Reason & Reflection

Recently, I joined BrightStar Labs  ( https://brightstarlabs.ai/ ) as an affiliate researcher, where I’ve been working on a class of systems known as Emergent Models (EMs). They’re not built like neural networks or traditional programs. Instead, they evolve from very simple beginnings: just a line or grid of colored cells following a small set of update rules. Over time, patterns emerge. Some of those patterns perform tasks that resemble basic forms of computation. At first, EMs may seem abstract or even mechanical. But the more I work with them, the more they raise interesting questions. These questions aren't just about computation, but about how we understand concepts like memory, intelligence, and structure. Intelligence Without Intent? One of the main features of an EM is that it’s not designed in the usual sense. There's no optimizer telling it what to do, and no fixed model architecture. Instead, its behavior is shaped by initial conditions and a rule table that dete...

When we think of memory, we usually imagine it as a kind of mental photo album. It holds everything we’ve done, felt, and learned. Memory, we assume, is how we remember the past . But what if that’s not quite true? Some neuroscientists now believe that memory might not exist primarily to store the past, but to predict the future . According to this view, our brains use memory less like a history book and more like a simulation engine: constantly modeling what could happen next, so we can make better decisions. If this is right, then memory isn’t about where we’ve been. It’s about where we’re going. Memory as a Tool for Prediction Research in neuroscience and cognitive science has shown that the same parts of the brain that help us remember the past (especially the hippocampus)  are active when we imagine the future . In fact, when people with damage to this area try to picture future events, they often struggle. They can’t visualize upcoming possibilities, even though nothing...

String theory is one of the most ambitious and imaginative ideas in modern physics. It aims to do something no other theory has done: unify all the fundamental forces of nature ( gravity, electromagnetism, the strong nuclear force, and the weak nuclear force) into a single framework. It replaces point-like particles with tiny vibrating strings , whose vibrations determine the type of particle you observe. But despite its promise, string theory is also one of the most controversial theories, because right now, it can't be tested . So this leads to a deep philosophical question: If a theory explains everything but can’t be tested, does it still count as science? In string theory, fundamental particles like electrons, protons, and quarks are represented as tiny vibrating strings. The type of particle is determined by the string’s vibrational pattern, similar to how different notes come from the same guitar string. Tripathi, A. (2024, March 24). String Theory: Dimensional Implicatio...

Imagine applying for a job and being instantly rejected—not by a person, but by an algorithm. Or imagine a judge using software to predict if someone is likely to commit a crime again—and using that score to decide how long they’ll be in prison. These aren’t science fiction stories. They’re happening today, and they raise an important question: is it ethical to let machines shape such major parts of our lives? What Is Predictive AI? Predictive AI is a kind of artificial intelligence that uses past data to guess what might happen in the future. For example, a system could be trained on years of data about employees to predict who’s likely to succeed in a certain role. Or it could use crime statistics to estimate the risk of someone reoffending after release from jail. These systems look at patterns that humans might miss—but that doesn’t mean they’re always right, or fair. The Problem of Bias One major issue with predictive AI is that it can inherit bias from the data it’s trained ...

What does it mean for nothing to exist? At first, the question sounds simple, even a little silly. But both scientists and philosophers have struggled with the idea of "nothing" for centuries. Is empty space truly empty? Can “nothingness” actually exist, or is it just a word we use when we don’t know what else to say? In this post, we’ll explore how science and philosophy look at the idea of nothingness—from ancient views of the void to modern physics and quantum theory—and ask whether nothing is ever really… nothing. Nothing in Philosophy: The Ancient Void Philosophers have debated the concept of nothingness for thousands of years. In ancient Greece, thinkers like Parmenides argued that “nothing” cannot exist at all. To him, the very act of thinking or speaking about “nothing” meant that it was something , which made the idea of true nothingness impossible. On the other hand, Democritus , who imagined the world as made of tiny atoms, believed that atoms moved through an ...

Virtual Reality (VR) allows people to step into digital worlds that feel surprisingly real. By wearing special headsets or using controllers, users can move around and interact with objects that only exist as computer code. Some find these virtual experiences exciting and full of potential. Others worry about the impact they might have on our ideas about right and wrong, as well as on our understanding of what is truly “real.” In this post, we will explore how VR challenges our sense of reality, examine moral issues that come up in virtual worlds, and consider how spending time in simulations could change our identities in everyday life. Apple Vision Pro, a glimpse into the next generation of immersive virtual and augmented experiences. "Introducing Apple Vision Pro: Apple’s first spatial computer", Apple, https://www.apple.com/newsroom/2023/06/introducing-apple-vision-pro/ What Counts as “Real” in Virtual Worlds? One of the biggest questions about VR is whether experience...

When we look at the universe, it seems almost perfectly set up for the existence of life. Many of the laws of physics work in just the right way to allow stars to form, planets to exist, and complex life to develop. This idea that our universe is “fine-tuned” for life has led to many discussions about what it really means. Some believe it might be just a lucky accident, while others think there could be a deeper reason. These debates bring us to the Anthropic Principle, which is a way of explaining why we see the universe as so well suited for living things. The Puzzle of Fine-Tuning Scientists have found that if certain physical laws or constants—such as the strength of gravity or the charge on the electron—were slightly different, stars might not form or atoms might not stay together. If that happened, life as we know it would not be possible. The universe’s seeming “perfect fit” for life is sometimes called the “fine-tuning” problem, because it is as though these constants were set ...

In my AP Calculus BC class, we recently explored Taylor series—a topic that combines mathematical precision with philosophical depth. As we worked through how derivatives at a single point can approximate a function's behavior nearby, I started thinking about how this process reflects larger ideas about knowledge and prediction. While Taylor series are tools for solving problems in calculus, they are also windows into how local information can illuminate broader patterns and behaviors. What Is a Taylor Series? The Taylor series uses information about a function's derivatives at a single point to approximate that function near the point. The formula for a Taylor series expansion around a point a  is: f ( x ) ≈ f ( a ) + f ′ ( a ) ( x − a ) + f ′ ′ ( a ) 2 ! ( x − a ) 2 + f ( 3 ) ( a ) 3 ! ( x − a ) 3 + ⋯ This formula is built step by step, incorporating more terms to reflect higher-order behaviors of the function. The zeroth term, f ( a ) f(a) , gives the function's value at...

Cryptography is often seen as a way to eliminate trust. It secures our communications, safeguards sensitive data, and even enables decentralized systems like cryptocurrencies (shoutout Dogecoin). At its heart, cryptography is a promise: you don’t need to trust people or institutions; you just need to trust the math. But this promise, while compelling, isn’t as simple as it seems. Trust doesn’t disappear in cryptography—it shifts. It moves from people and systems to algorithms, keys, and the humans who design them. This shift raises questions about the nature of trust in a world increasingly mediated by encryption. Trustless Systems: The Ideal Vision One of cryptography’s primary goals is to create systems that don’t rely on trust in third parties. End-to-end encryption (E2EE) is a clear example of this ambition. With E2EE, only the sender and recipient can read the content of a message. Not even the service provider has access, which means users don’t need to trust their data is safe—t...