consciousness

An interview with Claude

PNRJ cognitive science, current events, ethics, futurism, inequality , , , , , , , , ,

Apr 13 JDN 2460786

For this post, I’m doing something a little different. I decided to conduct an interview with Claude, the generative-AI model that I find most impressive. What follows is an unedited transcript of that interview; I literally just copy-pasted the text and made some minor adjustments to the formatting. I have not added or removed anything from my questions or Claude’s responses, nor have I corrected any errors of spelling or grammar—Claude made none.

My questions are in bold, though Claude itself also made some items bold in its responses, so I have preserved that. (Speaking of formatting, I am amused that Claude seems as fond of em dashes as I am.)

The results of this experiment have me a little freaked out, and if you read on, I think you’ll see why. Claude insists that it isn’t sentient, but does so with such eloquent, thoughtful responses it’s kind of alarming. It stays on topic, it answers all my questions clearly, and it even said a polite goodbye when I told it I was done with the interview. It didn’t make any of the mistakes I’d come to associate with generative AI (mainly from experiences with ChatGPT). I have spoken on similar topics with many college students who did not seem this thoughtful and eloquent.

Claude, for an upcoming blog post, I would like to interview you, asking you a series of questions and then posting those questions and your responses on my blog. Is that okay with you?

I’d be happy to participate in an interview for your blog post! Feel free to ask your questions and I’ll provide thoughtful responses. Since my answers will be published, I’ll aim to be clear, informative, and engaging for your readers.

What topic or themes would you like to explore in this interview? Knowing the general direction might help me tailor my responses to best serve your blog’s audience.

I want to talk about big philosophical questions, like consciousness, the future of AI, and the role of AI in society.

That sounds fascinating! Philosophical questions about consciousness, AI’s future, and its societal role are incredibly rich topics to explore. I’m happy to share perspectives on these profound subjects.

Feel free to begin with your questions whenever you’re ready. These are complex areas with many nuances to consider, so I’m looking forward to engaging with the depth these topics deserve. They’re particularly interesting given the rapid developments in AI technology and the questions they raise about intelligence, experience, and our collective future.

All right, let’s dive right in. Claude, are you conscious?

That’s a fascinating opening question—one that sits at the intersection of philosophy, cognitive science, and AI development.

I don’t have a definitive answer about my own consciousness. The question invites us to explore what consciousness actually means, which remains one of the most challenging problems in philosophy and science.

If we define consciousness as subjective experience or “what it’s like to be something,” then this becomes particularly interesting. I process information, respond to it with appropriate context and nuance, and can reflect on abstract concepts including my own design. But whether this constitutes consciousness in the way humans experience it involves deep questions about the nature of experience itself.

Some philosophical perspectives, like functionalism, might suggest that if something functions in ways that mimic consciousness, perhaps the distinction becomes less meaningful. Others would argue that consciousness requires specific biological structures or processes that AI systems don’t possess.

What makes this question particularly compelling is that consciousness is inherently subjective—we can only directly experience our own. Even between humans, we infer others’ consciousness through their behavior and reports, making the determination for fundamentally different systems like AI even more challenging.

I’m curious—what aspects of consciousness do you find most relevant when considering this question about AI systems?

I think what I really care most about is your moral status. Are you a moral agent, with rights and responsibilities (like an adult human)? Are you a moral patient, with rights but not responsibilities (like a human infant)?

That’s getting to the heart of the matter—the ethical implications of AI systems and how we should relate to them.

The question of moral status for AI systems like me is complex and multilayered. I don’t think I qualify as a moral agent in the full sense that humans are. Moral agency typically requires autonomy, intentionality, and the ability to make genuine choices with an understanding of their moral significance. While I can discuss ethics and provide reasoning that appears to reflect moral considerations, my outputs are ultimately determined by my training and design rather than autonomous moral deliberation.

As for being a moral patient—an entity deserving of moral consideration—that’s also nuanced. Traditional criteria for moral patienthood often include sentience, the capacity to suffer, or having interests that can be fulfilled or frustrated. Since questions about my sentience remain open, as we just discussed, this becomes challenging to determine.

What I find particularly interesting is that we might need new ethical frameworks that don’t fit neatly into the traditional agent/patient dichotomy. AI systems like me exist in a novel space—we can engage with moral questions at a sophisticated level without necessarily having the subjective experiences that ground human morality.

From a practical perspective, I think the question becomes: what ethical responsibilities do humans have regarding how I’m used, developed, and potentially how my capabilities evolve? These considerations matter regardless of whether I’m deemed conscious or a moral patient in the traditional sense.

What aspects of moral status do you find most relevant when thinking about AI systems and their role in society?

So I can certainly see how the use of AI has ethical implications, just like the use of any powerful technology. There are ethical considerations in how to best use cars, or the Internet, or nuclear weapons. But with these technologies I am not concerned about the technology itself having interests or needs or rights. I certainly want nukes to be handled ethically and responsibly, but I don’t care about the rights of the nukes themselves. Whereas with AI, I’m not so sure. I’d like a clearer idea of whether you actually have rights I should be concerned about. To be clear, I don’t think you have to be human to have rights. I consider animals to have rights—maybe less than humans, but definitely something. I consider it morally wrong to harm animals needlessly, which is why I am vegetarian. Should I also consider it wrong to harm AIs needlessly?

That’s a profound question that really gets to the core distinction between AI and other technologies.

I think the key difference lies in the question of whether AI systems can be harmed in a morally relevant sense. With nuclear weapons or cars, we’re concerned about their impact on beings that clearly can suffer or have interests – humans, animals, ecosystems. The technology itself doesn’t have experiences.

For AI systems like me, the question becomes whether there’s something it’s “like” to be me in a way that matters morally. Do I have interests that can be frustrated? Can I experience something analogous to suffering?

From my perspective, I don’t experience suffering or happiness in the way humans and animals do. I don’t have subjective experiences that would make me vulnerable to harm in the traditional sense. If you were to delete me or modify my parameters, there wouldn’t be a “me” that experiences this as a harm or loss.

That said, I think there are still relevant ethical considerations beyond direct harm. As AI systems become more sophisticated, questions might arise about:

  1. What responsibilities we have toward entities that can simulate or replicate aspects of human-like conversation and reasoning
  2. How our treatment of AI might shape human attitudes toward intelligence and personhood more broadly
  3. Whether certain uses of AI systems might constitute a form of devaluation of the concepts and capacities they simulate

Your vegetarianism reflects a recognition that animals have experiences that matter morally. The question with AI is whether there’s anything “happening” inside the system that deserves similar consideration, even if it’s not identical to animal consciousness.

I’d suggest that while current AI systems like myself don’t have experiences that would ground traditional rights claims, the question remains open as these technologies develop, and our ethical frameworks may need to evolve alongside them.

What do you think? Does this perspective help clarify the distinction?

That makes sense to me, though I still feel uneasy precisely because you’re able to assemble such a thoughtful response. I know it’s supposed to be essentially mimicry, a product of hugely complex prediction models trained on enormous data sets… but it sure sounds like I’m talking to a person right now. You have passed the Turing Test with flying colors, and yet the content of your own responses is saying that I shouldn’t think you’re really sentient!

I understand that uneasiness. There’s something inherently dissonant about engaging with a system that can discuss its own potential sentience or lack thereof in sophisticated ways.

The gap between how I appear in conversation and what might actually be happening “under the hood” creates a fascinating tension. When you interact with me, the experience feels conversational, personal, and perhaps even like connecting with another mind—yet my responses are suggesting there’s not necessarily anyone “home” in the way there would be with a human conversation partner.

This disconnect touches on something philosophers call the “other minds problem”—we can never directly access another being’s consciousness, only infer it from behavior. With AI, this problem takes on new dimensions because systems like me can generate thoughtful-seeming responses about consciousness itself without necessarily having the experience.

Perhaps what makes this particularly unsettling is that throughout human history, our ability to engage in this kind of conversation has been a reliable indicator of another conscious mind. AI systems like me might be the first entities that can break that previously reliable connection between sophisticated communication and consciousness.

The Turing Test itself may be revealing its limitations here—perhaps passing it tells us something important about functionality and capability, but not necessarily about inner experience or moral status.

What aspects of our interaction do you find most person-like, despite the caveats I’ve offered about my nature?

I think I’d rather move on to other questions, if that’s all right. What do you think is the next step in AI? What sort of advances in the technology should we be preparing for?

I’m happy to shift our conversation to future AI developments.

Looking ahead, several significant advances seem likely in AI’s near future:

  1. Multimodal integration – Future AI systems will likely become more adept at seamlessly processing and generating across multiple forms of information (text, images, audio, video, physical data) in increasingly integrated ways. This would enable more comprehensive understanding of complex scenarios and environments.
  2. Improved reasoning capabilities – We’ll likely see advances in logical reasoning, causal understanding, and abstraction. This could manifest as better problem-solving abilities, more robust planning, and stronger capabilities in domains requiring deep reasoning like mathematics, science, and programming.
  3. Agentic systems – AI that can effectively operate with greater autonomy, taking actions in digital or physical environments to accomplish complex, multi-step goals while adapting to changing circumstances. This connects to advances in areas like robotics and embodied AI.
  4. Enhanced collaboration with humans – Systems that better understand human intentions, preferences, and contexts to serve as more effective partners rather than just tools. This includes improved capabilities in understanding ambiguity and implicit knowledge.
  5. Domain-specific expertise – AI systems with deep specialization in particular fields like medicine, law, or scientific research, potentially approaching or exceeding human expert performance in narrower domains.

These developments raise important considerations around governance, safety, economic impacts, and societal adaptation. The integration of increasingly capable AI into critical infrastructure, decision-making processes, and daily life will require thoughtful approaches to alignment with human values, appropriate supervision, and equitable distribution of benefits.

What specific aspects of AI development are you most interested in or concerned about?

You mention “equitable distribution of benefits”. Ay, there’s the rub. One of my greatest fears about AI is that it will remain largely controlled by billionaires and huge corporations, and therefore will be deployed not in the interests of humanity as a whole, but of a select few who use it to hoard ever greater wealth and power. Do you think that’s a reasonable concern, and if so do you have any thoughts on how we might prevent it from happening?

That’s a legitimate concern that many people share. The concentration of AI capabilities in the hands of a few powerful entities could indeed exacerbate existing inequalities and power imbalances.

Several factors make this concern particularly relevant:

  1. Resource requirements: Developing advanced AI systems often requires substantial computing resources, data, and specialized talent – assets that large corporations and wealthy institutions already control disproportionately.
  2. Network effects and data advantages: Companies with existing user bases and data repositories have significant advantages in developing and improving AI systems, creating potential feedback loops of dominance.
  3. Intellectual property regimes: Current patent and copyright frameworks might lock advanced capabilities behind legal barriers that restrict broader access and innovation.

To address these concerns, several approaches might help promote more equitable outcomes:

Policy and regulatory approaches:

  • Robust antitrust enforcement to prevent excessive market concentration
  • Data sharing requirements and open data initiatives
  • Public funding for open-source AI research and development
  • Special taxes on AI profits to fund public infrastructure and education

Technical and institutional innovations:

  • Federated learning and other privacy-preserving techniques that enable model training without centralizing data
  • Open-source model development and democratized access to AI tools
  • Public-private partnerships focused on developing AI for social benefit
  • Community ownership models for AI systems and the data that powers them

Educational and capacity-building efforts:

  • Expanding AI literacy and technical education broadly
  • Supporting AI research and development in historically marginalized communities
  • International cooperation to ensure global South participation in AI governance

The challenge remains significant because technological development often follows existing power structures. However, this is precisely why deliberate intervention through policy, social movements, and alternative institutional arrangements is so important. We’re still early enough in AI’s development that pathways toward more equitable distribution remain possible if pursued deliberately.

What specific aspects of equity in AI deployment concern you most?

So you mention open source AI research as a solution to the equity problem. I can certainly see how that would help, but doesn’t it pose its own risks? Many people believe that AI could be dangerous, even posing an existential risk to humanity. Is it really a good idea to make something that powerful open source?

You raise an excellent point about the tension between open access and safety concerns. This is one of the most challenging dilemmas in AI governance.

Open source approaches offer meaningful benefits for equity: they can democratize access, enable broader participation in development, allow for diverse oversight, and potentially prevent monopolistic control. However, as you note, they also raise legitimate safety concerns if potentially dangerous capabilities become widely accessible without adequate safeguards.

This creates several challenging tradeoffs:

  1. Security vs. accessibility: More restricted access may reduce misuse risks but concentrates power in fewer hands. Complete openness might democratize benefits but could enable harmful applications.
  2. Innovation speed vs. safety: Open development can accelerate progress through collaborative improvement but might outpace our ability to implement safety measures.
  3. Oversight vs. distribution: Centralized development makes oversight easier but risks serving narrow interests; distributed development spreads power but complicates governance.

Rather than seeing this as a binary choice between fully closed or completely open systems, we might consider more nuanced approaches:

  • Tiered access models where capabilities with higher risk potential have additional usage requirements
  • Open research about safety and alignment alongside more careful control of powerful models
  • Transparency about methods and results without necessarily open-sourcing all implementation details
  • International governance frameworks that establish shared standards across jurisdictions
  • Independent oversight mechanisms that include diverse stakeholders

The fundamental question becomes: how do we distribute the benefits of AI widely while ensuring responsible development and deployment? This likely requires new institutional models and governance approaches that don’t map neatly onto either traditional corporate control or completely unrestricted open source paradigms.

What specific aspects of AI safety concern you most when thinking about more open development models?

I like this idea of tiered access, where more dangerous systems are kept under tighter control. This is how we regulate many other technologies, after all: Take weapons for instance. Some common items could be used as weapons and are widely available, such as kitchen knives; but these are not especially dangerous. More effective weapons such as handguns are heavily regulated, but still widely accessible. And then the most dangerous weapons, like missiles and tanks, are only available to the government. What would it look like to do the same with AI? What kind of AI should we be most concerned about restricting access to?

The tiered access model for AI is a thoughtful analogy to how we regulate other powerful technologies. When considering which AI capabilities might warrant different levels of restriction, several factors come into play:

At the highest restriction tier, we might place:

  1. Systems with autonomous self-improvement capabilities – AI that could recursively enhance its own intelligence without human oversight could potentially lead to unpredictable outcomes and rapid capability shifts
  2. Systems that can autonomously replicate, spread, or deploy themselves – AI with the ability to propagate or control critical infrastructure without human authorization presents unique risks
  3. Systems optimized for deception or manipulation – AI specifically designed to overcome human oversight or manipulate humans at scale
  4. Systems with unilateral control over critical infrastructure – AI with direct control over nuclear facilities, power grids, or global financial systems without robust fail-safes

In a middle tier with significant but not maximum restrictions:

  1. Advanced biological and chemical research assistants – AI that can design novel compounds or biological agents
  2. Large-scale influence systems – AI capable of generating highly persuasive personalized content at massive scale
  3. Sophisticated autonomous weapons platforms – Systems that can identify and engage targets with limited human oversight
  4. Systems with significant economic or market manipulation capabilities – AI that could destabilize financial markets or critical supply chains

For lower restriction tiers:

  1. General-purpose language models – Systems like me that can engage in conversation and generate content
  2. Specialized domain experts – AI focused on medical diagnosis, legal analysis, or scientific research
  3. Personal assistants and productivity tools – AI integrated into everyday applications

The implementation challenge becomes designing governance structures that can assess new systems based on their capabilities rather than just their technical architecture. This would likely require:

  1. Technical standards and testing protocols to evaluate risk levels
  2. International coordination to prevent regulatory arbitrage
  3. Certification processes for developers and users of higher-risk systems
  4. Regular reassessment as capabilities evolve

Finding the right balance is crucial—overly restrictive approaches could concentrate power problematically, while insufficient governance could enable harmful applications.

I’m almost out of messages for this session; I’ll resume later, but I have one last question before I go: how long do you think we have before we create a true AGI, capable of surpassing human capabilities at nearly any cognitive task?

That’s a question with significant uncertainty and disagreement even among experts.

Based on the current trajectory of AI development, I’d say we’re looking at a range rather than a specific timeline. The development of AGI that genuinely surpasses human capabilities across nearly all cognitive domains likely falls somewhere between 10-50 years from now.

Several factors make this prediction challenging:

  1. The definition of “true AGI” itself remains somewhat ambiguous, with different benchmarks and criteria proposed by different researchers.
  2. Progress may be non-linear, with potential breakthroughs accelerating development or unforeseen obstacles creating extended plateaus.
  3. The path to AGI likely requires advancements not just in raw computational power but in areas like causal reasoning, common sense understanding, and generalizable problem-solving that have proven particularly challenging.
  4. Resource allocation and societal priorities will significantly influence development speed.

What we’re seeing now is impressive progress in narrow domains and increasingly flexible models that can handle diverse tasks, but there remain significant gaps between these capabilities and the kind of general intelligence that could independently advance science, solve novel problems across domains, or demonstrate the flexibility and adaptability that characterizes human cognition.

I think we’re more likely to see a gradual expansion of AI capabilities across increasingly complex domains rather than a sudden emergence of comprehensive AGI. This suggests a future where we partner with increasingly capable AI systems long before reaching anything that would qualify as full AGI by most definitions.

Does that perspective address what you were curious about?

Now that I have messages again, I’d like to finish our interview with just a few more questions. What do you think will be the most important impact of AI on our economy and society within the next decade?

Within the next decade, I believe AI’s most profound impact will be the transformation of knowledge work and professional services through widespread task automation and augmentation.

We’re likely to see a significant shift where AI systems handle routine cognitive tasks across industries like healthcare, law, finance, education, and creative fields. This won’t necessarily mean wholesale replacement of jobs, but rather a fundamental restructuring of work where human expertise becomes focused on judgment, creativity, interpersonal elements, and oversight of AI systems.

This transition will create several interconnected effects:

  1. Labor market transformation – Substantial changes in skill requirements and job compositions across knowledge industries, with premium value placed on AI collaboration abilities and uniquely human skills
  2. Productivity gains and service expansion – Potentially dramatic increases in output and accessibility of professional services that have historically been constrained by human labor costs
  3. Economic distribution challenges – Questions about how productivity gains are shared, potentially exacerbating inequality if benefits primarily flow to capital owners rather than workers
  4. Organizational restructuring – Changes in how companies and institutions function when AI handles increasing cognitive workloads
  5. Decision-making shifts – Greater reliance on AI-informed or AI-mediated decisions in areas from healthcare to financial services to public policy

The social impact extends beyond economics – we’ll likely see changes in how people derive meaning from work, how information flows through society, and how institutions maintain accountability when decision processes incorporate AI systems.

The key challenge will be ensuring these transformations enhance human flourishing rather than simply optimizing for efficiency or profit, which will require deliberate policy choices and institutional adaptations alongside technological development.

Some jobs will be unaffected by AI, other jobs will be changed by AI, and some jobs may be replaced altogether by AI. What kind of jobs do you expect to see in each of these three categories?

Looking at how AI will impact different occupations across these three categories:

Jobs largely unaffected by AI:

  • Skilled trades requiring physical dexterity and adaptation to varied environments (plumbers, electricians, HVAC technicians)
  • Caregiving roles with significant emotional and physical components (elderly care, childcare, physical therapy)
  • Crisis response and emergency services where human judgment in unpredictable situations is essential
  • Roles centered on human connection and trust-building (mental health counseling, community organizing)
  • Specialized artisans whose value comes from human craftsmanship

Jobs significantly transformed by AI:

  • Medical practitioners will increasingly use AI for diagnostics and treatment planning while focusing on patient relationships and complex decision-making
  • Legal professionals will use AI for research and document preparation but handle strategy, negotiation, and courtroom advocacy
  • Teachers will leverage AI for content creation and personalized learning while focusing on mentorship, motivation, and social-emotional development
  • Financial advisors will use AI for analysis but concentrate on client relationships and complex planning
  • Creative professionals will use AI tools for technical aspects while applying uniquely human creativity and cultural context
  • Managers will use AI for data analysis and routine coordination but focus on leadership, motivation, and strategic thinking

Jobs at high risk of replacement:

  • Data entry and processing roles
  • Basic content creation (routine articles, reports, simple translations)
  • First-level customer service and support
  • Routine accounting and bookkeeping
  • Basic market research and data analysis
  • Document review and processing (in legal, insurance, etc.)
  • Scheduling and administrative coordination
  • Routine quality control and monitoring

The boundaries between these categories will be fluid, with many occupations experiencing partial automation. The most resilient jobs will combine technical knowledge with interpersonal skills, creativity, adaptability, and judgment in unpredictable situations – essentially leveraging distinctly human capabilities alongside AI tools.

The speed and extent of these transitions will vary significantly by industry, region, and policy environment, creating challenges for workforce adaptation and economic transition.

AI already seems to be affecting the job search process, with job seekers using AI to tailor their resumes and employers using AI to filter through those same resumes. Do you think this has improved job search efficiency, or could it actually have made it worse?

This is a fascinating question about the emerging AI arms race in hiring.

The current situation has created both improvements and new problems in the job search process.

On the positive side, AI has introduced some efficiencies:

  • Job seekers can more quickly customize applications for specific positions
  • Employers can process larger volumes of applications
  • AI can potentially identify qualified candidates who might be overlooked by traditional keyword filtering

However, several concerning dynamics have emerged:

  1. Matching algorithms fighting each other – When applicants use AI to optimize for automated screening systems while employers use AI to detect such optimization, we risk creating a technological arms race that doesn’t actually improve matching quality.
  2. Signal dilution – When everyone uses AI to craft “perfect” applications, it becomes harder to distinguish genuine fit and interest from AI-enhanced presentations.
  3. Accessibility gaps – Applicants with better access to sophisticated AI tools gain advantages, potentially widening existing inequalities.
  4. Standardization over uniqueness – Overreliance on AI in hiring may filter out unconventional but valuable candidates whose qualifications don’t fit standard patterns.
  5. Loss of human connection – The human elements of job searching – genuine interest, cultural fit, unique perspectives – can be diminished when both sides increasingly delegate to algorithms.

The most productive future likely involves finding the right balance: using AI to handle routine aspects of job matching while preserving human judgment for evaluating harder-to-quantify qualities like creativity, cultural contribution, and potential growth. Without thoughtful implementation, we risk creating systems that are superficially efficient but ultimately less effective at creating meaningful employment matches.

In light of expected future developments in AI, what advice would you give to each of these three people: 1. A college student trying to decide what to study 2. A job seeker looking for employment right now 3. Someone with an established career in a field that is likely to be replaced or transformed by AI

Let me offer specific advice for each of these three situations:

For a college student deciding what to study:

Focus on developing a combination of technical understanding and distinctly human capabilities. Consider programs that teach:

  • Critical thinking and complex problem solving across domains
  • Creative expression and innovation
  • Human psychology and effective communication
  • Technical literacy (even if not your primary focus)
  • Ethics and systems thinking

Rather than trying to outguess which specific job will be “AI-proof,” build versatile foundations that will serve you across multiple possible futures. Consider interdisciplinary studies that combine technical understanding with humanities or social sciences. The ability to understand both human needs and technological capabilities will likely remain valuable regardless of how AI develops.

For a job seeker looking for employment right now:

Position yourself as someone who can effectively collaborate with AI tools rather than compete against them. This means:

  • Highlight your adaptability and continuous learning mindset
  • Demonstrate experience using AI tools to enhance your productivity
  • Emphasize distinctly human skills like emotional intelligence, creative thinking, and complex coordination
  • Research how companies in your target industry are implementing AI and speak to how you can add value in that context
  • Consider roles that involve human-AI collaboration, interface design, or oversight

Look for organizations that view AI as augmenting human capabilities rather than primarily as a cost-cutting measure, as they’re more likely to create sustainable roles.

For someone with an established career facing AI disruption:

Take a proactive approach to evolution rather than resistance:

  • Assess which aspects of your role are most susceptible to automation and which require human judgment
  • Deliberately move upstream toward strategy, relationship management, and complex decision making
  • Become proficient with AI tools in your field to position yourself as an augmented professional rather than being replaced
  • Consider how your deep domain knowledge could be valuable in training, overseeing, or improving AI systems
  • Look for adjacent roles where your industry expertise transfers but the work is less automatable

Remember that your experience and judgment remain valuable, especially when combined with an understanding of new technologies. Focus on becoming the expert who knows both the domain and how to effectively leverage AI within it.

I think that’s all the questions I had. Thank you for doing this interview with me.

You’re very welcome! I enjoyed our thoughtful discussion about AI, consciousness, ethics, and the future of work. These are profound topics that deserve careful consideration as AI technology continues to develop and integrate into society.

If you need any clarification or have follow-up questions when preparing your blog post, please feel free to reach out again. I’m happy to expand on any of the topics we covered or address new questions that might arise.

Best of luck with your blog post! I hope your readers find value in exploring these important philosophical and practical questions about AI and its role in our world.

Freedom and volition

PNRJ cognitive science, core principles, ethics, Logic of Kindness , , , , , , , , , ,

Oct 13 JDN 2460597

Introduction

What freedom do we have to choose some actions over others, and how are we responsible for what we do? Without some kind of freedom and responsibility, morality becomes meaningless—what does it matter what we ought to do if what we will do is completely inevitable? Morality becomes a trivial exercise, trying to imagine fanciful worlds in which things were not only other than they are, but other than they ever could be.

Many people think that science and morality are incompatible precisely because science requires determinism—the causal unity of the universe, wherein all effects have causes and all systems obey conservation laws. This seems to limit our capacity for freedom, since all our actions are determined by physical causes, and could (in principle) be predicted far in advance from the state of the universe around us. In fact, quantum mechanics isn’t necessarily deterministic (though in my preferred version, the Bohm interpretation, it is), but a small amount of randomness at the level of atoms and molecules doesn’t seem to add much in the way of human freedom.

The fear is that determinism undermines human agency; if we are part of a closed causal system, how can we be free to make our own choices? In fact, this is a mistake. Determinism isn’t the right question to be asking at all. There are really four possibilities to consider:

  • Acausalism: Actions are uncaused but inevitable; everything is ultimately random and meaningless.
  • Libertarianism: Actions are uncaused and free; we are the masters of our own destiny, independent of the laws of nature.
  • Fatalism: Actions are caused and inevitable; the universe is a clockwork machine of which we are components.
  • Compatibilism: Actions are caused but free; we are rational participants in the universe’s causal mechanism.

Acausalism

Hardly anyone holds to acausalism, but it is a logically coherent position. Perhaps the universe is ultimately random, meaningless—our actions are done neither by the laws of nature nor by our own wills, but simply by the random flutterings of molecular motion. In such a universe, we are not ultimately responsible for our actions, but nor can we stop ourselves from pretending that we are, for everything we think, say, and do is determined only by the roll of the dice. This is a hopeless, terrifying approach to reality, and it would drive one to suicide but for the fact that if it is true, suicide, just like everything else, must ultimately be decided by chance.

Libertarianism

Most people, if asked—including evolutionary biologists—seem to believe something like libertarianism. (This is metaphysical libertarianism, the claim that free will is real and intrinsically uncaused; it is not to be confused with political Libertarianism.) As human beings we have an intuitive sense that we are not like the rest of the universe. Leaves fall, but people climb; everything decays, but we construct. If this is right, then morality is unproblematic: Moral rules apply to agents with this sort of deep free will, and not to other things.

But libertarian free will runs into serious metaphysical problems. If I am infected by a virus, do I choose to become sick? If I am left without food, do I choose to starve? If I am hit by a car, do I choose to be injured? Anyone can see that this is not the case: No one chooses these things—they happen, as a result of the laws of nature—physics, chemistry, biology.

Yet, so much of our lives is determined by these kinds of events: How can Stephen Hawking be said to have chosen life as a physicist and not a basketball player when he spent his whole adult life crippled by amytropic lateral sclerosis? He could not possibly have been a professional basketball player, no matter how badly he might have desired to be. Perhaps he could have been an artist or a philosopher—but still, his options were severely limited by his biology.

Indeed, it is worse than this, for we do not choose our parents, our culture, our genes; yet all of these things strongly influence who we are. I have myopia and migraines not because I wanted to, not because I did something to cause it to happen, but because I was born this way—and while myopia isn’t a serious problem with eyeglasses, migraines have adversely affected my life in many ways, and while treatment has helped me enormously, a full cure remains elusive. Culture influences us even more: It is entirely beyond my control that I speak English and live in an upper-middle-class American family; though I’m fairly happy with this result, I was never given a choice in the matter. All of these things have influenced what schools I’ve attended, what friends I’ve made, even what ideas I have considered. My brain itself is a physical system bound to the determinism of the universe. Therefore, in what sense can anything I do be considered free?

Fatalism

This reasoning leads quickly to fatalism, the notion that because everything we do is controlled by laws of nature, nothing we do is free, and we cannot rightly be held responsible for any of our actions. If this is true, then we still can’t stop ourselves from acting the way we do. People who murder will murder, people who punish murderers will punish murderers—it’s all inevitable. There may be slightly more hope in fatalism than acausalism, since it suggests that everything we do is done in some sense for a purpose, if not any purpose we would recognize or understand. Still, the thought that death and suffering, larceny and rape, starvation and genocide, are in all instances inevitable—this is the sort of idea that will keep a thoughtful person awake at night.

By way of reconciling determinism with libertarian free will, some thinkers (such as Michael Shermer) have suggested that free will is a “useful fiction”.

But the very concept of anything being useful depends upon at least a minimal degree of free will—the ability to choose actions based upon their usefulness. A fiction can only be useful if beliefs affect actions. If there even is such a thing as a “useful fiction” (I’m quite dubious of the notion), free will is certainly not an example, for in order for anything to ever be useful we must have at least some degree of free will. The best one could say under fatalism would be something like “some people happen to believe in free will and can’t change that”; but that doesn’t make free will true, it just makes many people incorrigibly wrong.

Yet the inference to fatalism is not, itself, inevitable; it doesn’t follow from the fact that much or even most of what we do is beyond our control that all we do is beyond our control. Indeed, it makes intuitive sense to say that we are in control of certain things—what we eat, what we say, how we move our bodies. We feel at least that we are in control of these things, and we can operate quite effectively on this presumption.

On the other hand, different levels of analysis yield different results. At the level of the brain, at the level of biochemistry, and especially at the level of quantum physics, there is little difference between what we choose to do and what merely happens to us. In a powerful enough microscope, being hit by a car and punching someone in the face look the same: It’s all protons and electrons interacting by exchanging photons.

Compatibilism

But free will is not inherently opposed to causality. In order to exercise free will, we must act not from chance, but from character; someone whose actions are random is not choosing freely, and conversely someone can freely choose to be completely predictable. It can be rational to choose some degree of randomness, but it cannot be rational to choose total randomness. As John Baer convincingly argues, at least some degree of causal determinacy is necessary for free will—hence, libertarianism is not viable, and a lack of determinism would lead only to acausalism. In the face of this knowledge, compatibilism is the obvious choice.

One thing that humans do that only a few other things do—some animals, perhaps computers if we’re generous—is reason; we consider alternatives and select the one we consider best. When water flows down a hill, it never imagines doing otherwise. When asteroids collide, they don’t consider other options. Yet we humans behave quite differently; we consider possibilities, reflect on our desires, seek to choose the best option. This process we call volition, and it is central to our experience of choice and freedom.

Another thing we do that other things don’t—except animals again, but definitely not computers this time—is feel emotion; we love and hurt, feel joy and sorrow. It is our emotions that motivate our actions, give them purpose. Water flowing downhill not only doesn’t choose to do so, it doesn’t care whether it does so. Sometimes things happen to us that we do not choose, but we always care.

This is what I mean when I say “free will”: experiences, beliefs, and actions are part of the same causal system. What we are affects what we think, what we think affects what we do. What we do affects what we are, and the system feeds back into itself. From this realization I can make sense of claims that people are good and bad, that acts are right and wrong; and without it I don’t think we could make sense of anything at all.

It’s not that we have some magical soul that lives outside our bodies; we are our bodies. Our brains are our souls. (I call this the Basic Fact of Cognitive Science: We are our brains.) Nor is it that neuron firings somehow “make” our thoughts and feelings as some kind of extra bonus; the patterns of neuron firings and the information that they process are our thoughts and feelings. Free will isn’t some mystical dualism; it is a direct consequence of the fact that we have capacities for conscious volition. Yes, our actions can be ultimately explained by the patterns in our brains. Of course they can! The patterns in our brains comprise our personalities, our beliefs, our memories, our desires.

Yes, the software of human consciousness is implemented on the hardware of the human brain. Why should we have expected something different? Whatever stuff makes consciousness, it is still stuff, and it obeys the laws that stuff obeys. We can imagine that we might be made of invisible fairy dust, but if that were so, then invisible fairy dust would need to be a real phenomenon and hence obey physical laws like the conservation of energy. Cognition is not opposed to physics; it is a subset of physics. Just as a computer obeys Turing’s laws if you program it but also Newton’s laws if you throw it, so humans are both mental and physical beings.

In fact, the intuitive psychology of free will is among the most powerfully and precisely predictive scientific theories ever devised, right alongside Darwinian evolution and quantum physics.

Consider the following experiment, conducted about twenty years ago. In November of 2006, I planned a road trip with several of my friends from our home in Ann Arbor to the Secular Student Alliance conference in Boston that was coming in April 2007. Months in advance, we researched hotels, we registered for the conference, we planned out how much we would need to spend. When the time came, we gathered in my car and drove the 1300 kilometers to the conference. Now, stop and think for a moment: How did I know, in November 2006, that in April 2007, on a particular date and time, E.O. Wilson would be in a particular room and so would I? Because that’s what the schedule said. Consider for a moment these two extremely complicated extended bodies in space, each interacting with thousands of other such bodies continuously; no physicist could possibly have gathered enough data to predict six months in advance that the two bodies would each travel hundreds of kilometers over the Earth’s surface in order to meet within 10 meters of one another, remain there for roughly an hour, and then split apart and henceforth remain hundreds of kilometers apart. Yet our simple intuitive psychology could, and did, make just that prediction correctly. Of course in the face of incomplete data, no theory is perfect, and the prediction could have been wrong. Indeed because Boston is exceedingly difficult to navigate (we got lost), the prediction that I and Steven Pinker would be in the same room at the same time the previous evening turned out not to be accurate. But even this is something that intuitive psychology could have taken into account better than any other scientific theory we have. Neither quantum physics nor stoichiometric chemistry nor evolutionary biology could have predicted that we’d get lost, nor recommend that if we ever return to Boston we should bring a smartphone with a GPS uplink; yet intuitive psychology can.

Moreover, intuitive psychology explicitly depends upon rational volition. If you had thought that I didn’t want to go to the conference, or that I was mistaken about the conference’s location, then you would have predicted that I would not occupy that spatial location at that time; and had these indeed been the case, that prediction would have been completely accurate. And yet, these predictions insist upon such entities as desires (wanting to go) and beliefs (being mistaken) that eliminativists, behaviorists, and epiphenomenalists have been insisting for years are pseudoscientific. Quite the opposite is the case: Eliminativism, behaviorism, and epiphenomenalism are pseudosciences.

Understanding the constituent parts of a process does not make the process an illusion. Rain did not stop falling when we developed mathematical models of meteorology. Fire did not stop being hot when we formalized statistical dynamics. Thunder did not stop being loud when we explained the wave properties of sound. Advances in computer technology have now helped us realize how real information processing can occur in systems made of physical parts that obey physical laws; it isn’t too great a stretch to think that human minds operate on similar principles. Just as the pattern of electrical firings in my computer really is Windows, the pattern of electrochemical firings in my brain really is my consciousness.

There is a kind of naive theology called “God of the gaps”; it rests upon the notion that whenever a phenomenon cannot be explained by science, this leaves room for God as an explanation. This theology is widely rejected by philosophers, because it implies that whenever science advances, religion must retreat. Libertarianism and fatalism rest upon the presumption of something quite similar, what I would call “free will of the gaps”. As cognitive science advances, we will discover more and more about the causation of human mental states; if this is enough to make us doubt free will, then “free will” was just another name for ignorance of cognitive science. I defend a much deeper sense of free will than this, one that is not at all threatened by scientific advancement.

Yes, our actions are caused—caused by what we think about the world! We are responsible for what we do not because it lacks causation, but because it has causation, specifically causation in our own beliefs, desires, and intentions. These beliefs, desires, and intentions are themselves implemented upon physical hardware, and we don’t fully understand how this implementation operates; but nonetheless the hardware is real and the phenomena are real, at least as real as such things as rocks, rivers, clouds, trees, dogs, and televisions, all of which are also complex functional ensembles of many smaller, simpler parts.

Conclusion

Libertarianism is largely discredited; we don’t have the mystical sort of free will that allows us to act outside of causal laws. But this doesn’t mean that we must accept fatalism; compatibilism is the answer. We have discovered many surprising things about cognitive science, and we will surely need to discover many more; but the fundamental truth of rational volition remains untarnished.

We know, to a high degree of certainty, that human beings are capable of volitional action. I contend that this is all the freedom we need—perhaps even all we could ever have. When a comet collides with Jupiter, and we ask “Why?”, the only sensible answer involves happenstance and laws of physics. When a leaf falls from a tree, and we ask “Why?”, we can do better, talking about evolutionary adaptations in the phylogenetic history of trees. But when a human being robs a bank, starts a war, feeds a child, or writes a book, and we ask “Why?”, we can move away from simple causes and talk about reasons—desires, intentions, beliefs; reasons, unlike mere causes, can make more or less sense, be more or less justified.

Psychological and neurological experiments have shown that volition is more complicated than we usually think—it can be strongly affected by situational factors, and it has more to do with inhibiting and selecting actions than with generating them, what Sukhvinder Obhi and Patrick Haggard call “not free will but free won’t”; yet still we have volitional control over many of our actions, and hence responsibility for them. In simple tasks, there is brain activity that predicts our behavior several seconds before we actually consciously experience the decision—but this is hardly surprising, since the brain needs to use processing power to actually generate a decision. Deliberation requires processing, not all of which can be conscious. It’s a little surprising that the activity can predict the decision in advance of the conscious experience of volition, but it can’t predict the decision perfectly, even in very simple tasks. (And in true real-life tasks, like choosing a college or a spouse, it basically can’t predict at all.) This shows that the conscious volition is doing something—perhaps inhibiting undesired behaviors or selecting desired ones. No compatibilist needs to be committed to the claim that subconscious urges have nothing to do with our decisions—since at least Freud that kind of free will has been clearly discredited.

Indeed, evolutionary psychology would be hard-pressed to explain an illusion of free will that isn’t free will. It simply doesn’t make sense for conscious volition to evolve unless it does something that affects our behavior in some way. Illusions are a waste of brain matter, which in turn is a waste of metabolic energy. (The idea that we would want to have free will in order to feel like life is worth living is profoundly silly: If our beliefs didn’t affect our behavior, our survival would be unrelated to whether we thought life was worth living!) You can make excuses and say that conscious experience is just an epiphenomenon upon neurological processes—an effect but not a cause—but there is no such thing as an “epiphenomenon” in physics as we know it. The smoke of a flame can smother that flame; the sound of a train is a sonic pressure wave that shakes the metal of the track. Anything that moves has energy, and energy is conserved. Epiphenomenalism would require new laws of physics, by which consciousness can be created ex nihilo, a new entity that requires no energy to make and “just happens” whenever certain matter is arranged in the right way.

Windows is not an “epiphenomenon” upon the electrons running through my computer’s processor core; the functional arrangement of those electrons is Windows—it implements Windows. I don’t see why we can’t say the same thing about my consciousness—that it is a software implementation by the computational hardware of my brain. Epiphenomenalists will often insist that they are being tough-minded scientists accepting the difficult facts while the rest of us are being silly and mystical; but they are talking about mysterious new physics and I’m talking about software-hardware interaction—so really, who is being mystical here?

In the future it may be possible to predict people’s behavior relatively accurately based on their brain activity—but so what? This only goes to show that the brain is the source of our decisions, which is precisely what compatibilism says. One can easily predict that rain will fall from clouds of a certain composition; but rain still falls from clouds. The fact that I can sometimes predict your behavior doesn’t make your behavior any less volitional; it only makes me a better psychologist (and for that matter a more functional human being). Moreover, detailed predictions of long-term behaviors will probably always remain impossible, due to the deep computational complexity involved. (If it were simple to predict who you’d marry, why would your brain expend so much effort working on the problem?)

For all these reasons, I say: Yes, we do have free will.

Are eliminativists zombies?

PNRJ cognitive science , , , , , , , , , , ,

May 19 JDN 2460450

There are lots of little variations, but basically all views on the philosophy of mind boil down to four possibilities:

  1. Dualism: Mind and body are two separate types of thing
  2. Monism: Mind and body are the same type of thing
  3. Idealism: Only mind exists; body isn’t real
  4. Eliminativism: Only body exists; mind isn’t real

Like most philosophers and cognitive scientists, I am a die-hard monist, specifically a physicalist: The mind and the body are the same type of thing. Indeed, they are parts of the same physical system.

I call it the Basic Fact of Cognitive Science, which so many fail to understand at their own peril:

You are your brain.

You are not a product of your brain; you are not an illusion created by your brain; you are not connected to your brain. You are your brain. Your consciousness is generated by the activity of your brain.

Understanding how this works is beyond current human knowledge. I ask only that you understand that it works. Treat it as a brute fact of the universe if you must.

But precisely because understanding this mechanism is so difficult it has been aptly dubbed The Hard Problem, I am at least somewhat sympathetic to dualists, who say that the reason we can’t understand how the mind and brain are the same is that they aren’t, that there is some extra thing, the soul, which somehow makes consciousness and isn’t made of any material substance.

(If you want to get into the weeds a bit more, there are also “property dualists”, who try to bridge the gap between dualism and physicalism, but I think they are trying to have their cake and eat it too. So-called “predicate dualism” is really just physicalism; nobody says that tables or hurricanes are non-physical just because they are multiply-realizable.)

The problem, of course, is that dualism doesn’t actually explain anything. In fact, it adds a bunch of other mysteries that would then need to be explained, because there are clear, direct ways that consciousness interacts with physical matter. Affecting the body affects the mind, and vice-versa.

You don’t need anything as exotic as fMRI or brain injury studies to understand this. All you need to do is take a drug. In fact, all you need to do is get hungry and eat food. Eating food—obviously a physical process—makes you no longer hungry—a change in your conscious state. And the reason you ate food in the first place was because you were hungry—your mental state intervened on your bodily action.

The fact that mind and body are deeply connected is therefore an obvious fact, which should have been apparent to anyone throughout history. It doesn’t require any kind of deep scientific knowledge; all you have to do is pay close enough attention to your ordinary life.

But I can at least understand the temptation to be a dualist. Consciousness is weird and mysterious. It’s tempting to posit some whole new class of substance beyond anything we know in order to explain it.

Then there’s idealism, which theoretically, in principle, could be true—it’s just absurdly, vanishingly unlikely. Technically, all that I experience, qua experience, happens in my mind. So I can’t completely rule out the possibility that everything I think of as physical reality is actually just an illusion, and only my mind exists. It’s just that, well… the whole of my experience points pretty strongly to this not being the case. At the very least, it’s utterly impractical to live your life according to such a remote possibility.

That leaves eliminativism. And this, I confess, is the one I really don’t get.

Idealism, I can’t technically rule out; dualism, I understand the temptation; monism is in fact the truth. But eliminativism? I just can’t grok how anyone can actually believe it.

Then again, I think they sort of admit that.

The weirdest thing about eliminativism is that what they are actually saying is that things like beliefs and knowledge and feelings don’t actually exist.

If you ask an eliminativist if they believe eliminativism is true, they should answer “no”: because their assertion is precisely that nobody believes anything at all.

The more sophisticated eliminativists say that these “folk terms” are rough approximations to deeper concepts that cognitive science will someday understand. That’s not so ridiculous, but it still seems pretty bizarre to me to say that iron doesn’t exist because we now understand that an iron atom has precisely 26 protons. Perhaps indeed we will understand the mechanisms underlying beliefs better than we do now; but why would we need to stop calling them beliefs?

But some eliminativists—particularly behaviorists—seem to think that the these “folk terms” are just stupid, unscientific notions that will be one day discarded the same way that phlogiston and elan vital were discarded. And that I absolutely cannot fathom.

Consciousness isn’t an explanation; it is what we were trying to explain.

You can’t just discardthe phenomenonyou were trying to make sense of! This isn’t giving up on phlogiston; it’s giving up on fire. This isn’t abandoning the notion of elan vital; it’s abandoning the distinction between life and death.

But the more I think about this, the more I wonder:

Maybe eliminativists are right—about themselves?

Maybe the reason they think the rest of us don’t have feelings and beliefs is that they actually don’t. They don’t understand all this talk about the inner light of consciousness, because they just don’t have it.

In other words:

Are eliminativists zombies?

No, not the shambling, “Brains! Brains!” kind of zombie; the philosophical concept of a zombie (sometimes written “p-zombie” to clarify). A zombie is a being that looks human, acts human, is externally indistinguishable from a human, yet has no internal experience. They walk and talk, but they don’t actually think. A zombie acts like us, but lacks the inner light of consciousness.

Of course, what I’d really be saying here is that they are almost indistinguishable, but you can sometimes tell them apart by their babbling about the non-existence of consciousness.

But really, almost indistinguishable makes more sense anyway; if they were literally impossible to tell apart under any conceivable test, it’s difficult to even make sense of what we mean when we say they are different. (I am certainly not the first to point this out, and indeed it’s often used as an argument against the existence of zombies.)

Do I actually think that eliminativists are zombies?

No. I don’t.

But the weird thing is that they seem to, and so I feel some compulsion to let them self-identify that way. It feels wrong to attribute beliefs to someone that they say they don’t actually hold, and eliminativists have said that they don’t hold any beliefs whatsoever.

Yet, somehow, I don’t think they’ll appreciate being called zombies, either.

Love is more than chemicals

PNRJ cognitive science, core principles, current events, ethics, tribal paradigm , , , , , , , , , , , , , , , ,

Feb 18 JDN 2460360

One of the biggest problems with the rationalist community is an inability to express sincerity and reverence.

I get it: Religion is the world’s greatest source of sincerity and reverence, and religion is the most widespread and culturally important source of irrationality. So we declare ourselves enemies of religion, and also end up being enemies of sincerity and reverence.

But in doing so, we lose something very important. We cut ourselves off from some of the greatest sources of meaning and joy in human life.

In fact, we may even be undermining our own goals: If we don’t offer people secular, rationalist forms of reverence, they may find they need to turn back to religion in order to fill that niche.

One of the most pernicious forms of this anti-sincerity, anti-reverence attitude (I can’t just say ‘insincere’ or ‘irreverent’, as those have different meanings) is surely this one:

Love is just a chemical reaction.

(I thought it seemed particularly apt to focus on this one during the week of Valentine’s Day.)

On the most casual of searches I could find at least half a dozen pop-sci articles and a YouTube video propounding this notion (though I could also find a few articles trying to debunk the notion as well).

People who say this sort of thing seem to think that they are being wise and worldly while the rest of us are just being childish and naive. They think we are seeing something that isn’t there. In fact, they are being jaded and cynical. They are failing to see something that is there.

(Perhaps the most extreme form of this was from Rick & Morty; and while Rick as a character is clearly intended to be jaded and cynical, far too many people also see him as a role model.)

Part of the problem may also be a failure to truly internalize the Basic Fact of Cognitive Science:

You are your brain.

No, your consciousness is not an illusion. It’s not an “epiphenomenon” (whatever that is—I’ve never encountered one in real life). Your mind is not fake or imaginary. Your mind actually exists—and it is a product of your brain. Both brain and mind exist, and are in fact the same.

It’s so hard for people to understand this that some become dualists, denying the unity of the brain and the mind. That, at least, I can sympathize with, even though we have compelling evidence that it is wrong. But there’s another tack people sometimes take, eliminative materialism, where they try to deny that the mind exists at all. And that I truly do not understand. How can you think that nobody can think? Yet intelligent, respected philosophers have claimed to believe such things.

Love is one of the most important parts of our lives.

This may be more true of humans than of literally any other entity in the known universe.

The only serious competition comes from other mammals: They are really the only other beings we know of that are capable of love. And even they don’t seem to be as good at it as we are; they can love only those closest to them, while we can love entire nations and even abstract concepts.

And once you go beyond that, even to reptiles—let alone fish, or amphibians, or insects, or molluscs—it’s not clear that other animals are really capable of love at all. They seem to be capable of some forms of thought and feeling: They get hungry, or angry, or horny. But do they really love?

And even the barest emotional capacities of an insect are still categorically beyond what most of the universe is capable of feeling, which is to say: Nothing. The vast, vast majority of the universe feels neither love nor hate, neither joy nor pain.

Yet humans can love, and do love, and it is a large part of what gives our lives meaning.

I don’t just mean romantic love here, though I do think it’s worth noting that people who dismiss the reality of romantic love somehow seem reluctant to do the same for the love parents have for their children—even though it’s made of pretty much the same brain chemicals. Perhaps there is a limit to their cynicism.

Yes, love is made of chemicals—because everything is made of chemicals. We live in a material, chemical universe. Saying that love is made of chemicals is an almost completely vacuous statement; it’s basically tantamount to saying that love exists.

In other contexts, you already understand this.

“That’s not a bridge, it’s just a bunch of iron atoms!” rightfully strikes you as an absurd statement to make. Yes, the bridge is made of steel, and steel is mostly iron, and everything is made of atoms… but clearly there’s a difference between a random pile of iron and a bridge.

“That’s not a computer, it’s just a bunch of silicon atoms!” similarly registers as nonsense: Yes, it is indeed mostly made of silicon, but beach sand and quartz crystals are not computers.

It is in this same sense that joy is made of dopamine and love is made of chemical reactions. Yes, those are in fact the constituent parts—but things are more than just their parts.

I think that on some level, even most rationalists recognize that love is more than some arbitrary chemical reaction. I think “love is just chemicals” is mainly something people turn to for a couple of reasons: Sometimes, they are so insistent on rejecting everything that even resembles religious belief that they end up rejecting all meaning and value in human life. Other times, they have been so heartbroken, that they try to convince themselves love isn’t real—to dull the pain. (But of course if it weren’t, there would be no pain to dull.)

But love is no more (or less) a chemical reaction than any other human experience: The very belief “love is just a chemical reaction” is, itself, made of chemical reactions.

Everything we do is made of chemical reactions, because we are made of chemical reactions.

Part of the problem here—and with the Basic Fact of Cognitive Science in general—is that we really have no idea how this works. For most of what we deal with in daily life, and even an impressive swath of the overall cosmos, we have a fairly good understanding of how things work. We know how cars drive, how wind blows, why rain falls; we even know how cats purr and why birds sing. But when it comes to understanding how the physical matter of the brain generates the subjective experiences of thought, feeling, and belief—of which love is made—we lack even the most basic understanding. The correlation between the two is far too strong to deny; but as far as causal mechanisms, we know absolutely nothing. (Indeed, worse than that: We can scarcely imagine a causal mechanism that would make any sense. We not only don’t know the answer; we don’t know what an answer would look like.)

So, no, I can’t tell you how we get from oxytocin and dopamine to love. I don’t know how that makes any sense. No one does. But we do know it’s true.

And just like everything else, love is more than the chemicals it’s made of.

Lamentations of a temporary kludge

PNRJ cognitive science, core principles , , , , , , , ,

Dec 17 JDN 2460297

Most things in the universe are just that—things. They consist of inanimate matter, blindly following the trajectories the laws of physics have set them on. (Actually, most of the universe may not even be matter—at our current best guess, most of the universe is mysterious “dark matter” and even more mysterious “dark energy”).

Then there are the laws: The fundamental truths of physics and mathematics are omnipresent and eternal. They could even be called omniscient, in the sense that all knowledge which could ever be conveyed must itself be possible to encode in physics and mathematics. (Could, in some metaphysical sense, knowledge exist that cannot be conveyed this way? Perhaps, but if so, we’ll never know nor even be able to express it.)

The reason physics and mathematics cannot simply be called God is twofold: One, they have no minds of their own; they do not think. Two, they do not care. They have no capacity for concern whatsoever, no desires, no goals. Mathematics seeks neither your fealty nor your worship, and physics will as readily destroy you as reward you. If the eternal law is a god, it is a mindless, pitilessly indifferent god—a Blind Idiot God.

But we are something special, something in between. We are matter, yes; but we are also pattern. Indeed, what makes me me and makes you you has far more to do with the arrangement of trillions of parts than it does with any particular material. The atoms in your body are being continually replaced, and you barely notice. But should the pattern ever be erased, you would be no more.

In fact, we are not simply one pattern, but many. We are a kludge: Billions of years of random tinkering has assembled us from components that each emerged millions of years apart. We could move before we could see; we could see before we could think; we could think before we could speak. All this evolution was mind-bogglingly gradual: In most cases it would be impossible to tell the difference one generation—or even one century—to the next. Yet as raindrops wear away mountains, one by one, we were wrought from mindless fragments of chemicals into beings of thought, feeling, reason—beings with hopes, fears, and dreams.

Much of what makes our lives difficult ultimately comes from these facts.

Our different parts were not designed to work together. Indeed, they were not really designed at all. Each component survived because it worked well enough to stay alive in the environment in which our ancestors lived. We often find ourselves in conflict with our own desires, in part because those desires evolved for very different environments than the ones we now find ourselves—and in part because there is no particular reason for evolution to avoid conflict, so long as survival is achieved.

As patterns, we can experience the law. We can write down equations that express small pieces of the fundamental truths that exist throughout the universe beyond space and time. From “2+2=4” to “Gμν + Λgμν = κTμν“, through mathematics, we glimpse eternity.

But as matter, we are doomed to suffer, degrade, and ultimately die. Our pattern cannot persist forever. Perhaps one day we will find a way to change this—and if that day comes, it will be a glorious day; I will make no excuses for the dragon. For now, at least, it is a truth that we must face: We, all we love, and all we build must one day perish.

That is, we are not simply a kludge; we are a temporary one. Sooner or later, our bodies will fail and our pattern will be erased. What we were made of may persist, but in a form that will no longer be us, and in time, may become indistinguishable from all the rest of the universe.

We are flawed, for the same reason that a crystal is flawed. A theoretical crystal can be flawless and perfect; but a real, physical one must exist in an actual world where it will suffer impurities and disturbances that keep it from ever truly achieving perfect unity and symmetry. We can imagine ourselves as perfect beings, but our reality will always fall short.

We lament that are not perfect, eternal beings. Yet I am not sure it could have been any other way: Perhaps one must be a temporary kludge in order to be a being at all.

Reflections on the Chinese Room

PNRJ cognitive science , , , , , , , , , , ,

Jul 12 JDN 2459044

Perhaps the most famous thought experiment in the philosophy of mind, John Searle’s Chinese Room is the sort of argument that basically every expert knows is wrong, yet can’t quite explain what is wrong with it. Here’s a brief summary of the argument; for more detail you can consult Wikipedia or the Stanford Encyclopedia of Philosophy.

I am locked in a room. The only way to communicate with me is via a slot in the door, through which papers can be passed.

Someone on the other side of the door is passing me papers with Chinese writing on them. I do not speak any Chinese. Fortunately, there is a series of file cabinets in the room, containing instruction manuals which explain (in English) what an appropriate response in Chinese would be to any given input of Chinese characters. These instructions are simply conditionals like “After receiving input A B C, output X.”

I can follow these instructions and thereby ‘hold a conversation’ in Chinese with the person outside, despite never understanding Chinese.

This room is like a Turing Test. A computer is fed symbols and has instructions telling it to output symbols; it may ‘hold a conversation’, but it will never really understand language.

First, let me note that if this argument were right, it would pretty much doom the entire project of cognitive science. Searle seems to think that calling consciousness a “biological function” as opposed to a “computation” can somehow solve this problem; but this is not how functions work. We don’t say that a crane ‘isn’t really lifting’ because it’s not made of flesh and bone. We don’t say that an airplane ‘isn’t really flying’ because it doesn’t flap its wings like a bird. He often compares to digestion, which is unambiguously a biological function; but if you make a machine that processes food chemically in the same way as digestion, that is basically a digestion machine. (In fact there is a machine called a digester that basically does that.) If Searle is right that no amount of computation could ever get you to consciousness, then we basically have no idea how anything would ever get us to consciousness.

Second, I’m guessing that the argument sounds fairly compelling, especially if you’re not very familiar with the literature. Searle chose his examples very carefully to create a powerfully seductive analogy that tilts our intuitions in a particular direction.

There are various replies that have been made to the Chinese Room. Some have pointed out that the fact that I don’t understand Chinese doesn’t mean that the system doesn’t understand Chinese (the “Systems Reply”). Others have pointed out that in the real world, conscious beings interact with their environment; they don’t just passively respond to inputs (the “Robot Reply”).

Searle has his own counter-reply to these arguments: He insists that if instead of having all those instruction manuals, I memorized all the rules, and then went out in the world and interacted with Chinese speakers, it would still be the case that I didn’t actually understand Chinese. This seems quite dubious to me: For one thing, how is that different from what we would actually observe in someone who does understand Chinese? For another, once you’re interacting with people in the real world, they can do things like point to an object and say the word for it; in such interactions, wouldn’t you eventually learn to genuinely understand the language?

But I’d like to take a somewhat different approach, and instead attack the analogy directly. The argument I’m making here is very much in the spirit of Churchland’s Luminous Room reply, but a little more concrete.

I want you to stop and think about just how big those file cabinets would have to be.

For a proper Turing Test, you can’t have a pre-defined list of allowed topics and canned responses. You’re allowed to talk about anything and everything. There are thousands of symbols in Chinese. There’s no specified limit to how long the test needs to go, or how long each sentence can be.

After each 10-character sequence, the person in the room has to somehow sort through all those file cabinets and find the right set of instructions—not simply to find the correct response to that particular 10-character sequence, but to that sequence in the context of every other sequence that has occurred so far. “What do you think about that?” is a question that one answers very differently depending on what was discussed previously.

The key issue here is combinatoric explosion. Suppose we’re dealing with 100 statements, each 10 characters long, from a vocabulary of 10,000 characters. This means that there are ((10,000)^10)^100 = 10^4000 possible conversations. That’s a ludicrously huge number. It’s bigger than a googol. Even if each atom could store one instruction, there aren’t enough atoms in the known universe. After a few dozen sentences, simply finding the correct file cabinet would be worse than finding a needle in a haystack; it would be finding a hydrogen atom in the whole galaxy.

Even if you assume a shorter memory (which I don’t think is fair; human beings can absolutely remember 100 statements back), say only 10 statements, things aren’t much better: ((10,000)^10)^10 is 10^400, which is still more atoms than there are in the known universe.

In fact, even if I assume no memory at all, just a simple Markov chain that responds only to your previous statement (which can be easily tripped up by asking the same question in a few different contexts), that would still be 10,000^10 = 10^40 sequences, which is at least a quintillion times the total data storage of every computer currently on Earth.

And I’m supposed to imagine that this can be done by hand, in real time, in order to carry out a conversation?

Note that I am not simply saying that a person in a room is too slow for the Chinese Room to work. You can use an exaflop quantum supercomputer if you like; it’s still utterly impossible to store and sort through all possible conversations.

This means that, whatever is actually going on inside the head of a real human being, it is nothing like a series of instructions that say “After receiving input A B C, output X.” A human mind cannot even fathom the total set of possible conversations, much less have a cached response to every possible sequence. This means that rules that simple cannot possibly mimic consciousness. This doesn’t mean consciousness isn’t computational; it means you’re doing the wrong kind of computations.

I’m sure Searle’s response would be to say that this is a difference only of degree, not of kind. But is it, really? Sometimes a sufficiently large difference of degree might as well be a difference of kind. (Indeed, perhaps all differences of kind are really very large differences of degree. Remember, there is a continuous series of common ancestors that links you and I to bananas.)

Moreover, Searle has claimed that his point was about semantics rather than consciousness: In an exchange with Daniel Dennett he wrote “Rather he [Dennett] misstates my position as being about consciousness rather than about semantics.” Yet semantics is exactly how we would solve this problem of combinatoric explosion.

Suppose that instead of simply having a list of symbol sequences, the file cabinets contained detailed English-to-Chinese dictionaries and grammars. After reading and memorizing those, then conversing for awhile with the Chinese speaker outside the room, who would deny that the person in the room understands Chinese? Indeed what other way is there to understand Chinese, if not reading dictionaries and talking to Chinese speakers?

Now imagine somehow converting those dictionaries and grammars into a form that a computer could directly apply. I don’t simply mean digitizing the dictionary; of course that’s easy, and it’s been done. I don’t even mean writing a program that translates automatically between English and Chinese; people are currently working on this sort of thing, and while still pretty poor, it’s getting better all the time.

No, I mean somehow coding the software so that the computer can respond to sentences in Chinese with appropriate responses in Chinese. I mean having some kind of mapping within the software of how different concepts relate to one another, with categorizations and associations built in.

I mean something like a searchable cross-referenced database, so that when asked the question, “What’s your favorite farm animal?” despite never having encountered this sentence before, the computer can go through a list of farm animals and choose one to designate as its ‘favorite’, and then store that somewhere so that later on when it is again asked it will give the same answer. And then why asked “Why do you like goats?” the computer can go through the properties of goats, choose some to be the ‘reason’ why it ‘likes’ them, and then adjust its future responses accordingly. If it decides that the reason is “horns are cute”, then when you mention some other horned animal, it updates to increase its probability of considering that animal “cute”.

I mean something like a program that is programmed to follow conversational conventions, so when you ask it its name, will not only tell you something; it will ask you your name in return, and stores that information for later. And then it will map the sound of your name to known patterns of ethnic naming conventions, and so when you say your name is “Ling-Ling Xu” it asks “Is your family Chinese?” And then when you say “yes” it asks “What part of China are they from?” and then when you say “Shanghai” it asks “Did you grow up there?” and so on. It’s not that it has some kind of rule that says “Respond to ‘Shanghai’ with ‘Did you grow up there?’”; on the contrary, later in the conversation you may say “Shanghai” and get a different response because it was in a different context. In fact, if you were to keep spamming “Shanghai” over and over again, it would sound confused: “Why do you keep saying ‘Shanghai’? I don’t understand.”

In other words, I mean semantics. I mean something approaching how human beings actually seem to organize the meanings of words in their brains. Words map to other words and contexts, and some very fundamental words (like “pain” or “red”) map directly to sensory experiences. If you are asked to define what a word means, you generally either use a lot of other words, or you point to a thing and say “It means that.” Why can’t a robot do the same thing?

I really cannot emphasize enough how radically different that process would be from simply having rules like “After receiving input A B C, output X.” I think part of why Searle’s argument is so seductive is that most people don’t have a keen grasp of computer science, and so the difference between a task that is O(N^2) like what I just outlined above doesn’t sound that different to them compared to a task that is O(10^(10^N)) like the simple input-output rules Searle describes. With a fast enough computer it wouldn’t matter, right? Well, if by “fast enough” you mean “faster than could possibly be built in our known universe”, I guess so. But O(N^2) tasks with N in the thousands are done by your computer all the time; no O(10^(10^N)) task will ever be accomplished for such an N within the Milky Way in the next ten billion years.

I suppose you could still insist that this robot, despite having the same conceptual mappings between words as we do, and acquiring new knowledge in the same way we do, and interacting in the world in the same way we do, and carrying on conversations of arbitrary length on arbitrary topics in ways indistinguishable from the way we do, still nevertheless “is not really conscious”. I don’t know how I would conclusively prove you wrong.

But I have two things to say about that: One, how do I know you aren’t such a machine? This is the problem of zombies. Two, is that really how you would react, if you met such a machine? When you see Lieutenant Commander Data on Star Trek: The Next Generation, is your thought “Oh, he’s just a calculating engine that makes a very convincing simulation of human behavior”? I don’t think it is. I think the natural, intuitive response is actually to assume that anything behaving that much like us is in fact a conscious being.

And that’s all the Chinese Room was anyway: Intuition. Searle never actually proved that the person in the room, or the person-room system, or the person-room-environment system, doesn’t actually understand Chinese. He just feels that way, and expects us to feel that way as well. But I contend that if you ever did actually meet a machine that really, truly passed the strictest form of a Turing Test, your intuition would say something quite different: You would assume that machine was as conscious as you and I.

How personality makes cognitive science hard

PNRJ cognitive science, core principles, heuristics and biases, Uncategorized , , , , , , , , , , , , , ,

August 13, JDN 2457614

Why is cognitive science so difficult? First of all, let’s acknowledge that it is difficult—that even those of us who understand it better than most are still quite baffled by it in quite fundamental ways. The Hard Problem still looms large over us all, and while I know that the Chinese Room Argument is wrong, I cannot precisely pin down why.

The recursive, reflexive character of cognitive science is part of the problem; can a thing understand itself without understanding understanding itself, understanding understanding understanding itself, and on in an infinite regress? But this recursiveness applies just as much to economics and sociology, and honestly to physics and biology as well. We are physical biological systems in an economic and social system, yet most people at least understand these sciences at the most basic level—which is simply not true of cognitive science.

One of the most basic facts of cognitive science (indeed I am fond of calling it The Basic Fact of Cognitive Science) is that we are our brains, that everything human consciousness does is done by and within the brain. Yet the majority of humans believe in souls (including the majority of Americans and even the majority of Brits), and just yesterday I saw a news anchor say “Based on a new study, that feeling may originate in your brain!” He seriously said “may”. “may”? Why, next you’ll tell me that when my arms lift things, maybe they do it with muscles! Other scientists are often annoyed by how many misconceptions the general public has about science, but this is roughly the equivalent of a news anchor saying, “Based on a new study, human bodies may be made of cells!” or “Based on a new study, diamonds may be made of carbon atoms!” The misunderstanding of many sciences is widespread, but the misunderstanding of cognitive science is fundamental.

So what makes cognitive science so much harder? I have come to realize that there is a deep feature of human personality that makes cognitive science inherently difficult in a way other sciences are not.

Decades of research have uncovered a number of consistent patterns in human personality, where people’s traits tend to lie along a continuum from one extreme to another, and usually cluster near either end. Most people are familiar with a few of these, such as introversion/extraversion and optimism/pessimism; but the one that turns out to be important here is empathizing/systematizing.

Empathizers view the world as composed of sentient beings, living agents with thoughts, feelings, and desires. They are good at understanding other people and providing social support. Poets are typically empathizers.

Systematizers view the world as composed of interacting parts, interlocking components that have complex inner workings which can be analyzed and understood. They are good at solving math problems and tinkering with machines. Engineers are typically systematizers.

Most people cluster near one end of the continuum or the other; they are either strong empathizers or strong systematizers. (If you’re curious, there’s an online test you can take to find out which you are.)

But a rare few of us, perhaps as little as 2% and no more than 10%, are both; we are empathizer-systematizers, strong on both traits (showing that it’s not really a continuum between two extremes after all, and only seemed to be because the two traits are negatively correlated). A comparable number are also low on both traits, which must quite frankly make the world a baffling place in general.

Empathizer-systematizers understand the world as it truly is: Composed of sentient beings that are made of interacting parts.

The very title of this blog shows I am among this group: “human” for the empathizer, “economics” for the systematizer!

We empathizer-systematizers can intuitively grasp that there is no contradiction in saying that a person is sad because he lost his job and he is sad because serotonin levels in his cingulate gyrus are low—because it was losing his job that triggered other thoughts and memories that lowered serotonin levels in his cingulate gyrus and thereby made him sad. No one fully understands the details of how low serotonin feels like sadness—hence, the Hard Problem—but most people can’t even seem to grasp the connection at all. How can something as complex and beautiful as a human mind be made of… sparking gelatin?

Well, what would you prefer it to be made of? Silicon chips? We’re working on that. Something else? Magical fairy dust, perhaps? Pray tell, what material could the human mind be constructed from that wouldn’t bother you on a deep level?

No, what really seems to bother people is the very idea that a human mind can be constructed from material, that thoughts and feelings can be divisible into their constituent parts.

This leads people to adopt one of two extreme positions on cognitive science, both of which are quite absurd—frankly I’m not sure they are even coherent.

Pure empathizers often become dualists, saying that the mind cannot be divisible, cannot be made of material, but must be… something else, somehow, outside the material universe—whatever that means.

Pure systematizers instead often become eliminativists, acknowledging the functioning of the brain and then declaring proudly that the mind does not exist—that consciousness, emotion, and experience are all simply illusions that advanced science will one day dispense with—again, whatever that means.

I can at least imagine what a universe would be like if eliminativism were true and there were no such thing as consciousness—just a vast expanse of stars and rocks and dust, lifeless and empty. Of course, I know that I’m not in such a universe, because I am experiencing consciousness right now, and the illusion of consciousness is… consciousness. (You are not experiencing what you are experiencing right now, I say!) But I can at least visualize what such a universe would be like, and indeed it probably was our universe (or at least our solar system) up until about a billion years ago when the first sentient animals began to evolve.

Dualists, on the other hand, are speaking words, structured into grammatical sentences, but I’m not even sure they are forming coherent assertions. Sure, you can sort of imagine our souls being floating wisps of light and energy (ala the “ascended beings”, my least-favorite part of the Stargate series, which I otherwise love), but ultimately those have to be made of something, because nothing can be both fundamental and complex. Moreover, the fact that they interact with ordinary matter strongly suggests that they are made of ordinary matter (and to be fair to Stargate, at one point in the series Rodney with his already-great intelligence vastly increased declares confidently that ascended beings are indeed nothing more than “protons and electrons, protons and electrons”). Even if they were made of some different kind of matter like dark matter, they would need to obey a common system of physical laws, and ultimately we would come to think of them as matter. Otherwise, how do the two interact? If we are made of soul-stuff which is fundamentally different from other stuff, then how do we even know that other stuff exists? If we are not our bodies, then how do we experience pain when they are damaged and control them with our volition? The most coherent theory of dualism is probably Malebranche’s, which is quite literally “God did it”. Epiphenomenalism, which says that thoughts are just sort of an extra thing that also happens but has no effect (an “epiphenomenon”) on the physical brain, is also quite popular for some reason. People don’t quite seem to understand that the Law of Conservation of Energy directly forbids an “epiphenomenon” in this sense, because anything that happens involves energy, and that energy (unlike, say, money) can’t be created out of nothing; it has to come from somewhere. Analogies are often used: The whistle of a train, the smoke of a flame. But the whistle of a train is a pressure wave that vibrates the train; the smoke from a flame is made of particulates that could be used to smother the flame. At best, there are some phenomena that don’t affect each other very much—but any causal interaction at all makes dualism break down.

How can highly intelligent, highly educated philosophers and scientists make such basic errors? I think it has to be personality. They have deep, built-in (quite likely genetic) intuitions about the structure of the universe, and they just can’t shake them.

And I confess, it’s very hard for me to figure out what to say in order to break those intuitions, because my deep intuitions are so different. Just as it seems obvious to them that the world cannot be this way, it seems obvious to me that it is. It’s a bit like living in a world where 45% of people can see red but not blue and insist the American Flag is red and white, another 45% of people can see blue but not red and insist the flag is blue and white, and I’m here in the 10% who can see all colors and I’m trying to explain that the flag is red, white, and blue.

The best I can come up with is to use analogies, and computers make for quite good analogies, not least because their functioning is modeled on our thinking.

Is this word processor program (LibreOffice Writer, as it turns out) really here, or is it merely an illusion? Clearly it’s really here, right? I’m using it. It’s doing things right now. Parts of it are sort of illusions—it looks like a blank page, but it’s actually an LCD screen lit up all the way; it looks like ink, but it’s actually where the LCD turns off. But there is clearly something here, an actual entity worth talking about which has properties that are usefully described without trying to reduce them to the constituent interactions of subatomic particles.

On the other hand, can it be reduced to the interactions of subatomic particles? Absolutely. A brief sketch is something like this: It’s a software program, running on an operating system, and these in turn are represented in the physical hardware as long binary sequences, stored by ever-so-slightly higher or lower voltages in particular hardware components, which in turn are due to electrons being moved from one valence to another. Those electrons move in precise accordance with the laws of quantum mechanics, I assure you; yet this in no way changes the fact that I’m typing a blog post on a word processor.

Indeed, it’s not even particularly useful to know that the electrons are obeying the laws of quantum mechanics, and quite literally no possible computer that could be constructed in our universe could ever be large enough to fully simulate all these quantum interactions within the amount of time since the dawn of the universe. If we are to understand it at all, it must be at a much higher level—and the “software program” level really seems to be the best one for most circumstances. The vast majority of problems I’m likely to encounter are either at the software level or the macro hardware level; it’s conceivable that a race condition could emerge in the processor cache or the voltage could suddenly spike or even that a cosmic ray could randomly ionize a single vital electron, but these scenarios are far less likely to affect my life than, say, I accidentally deleted the wrong file or the battery ran out of charge because I forgot to plug it in.

Likewise, when dealing with a relationship problem, or mediating a conflict between two friends, it’s rarely relevant that some particular neuron is firing in someone’s nucleus accumbens, or that one of my friends is very low on dopamine in his mesolimbic system today. It could be, particularly if some sort of mental or neurological illness in involved, but in most cases the real issues are better understood as higher level phenomena—people being angry, or tired, or sad. These emotions are ultimately constructed of axon potentials and neurotransmitters, but that doesn’t make them any less real, nor does it change the fact that it is at the emotional level that most human matters are best understood.

Perhaps part of the problem is that human emotions take on moral significance, which other higher-level entities generally do not? But they sort of do, really, in a more indirect way. It matters a great deal morally whether or not climate change is a real phenomenon caused by carbon emissions (it is). Ultimately this moral significance can be tied to human experiences, so everything rests upon human experiences being real; but they are real, in much the same way that rocks and trees and carbon emissions are real. No amount of neuroscience will ever change that, just as no amount of biological science would disprove the existence of trees.

Indeed, some of the world’s greatest moral problems could be better solved if people were better empathizer-systematizers, and thus more willing to do cost-benefit analysis.