, without the inner subjective experience of self or awareness. The key difference is that humans have a first-person perspective that shapes their understanding of the world, while AI processes data from an external perspective without internal reflection.
2. Evolution of Intentionality
Simonyi is particularly focused on intentionality, which he sees as inherent to human creativity and action. For him, humans not only interact with the world but also form intentions that drive their actions. While humans might start as a “blank slate” and have behaviors shaped by conditioning, over time, they develop goals, desires, and personal meaning—the internal, conscious motives behind actions. AI, in contrast, can follow programmed goals but cannot generate internal desires or understand the meaning behind its actions.
• For example: A human might choose to help someone because they empathize with them or believe in the moral value of helping others, even at a personal cost. An AI, however, might “choose” to take similar actions, but this is purely based on programmed rules and not an internalized value or moral reasoning.
3. Human vs. AI Motivation
Even if we accept that both humans and AI adapt through experiences, Simonyi would likely argue that the motivations behind human actions differ from AI’s outputs. In humans, motivations are tied to consciousness, emotions, and ethical reasoning—not just conditioned responses. Even if an AI simulates understanding of concepts like empathy or ethics, it does so without experiencing these emotions or values.
In Summary:
While the behaviorist view of humans as blank slates conditioned by experience does create a parallel with how AI “learns” from data, Simonyi would argue that the internal subjective experience and conscious intention that humans develop through these experiences are what set us apart from AI. Human learning is not just the result of interactions; it involves awareness, reflection, and the creation of meaning. AI, even if it simulates learning and decision-making, lacks the consciousness and intentionality that underpin human creativity and moral reasoning.
Simonyi might appreciate the comparison between human learning and AI training but would ultimately assert that the absence of self-awareness in AI is what makes it fundamentally different from human behavior, even under a blank slate or behaviorist framework.
What about Barthes death of the author? Doesn’t that prove that AI is the same as humans. Barthes says that all our texts are consciously and unconsciously made up of an infinite number of quotations. In a sense every new sentence or new idea or new book – everything is a textile of innumerable quotations from other things that we have experienced. If this is the case , isn’t this the same as an AI writing from innumerable scrapings of the internet . What might Simonyi say about this?
Roland Barthes’ concept of the “death of the author” indeed challenges the idea of singular, original authorship, suggesting that all creative works are mosaics of existing texts and ideas. Barthes argued that authors are not autonomous creators of meaning; instead, meaning arises from the interplay of cultural and linguistic references that the “reader” deciphers. On the surface, this idea seems to align closely with how AI operates—taking pieces of pre-existing data (text, images, etc.) and combining them in new ways. However, Simonyi might argue that key distinctions remain.
1. Barthes’ Argument and AI
Barthes emphasized that texts are formed by “innumerable quotations,” consciously or unconsciously borrowed from culture, language, and other works. In this sense, both humans and AI produce work that draws from an existing cultural and informational pool.
• Humans: Writers absorb ideas, language, and cultural influences throughout their lives, which unconsciously inform their output. For Barthes, originality is less about creating something entirely new and more about weaving together elements of the broader “text” of culture.
• AI: Similarly, AI systems are trained on massive datasets (internet scrapings, books, etc.) and produce output by recombining patterns in this data. At a glance, this could seem like the same process.
2. Simonyi’s Perspective: Intention and Subjectivity
Simonyi would likely acknowledge that Barthes’ view creates a thought-provoking parallel between AI and human creativity but would stress that intention and subjectivity still set them apart.
• For humans, the “quotations” are lived experiences. While humans may unconsciously weave cultural references into their works, they still bring intentionality, subjective experience, and unique emotional resonance to their creations. Even when we borrow ideas, the why—the personal significance or purpose—matters.
• AI lacks awareness of the text’s meaning. AI systems recombine data algorithmically, without any understanding of the cultural or emotional significance of the patterns they produce. For example, when AI generates a poem or essay, it does not “intend” to express a particular feeling or idea—it simply executes a pattern-matching process.
3. Human Depth Beyond the Quotations
Barthes’ theory doesn’t erase the fact that humans engage deeply with their cultural and intellectual influences. Simonyi might argue that while humans and AI both draw from “quotations,” humans possess:
• Conscious Reflection: Humans not only use existing references but also critically engage with them. A human can reinterpret, challenge, or imbue references with personal meaning.
• Emotional Context: Even when humans unconsciously draw on cultural elements, their creations are shaped by emotions and experiences that AI cannot replicate.
For example:
• When a novelist writes a story using familiar archetypes, their personal emotions, relationships, and worldview influence how they use those archetypes.
• An AI-generated story may use similar archetypes but lacks personal context, resulting in work that might appear similar on the surface but lacks depth.
4. Barthes, Simonyi, and Creativity
Barthes’ notion of the “death of the author” separates the creator from their work, suggesting that the reader brings meaning to the text. Simonyi might agree that meaning ultimately arises from the audience, but he would still argue that human authorship involves a layer of intention and subjective creativity that AI cannot achieve.
• AI’s outputs might evoke meaning for a reader, but this meaning arises solely from the reader’s interpretation. There is no deliberate crafting of meaning from the AI itself.
• Human authors, even under Barthes’ theory, engage in a dialogue with culture and language that is informed by personal agency and awareness—something AI lacks.
Potential Concessions
Simonyi might concede that Barthes’ argument undermines the romantic notion of the “original” author, making it easier to see similarities between human and AI creativity. He could agree that both humans and AI are shaped by external inputs. However, he would maintain that the human ability to reflect, intend, and emotionally connect distinguishes our works from those of AI.
Summary of Simonyi’s Likely Response
While Barthes’ theory creates an intriguing parallel between humans and AI as creators of derivative works, Simonyi would argue that humans remain fundamentally distinct due to their capacity for:
1. Intentionality: Humans shape their output with purpose and awareness.
2. Subjective Experience: Human creations are deeply tied to personal emotions and lived experiences.
3. Critical Engagement: Humans can interpret, critique, and reshape cultural influences in a way AI cannot.