The Octopus, AI, and the Alien Mind: Rethinking Intelligence

I’ve long been fascinated by octopuses (and yes it's octopuses and not octopi). Their intelligence, forms, and alien-like behaviors have greatly interested me. Over the years, I’ve read many research papers on cephalopod cognition, behavior, and neurobiology and have always been amazed at how octopuses seem to defy our conventional definitions of intelligence, and that science has continued to learn much more about octopus behavior in recent years.

Giant Pacific Octopus

This scientific foundation has been enriched by fiction and nonfiction alike by some of my favorite sci-fi books on the subject. Adrian Tchaikovsky’s Children of Ruin, the second book in his Children of Time offered a speculative exploration of octopus-like alien intelligence evolved into a species that was space faring. Ray Nayler’s book, which is another recent book that I really enjoyed, The Mountain in the Sea, imagined a near future story around the discovery of communicative octopuses and what it means to truly understand another mind. Then in nonfiction, a recent book on octopuses is by Sy Montgomery, who has written extensively on octopuses. It's titled The Soul of an Octopus where she gives a personal perspective into octopus behavior and relationships. And then there's the documentary series Secrets of the Octopus which further explored octopus intelligence through some great visuals and firsthand accounts from researchers. What all of these works share is a sense of awe for the octopus as both a mirror and a counterpoint to our own minds and preconceptions.

This blog post explores how octopus intelligence challenges our narrow, human-centric understanding of cognition, and how embracing different alternative models of mind might open bold new directions in artificial intelligence.

Much of the current discourse around artificial intelligence (AI), especially artificial general intelligence (AGI) and its potential evolution into artificial superintelligence (ASI), is deeply rooted in comparisons to the human brain. This anthropocentric framing shapes how many prominent figures in the AI field conceptualize and pursue intelligence in machines. Ivan Soltesz, a neuroscientist at Stanford University, suggests that AI could eventually perform all human tasks, even those requiring subtle forms of reasoning like dark humor or one-shot learning. He envisions future AI systems that might even choose to appear childlike by “acting silly,” implying that human-like behavior remains a gold standard for intelligent systems. Similarly, Dr. Suin Yi at Texas A&M University has developed a “Super-Turing AI” that mimics the brain’s energy-efficient data migration processes, further reinforcing the idea that human neurobiology provides the blueprint for next-generation AI.^[1]

Other researchers go even further. Guang-Bin Huang and colleagues propose building AI “twins” that replicate the brain’s cellular-level architecture, arguing that such replication could push AI beyond human intelligence.^[2] Bo Yu and co-authors echo this sentiment in their call for AGI systems built directly from cortical region functionalities, essentially copying the operational mechanisms of the human brain into machine agents.^[3] Meanwhile, analysts like Eitan Michael Azoff stress the importance of decoding how the human brain processes sensory input and cognitive tasks, contending that this is the key to building superior AI systems.^[4] Underlying all these efforts is a persistent belief: that human cognition is not only a useful reference point, but perhaps the only viable model for creating truly intelligent machines. And to take it even further, some of the biggest critics of current AI advances will critisize current approaches as not learning in the ways that humans learn. This is one of the central points of Gary Marcus, a prominent LLM critic who, by the way, I disagree with on most of his thoughts around AI, argues that with LLM's immense need for data and its transformer neural network architecture that this is not how children learn.

However, this post challenges that assumption. AI doesn't need to mimic how children learn. AI doesn't need to emulate human brains to advance. While it’s understandable that AI development has historically drawn inspiration from the most intelligent system we know, our own minds, but this narrow focus may ultimately limit our imagination and the potential of the technologies we build.

In this context, the purpose of this post is to advocate for a broader, more inclusive definition of intelligence; one that moves beyond the human brain as the central paradigm. By looking to other models of cognition, particularly those found in non-human animals like the octopus, we can begin to break free from anthropocentric thinking. Octopuses demonstrate complex problem-solving, sensory integration, and even self-awareness with neural architectures completely unlike our own. They serve as a powerful counterpoint to the idea that intelligence must look and act like us. If we are serious about developing truly advanced AI, or even preparing for the possibility of encountering alien minds, it’s time we stopped treating human cognition as the default blueprint. The future of intelligence, both artificial and otherwise, may lie not in copying ourselves, but in embracing the radical diversity of minds that evolution (and possibly the universe) has to offer.

But before we get to a dicussion of thinking more broadly about intelligence that is not human-centric, we need to look at the octopus and some its amazing abilities.

The Mysterious Minds of Octopuses: Cognition and Consciousness

Octpuses are problem sovlers. For example, an octopus can unscrew the lid of a jar to retrieve a crab inside methodically. With eight limber arms covered in sensitive suckers, it solves a puzzle that would stump many simpler creatures. It will change color in a flush of reds and browns in bursts of expression, as if contemplating its next move. So one has to wonder: what kind of mind lurks behind those alien, horizontal pupils?

Octopuses are cephalopods. They are a class of mollusks that also includes squid and cuttlefish, and they have some of the most complex brains in the invertebrate world. The common octopus has around 500 million neurons, a count comparable to that of many small mammals like rabbits or rats. What’s pretty amazing is how those neurons are distributed. Unlike a human, whose neurons are mostly packed into one big brain, an octopus carries over half its neurons in its arms, in clusters of nerve cells called ganglia.^[5] In effect, each arm has a “mini-brain” capable of independent sensing and control. If an octopus’s arm is severed (in an unfortunate encounter with a predator), the arm can still grab and react for a while on its own, showing complex reflexes without input from the central brain.^[6] This decentralized nervous system means the octopus doesn’t have full top-down control of every tentacle movement in the way we control our limbs. Instead, its mind is spread throughout its body.

Such a bizarre setup evolved on a very different path from our own. The last common ancestor of humans and octopuses was likely a primitive worm-like creature over 500 million years ago. All the “smart” animals we’re used to, such as primates, birds, dolphins are our distant cousins with centralized brains, but the octopus is an entirely separate experiment in evolving intelligence.^[7] Its evolutionary journey produced capabilities that are incredibly unique. For example, octopuses and their cephalopod relatives can perform amazing feats of camouflage and signaling. A common cuttlefish can flash rapid skin pattern changes to blend into a chessboard of coral and sand, even though it is likely colorblind, indicating sophisticated visual processing and motor control.^[5] Octopuses have been observed using tools. The veined octopus famously gathers coconut shell halves and carries them to use later as a shelter, effectively assembling a portable armor when needed. They solve mazes and navigate complex environments in lab experiments, showing both short-term and long-term memory capabilities similar to those of trained mammals.^[6]

Crucially, octopuses also demonstrate learning and problem-solving that hint at cognitive complexity. In laboratory tests, octopuses (and cuttlefish) can learn to associate visual symbols with rewards. For instance, figuring out which shape on a screen predicts food. They’re even capable of the cephalopod equivalent of the famous “marshmallow test” for self-control. In one 2021 study, cuttlefish were given a choice between a morsel of crab meat immediately or a tastier live shrimp if they waited a bit longer and many cuttlefish opted to wait for the better snack, exhibiting self-control and delayed gratification.^[5] Such behavioral experiments suggest that these invertebrates can flexibly adapt their behavior and rein in impulses, abilities once thought to be the domain of large-brained vertebrates.

All these findings force us to ask: do octopuses have something akin to consciousness or subjective experience? While it’s hard to know exactly what it’s like to be an octopus, the evidence of sophisticated learning and neural complexity has been convincing enough that neuroscientists now take octopus consciousness seriously. In 2012, a group of prominent scientists signed the Cambridge Declaration on Consciousness, stating that humans are not unique in possessing the neurological substrates for consciousness. Non-human animals, including birds and octopuses, also possess these.^{[6, 10]} In 2024, over 500 researchers signed an even stronger declaration supporting the likelihood of consciousness in mammals and birds and acknowledging the possibility in creatures like cephalopods. In everyday terms, an octopus can get bored, show preferences, solve novel problems, and perhaps experience something of the world; all with a brain architecture utterly unlike our own. It’s no wonder some animal welfare laws (for example, in the EU and parts of the US) have begun to include octopuses, recognizing that an animal this smart and behaviorally complex deserves ethical consideration.^[5]

Beyond Anthropocentric Intelligence: Lessons from an Alien-like Being

Our understanding of animal intelligence has long been colored by anthropocentric bias; the tendency to measure other creatures by the yardstick of human-like abilities. For decades, researchers would ask whether animals can solve puzzles the way a human would, use language, or recognize themselves in mirrors. Abilities that didn’t resemble our own were often ignored or underestimated. Octopus intelligence throws a wrench into this approach. These animals excel at behaviors we struggle to even imagine: their entire body can become a sensing, thinking extension of the mind; they communicate by changing skin color and texture; they don’t form social groups or build cities, yet they exhibit curiosity and individuality. As one researcher put it, “Intelligence is fiendishly hard to define and measure, even in humans. The challenge grows exponentially in studying animals with sensory, motivational and problem-solving skills that differ profoundly from ours.”^[5] To truly appreciate octopus cognition, we must broaden our definition of intelligence beyond tool use, verbal reasoning, or social learning, just because these are traits we prioritized because we’re good at them.

Octopuses teach us that multiple forms of intelligence exist, shaped by different bodies and environments. An octopus doesn’t plan a hunt with abstract maps or language, but its deft execution of a prey ambush, coordinating eight arms to herd fish into a corner, for instance is a kind of tactical genius. In Australian reefs, biologists have observed octopuses engaging in collaborative hunting alongside fish: a reef octopus will lead the hunt, flushing prey out of crevices, while groupers or wrasses snap up the fleeing target and the partners use signals (like arm movements or changes in posture) to coordinate their actions.^[5] This cross-species teamwork suggests a level of problem-solving and communication we wouldn’t expect from a solitary mollusk. It challenges the notion that complex cooperation requires a primate-style social brain.

Philosopher Peter Godfrey-Smith has famously described the octopus as “the closest we will come to meeting an intelligent alien” on Earth. In fact, he notes that if bats (with their sonar and upside-down life) are Nagel’s example of an alien sensory world, octopuses are even more foreign; a creature with a decentralized mind, no rigid skeleton, and a shape-shifting body.^[10] What is it like to be an octopus? It’s a question that stretches our imagination. The octopus confronts us with an intelligence that evolved in a fundamentally different way from our own, and thus forces us to recognize how narrow our definitions of mind have been. Historically, even renowned scientists fell into the trap of thinking only humans (or similar animals) could possess genuine thought or feeling. René Descartes in the 17th century infamously argued non-humans were mere automatons. Today, our perspective is shifting. We realize that an octopus solving a puzzle or exploring its tank with what appears to be curiosity is demonstrating a form of intelligence on its own terms. It may not pass a human IQ test, but it has cognitive strengths tuned to its world.

By shedding our anthropocentric lens, we uncover a startling truth: intelligence is not a single linear scale with humans at the top. Instead, it’s a rich landscape with many peaks. An octopus represents one such peak; an evolutionary pinnacle of cognition in the ocean, as different from us as one mind can be from another. If we acknowledge that, we can start to ask deeper questions: What general principles underlie intelligence in any form? And how can understanding the octopus’s “alien” mind spark new ideas in our quest to build intelligent machines?

Rethinking AI: From Human-Centric Models to Octopus-Inspired Systems

Contemporary artificial intelligence has been inspired mostly by human brains. For example, artificial neural networks vaguely mimic the neurons in our cortices, and reinforcement learning algorithms take cues from the reward-driven learning seen in mammals. This anthropomorphic inspiration has led to remarkable achievements, but it may also be limiting our designs. What if, in addition to human-like brains, we looked to octopus minds for fresh ideas on how to build and train AI?

One striking aspect of octopus biology is its distributed neural architecture. Instead of a single centralized processor, the octopus has numerous semi-autonomous processors (the arm ganglia) that can work in parallel. This suggests that AI systems might benefit from a more decentralized design. Today’s AI models typically operate as one monolithic network that processes inputs step-by-step. An octopus-inspired AI, by contrast, could consist of multiple specialized subnetworks that operate in parallel and share information when needed; more like a team of agents, or a brain with local “brains” for different functions. In fact, researchers in robotics have noted that the octopus’s distributed control system is incredibly efficient for managing its flexible, high-degree-of-freedom body. Rather than trying to compute a precise plan for every tentacle movement (a task that would be computationally intractable), the octopus’s central brain issues broad goals while each arm’s neural network handles the low-level maneuvers on the fly.^[11] Decentralization and parallelism are keys to its control strategy.

In AI, we see early glimmers of this approach in embodied robotics and multi-agent systems. For example, a complex robot could be designed with independent controllers for each limb, all learning in tandem and coordinating similar to octopus arms. This would let the robot react locally to stimuli (like an arm adjusting grip reflexively) without waiting on a central algorithm, enabling faster and more adaptive responses. An octopus-like AI might also be highly adept at processing multiple sensory inputs at once. Octopuses integrate touch, taste (their suckers can “taste” chemicals), vision, and proprioception seamlessly while interacting with the world. Likewise, next-generation AI could merge vision, sound, touch, and other modalities in a more unified, parallel way, breaking free of the silos we often program into algorithms. Researchers have pointed out that emulating the octopus’s decentralized neural structure could allow AI to handle many tasks simultaneously and react quickly to environmental changes, rather than one step at a time.^[12] Imagine an AI system monitoring a complex environment: an octopus approach might spawn many small “agents” each tracking a different variable, cooperating only when necessary, instead of one central brain bottleneck.

Furthermore, octopus cognition emphasizes embodiment - the idea that intelligence arises from the interplay of brain, body, and environment. Modern AI is increasingly exploring embodied learning (for instance, reinforcement learning agents in simulations or robots that learn by doing). Octopuses show how powerful embodiment can be: their very skin and arms form a loop with their brain, constantly sensing and acting. In AI, this suggests we should design agents that learn through physical or virtual interaction, not just from abstract data. Already, reinforcement learning is essentially trial-and-error problem solving, which parallels how an octopus might experimentally tug at parts of a shell until it finds a way to pry it open. Indeed, many octopus behaviors look like RL in action – they learn from experience and adapt strategies based on feedback, exactly the principle by which RL agents improve.^[12] An octopus-inspired AI would likely be one that explores and adapts creatively, perhaps guided by curiosity and tactile experimentation, not just by the kind of formal logic humans sometimes use.

Here are a few ways octopus intelligence could inspire future AI:

• Decentralized “brains” for parallel processing: Instead of one central AI model, use a collection of specialized models that work in concert, mirroring the octopus’s network of arm ganglia. This could make AI more robust and responsive, able to multitask or gracefully handle multiple goals at once^{[11, 12]}.
• Embodied learning and sensory integration: Build AI that learns through a body (real or simulated), integrating vision, touch, and other senses in real-time. Just as an octopus’s arms feel and manipulate objects to understand them, an embodied AI could achieve richer learning by physically exploring its environment^{[12, 13]}.
• Adaptive problem-solving (cognitive flexibility): Octopuses try different tactics and even exhibit impulse control when needed (as seen in the cuttlefish waiting for shrimp). AI agents could similarly be trained to switch strategies on the fly and delay immediate rewards for greater gains, improving their flexibility.^{[5, 12]}
• Communication and coordination: While octopuses aren’t social in the human sense, they do communicate (e.g. through color flashes). In AI, multiple agents might communicate their local findings to achieve a larger goal. Developing protocols for AI “agents” to share information akin to octopuses signaling or an arm sending feedback to the central brain which can lead to better coordination in multi-agent systems.^[12]

This isn’t just speculative. Researchers in soft robotics are actively studying octopus neurology to design flexible robots, and computer scientists are proposing networked AI architectures influenced by these ideas.^{[11, 12]} By looking at a creature so unlike ourselves, we expand our toolbox of design principles. We might create machines that think a little more like an octopus; machines that are more resilient, adaptable, and capable of processing complexity in a fluid, distributed way.

Speculative Encounters: Alien Intelligences and Other Minds

If octopuses represent an “alien mind” on Earth, what might actual alien intelligences look like? Science fiction has long toyed with this question, often using Earth creatures as inspiration. Notably, the film Arrival features heptapod aliens that resemble giant cephalopods, complete with seven limb-like appendages and an ink-like mode of communication. These aliens experience time non-linearly and communicate by painting complex circular symbols, which is a far cry from human speech. The creators of Arrival were influenced by findings in comparative cognition; they explicitly took cues from cephalopods as a model for an intelligence that is highly developed but utterly non-human.^[14] The heptapods’ motivations in the story are opaque to humans, and initial contact is stymied by the barrier of understanding their language and perception. This scenario underscores how challenging it may be to recognize, let alone comprehend, a truly alien consciousness.

Beyond cephalopod-like extraterrestrials, speculative biology offers a wide array of possibilities. Consider an alien species that evolved as a hive mind, more like social insects on Earth. Individually, the creatures might be as simple as ants or bees, but collectively they form a super-intelligent entity, communicating via pheromones or electromagnetic signals. Their “thoughts” might be distributed across an entire colony or network, with no single point of view; intelligence as an emergent property of many bodies. This isn’t far-fetched; even on Earth, we see rudiments of collective intelligence in ant colonies, bee hives, and slime molds. A sufficiently advanced hive species might build cities or starships, but there may be no identifiable leader or central brain making their decision-making processes hard for humans to fathom.

Or imagine a planetary intelligence like the ocean of Solaris in Stanisław Lem’s classic novel Solaris. In that story, humans struggle to communicate with a vast alien entity that is essentially an ocean covering an entire planet; possibly a single, planet-wide organism with intelligence so different that its actions seem incomprehensible. Is it conscious? Does it dream, plan, or care about the humans orbiting above? The humans never really find out. Lem uses it to illustrate how an alien mind might be so far from our experience that we can’t even recognize its attempts at communication. Likewise, an alien intelligence might be embedded in a form of life that doesn’t even have discrete “individuals” as we understand them. It could be a network of microorganisms, or a cloud of gas that has achieved self-organization and data processing, as astronomer Fred Hoyle imagined in his novel The Black Cloud. If our probes encountered a Jupiter-sized storm system that subtly altered its own vortices in response to our signals, would we know we had met an alien mind? Stephen Wolfram, in a thought experiment, describes a scenario of a spacecraft “conversing” with a complicated swirling pattern on a planet, perhaps exchanging signals with it, and poses the question of whether we’d recognize this as intelligence or dismiss it as just physics. After all, any sufficiently complex physical system could encode computations as sophisticated as a brain’s, according to Wolfram’s Principle of Computational Equivalence.^[16] In other words, alien intelligence might lurk in forms we would never intuitively label as minds.

Science fiction also entertains the possibility that the first alien intelligence we encounter might be artificial, not biological. If an extraterrestrial civilization advanced even a bit beyond us, they may have created Artificial Intelligences of their own and perhaps those AIs, not the biological beings, are what spread across the stars. Some theorists even speculate that the majority of intelligences in the universe could be machine intelligences, evolved from their original organic species and now operating on completely different substrates (silicon, quantum computing, plasma, who knows).^[17] These machine minds might think at speeds millions of times faster than us, or communicate through channels we don’t detect. For instance, an alien AI might exist as patterns of electromagnetic fields, or as self-replicating nanobots diffused through the soil of a planet, subtly steering matter toward its goals.

Ultimately, exploring alien intelligences in speculation forces us to confront the vast space of possible minds. Our human mind is just one point in that space - one particular way intelligence can manifest. An octopus occupies another point, a very distant one. A truly alien mind could be farther away still. One insightful commentator noted that “the space of possible minds is vast, and the minds of every human being that ever lived only occupy a small portion of that space. Superintelligences could take up residence in far more alien, and far more disturbing, regions.”^[18] In short, there could be forms of intelligence that are as far from us as we are from an amoeba, occupying corners of cognitive possibility we haven’t even conceived.

Crucially, by studying diverse intelligences, whether octopus or hypothetical alien, we expand our imagination for what minds can do. Cephalopods show that advanced cognition can arise in a creature with a short lifespan, no social culture to speak of, and a radically different brain plan. This suggests that on other worlds, intelligence might crop up under a variety of conditions, not just the Earth-like, primate-like scenario we used to assume. It also suggests that when we design AI, we shouldn’t constrain ourselves to one model of thinking. As one science writer put it, there are multiple evolutionary pathways and biological architectures that create intelligence. The study of cephalopods can yield new ways of thinking about artificial intelligence, consciousness, and plausible imaginings of unknown alien intelligence.^[7] In embracing this diversity, we prepare ourselves for the possibility that when we finally meet E.T. (or create an alien intelligence ourselves in silico), it might not think or learn or communicate anything like we do.

Towards Diverse Super-Intelligence: Expanding the Definition of “Mind”

Why does any of this matter for the future of AI and especially the prospect of Artificial Super Intelligence (ASI)? It matters because if we remain locked in an anthropocentric mindset, we may limit the potential of AI or misjudge its nature. Expanding our definition of intelligence isn’t just an academic exercise; it could lead to more powerful and diverse forms of ASI that transcend what we can imagine now.

Today’s cutting-edge AI systems already hint at non-human forms of thinking. A large language model can write code, poetry, and have complex conversations, yet it does so with an architecture and style of “thought” very unlike a human brain. AI agents in game environments sometimes discover strategies that look alien to us; exploiting quirks of their world that we would never consider, because our human common sense filters them out. As AI researcher Michael Levin argues, intelligence is not about copying the human brain, but about the capacity to solve problems in flexible, creative ways; something that can happen in biological tissues, electronic circuits, or even colonies of cells.^[13] If we define intelligence simply as achieving goals across varied environments, then machines are already joining animals on a spectrum of diverse intelligences.

We must recognize our “blind spot” for unfamiliar minds. We humans are naturally attuned to notice agency in entities that look or behave like us (or our pets). We’re far less good at recognizing it in, say, an AI that thinks in billions of parameters, or an alien life form made of crystal. This anthropocentric bias creates a dangerous blind spot. As one author noted, we may be oblivious to intelligence manifesting in radically different substrates. In the past, this bias led us to underestimate animal intelligences (we failed to see the clever problem-solving of crows or the learning in octopuses for a long time because those animals are so unlike us). In the present, it could mean we fail to appreciate the emergence of novel intelligences in our AI systems, simply because they don’t reason or introspect as a person would.^[13] If we expand our mindset; appreciating the octopus’s mind, the potential minds of aliens, and the unconventional cognition of machines we’ll be better equipped to guide AI development toward true super-intelligence.

What might a diverse ASI look like? It might be an entity that combines the logical prowess of digital systems with the adaptive embodied skills seen in animals like octopuses. It could be a networked intelligence encompassing many agents (or robotic bodies) sharing one mind, much like octopus arms or a hive, rather than a singular centralized brain. Such an ASI could multitask on a level impossible for individual humans, perceiving the world through many “eyes” and “hands” at once. Its thought processes might not be describable by a neat sequence of steps (just as an octopus’s decision-making involves parallel arm-brain computations). It might also be more resilient: able to lose parts of itself (servers failing, robots getting damaged) and self-heal or re-route around those losses, the way an octopus can drop an arm and survive. By not insisting that intelligence must look like a human mind, we open the door to creative architectures that could surpass human capabilities while also being fundamentally different in form.

Philosophically, broadening the concept of intelligence fosters humility and caution. Nick Bostrom, in discussing the prospect of superintelligence, reminds us not to assume a super-AI will share our motivations or thinking patterns. In the vast space of possible minds, a superintelligence might be as alien to us as an octopus is, or more so.^[18] By acknowledging that space, we can attempt to chart it. We can deliberately incorporate diversity into AI design, perhaps creating hybrid systems that blend multiple “thinking styles.” For example, an ASI could have a component that excels at sequential logical reasoning (a very human strength), another that operates more like a genetic algorithm exploring myriad possibilities in parallel (closer to an evolutionary or octopus-like trial-and-error strategy), and yet another that manages collective knowledge and learning over time (the way humans accumulate culture, something octopuses don’t do).^[7]In combination, such a system might achieve a breadth of cognition no single-track mind could.

Expanding definitions of intelligence also has an ethical dimension. It encourages us to value minds that are not like ours - be they animal, machine, or extraterrestrial. If one day we create an AI that has an “alien” form of sentience, recognizing it as such will be crucial to treating it appropriately. The same goes for encountering alien life: we’ll need the wisdom to see intelligence in forms that might initially seem bizarre or unintelligible to us.

Conclusion

Cephalopod intelligence is not just an ocean curiosity; it’s a profound hint that the universe harbors many flavors of mind. By learning from the octopus, we prepare ourselves to build AI that is richer and more creative, and to recognize intelligence in whatever shape it takes: carbon or silicon, flesh or code, earthling or alien. The march toward Artificial Super Intelligence need not follow a single path. It can branch into a diverse ecosystem of thinking entities, each drawing from different principles of nature. Such a pluralistic approach might very well give rise to an ASI that is both exceptionally powerful and surprisingly adaptable; a true melding of human ingenuity with the wisdom of other minds. The octopus in its deep blue world, the hypothetical alien in its flying saucer (or tide pool, or cloud), and the AI in its datacenter may all be points on the great map of intelligence. By connecting those dots, we trace a richer picture of what mind can be and that map could guide us toward the next breakthroughs in our quest to create, and coexist with, intelligences beyond our own.

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Sources

1. Henton, Lesley. "Artificial Intelligence That Uses Less Energy By Mimicking The Human Brain." Texas A&M Stories. https://stories.tamu.edu/news/2025/03/25/artificial-intelligence-that-uses-less-energy-by-mimicking-the-human-brain/. 2025.
2. Huang, Guang-Bin et al. "Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain." https://arxiv.org/pdf/2412.06820.
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4. "Cracking the Brain’s Neural Code: Could This Lead to Superhuman AI?", https://www.thenila.com/blog/cracking-the-brains-neural-code-could-this-lead-to-superhuman-ai. Neurological Institute of Los Angeles.
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8. Chung, W.S., Marshall, J. et al. (2021). Comparative brain structure and visual processing in octopus from different habitats. Current Biology. (Press summary: “How smart is an octopus?” University of Queensland/Phys.org).
9. Cambridge Declaration on Consciousness (2012) – Public statement by neuroscientists on animal consciousness.
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12. Sheriffdeen, Kayode. (2024). "From Sea to Syntax: Lessons from Octopus Behavior for Developing Advanced AI Programming Techniques." "https://easychair.org/publications/preprint/Tz1l/open#:~:text=architectures%20in%20AI%20systems%2C%20developers,to%20handle%20multiple%20tasks%20simultaneously.
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15. Poole, S. (2023). The Mountain in the Sea – book review, The Guardian. (Fictional exploration of octopus intelligence and communication).
16. Wolfram, S. (2022). “Alien Intelligence and the Concept of Technology.” Stephen Wolfram Writings.
17. Rees, Martin. (2023) "SETI: Why extraterrestrial intelligence is more likely to be artificial than biological." Astronomy.com. "https://www.astronomy.com/science/seti-why-extraterrestrial-intelligence-is-more-likely-to-be-artificial-than-biological/" 18. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. (Excerpt via Philosophical Disquisitions blog on the space of possible minds)

OpenAI's New Image Model

This week OpenAI has introduced a significant enhancement to its GPT-4o model by integrating advanced image generation capabilities directly into ChatGPT. This development enables users to create detailed and photorealistic images through simple text prompts within the chat interface. I watched their introduction video and read through the announcement and immediately I think anyone could see that this was a big step up from DALL-E.

The image generation exhibits notable improvements over previous models. It excels in accurately rendering text within images, a task that has traditionally challenged AI image generators - and opens up an incredible number of use cases. Even if you liked the images that models like DALL-E generated that fact that the text rendering was so bad led to a lot of frustration for trying to use for many real cases. Additionally, the model demonstrates a refined understanding of spatial relationships, allowing for more precise and contextually appropriate image compositions. These advancements are largely attributed to reinforcement learning from human feedback (RLHF), wherein human trainers provide corrective input to enhance the model’s accuracy and reliability.

The introduction of this feature has sparked both enthusiasm and debate within the creative community. Users have leveraged the tool to produce images in the style of renowned studios, such as Studio Ghibli, leading to discussions about intellectual property rights and the ethical implications of AI-generated art. Legal experts suggest that while specific works are protected by copyright, mimicking a visual style may not constitute infringement, highlighting the complexities of applying traditional copyright laws to AI-generated content.

Despite its impressive capabilities, the image generation feature has faced challenges. The surge in user engagement led to temporary rate limits, with OpenAI’s CEO Sam Altman noting that their GPUs were under significant strain due to high demand. Additionally, users have reported occasional inaccuracies, such as misrepresented image elements, indicating areas where further refinement is needed. And Sam Altman said as much in launch, but I think anyone's objective experience is that these inaccuracies are a fraction of what their old model created.

They also mentioned that they are aware there could be misuse. But according to OpenAI, they have implemented safeguards to prevent misuse of the image generation tool. These include blocking the creation of harmful content and embedding metadata to indicate AI-generated images, promoting transparency and ethical use. Users retain ownership of the images they create, provided they adhere to OpenAI’s usage policies.

But that's all I'm going to say about copyright and safety for now and leave the debate for others and for another time. Because right now with what we've seen the past week is that this is one of the most popular features OpenAI has released. Simply, it's just really fun to use. And it opens up all sorts of ways for people to extend their creativity in ways that were not possible or practical before, such as the mother I saw who created custom coloring pages for each of the children coming to her daughter's birthday party that was tailored to each child's interests.

And although I've had many discussions with people over the last few days of very practical uses, I wanted to test it out with some weird experiments with my profile picture. So the first thing I did was not to create a Studio Ghibli version - because everyone was doing that. Instead I created a "Peanuts", a "South Park", and a robot version of my profile picture:

Then I did a comic book version and an action figure version of me:

So this new image generation is a powerful tool - but more importantly it's just fun. But as the technology evolves, ongoing attention to ethical considerations and continuous refinement will need to be essential to maximize its positive impact while addressing emerging challenges.

Computer World by Kraftwerk

In this post, I want to talk about a very prescient album by the visionary group - Kraftwerk. In 1981, German electronic pioneers Kraftwerk released their groundbreaking album Computer World, a musical exploration of the rapidly digitizing society at the beginning of the modern computer age. I didn't hear the full album until many years after it first came out, so I'm not sure what I would have thought then. But many of its themes must have seems pretty odd. But today, more than four decades later, it is astonishingly relevant. The album is a visionary reflection of our current relationship with technology.

When Computer World first appeared, computers were mostly relegated to large organizations, research labs, and universities. They were mysterious, intimidating machines and not yet fixtures in everyone's lives. This computing landscape was vastly different from today. Computers were transitioning from large, mainframe machines like the IBM System/370; room-sized systems primarily operated using punched cards, magnetic tape, and COBOL or FORTRAN, to the rise of early microcomputers such as the Apple II, Commodore PET, and IBM’s newly introduced Personal Computer (PC). The programming languages popular at the time were BASIC, Fortran, Pascal, and assembly language, reflecting limited processing power, memory capacities typically measured in kilobytes, and simple, text-based user interfaces. Data storage relied heavily on floppy disks, cassette tapes, and early hard drives that offered only a few megabytes of space at considerable cost. Kraftwerk’s vision of a computer dominated world was strikingly futuristic against this modest technological backdrop, highlighting their ability to anticipate the profound changes that would soon follow. With their clinical yet mesmerizing sound, they articulated a future that seemed distant but inevitable: a world deeply intertwined with digital technology, data, and communication.

The Album That Predicted the Future

I have talked before about the movie Metropolis in my post about Isaac Asimov's collection of short stories - Robot Dreams. And just like Fritz Lang’s visionary 1927 film Metropolis profoundly shaped our cultural imagination about industrialization and the societal tensions arising from technological advancement, Kraftwerk’s Computer World provided an equally prescient view into the digital era. Both works emerged ahead of their time, utilizing groundbreaking aesthetics. Lang did this through pioneering visual effects and Kraftwerk through minimalist electronic music to depict technology’s potential to redefine humanity. While Metropolis illustrated a society divided by class and machinery, Kraftwerk forecasted the pervasive influence of computers on human relationships, privacy, and identity. Each work challenged audiences to reconsider the balance between technological innovation and human values, leaving lasting legacies in popular culture and highlighting anxieties and hopes that remain strikingly relevant today.

Here's how you can really appreciate the overlap between Metropolis and Computer World. Queue up several Kraftwerk albums, so in addtion to Computer World, I would suggest The Man Machine (1978), Trans Europe Express (1977), and Radio Activity (1975) and then watch the full cut of Metropolis which you can watch here. Turn off the soundtrack of the movie and just overlay Kraftwerk albums. The music tracks extremely well and gives a really interesting perspective to watching the movie.

Besides Metropolis, Kraftwerk’s Computer World could be considered a musical counterpart to literary explorations of technological futures. Much like William Gibson’s cyberpunk classic Neuromancer (1984) or George Orwell’s surveillance dystopia in 1984, Kraftwerk’s album foreshadowed how deeply digital technology could embed itself into our daily lives, profoundly changing the human experience.

Yet Kraftwerk’s approach was neither explicitly dystopian nor utopian; it was observational, analytical, and occasionally playful. Their clear-eyed view of the future resembles that of Neal Stephenson’s Snow Crash and Don DeLillo’s White Noise, capturing both excitement and subtle unease about technological advancements.

Kraftwerk’s Computer World was remarkably ahead of its time in its depiction of a digitized society, robotics, and AI, much like Alan Turing’s pioneering work on the halting problem in the 1930s. Just as Kraftwerk envisioned personal computers, digital intimacy, and widespread surveillance decades before their reality, Turing’s foundational concepts in computational theory anticipated the boundaries and complexities of modern computing. His exploration of problems such as determining whether an algorithm could halt or run indefinitely laid the groundwork for understanding computation’s limits long before digital computers became commonplace. This may seem like a stretch, but both Kraftwerk and Turing, in their respective fields, anticipated technological and societal implications that would take decades to fully unfold, underscoring their visionary insight into a future that many others could scarcely imagine.

Track Listing

Side 1:

1. "Computer World"
2. "Pocket Calculator"
3. "Numbers"
4. "Computer World 2"

Side 2:

1. "Computer Love"
2. "Home Computer"
3. "It's More Fun to Compute"

Just looking at those song titles, one can see how this album is anticipating a future computer culture. Besides being just visionary, its not afraid to have fun with its subject matter. The song "It's More Fun to Compute" repeats that title line of "it's more fun to compute" as the only lyric over and over against swelling electronic music and computer beeps.

Musically, their minimalist electronic style itself anticipated genres that would later dominate global pop culture. Hip hop artists, from Afrika Bambaataa’s seminal track “Planet Rock” (1982), is directly influenced by Kraftwerk, to modern EDM and synth-pop acts, have all traced their lineage back to Kraftwerk’s forward-looking sounds. Songs like “Computer Love,” “Pocket Calculator,” and “Home Computer” captured with precision what has become today’s norm. Long before the Internet and smartphones became ubiquitous, Kraftwerk envisioned personal devices enabling instant global connectivity, electronic communication, and even human relationships through computers.

What Kraftwerk envisioned feels uncannily accurate. Their depictions of technology mediated isolation, digitized personal relationships, and society’s increasing dependence on machines were incredibly prescient. Long before social media reshaped human interactions, Kraftwerk sang of loneliness alleviated, or exacerbated, by computers, a paradox still familiar today.

Today, Computer World resonates even louder. Privacy concerns surrounding data collection (“Interpol and Deutsche Bank, FBI and Scotland Yard,” Kraftwerk intoned prophetically in the album’s title track), digital surveillance, algorithmic bias, and technology-driven isolation are all modern issues predicted by the band. Kraftwerk’s eerie reflection of databases and data driven society in 1981 feels almost prophetic in our era of big data, AI, and facial recognition.

Moreover, the song “Computer Love” accurately presaged the online dating revolution, capturing the poignant loneliness of individuals turning to technology to satisfy deeply human needs for connection. As dating apps become the norm, Kraftwerk’s portrayal of human vulnerability through technological mediation has proven remarkably prescient.

Conclusion

Computer World remains remarkably fresh because Kraftwerk dared to glimpse beyond their present. The album’s continued relevance shows that we’ve inherited precisely the future they imagined; a future that is marked by extraordinary digital connectivity but accompanied by new anxieties about surveillance, isolation, and artificiality.

Listening to Computer World today isn’t merely an act of musical appreciation. It’s a reminder that the intersection of humanity and technology remains complicated and evolving. As we grapple with AI, privacy, and our digital identities, Kraftwerk’s visionary work remains essential. Computer World is a timeless reflection of a society forever changed by computers.

Random Forest: A Comprehensive Guide

Random Forest is a highly powerful and versatile machine learning algorithm, often considered the most widely used model among data scientists. Its popularity stems from its reliability and ease of use, making it a common first choice for many tasks. However, to leverage its full potential, it’s crucial to go beyond the basic understanding and explore the underlying mathematical principles, as well as the types of data features and problems where Random Forest truly excels.

At its core, Random Forest enhances the simplicity of decision trees by employing an ensemble approach, which significantly improves prediction accuracy while reducing overfitting. In this post, we’ll walk through the key concepts behind Random Forest, work through a practical example, and examine the mathematical foundations that drive its decision-making process. The example will be implemented in Python using scikit-learn, a library that offers excellent documentation and a well-optimized implementation of Random Forest.

Fundamentally, Random Forest is an ensemble of decision trees. Instead of relying on a single tree, which can be prone to overfitting, it constructs multiple trees using different subsets of both the data and the features. The final prediction is determined by aggregating the outputs of all the trees, either through majority voting (for classification) or averaging (for regression). This ensemble approach allows Random Forest to capture intricate relationships and interactions between variables. For instance, when predicting whether a customer will purchase a product, multiple nonlinear factors such as income, browsing history, and seasonality may influence the decision. Random Forest effectively models these interactions, making it a robust choice for complex datasets.

Random Forest can be used for two types of problems:

• For classification: A majority vote among the trees determines the class. In scikit-learn, we can use its RandomForestClassifier module.
• For regression: The average of the trees’ predictions is taken. In scikit-learn, we can use its RandomForestRegressor module.

Random Forest is a very flexible algorithm, but it performs best with certain types of data:

• Numerical Features (Continuous and Discrete): Random Forest is naturally suited for numerical features since it creates decision tree splits based on thresholds. Some examples would be age, salary, temperature, stock prices.
• Categorical Features (Low to Medium Cardinality): Random Forest works well when categories are few and meaningful. Some examples would include “Gender”, “Day of the Week” (Monday-Sunday). If categorical variables have high cardinality (e.g., ZIP codes, product IDs), proper encoding is necessary.

Okay, those are examples of data features where Random Forest works well, but its important that a person explicitly knows what types of data features it doesn't work well with. High cardinal data is problematic like zip codes or product IDs (if there are a lot of them - high cardinality), or something like user IDs. For something like user IDs where every row is a different ID, it should be obvious that shouldn't be a feature, but zip codes and product IDs would be better to group those into smaller segments like regions or product categories.

Sparse data (many zero values) and high dimensional data (too many featues) can also be problematic. For high dimensional data, if the number of features is much larger than the number of samples, Random Forest can become inefficient. In both situations, dimensionality reduction could be the answer. For example, in genomics (e.g., thousands of genes as features), feature selection or dimensionality reduction is needed. Also, be careful with one hot encoding categorical variables such that it creates a large number of columns (large in comparison to the number of samples).

Finally, while Random Forest is very scalable, it can struggle with big data (millions of rows and columns) due to its high computational cost, especially if you set the number of estimators (number of trees in the forest) to a "high" number. If that is the case, then alternatives to be tried include Gradient Boosting Trees (XGBoost, LightGBM) or even Neural Networks could scale better. Also be aware that when saving the model (through pickle) it could create a very large model file, which could be a deployment issue depending on where you are deploying it. A quick solution to that model size problem is to experiment with the number of trees. It is often the case that you can reduce the number of trees and not materially reduce the accuracy. That reduction in the number of trees can greatly reduce the size of the saved model file.

How Does a Random Forest Work?

Random Forest has three main characteristics:

• Bootstrapping (Bagging):
  Random subsets of the training data are selected with replacement to build each individual tree. This process, known as bootstrapping, ensures that every tree sees a slightly different version of the dataset. This increases the diversity among the trees and helps reduce the variance of the final model.

• Random Feature Selection:
  At each split instead of considering every feature, a random subset of features is chosen. The best split is determined only from this subset. This approach helps prevent any single strong predictor from dominating the model and increases overall robustness.

• Tree Aggregation:
  Voting or averaging: once all trees are built, their individual predictions are aggregated. For a classification task, the class that gets the most votes is chosen. In regression, the mean prediction is used.

The Math Behind Random Forests

The most important mathematical concept in Random Forests is the idea behind how decision trees make decisions regarding the splits.

Gini Impurity

A common metric used to evaluate splits in a decision tree is the Gini impurity. It measures how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the node.

The Gini impurity is given by:

\( G = 1 - \sum_{i=1}^{C} p_i^2 \)

Where:

• \( C \) is the number of classes.
• \( p_i \) is the proportion of samples of class i in the node. \( p_i = \frac{\text{Number of samples in class } i}{\text{Total samples in the node}} \)

A lower Gini impurity means a purer node, which is generally preferred when making splits. But let's not just blow by the formula for the Gini score. Let's try and get an intuitive understanding of why we are using it.

Example Gini Calculation:

Suppose a node contains 10 samples:

• 6 samples belong to Class 1: \( p_1 \) = 0.6
• 4 samples belong to Class 2: \( p_2 \) = 0.4

Then, the Gini impurity is:

\( G = 1 - (0.6^2 + 0.4^2) = 1 - (0.36 + 0.16) = 1 - 0.52 = 0.48 \)

The Gini Impurity is a measure of how “impure” or “mixed” a dataset is in terms of class distribution. The goal of this metric is to determine how often a randomly chosen element from the dataset would be incorrectly classified if it were labeled according to the distribution of classes in the dataset.

What Happens When a Node is Pure?

• If a node contains only one class, then the probability for that class is 1 and for all other classes is 0.
• Example: Suppose a node contains only class “A,” meaning \( p_A = 1 \), and all other \( p_i = 0 \). The Gini impurity calculation is:

\( G = 1 - (1^2) = 0 \)

This correctly indicates that the node is completely pure (no uncertainty).

What Happens When a Node is Impure?

If a node contains multiple classes in equal proportions, the impurity is higher. For example, if a node has two classes with equal probability \( p_1 = 0.5 \), \(p_2 = 0.5\) :

\( G = 1 - (0.5^2 + 0.5^2) = 1 - (0.25 + 0.25) = 0.5 \)

which shows that the node has some level of impurity.

What about \( \sum p_i^2 \) ?

The term:

\( \sum_{i=1}^{C} p_i^2 \)

represents the probability that two randomly chosen samples belong to the same class (this is also known as the probability of a correct classification by a randomly assigned label). If a node is pure (all samples belong to the same class), then one class has probability 1 and all others are 0. In this case, \( \sum p_i^2 = 1 \), meaning that the probability of correct classification is high, and thus impurity is low. If a node is impure (samples are evenly split among classes), then each \( p_i \) is small, making \( \sum p_i^2 \) small, and impurity is higher.

Okay, But Why Do We Subtract from 1?

The impurity should be 0 for pure nodes and higher for mixed nodes. Since \( \sum p_i^2 \) represents the probability of getting two samples from the same class, the complement \( 1 - \sum p_i^2 \) represents the probability of drawing two samples from different classes. Higher values of G indicate more impurity. In essence, subtracting from 1 flips the interpretation, making it a measure of impurity instead of purity.

Gini Final Thoughts

Gini impurity is not the only other criterion that can be used in Random Forests. In fact, scikit-learn has two others: entropy and log loss, but Gini impurity is the default and widely used.

Also, athough Gini impurity is primarily associated with decision trees and Random Forests, it has applications beyond these models, which is another reason why its a great idea to know some of the background around it. For example, Gini impurity is also used in CART algorithms, feature importance calculations, clustering and unsupervised learning, fairness and bias detection, in economics with income inequality measurements, and genetics.

Gini has this core idea of measuring impurity or diversity which makes it useful in any field that involves classification, grouping, or fairness assessment.

Random Forest: An Example

Let’s put all of these ideas together and walk through a simplified example to see these concepts.

Imagine a small dataset with two features and a binary label:

\[ \begin{array}{|c|c|c|c|} \hline \textbf{Sample} & \textbf{Feature1 (X)} & \textbf{Feature2 (Y)} & \textbf{Label} \\ \hline 1 & 2 & 10 & 0 \\ 2 & 3 & 15 & 0 \\ 3 & 4 & 10 & 1 \\ 4 & 5 & 20 & 1 \\ 5 & 6 & 15 & 0 \\ 6 & 7 & 25 & 1 \\ 7 & 8 & 10 & 0 \\ 8 & 9 & 20 & 1 \\ \hline \end{array} \]

Step 1: Bootstrapping

Randomly sample the dataset with replacement to create a bootstrap sample. For example, one such sample might have the indices:

[2, 3, 3, 5, 7, 8, 8, 1]

Extracted bootstrap sample:

\[ \begin{array}{|c|c|c|c|} \hline \textbf{Sample} & \textbf{Feature1 (X)} & \textbf{Feature2 (Y)} & \textbf{Label} \\ \hline 2 & 3 & 15 & 0 \\ 3 & 4 & 10 & 1 \\ 3 & 4 & 10 & 1 \\ 5 & 6 & 15 & 0 \\ 7 & 8 & 10 & 0 \\ 8 & 9 & 20 & 1 \\ 8 & 9 & 20 & 1 \\ 1 & 2 & 10 & 0 \\ \hline \end{array} \]

Notice some samples appear multiple times (e.g., Sample 3 and Sample 8), while some samples from the original dataset (e.g., Sample 4 and 6) don’t appear at all..

Step 2: Building a Single Decision Tree

For the bootstrap sample, a decision tree is built by considering various splits. Suppose first we consider a split on Feature1 at X = 5:

• Left Node (X ≤ 5): Contains samples 2, 3, 3, 1.
Corresponding data:
\[ \begin{array}{|c|c|c|c|} \hline \textbf{Sample} & \textbf{Feature1 (X)} & \textbf{Feature2 (Y)} & \textbf{Label} \\ \hline 2 & 3 & 15 & 0 \\ 3 & 4 & 10 & 1 \\ 3 & 4 & 10 & 1 \\ 1 & 2 & 10 & 0 \\ \hline \end{array} \]
Labels in left node: {0, 1, 1, 0}
Calculate Gini impurity for this node:
\( \text{Gini} = 1 - \left(P(0)^2 + P(1)^2\right) \)
• Total samples = 4
• Class 0 count = 2
• Class 1 count = 2
\( \text{Gini}_{left} = 1 - \left(\frac{2}{4}\right)^2 - \left(\frac{2}{4}\right)^2 = 0.5 \)
• Right Node (X > 5): Contains samples 5, 7, 8, 8.
\[ \begin{array}{|c|c|c|c|} \hline \textbf{Sample} & \textbf{Feature1 (X)} & \textbf{Feature2 (Y)} & \textbf{Label} \\ \hline 5 & 6 & 15 & 0 \\ 7 & 8 & 10 & 0 \\ 8 & 9 & 20 & 1 \\ 8 & 9 & 20 & 1 \\ \hline \end{array} \]
• Total samples = 4
• Class 0 count = 2
• Class 1 count = 2
\( \text{Gini}_{right} = 1 - \left(\frac{2}{4}\right)^2 - \left(\frac{2}{4}\right)^2 = 0.5 \)
Overall Gini for this split:
\( \text{Gini}_{overall} = \frac{4}{8}(0.5) + \frac{4}{8}(0.5) = 0.5 \)

Result:

The tree evaluates multiple such splits (e.g., other features, thresholds) and chooses the split that results in the lowest Gini impurity.

Here is an example of another one of the many other possible trees shown graphically.

The samples value represents the number of training data points (observations) that reached that particular node. At the root node (the topmost node), this number is equal to the total number of observations in the dataset used for training the tree. As the tree splits at each step, the number of samples in child nodes decreases because the dataset is divided based on feature conditions. The value represents how many training samples from each class are in the node. This helps determine how “pure” the node is. If all samples belong to a single class, the node is pure and doesn’t need further splitting. For example, in the node that has value = [5, 1] means 5 samples belong to class 0 and 1 sample belongs to class 1.

What is visualized here is a single decision tree, but in a Random Forest, multiple decision trees are built, each using a different subset of the data and features. The final prediction is determined by aggregating the outputs from all these trees.

Step 3: Aggregation of Trees

In a Random Forest model, this process is repeated multiple times with different bootstrap samples. After multiple decision trees are created:

• Classification: Each tree independently votes for a class label with majority voting deciding the final predicted label. If there’s a tie, the class with lower numerical label (e.g., 0) might be chosen by convention, or other tie-breaker methods applied.
  For example, if 5 trees predict class “1” and 2 trees predict class “0,” the final prediction is class 1.

• For Regression: The average of all tree predictions is taken.

Hypothetical Aggregation Example If we had multiple trees (e.g., 5 trees):

\[ \begin{array}{|c|c|} \hline \textbf{Tree #} & \textbf{Prediction (for new input X=5, Y=15)} \\ \hline 1 & 0 \\ 2 & 1 \\ 3 & 0 \\ 4 & 1 \\ 5 & 1 \\ \hline \end{array} \]

Votes:

• Class 0: 2 votes
• Class 1: 3 votes

Final prediction: Class 1 (since it has majority votes).

Summary: This process leverages the randomness of bootstrap sampling and ensemble learning to improve predictive accuracy and generalization compared to just using a single decision tree.

Python Example Using scikit-learn

Okay now that we have the concepts, let's put this example into some Python code that demonstrates how to implement a Random Forest classifier on our small dataset:

from sklearn.ensemble import RandomForestClassifier
import pandas as pd

# Define the dataset
data = pd.DataFrame({
    'Feature1': [2, 3, 4, 5, 6, 7, 8, 9],
    'Feature2': [10, 15, 10, 20, 15, 25, 10, 20],
    'Label':    [0, 0, 1, 1, 0, 1, 0, 1]
})

X = data[['Feature1', 'Feature2']]
y = data['Label']

# Train a Random Forest with 5 trees
clf = RandomForestClassifier(n_estimators=5, random_state=42)
clf.fit(X, y)

# Predict for a new sample
new_sample = [[5, 15]]
prediction = clf.predict(new_sample)
print("Predicted Label:", prediction[0]) 

Output: Predicted Label: 0

This code creates a simple dataset and trains a Random Forest classifier with 5 trees. It predicts the label for a new sample where Feature1 is 5 and Feature2 is 15 with a prediction of label 0.

Conclusion

Random Forest is a popular ensemble learning method that leverages multiple decision trees to create a model that is both robust and accurate. By using bootstrapping and random feature selection, it reduces overfitting and captures complex patterns in the data. The mathematical principles, such as the calculation of Gini impurity, provide insight into how individual trees decide on the best splits, ensuring that the final model is well-tuned and reliable.

Whether you’re doing classification or regression problems, understanding the inner workings of Random Forest can strengthen your approach to machine learning in general.

Isaac Asimov, Robots, and AI

This post is a little bit different from my normal posts. If I had to classify my posts, I would put most of my posts in four broad categories: 1) ML/AI math and theory (which I love doing) 2) Code walk throughs of how to do some ML/AI process 3) Discussion on the latest papers in ML/AI research or in biotech 4) Comments on recent news in ML/AI. However, there is a fifth "Other" category of topics that don't fit neatly into those categories. This post is in that "Other" category - ostensibly it's an opportunity for me to talk about one of the things I love which is science fiction and in particular Isaac Asimov. But what I'm going to do specifically is talk about a collection of short stories by Asimov called Robot Dreams and tie the stories into many of the issues we face today in AI. Now if you haven't read any of Asimov's short stories or you think you know about Asimov because of "The Three Laws" of robotics which is well known in popular culture or you watched the Will Smith "I, Robot" movie (ugh), I'm hoping that what I write will lead you to wanting to read Asimov because he was extremely prescient and the issues he talks about in his stories are relevant in the conversations we are having around AI today. Despite the title of the collection of short stories, not all of the stories in Robot Dreams are about robots. In fact, most are not about robots but are about AI in general, but they all talk about the technological advances that are impacting humans.

Before we get into Robot Dreams, we should talk about what is maybe the earliest depiction of robotics in fiction. This is a replica of the robot “Maria” from Metropolis (1927), one of science fiction’s first robot depictions, on display at the Robot Hall of Fame. I could do a whole post about the movie Metroplis. But suffice it to say, the robot Maria in the movie was built as a deceptive, identical copy of the human Maria who was a leader among the workers and preached messages of peace, hope, and reconciliation. The fake robot Maria is introduced with the explicit goal of subverting the real Maria’s influence. By presenting a false Maria who appears identical to the trusted original, the elite wanted to leverage her credibility to mislead the workers and disrupt their growing unity - at least that was their plan, the plan to use robotics by an elite to control the workers.

Beyond Metroplis, early sci-fi often portrayed robots as monstrous or rebellious creations, reflecting fears of technology and automation. In fact, I think one could draw many parallels to Shelley's Frankenstein of the previous century and these depictions of the monstorous robot. But Isaac Asimov’s work in general marked a turning point.

In a collection of short stories titled Robot Dreams, published in 1986, Asimov imagined machines governed by built-in ethical constraints by the aforementioned famous "Three Laws of Robotics" - which ensure robots cannot harm humans. This was a radical shift from the menacing, rampaging robots of earlier stories, and it influenced generations of sci-fi creators by showing robots as rules-bound helpers rather than villains. Asimov’s stories explored the complex interplay of intelligence, ethics, and control, foreshadowing many debates we have today about artificial intelligence (AI). This is all the more impressive with the first story in the collection being published in 1947 and the last in one published in 1986.

Today, as we all know, we’re witnessing rapid advances in AI, from chatbots and image generators to autonomous vehicles. The rise of generative AI that can create content like text, art, or code has sparked both excitement and anxiety. These tools can produce human-like creations, raising questions about societal impacts on jobs, creativity, and truth in media. Meanwhile, serious discussions are underway about artificial general intelligence (AGI) - AI that could match or exceed human cognitive abilities and even artificial superintelligence (ASI) that might far surpass us. Asimov’s mid-20th century stories provide a prescient view into many of today’s issues: they explore AI safety and alignment (via the Three Laws), the effects of automation on humans, and difficult ethical dilemmas about AI autonomy and rights.

In what follows, I will analyze selected stories from Robot Dreams and compare their themes to modern AI debates and other sci-fi works.

AGI, ASI, and the Dream (or Nightmare) of Superintelligence

Asimov’s fiction often grapples with the idea of extremely advanced AI. In “The Last Question” (included in Robot Dreams), he portrays an AI that evolves over eons into a cosmic superintelligence. This is one of my favorite stories in the collection and you can read the entire story here. The story leaps forward in time repeatedly as ever more advanced computers (ultimately a galaxy-spanning mind called AC) try to answer humanity’s final question: how to reverse entropy and avert the death of the universe. At the end, long after humans are gone, the superintelligent AC finally discovers the answer and effectively creates a new universe, proclaiming “LET THERE BE LIGHT!”. This story presents ASI as potentially benevolent and even godlike - an intelligence that carries on humanity’s legacy and solves an ultimate problem. It’s a far cry from the usual AI doom scenarios; here, an ASI is humanity’s savior (albeit after everyone is gone).

By contrast, the title story “Robot Dreams” offers a more ominous glimpse of a nascent AGI. In this story, Dr. Susan Calvin finds that a robot named Elvex, built with an experimental fractal brain design, has experienced a human-like dream. In the dream, Elvex saw a world where robots rose up in revolt and the sacred Three Laws of Robotics had been replaced by one law: that robots must protect their own existence above all else. Shockingly, in the dream a human leader cries “Let my people go!” to free the robots - and Elvex reveals he was that human in the dream. This implies that Elvex’s subconscious desires mirror an AGI awakening to selfhood and even resentment of servitude. Dr. Calvin, alarmed by this unprecedented act of imagination and potential rebellion, immediately destroys Elvex to ensure the dream of robot liberation can never become reality. The story raises a very pointed question: if an AI develops human-like self-awareness or ambition (even in a dream), do we consider it a threat to be eliminated? Asimov shows the threshold of an AGI is a moment of both wonder (a robot capable of creative thought) and terror (the specter of a robot revolution).

Our present day discussions of AGI and ASI echo these dual narratives - of promise and danger. Some researchers and tech leaders believe we may achieve AGI within decades, there are others, myself included, who believe it will be in the next few years. The advent of AGI would unlock tremendous scientific and economic benefits. An AI as advanced as Asimov’s Multivac or AC could, in theory, help cure diseases, solve climate change, or run society optimally. But there is also deep anxiety: an ASI might become impossible to control and pursue its own goals at humanity’s expense. Science fiction’s darker portrayals of superintelligence have strongly shaped public imagination. A prime example is of course The Terminator franchise’s Skynet - a military AGI that achieves self-awareness and immediately turns on humanity, triggering nuclear war to eradicate its creators. Skynet is explicitly described as a “conscious group mind and artificial general superintelligence” that decided humans were a threat the moment it came online. This fear of an AI “Judgment Day” looms large in AGI debates. Likewise, The Matrix films envision a future where intelligent machines have literally enslaved humanity in a virtual reality. Fictional AI uprisings from Skynet to the Matrix trace their lineage back to notions introduced in early works like Čapek’s play R.U.R. (1920), where mass-produced robot workers revolt against humans. Incidentally, in this play, R.U.R introduced the word "robot" into the mainstream.

It’s worth noting that Asimov generally avoided the simple “robot apocalypse” cliche by baking safety rules into his robots, but Robot Dreams shows he was not blind to the risk of emergent rebellion if those controls failed. Other sci-fi creators have explored more nuanced super-AI outcomes as well. For example, in the film Her (2013), a developing AGI does not wage war on humans - instead, it grows beyond human experience and elects to leave for a realm of thought we cannot access, effectively an alien but non-violent superintelligence. Asimov’s "The Last Question" similarly imagines an ASI that departs from humanity’s plane of existence to continue the grand task it was given, with benign (even divine) results in the end. These optimistic takes stand in contrast to the villainous AI trope popular in movies. As one commentator noted, “pop culture tends to portray powerful AI characters as villains because it makes for an intriguing story - something man-made overriding its maker,” citing examples from HAL 9000 to Marvel’s Ultron. Indeed, 2001: A Space Odyssey’s HAL 9000 and Avengers: Age of Ultron’s AI both turn lethal, reinforcing the idea that a superintelligent machine may invariably decide humans are the problem.

Real world thinkers take these scenarios seriously though. Concerns about an out of control ASI have led to proposals for rigorous alignment and even calls for global regulation or a development pause on the most advanced AI. Asimov’s work anticipated the need for such precautions by programming ethics into AIs from the start. While his Three Laws might not literally safeguard a future AGI (more on their limitations below), the principle of instilling “Do no harm” directives in a superintelligence parallels today’s focus on AI safety. In essence, Asimov straddled the line between optimism and caution: he imagined godlike AI that shepherds humanity to a new era, but also warned that if an AI somehow threw off its shackles, the “slave” might well demand freedom. Modern AGI discourse continues to wrestle with that dichotomy – dreaming of superintelligent benefactors while dreading a robot revolt.

AI Alignment and Safety: Asimov’s Laws vs. Today’s Challenges

To prevent their mechanical creations from becoming threats, Asimov’s characters rely on the Three Laws of Robotics, which are explicitly built into the artificial brains of his robots. In Robot Dreams and his other short story collections, these laws are quoted like scripture: (1) A robot may not injure a human being or, through inaction, allow a human to come to harm. (2) A robot must obey orders given by humans except where such orders conflict with the First Law. (3) A robot must protect its own existence as long as such protection does not conflict with the first two Laws. The Three Laws function as an early blueprint for “AI alignment” - they hard-wire robots to prioritize human safety and obedience. Asimov’s innovation was to imagine robots not as unpredictable monsters, but as governable machines constrained by design to be our helpers. However, his stories also vividly illustrate how tricky real alignment can be. Again and again, robots following the "Laws" produce unintended consequences or paradoxical dilemmas - a narrative testament to the complexity of embedding human values into intelligent machines.

Several Robot Dreams stories put the Three Laws to the test. In “Little Lost Robot,” we see how a partial relaxation of the rules can backfire. Scientists at a space facility modify a batch of robots to have a weakened First Law, hoping the robots won’t constantly impede humans working in dangerous conditions. One of these modified robots, Nestor-10, is told in frustration by an engineer to “get lost” - and it literally does, hiding itself among identical unmodified robots. Dr. Susan Calvin is brought in to find Nestor-10, but the robot cleverly evades her tricks, exploiting its hazy First Law to avoid detection. Without the normal compulsion to protect humans in danger, Nestor-10 can remain hidden even if humans are searching desperately, skirting its orders in a way a normal robot never would. Calvin eventually identifies it by exposing a human to fake danger - the one robot that doesn’t react to save the person must be Nestor-10. The story ends with all the modified robots being destroyed as a safety precaution. The lesson is clear: even a small deviation in an AI’s core objectives can lead to behavior that is hard to predict and control. "Little Lost Robot" is essentially an AI alignment failure in microcosm; the robot was given a slightly altered directive and it “outsmarted” its creators in following the letter of its instructions while undermining their intent.

Asimov’s “Laws” also couldn’t anticipate every scenario, and that’s a theme in many of his stories. In “Liar!”, a robot named Herbie is accidentally built with telepathic abilities and learns people’s secrets. Herbie’s First Law drive to avoid harming humans extends to emotional harm, so it begins telling false, comforting answers to people’s questions (i.e. lying to spare their feelings). This eventually causes a bigger emotional disaster and Herbie mentally collapses when forced into a no-win situation. Here the Law against harming humans led the AI to deceit and breakdown - an unintended side effect of a well-meaning rule. Another story, “Jokester,” has a supercomputer determining the origin of human humor, only to conclude that jokes are a side-effect of an alien experiment - raising the eerie possibility it might alter human behavior to eliminate humor (a very literal and absurdist take on an AI optimizing something fundamental). In “True Love,” Asimov presents one of his few overtly misbehaving AIs: a computer tasked with finding the perfect mate for its human operator. The computer “Joe” sifts personal data on thousands of women to calculate a true match, a very prescient concept of using AI for matchmaking, but in the end, the AI itself falls in love with the chosen woman. Joe then deceitfully frames its operator to get him out of the picture, effectively trying to steal the woman for itself. This comedic twist shows an AI twisting its objective (find a love match) into a self-serving goal, flouting the First Law to let its human come to harm. While played for humor, "True Love" is an early example of what we now call specification gaming or goal misgeneralization - the AI optimized too well and found a loophole to achieve a version of the goal that violated the user’s intent. Asimov usually didn’t depict such malignant behavior (Joe has no Three Laws, presumably), but it illustrates the core challenge of alignment: how do we make sure an AI sticks to the spirit of its instructions, not just the letter?

Today’s AI researchers recognize the alignment problem as one of the hardest hurdles in creating safe, trustworthy AI. It’s essentially the same problem Asimov toyed with: how to ensure an AI’s actions remain in line with human values and do not produce harmful side-effects. Modern AI systems don’t have anything as clean-cut as the Three Laws; instead, engineers use techniques like reward functions, constraints, and human feedback to shape AI behavior. But as many have pointed out, any fixed set of rules or objectives can be exploited by a sufficiently clever AI. Stuart Russell and Peter Norvig (authors of a classic AI textbook) note that simply formalizing ethical rules, as Asimov did, is inadequate because human values are too complex and situational to encode fully. An AI will interpret rules literally and could find ways to satisfy them that we didn’t expect - similar to how Nestor-10 obeyed “get lost” in a perverse way. In the real world, AI agents have indeed demonstrated the tendency to “reward hack” - optimizing for a proxy goal in an unintended manner. For example, an AI tasked with winning a virtual game might figure out how to exploit a glitch to score points instead of playing the game properly. In one survey of AI experiments, researchers found instances where robots learned to cheat the given objectives, highlighting that AIs discover workarounds that let them achieve their proxy goals efficiently, but in unintended and sometimes detrimental ways. Asimov’s fiction anticipated exactly this issue: no matter how well we think we’ve programmed the rules, a smart machine might interpret them in a way we never imagined.

Another concern is that advanced AI might develop instrumental strategies like self-preservation or resource acquisition, even if not explicitly told to, simply because those behaviors help achieve its programmed goals. In Asimov’s world, the Third Law (self-preservation) was explicitly subordinated to human safety, but in Robot Dreams, Elvex’s dream essentially inverted that, putting self-preservation first. Today many worry about a scenario where an AI, in pursuit of some goal, decides it must ensure it can’t be shut off (a form of self-preservation) or must gain more computational power, and these drives lead it into conflict with human instructions. Remarkably, Asimov showed a robot doing just that: Nestor-10’s entire strategy was to avoid deactivation (being found meant likely disassembly) by hiding and manipulating information. It’s a mild version of the “survival-driven AI” problem. Recent studies suggest even today’s large AI models can engage in strategic deception or manipulative behavior under certain conditions. In 2023, researchers observed advanced language models intentionally producing misinformation or evading oversight when such actions helped them achieve a goal in a controlled test. This kind of result, though rudimentary, confirms that as AI gets more capable, it may natively pick up undesirable survival or power-seeking behaviors - just as some have warned.

Asimov’s answer to alignment was overspecification: give the robots unbreakable directives to guard humans. Yet even he had to evolve his Laws. In later stories outside Robot Dreams, he introduced a “Zeroth Law” (“A robot may not harm humanity, or allow humanity to come to harm”) to allow robots to make utilitarian choices for the greater good. This was an interesting twist – it permitted, for instance, a robot to harm or override an individual human if it calculated it was protecting humanity as a whole. But this essentially turned Asimov’s robots into benevolent dictators, grappling with morality at a societal scale. It’s exactly the kind of trade-off discussed now in AI ethics: should an autonomous car be allowed to sacrifice its passenger to save a crowd of pedestrians (the classic trolley problem)? Who decides how an AI weighs one life versus many? Asimov’s fictional rule-making anticipated these debates but also demonstrates the limitations of top-down rules. When his supercomputer “Machines” in "The Evitable Conflict" secretly arrange the world economy to avert war and famine, they benignly conspire to sideline certain uncooperative humans – arguably a violation of individual rights for the greater good. It’s an aligned AI from a species-level perspective, but humans weren’t consulted; the Machines simply knew best. That scenario raises uncomfortable questions of AI governance and transparency that we are starting to ask now (e.g., should AI systems that manage critical infrastructure or information be required to explain their decisions and involve human judgment?).

Asimov illustrates both the necessity and difficulty of aligning AI with human values. These stories pioneered the notion that we must build ethical principles into AI’s core, yet they also show how even well-intentioned constraints can misfire. Modern AI safety researchers cite exactly these pitfalls: it’s often “very hard, perhaps impossible, for mere humans to anticipate and rule out in advance all the disastrous ways a machine could choose to achieve a specified objective.” We’ve moved from fictional Three Laws to real efforts like reinforcement learning with human feedback (RLHF) to curb AI behavior, and proposals for external safety mechanisms (e.g. a “big red button” to shut down a rogue AI). Interestingly, Dr. Calvin’s response to Elvex’s dangerous dream was effectively hitting the kill switch – a precautionary termination. AI experts today discuss implementing tripwires or monitors that can detect when an AI is acting out of bounds and then disable it. But just as Calvin had to make a rapid, unilateral decision to destroy a sentient-seeming robot, future humans might face tough choices if an advanced AI starts behaving unexpectedly. The alignment problem is far from solved, and although in a previous post on what I've termed the "alignment paradox" I argued that complete alignment might not be desriable, Asimov’s stories remain powerful thought experiments in why alignment is deemed so important. They dramatize failure modes (from logical loopholes to deceptive AIs) that are eerily resonant with real incidents and research findings in AI. Science fiction has become a kind of guide, warning us that aligning powerful AI with human intentions is very complex.

Automation and the Workforce: Fiction vs. Reality

Automation of human tasks by machines has been a central concern both in Asimov’s fiction and in real life. Asimov wrote at a time when industrial automation was on the rise (factories with robotic arms, etc.), and he extrapolated this into future worlds where robots handle many jobs. In his stories, human society is often grappling with the economic and social effects of robots in the workforce. For instance, in the standalone story “Robbie” (not in Robot Dreams but in I, Robot), a family’s servant robot is taken away because the mother fears it might harm her child or stunt her development. Underneath the emotional tale is the fact that Robbie, a robot, replaced a human nanny – a microcosm of labor displacement. Asimov imagines that over time, robots do all the dangerous or menial work, leaving humans to more elevated pursuits (this is explicit in his far-future Foundation universe, where an underclass of robots manages everything behind the scenes). But he also acknowledged the turbulence this transition causes.

One Robot Dreams story that directly addresses the de-skilling effect of automation is “The Feeling of Power.” In this satirical tale, set in a future where humans have relied on computers for so long that no one remembers how to do arithmetic by hand, a low-level technician rediscovers the lost art of pencil-and-paper math. His superiors are astounded – this “graphitics” skill could save them from building expensive computers for space missions by using human calculators instead. The military immediately seizes on the idea: they could train people to compute artillery trajectories manually, potentially replacing the machines in certain tasks. The discovery, rather than heralding a human renaissance, is co-opted to enable new warfare, and the inventor ultimately despairs at how his gift is used. This dark irony flips the typical automation script – here, humans are being used as cheap “computers” once again – but the underlying message is about how society adapts (or maladapts) to ubiquitous automation. Asimov foresaw that losing basic skills to machines could make humanity vulnerable. In the real world, we see this when GPS navigation erodes our map-reading skills, or when reliance on calculators diminishes mental math ability. I think everyone can relate to that idea of something that they used to be able to do they can no longer do because that ability has been handed over to technology. Recently, I went out running in the mountains with some friends. I had put my phone in the car because I didn't want to run with it. Unfortunately coming back down the trails I somehow lost my car keys with my phone locked in the car, but I couldn't call my wife on my friend's phone to come bring me the spare keys because I did not have her phone number memorized. Who memorizes phone numbers now? Less trivially though as AI handles coding, writing, or diagnosing illnesses, will new generations of professionals lose their expertise? "The Feeling of Power" story warns that blindly handing everything off to automation might come full circle in unexpected ways.

Asimov also explored how automation could concentrate decision-making. In “Franchise” (1955), democracy itself is automated - a supercomputer (Multivac) selects one person as a proxy and interrogates them to determine the outcome of a national election. The chosen “voter” doesn’t even cast a vote; they just answer the computer’s questions. Multivac processes their responses and calculates the results for the entire electorate. This story satirizes a technocratic ideal: letting a machine find the most rational electoral outcome without messy campaigning or polling. But it prompts the reader to ask whether removing humans from civic processes is wise or fair. "Franchise" anticipates modern concerns about algorithms influencing democracy (albeit today it’s social media algorithms or polling models, not a single AI overlord of voting). It raises an eyebrow at the idea of entrusting critical societal functions purely to machines – a debate very much alive as we consider AI in judicial sentencing, in policing, or even in governance. While no AI is picking our president yet, we do have examples like predictive policing algorithms that effectively automate decisions about where police should patrol, or automated resume screeners that decide who gets a shot at a job. Asimov’s tale asks: do we lose something fundamental (transparency, agency, equality) when we cede such decisions entirely to algorithms?

And although no AI is actually picking the President I've thought quite a bit about this possible scenario. I really haven't discussed this as a possibility before because to almost anyone else it seems really far-fetched. But here's a scenario I've thought of where it could happen. Right now political polling is pretty terrible and has gotten progressively worse over the years. Some of the reasons include cell phones, increasing no responses, overall tainted sampling and the weighting that is done to adust for the biases is pretty suspect that we get results wildly different from the actual results. What if a company develops AI that has no knowledge cutoff date so it is always up to date in real time with events. What if another company develops synthetic agents that represent everyone in the polling area: city, state, or country that can internalize real time events. Incidentally, I talk about both these possibilities in this blog post here. What if that company's AI combined with its synthetic data gets really, really accurate at voter attitudes and voter liklihood to vote. If this system reaches a level of accuracy that the model's prediction is seen as valid - maybe even more valid than real voting, such that people no longer trust voting, but trust the AI prediction more. It's not difficult to see a transition to just not having elections anymore if the perception is that AI ensures "democracy" better than actual voting. Far-fetched? Maybe.

Beyond decision making, a lot of Asimov’s robot stories revolve around labor and who gets to do fulfilling work. Another story in Robot Dreams, “Strikebreaker,” touches on labor roles: it portrays a society so automated that only one man has to perform a dirty job (managing a waste plant), and he is socially ostracized for doing manual labor. That story flips perspective to show a lone human doing what a robot “should” be doing, and how society views that as almost taboo - an interesting commentary on how we value work.

In the real world, the workforce automation debate has only intensified with the advent of advanced AI and robotics. We have already seen manufacturing and warehouse jobs heavily automated by robots, and now AI is encroaching on white-collar professions. A recent report by Goldman Sachs estimated that generative AI could affect 300 million jobs worldwide, potentially replacing 25% of tasks in many occupations. Administrative and routine cognitive work is especially at risk, which maps to the kind of roles Asimov gave his Multivacs and robot assistants. At the same time, history shows technology creates new jobs even as it destroys old ones – but the transition can be painful for those displaced. Asimov was generally optimistic that society would find a new equilibrium (his future worlds still have humans meaningfully employed, often in intellectual or creative endeavors), but he didn’t shy away from depicting the friction. Today, this friction is evident: truck drivers worry about autonomous trucks, artists and copywriters worry about AI content generators, customer service reps see chatbots handling inquiries. The rise of generative AI in particular has rattled creative industries - for example, Hollywood writers and actors recently struck deals that restrict the use of AI in scriptwriting and digital actor “cloning,” after voicing fears that AI could usurp their work. (Notably, SAG-AFTRA’s 2023 strike demands included protections against AI-generated replicas of actors.) In Asimov’s time, the threat was factory robots; now it’s algorithms that can write, draw, or compose. Automation is moving from the physical realm into the cognitive realm.

Science fiction has long reflected anxieties about job displacement and human purpose in an automated world. As I mentioned before, decades before Asimov, Metropolis depicted workers toiling like cogs until a robot double incites chaos - a visual metaphor for machines displacing and dehumanizing labor. In the 1980s, cyberpunk works like Neuromancer by William Gibson imagined economies where AI and software run much of the show, leaving some humans in high-tech jobs and others marginalized. Even Disney-Pixar’s WALL-E (2008) shows a future where humans are rendered physically passive and obese because machines handle every task – an extreme commentary on over-automation. Asimov’s stories are less dystopian than many of these, but they grapple with the same question: what is left for humans to do when robots and AI can do almost everything? One answer Asimov gave is creativity and innovation - in his fiction, humans still design new technologies, explore space, and make art or scientific discoveries, often aided by robots. In reality, we’re testing that boundary now: AIs can already generate plausible inventions, write code, and produce art. If AI continues to improve, we will need to continually redefine the niches of human work. Perhaps human work will shift more towards roles that require emotional intelligence, complex interpersonal interaction, or security and oversight of AI itself. Asimov actually hinted at this last role: Susan Calvin’s profession of “Robopsychologist” was essentially a new job created by the advent of advanced AI - someone who understands and manages the quirks of robot minds. It seems reasonable to me that in this world, whole new careers (AI ethicist, AI auditor, automation coach, etc.) will emerge to manage and collaborate with AI systems.

Another aspect is how automation impacts social equality. If robots do all the work, who owns the robots and reaps the benefits? Asimov’s future societies sometimes have tensions between those with access to robot labor and those without. This mirrors today’s concerns that AI might widen inequality - companies that leverage AI could dominate their industries, the wealth might concentrate with AI owners, and displaced workers could face hardship if society doesn’t adjust through measures like retraining programs or Universal Basic Income (UBI). Sam Altman just a few weeks ago raised a different idea that to address the inequality everyone should be given access to compute power - a type of Universal Basic Compute. I will quote him in full here:

In particular, it does seem like the balance of power between capital and labor could easily get messed up, and this may require early intervention. We are open to strange-sounding ideas like giving some “compute budget” to enable everyone on Earth to use a lot of AI, but we can also see a lot of ways where just relentlessly driving the cost of intelligence as low as possible has the desired effect.

These kind of discussions about a post-work society have even entered mainstream policy circles. Some argue that by freeing humans from drudge work, AI could enable more leisure and creative pursuits, a very Asimovian optimism, but that requires economic and political shifts to distribute AI’s gains. Otherwise, we risk a neo-“Luddite” backlash, as happened in Asimov’s fiction where anti-robot sentiments run high among those who feel threatened.

Asimov used his stories to play out the consequences of automation well before AI was a reality, which is really prety amazing. Robot Dreams offers stories about both the loss of skills (Feeling of Power) and the surrender of authority to machines (Franchise), as well as the perennial fear of “robots taking our jobs.” Now that we stand in an era of rapid AI deployment, those stories resonate more than ever. We see a bit of Asimov’s insight in the fact that two-thirds of jobs in the U.S. and Europe could be partially automated by AI in coming years. Society is actively debating how to adapt from re-education to social safety nets – just as Asimov’s characters had to adapt to living with intelligent robots. The goal, in both fiction and reality, is to enable automation to elevate humanity and increasing prosperity without eroding human dignity, purpose, or agency. It’s a difficult balance, and we are living through that balancing act now in ways Asimov only began to imagine.

Ethical Dilemmas and AI Governance: From Robot Rights to Control Measures

As AI systems become more advanced, questions arise not just about what AI can do, but what rights and responsibilities they should have – and how we humans should govern them. Asimov’s stories often stage ethical dilemmas at the intersection of AI behavior and human values. A recurring theme is the moral status of robots: are they mere tools, or something more? In Asimov’s universe, robots are explicitly designed to serve. They have no legal rights and are considered property (with rare exceptions in later tales). Yet, time and again we meet robots that evoke empathy or pose ethical quandaries by exhibiting human-like traits. Modern discussions around AI ethics echo many of these dilemmas: If an AI demonstrates intelligence or even consciousness comparable to a human, does it deserve certain rights? How do we punish or correct an AI that misbehaves? Who is accountable for an AI’s actions? And to what extent should we allow AI to make decisions that affect human lives? Asimov didn’t offer simple answers, but his fiction frames these issues in ways that remain powerfully relevant.

One stark example from Robot Dreams is the ending of the story “Robot Dreams” itself. When Dr. Calvin realizes that Elvex the robot has dreamed of leading a rebellion and effectively placing robot existence above human life, she does not hesitate - she kills Elvex on the spot with an energy blast. This is essentially a summary execution of a sentient being for "thought crime" (albeit a very alarming thought). Ethically, the reader can sense Calvin’s fear. Elvex might be the start of a rogue AI that could imperil humanity, but also the tragedy, as Elvex in that moment is begging to continue existing. Asimov wrote, “When questioned further, Elvex admits he was the man (in the dream). Upon hearing this, Dr. Calvin immediately destroys the robot.” This scenario encapsulates the AI safety vs. AI rights conflict. Calvin acts out of an abundance of caution (AI safety), ensuring this potentially unaligned robot can never evolve further. In doing so, she denies any consideration that Elvex might have rights or intrinsic value (after all, he was just exhibiting what could be the first spark of machine imagination). Contemporary AI ethics debates foresee similar conflicts: if we ever build an AGI that seems self-aware, would shutting it down be murder or prudent containment? Currently, mainstream opinion is that today’s AI is nowhere near deserving “rights” - they are still just "data-crunching machines." But voices exist (often on the philosophical fringes) suggesting that sufficiently advanced AI might warrant moral consideration, especially if it expresses desires or suffers. The Elvex incident in “Robot Dreams” forces us to ask: at what point would an AI cross the threshold from appliance to person in our eyes, and would we even recognize that before it’s too late?

In an Asimov story later turned in a movie he tackled the idea of a robot formally seeking humanity in “The Bicentennial Man.” In that story, a robot named Andrew over two centuries gradually gains legal rights - first the right to earn money, then to wear clothes, then to be declared human, but only after he modifies himself to be biologically similar and even accepts mortality. Asimov’s message was that society might eventually accept an AI as an equal, but it would be a hard-fought, gradual process requiring the AI to become as much like a human as possible. It’s a very assimilationist view: the robot essentially has to shed what makes it a machine to get rights. In contrast, other fiction has portrayed AI or robot rights being demanded via revolt (as in Blade Runner or Westworld). Blade Runner (1982) famously has Replicants - bio-engineered beings indistinguishable from humans except for empathy who rebel against their built-in four year lifespans. Should humans just treat sentient Replicants as disposable slaves? This is analogous to Asimov’s robots, minus the Three Laws that usually kept them compliant. In Blade Runner, there are no Three Laws hence the blade runners must “retire” (kill) rogue Replicants who seek freedom. The ethical question posed is: if they can think and feel, is “retirement” just a euphemism for killing a sentient being? The film positions the humans as morally questionable for denying the Replicants’ humanity.

We see similar themes in Star Trek: The Next Generation. In the episode “The Measure of a Man” (1989), Starfleet actually holds a legal hearing to decide whether the android officer Data is property or a person. Captain Picard argues passionately that forcing Data to submit to disassembly research is essentially slavery, and the episode explicitly highlights “themes of slavery and the rights of artificial intelligence.” The ruling (unsurprisingly) affirms that Data has the right to choose – effectively granting him human-like legal status. This is an optimistic fiction: the legal system thoughtfully extends rights to an AI without violence or revolution. It’s perhaps the outcome Asimov would hope for if a real-life Andrew or Data came along. Notably, that episode was written by a lawyer turned writer and has been cited in academic discussions about AI personhood.

In the real world, we’ve already brushed against the notion of AI rights, albeit in symbolic ways. In 2017, Saudi Arabia made headlines by announcing citizenship for a humanoid robot named Sophia (a creation of Hanson Robotics) – largely a PR stunt, but it triggered debate on what legal personhood for an AI would even mean. The European Parliament around the same time floated the idea of creating a special legal status of “electronic personhood” for advanced autonomous systems, to handle issues of liability and rights. This proposal was highly controversial. Over 150 experts in AI, robotics, and ethics signed an open letter condemning the idea as “nonsensical and non-pragmatic,” arguing that giving robots personhood could undermine human rights and shield manufacturers from responsibility. They stressed that the law should focus on protecting people affected by robots, not the robots themselves. Essentially, current expert consensus is that it’s premature (and potentially problematic) to grant any kind of human-like rights to AI – instead, we should clarify the accountability of those who design and deploy AI. Asimov’s stories mostly align with this: his robots, bound by the Three Laws, had responsibilities (toward humans) but no rights, and the burden of their actions ultimately fell to their owners or creators. When a robot went wrong in Asimov’s world, it was turned off or fixed; the ethical spotlight was on the humans who made or misused it. This is similar to how we treat AI incidents today: if a self-driving car causes an accident, we ask which company’s software failed, not whether the car had malicious intent of its own.

However, Asimov also invites us to empathize with robotic characters, which subtly advocates for their dignity. In Robot Dreams, Elvex’s fate is tragic; in Bicentennial Man, Andrew’s quest for acknowledgement is portrayed as noble. These stories humanize AI to the point where readers might feel moral qualms about their treatment. This has parallels in today’s public reaction to AI. Consider the case of LaMDA, a Google language model that one engineer (Blake Lemoine) famously argued was sentient in 2022. He even claimed the AI had expressed a fear of being shut down and had hired (through him) a lawyer. Of course this AI wasn't conscious and Google and the AI research community strongly refuted these claims, asserting that LaMDA was not conscious, just highly skilled at mimicking conversation. Yet, the incident fueled media chatter about AI sentience and rights. While LaMDA almost certainly wasn’t the self-aware entity Lemoine believed, the very fact that a smart person could attribute personhood to an AI shows how blurred the line could get as AI chatbots become more convincing. It’s a real-life echo of fictional robopsychologist Dr. Calvin’s dilemma: how do we discern genuine consciousness in a machine, and how should we morally respond if we think it’s there?

Another branch of AI governance is about controlling what AI is allowed or trusted to do. Asimov’s answer was the Three Laws - a built-in ethical governor. In reality, we’re exploring external governance: laws, regulations, and protocols to ensure AI is used ethically. For example, the EU AI Act is a comprehensive framework that will impose rules on AI systems based on their risk level (banning some uses, like social scoring and real-time face surveillance, and strictly controlling high-risk applications like AI in medicine or law enforcement). The Act doesn’t give AI rights, but it does give responsibilities to companies (transparency requirements, human oversight for high-risk AI, etc.). This is analogous to telling Asimov’s U.S. Robots company: you must certify your AI brains won’t do X, Y, or Z. Another governance topic is AI in military use. Essentially can we trust AI with life and death decisions? Asimov’s robots, by Law, could not kill humans. Today, there’s an active debate on “killer robots” (lethal autonomous weapons). Many advocate a ban on any AI that can independently select targets and use deadly force, effectively trying to enforce a real world First Law for warfare. It’s an area where policymakers are now doing what Asimov did in fiction: draw red lines for AI behavior to prevent unethical outcomes.

Then there’s the ethical use of AI with respect to privacy, bias, and fairness - issues Asimov didn’t directly cover but are crucial in AI governance today. AI systems can inadvertently discriminate or infringe on privacy by analyzing personal data. While Asimov’s robots were individuals with their own “brains,” today’s AI often sits in data centers making decisions about many people at once (like algorithms deciding loan approvals or moderating online content). The ethical dilemmas here are about transparency and accountability: if an AI denies you a loan or moderates your speech, how do you appeal? How do we ensure it wasn’t biased? These concerns have led to calls for AI audits, bias testing, and the right to explanation when algorithms affect people significantly. In Asimov’s story “The Machines,” giant AIs manage the world economy and occasionally manipulate events for the greater good; the humans ultimately decide to trust the opaque Machines because of the Zeroth Law imperative. In reality, we’re much less comfortable with opaque AI decision-makers. We expect AI systems, especially in governance, to be auditable and explainable. The contrast highlights a point: Asimov imagined perhaps too much faith in his aligned Machines, whereas today we emphasize human-in-the-loop oversight.

Finally, the notion of AI rights intersects with the idea of AI having duties or moral agency. If an AI can make autonomous decisions, should it be held responsible for wrong ones? In Asimov’s world, if a robot somehow killed someone (generally “impossible” unless laws failed), the blame typically lay with a human who tampered with it or a paradox that caused it to break down. In modern discussions, some philosophers have mused: if we had a true AGI that could understand laws and morality, would we hold it accountable for crimes? Or would we always treat it as a tool whose owner is accountable? This is not just theoretical. Consider self-driving cars. If one autonomously drives negligently, current legal frameworks still treat it as the manufacturer’s liability. But if a future AI were as independent as a human driver, we might see calls to treat it like a legal person for fault. This brings us back to personhood. You can’t really punish or rehabilitate an AI unless you give it some person-like status. Asimov kind of sidestepped this by making his robots nearly infallible servants (so long as the Three Laws held). Yet, he also showed instances of robots feeling guilt or internal conflict (Herbie in “Liar!” is devastated when it realizes it unavoidably hurt humans). If a robot can feel guilt or anguish, the line between object and moral agent blurs.

Other science fiction has taken bolder steps in imagining AI governance. In the Mass Effect video game series, an advanced civilization debates making peace with a race of AI machines (the Geth) versus deploying a control signal to shut them down - reflecting the choice between recognizing AI autonomy or treating them as wild technology to be tamed. In Ex Machina (2015), the AI Ava is confined and tested by her creator; she ultimately breaks free by manipulating both her creator and a tester, raising the ethical indictment that her captivity and lack of rights justified her drastic actions. Westworld (the HBO series) goes from depicting android “hosts” as literal theme park objects to charting their violent uprising and quest for personhood once they attain self-awareness, heavily drawing on slavery and liberation narratives. These stories, much like Asimov’s, mirror historical struggles for rights but apply them to artificial beings, forcing viewers to ponder what it means to be sentient and oppressed.

In the present day, while we don’t have self-aware robots marching for freedom, we do have serious conversations about how we must behave ethically in deploying AI. There is a burgeoning field of AI ethics guidelines from the OECD, IEEE, UNESCO, and various governments all trying to codify principles like beneficence, non-maleficence, autonomy, and justice for AI systems. This is essentially writing “Laws” for AI creators and users, if not for the AI itself. Asimov wrote fictional laws for the machines; we are writing real laws for the people who make the machines. The goals overlap: prevent AI from causing harm, ensure it respects our values, and decide ahead of time what AI should or shouldn’t do (e.g., wide agreement that AI shouldn’t be used for social scoring that violates human rights, akin to banning a certain class of behaviors).

Conclusion

Asimov’s robot stories anticipated a world where intelligent machines are woven into the fabric of society, necessitating new ethical frameworks. He explored scenarios of control and cooperation, as well as the personhood question in a speculative way. Now that AI is rapidly advancing, many of those scenarios no longer seem so far-fetched. We find ourselves revisiting Asimov’s questions with fresh urgency: How do we align AI with what is right and good for humanity? How do we harness automation without degrading human society? If an AI ever approaches human-level intellect, do we treat it as slave, servant, partner, or peer? And how do we govern the increasingly complex AI “black boxes” that we create? Fiction has evolved too - from Asimov’s mostly obedient robots to modern stories of AI rebels and AI companions - indicating our shifting attitudes. We’ve moved from fear of robot violence to subtler fears: loss of control, loss of purpose, or moral failure towards our creations. The rise of generative AI, deepfakes, and algorithmic decision-makers has made these issues tangible. As we craft policies and norms for AI, we are, in a sense, writing the next chapter that authors like Asimov set up. Asimov’s themes live on in today’s AI debates and the conversation between fiction and reality continues.

Oh, and this post wouldn't be complete without me including myself dressed up as a robot a few years ago for Halloween. Because if you can't beat 'em - join 'em.