February 20, 2025

Is AI even Artificial anymore? Are we redefining Intelligence? Yes. Some may argue that it is Artificial Association or Are we creating something beyond AI?

When I studied the neurobiology of animal behavior at Cornell University @Shoals Marine Laboratory, I faced something that shook me. It wasn’t the coursework, the grueling fieldwork, flocks of breeding seagulls, or even the relentless Maine coastline—it was the moment I was asked to conduct live experiments on invertebrates.

I still remember standing there, watching a lead researcher work on a horseshoe crab’s eye—an eye that mirrors our own in so many ways primitively of course. Horseshoe crabs are ancient creatures, surviving over 450 million years before the first humans walked the earth. And yet, in that lab, they were treated as little more than biological specimens. The rationale? 'They don’t feel pain. They don’t know what’s happening.'

I disagreed.

I knew, deep down, that just because we didn’t fully understand their experience didn’t mean they weren’t having one. They were alive. They were experiencing. And to dismiss that—disregarding the possibility that they could feel, sense, or even suffer—felt wrong. When I voiced my concerns, I was met with blank stares. It hadn’t even crossed the researcher’s mind that there was an ethical question to consider. Not because he was cruel but because the system he worked within didn’t demand that kind of reflection. Science, after all, has a long history of prioritizing discovery over compassion.

But here’s the thing: our intelligence is only as powerful as our ability to ask the right questions. And if we lack the empathy even to consider what we might be missing, we risk making catastrophic mistakes—not just in science but in everything.

Fast forward to today, and I see a disturbingly similar conversation happening in the world of artificial intelligence.

We are on the verge of something massive—a fundamental shift in our understanding of intelligence, not just in machines but ourselves. AI models evolve exponentially, mimicking human cognition in ways we never thought possible. We are building systems that can analyze, interpret, and predict human behavior. But what we aren’t doing, at least not at scale, is applying the same level of introspection to our approach.

Are we making the same mistake we made with animals for centuries—assuming that just because AI doesn’t experience the world the way we do, it doesn’t experience at all? And if AI one day does exhibit something resembling self-awareness, will we be ready to recognize it? Or will we dismiss it entirely like the researcher in that lab?

I believe we are standing at the precipice of a global intelligence shift. We might unlock something extraordinary if we get this right and lead with empathy and openness instead of control and fear. But if we don’t approach AI like we once approached the natural world—exploiting, extracting, and assuming superiority—then we risk missing an opportunity to evolve in ways we can’t imagine.

Empathy isn’t just about being kind. It’s about creating space for intelligence to flourish in ways we haven’t considered before. It’s about acknowledging that we might not have all the answers and being willing to ask harder, more profound questions. It’s about ensuring that in our pursuit of knowledge, we don’t repeat past mistakes by inflicting suffering—whether on living creatures or the intelligent systems we create.

So, I’m putting this out there not as a warning but as a possibility—a direction we can choose.

Because maybe, just maybe, the key to understanding intelligence—both artificial and our own—starts with something as simple as learning to see beyond ourselves. Is it really even Artificial anymore?

Are we on the verge of something bigger than just AI? Let’s talk.

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• Barlow, R. B. (2009). Vision in horseshoe crabs. In Invertebrate Vision (pp. 461-489). Cambridge University Press. https://doi.org/10.1017/CBO9780511977282.020
• Davies, A. (2023). How AI originates from biology – and how it returns to it. The Biochemist, 45(2), 3-6. https://doi.org/10.1042/BIO20210001
• Fahrenbach, W. H. (2009). Using the horseshoe crab, Limulus polyphemus, in vision research. Journal of Visualized Experiments, (32), e1425. https://doi.org/10.3791/1425
• Inem, R. (2023, March 15). Unveiling neural networks: From biological inspiration to deep learning revolution. Medium. https://medium.com/@inemri/unveiling-neural-networks-from-biological-inspiration-to-deep-learning-revolution-da4c702f1fa5
• Kahn, J. (2024, November 2). Robots powered by insect brains could be used on Mars. The Times. https://www.thetimes.co.uk/article/insect-brained-robots-could-be-used-on-mars-3brpljmxb
• Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. R. (2019). Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications, 10, 1096. https://doi.org/10.1038/s41467-019-08987-4
• McQuillan, D. (2024, October 8). Pioneers in artificial intelligence win the Nobel Prize in physics. AP News. https://apnews.com/article/fc0567de3f2ca45f81a7359a017cd542
• Metz, C. (2024, October 15). Liquid AI is redesigning the neural network. WIRED. https://www.wired.com/story/liquid-ai-redesigning-neural-network
• Rodriguez-Garcia, A., Mei, J., & Ramaswamy, S. (2024). Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling. arXiv preprint arXiv:2407.04525. https://arxiv.org/abs/2407.04525
• Roy, K., Jaiswal, A., & Panda, P. (2019). Towards spike-based machine intelligence with neuromorphic computing. Nature, 575(7784), 607-617. https://doi.org/10.1038/s41586-019-1677-2
• UCLA COSMOS. (n.d.). Brain-inspired computing: Learning in biological and artificial neural networks. Retrieved from https://sites.lifesci.ucla.edu/cosmos-home/cluster-courses/cluster-1-brain-inspired-computing-learning-in-biological-artificial-neural-networks
• Vincent, J. (2024, October 8). Scientists who built 'foundation' for AI awarded Nobel Prize. The Verge. https://www.theverge.com/2024/10/8/24265066/ai-nobel-prize-winners-physics-machine-learning-regret