What if reality itself is a simulation?
My book Simulated Reality explores quantum mechanics, relativity, consciousness, AI, brain-machine interfaces, transhumanism, and the sustainable digital future — in a clear and accessible way.
https://t.co/8O0BpSpj3T
Read it FREE on Google Play Books & Apple Books
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An Exciting Journey into the World of Quantum Mechanics, Brain-Machine Interfaces, and Transhumanism
(a short intro video for the book Simulated Reality) https://t.co/JBABkDcvQt
In the age of AI, programming languages are no longer primarily for writing software, but for understanding how AI-generated applications work. (Which is why generating raw binaries directly would be a bad idea.)
Thanks. I read it. ;) There are many fascinating ideas in the paper — for example, the concept of “mortal computing.”
The biggest problem is that we still don’t have a good definition of consciousness, we don’t know how to measure it, and it is even questionable whether it is theoretically possible to determine with certainty whether a system is conscious or not.
What you say is true: if we too easily assume that machines are conscious, we may end up diminishing ourselves in the process. But this cuts both ways. Since we cannot directly measure consciousness, we may also make the opposite mistake in the future — dismissing a genuinely conscious system as “just simulating” consciousness.
After all, I only have direct access to my own conscious experience. From that perspective, I could just as easily assume that you are not conscious either, but merely extremely good at imitating consciousness. Right now, your biological brain is probably the strongest argument in favor of you actually being conscious, but even that argument may weaken as machines become increasingly brain-like.
So I don’t feel that I’ve gotten much closer to a solution — only that the problem is far more complex than many people initially assume.
It is entirely reasonable to argue that today’s AI models merely imitate consciousness, and that true self-awareness can only emerge in a biological brain. But if that is the case, then we must ask: what exactly is it about the brain that makes consciousness possible?
Can only living cells possess consciousness? If so, what makes cells fundamentally special? After all, cells are made of the same atoms as computers. Is consciousness possible only in carbon-based life forms, but impossible in silicon-based systems? What about machines built from carbon nanotubes or graphene — could they become conscious?
And if consciousness depends on certain structural or functional properties rather than biology itself, then perhaps it would be possible to build something even more advanced than the human brain — not merely a “computer,” but an artificial mind capable of hosting consciousness. In that case, one could imagine abandoning our fragile biological bodies and brains after death.
I think these are profoundly important questions. Without addressing them, any discussion about AI and consciousness feels incomplete — only half the lecture is being given. The missing half is the attempt to answer these deeper questions.
1/2 Why AI is unlikely to become conscious – my 2026 @TEDTalks is now online. What do you think about the prospects for 'conscious AI'? https://t.co/OoXFavR3eG
Einstein’s Relativity Explained Simply
What is Einstein’s theory of relativity really about — and why does it feel so difficult to understand?
https://t.co/FGGJzOYsQw
@protocollabs Take a look at @DeblinaSarkar59 's Autonomous and Surgery-free Brain-Computer Interface (BCI). Would be great to see an interview with her. https://t.co/rtXoEPTZ5H
LLM Fine-Tuning with Evolutionary Algorithms
Fine-tuning large language models using evolutionary algorithms is especially exciting because it doesn’t require gradient descent—only inference. You modify the network, evaluate how it performs, and iterate.
This approach avoids getting stuck in local minima and sidesteps many of the constraints associated with gradient-based optimization. Instead of relying on backpropagation, you explore the solution space more freely.
Several techniques can make inference-time models far more efficient than their original versions: distillation, quantization, 1-bit networks, specialized hardware, and more. When you take these advantages into account, evolutionary fine-tuning can potentially become significantly more efficient than traditional gradient descent–based methods.
https://t.co/lLBWEXXQDb
What comes after humanity?
Digital minds. Dyson swarms. Intergalactic civilizations.
This video explores the final evolution of intelligent life.
https://t.co/erENWWdvFl
Discover how nanobots could transform medicine, reshape manufacturing, and connect the human brain to advanced AI systems — with a special focus on Deblina Sarkar’s groundbreaking bio-hybrid approach. https://t.co/U6anlI3XVR