I'm having an existential crisis with this client.

We are spending millions of $s every year to make sure the product's performance is perfect. We are testing various scenarios, fine-tuning PLABs: the environment, application, middleware, infra,... And then we provide our recommendations to the client: "To handle load of XX parallel users focusing on YY, yy and Zy APIs, use <THIS> configuration".

And what the client does?
- take our recommendations and measure the wind speed outside
- if speed is <20m/s and milk hasn't gone bad yet, add 2x more instances of API X
- otherwise add 3xX, 1xY and give more CPUs to Z
- split the setup in half and deploy in 2 completely separate load-balanced prod environments.
- <do other "tweaking">

- bomb our team with questions "why do we have slow RTs?", "why did the env crash?", "why do we have all those errors?", "why has this been overlooked in PLABs?!?"

If you're improvising despite our recommendations, wtf are we doing here???

One day I will crack. Hopefully, not sometime soon.

All is well until one of the interviewers starts dropping questions like:
- Where do you see yourself in x years
- What gets you out of bed every day
etc.

that give you some nice mid interview existential dread.

I'll be honest, I've never understood why people say that numbers generated by a computer are pseudorandom and not random.
I know a lot of algorithms for number generation, and I implemented mine, based on time of invokation expressed in nanoseconds, taking digits, manipulating and transforming them. Then I analyzed the probability distribution and it's absolutely flat. So, if you know the Touring test, we can use a modified version of it. If I give you a sequence of random numbers generated by a computer and I give you a sequence of random numbers invented by a person, and you can't notice the difference, so the test is passed.
What's wrong on it?

I wonder how much of my life I've wasted waiting for code to compile

Exploring myself was always an existential dread for me, even in childhood.

For any arbitrary thing I always struggle to give straight answers to following questions:

Do I want it?

Do I like it?

The complexity of the topic itself doesn’t matter. From choosing what to eat to reading about ideologies, the fear and confusion remains.

The Turing Test, a concept introduced by Alan Turing in 1950, has been a foundation concept for evaluating a machine's ability to exhibit human-like intelligence. But as we edge closer to the singularity—the point where artificial intelligence surpasses human intelligence—a new, perhaps unsettling question comes to the fore: Are we humans ready for the Turing Test's inverse? Unlike Turing's original proposition where machines strive to become indistinguishable from humans, the Inverse Turing Test ponders whether the complex, multi-dimensional realities generated by AI can be rendered palatable or even comprehensible to human cognition. This discourse goes beyond mere philosophical debate; it directly impacts the future trajectory of human-machine symbiosis.

Artificial intelligence has been advancing at an exponential pace, far outstripping Moore's Law. From Generative Adversarial Networks (GANs) that create life-like images to quantum computing that solve problems unfathomable to classical computers, the AI universe is a sprawling expanse of complexity. What's more compelling is that these machine-constructed worlds aren't confined to academic circles. They permeate every facet of our lives—be it medicine, finance, or even social dynamics. And so, an existential conundrum arises: Will there come a point where these AI-created outputs become so labyrinthine that they are beyond the cognitive reach of the average human?

The Human-AI Cognitive Disconnection

As we look closer into the interplay between humans and AI-created realities, the phenomenon of cognitive disconnection becomes increasingly salient, perhaps even a bit uncomfortable. This disconnection is not confined to esoteric, high-level computational processes; it's pervasive in our everyday life. Take, for instance, the experience of driving a car. Most people can operate a vehicle without understanding the intricacies of its internal combustion engine, transmission mechanics, or even its embedded software. Similarly, when boarding an airplane, passengers trust that they'll arrive at their destination safely, yet most have little to no understanding of aerodynamics, jet propulsion, or air traffic control systems. In both scenarios, individuals navigate a reality facilitated by complex systems they don't fully understand. Simply put, we just enjoy the ride.

However, this is emblematic of a larger issue—the uncritical trust we place in machines and algorithms, often without understanding the implications or mechanics. Imagine if, in the future, these systems become exponentially more complex, driven by AI algorithms that even experts struggle to comprehend. Where does that leave the average individual? In such a future, not only are we passengers in cars or planes, but we also become passengers in a reality steered by artificial intelligence—a reality we may neither fully grasp nor control. This raises serious questions about agency, autonomy, and oversight, especially as AI technologies continue to weave themselves into the fabric of our existence.

The Illusion of Reality

To adequately explore the intricate issue of human-AI cognitive disconnection, let's journey through the corridors of metaphysics and epistemology, where the concept of reality itself is under scrutiny. Humans have always been limited by their biological faculties—our senses can only perceive a sliver of the electromagnetic spectrum, our ears can hear only a fraction of the vibrations in the air, and our cognitive powers are constrained by the limitations of our neural architecture. In this context, what we term "reality" is in essence a constructed narrative, meticulously assembled by our senses and brain as a way to make sense of the world around us. Philosophers have argued that our perception of reality is akin to a "user interface," evolved to guide us through the complexities of the world, rather than to reveal its ultimate nature. But now, we find ourselves in a new (contrived) techno-reality.

Artificial intelligence brings forth the potential for a new layer of reality, one that is stitched together not by biological neurons but by algorithms and silicon chips. As AI starts to create complex simulations, predictive models, or even whole virtual worlds, one has to ask: Are these AI-constructed realities an extension of the "grand illusion" that we're already living in? Or do they represent a departure, an entirely new plane of existence that demands its own set of sensory and cognitive tools for comprehension? The metaphorical veil between humans and the universe has historically been made of biological fabric, so to speak.