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Occam's Razor, the principle often summarized as "the simplest explanation is usually the best," is one of the most pervasive and practical heuristics in human thought.

The term "Singularity" has transcended niche scientific discourse to become a recurring motif in popular culture, featuring prominently in films, news articles, and public debate. Often depicted with dramatic flair, it evokes images of runaway artificial intelligence and fundamentally altered human existence. While sometimes sensationalized, the underlying concept warrants serious consideration, particularly as advancements in Artificial Intelligence (AI) accelerate.

General Motors' autonomous vehicle subsidiary, Cruise, embarked on a challenging path following a significant incident in October 2023. Initial efforts focused on a gradual operational restart, rebuilding trust, and enhancing safety after a pedestrian-dragging incident led to a nationwide shutdown. However, subsequent strategic shifts by GM dramatically altered Cruise's trajectory, culminating in the abandonment of its independent robotaxi ambitions in late 2024 and early 2025.

Game theory, the mathematical modeling of strategic decision-making, employs numerous concepts to help understand the dynamics of interaction. One of the most important and frequently cited among these is the zero-sum game. This concept describes situations where one participant's gain necessarily results in another participant's loss, and the total "winnings" remain constant, effectively summing to zero. Zero-sum games serve as fundamental models for competition and conflict, holding relevance across various domains, from sports and economics to politics.

Have you ever seen a robot, an animated figure, or even a video game character that was so lifelike it felt almost... unsettling? Did you struggle to tell if it was human or not, and did this uncertainty create a strange, unnerving feeling? If so, you've likely experienced the phenomenon known as the "uncanny valley." But what exactly is it, and why does it trigger such a strong reaction in us?

Have you ever wondered why modern technology, supposedly designed to make our lives easier and save us time, doesn't actually result in more free time? Why do we work just as much, or perhaps even more, than our grandparents, despite being surrounded by washing machines, dishwashers, computers, and smartphones? The answer lies in a phenomenon recognized back in the Industrial Revolution, known as the Jevons Paradox.

Reverse Polish Notation (RPN) is an efficient method for evaluating mathematical expressions, characterized by placing operators after their operands. This approach allows for the omission of parentheses, simplifying and clarifying the calculation process. Although it might seem different at first, using RPN significantly speeds up the execution of operations, especially in computer systems and programmable calculators.

The advancement of artificial intelligence is increasingly enabling LLM models to solve complex mathematical problems. But how well can they handle the logical challenges of an elementary school competition task? In a previous test, I examined the performance of various models, and now, with the release of OpenAI's new O3 model, I've conducted an updated comparison.

OpenAI recently introduced the o3-mini model, marking another step forward in the lineage of artificial intelligence systems optimized for reasoning capabilities. The new model can be particularly useful for those seeking AI-based support for solving technical or scientific problems.

Many people still associate graphics cards primarily with gaming, yet GPUs are capable of much more. Due to their architecture, they are excellently suited for parallel computations, which is essential for training and running deep learning models. Consider this: a modern LLM has billions of parameters, and all these parameters need to be managed simultaneously. This type of parallel processing is the true strength of GPUs, whereas traditional CPUs (central processing units) lag behind in this regard.

The advancement of machine learning and large language models (LLMs) has created computational challenges that require much more than simple hardware upgrades. The artificial intelligence explosion of recent years has generated specialized computing demands for which NVIDIA currently offers almost exclusive solutions.

Throughout the evolution of storage devices, numerous connection technologies have emerged, bringing revolutionary changes in both speed and efficiency. SATA, SAS, and M.2 connections are among the most common solutions today, but what are they used for, and how do they differ?

Efficient operation of Large Language Models (LLMs) heavily relies on the appropriate storage and processing of their weights. The chosen numerical format directly impacts the model's memory requirements, computational speed, and accuracy. Over the years, FP32 has gradually been supplemented or replaced by FP16 and BF16 for training, while INT8 and even lower-bit quantized formats are increasingly common for optimizing inference.

The development of AI models has progressed at an astonishing pace in recent years, but how do these systems perform when tasked with solving a 5th-grade math competition problem? In this test, I not only examine the models' problem-solving abilities but also provide insight into how effectively they can handle optimization problems.

The rapid development of Large Language Models (LLMs) poses new challenges in the field of computing. A crucial question for me is how GPUs perform when running these models. In this post, I aim to examine the performance of various GPUs through the concepts of TFLOPS (trillion floating-point operations per second) and TOPS (trillion operations per second). I will present the capabilities of individual models using a clear table, supplemented with brief explanations.

In the world of AI, closed system models like GPT-4 or Claude Sonnet have dominated the high-end solutions market so far, but accessing them often comes with high costs and limited options. However, the arrival of DeepSeek-V3 has opened a new era: this open-source language model not only offers competitive performance against the most well-known closed models, but also provides the opportunity to run it within your own infrastructure.

Graphics card prices have been sky-high for years, and this is due to complex, interconnected reasons. Often, a high-end graphics card (GPU) alone can cost more than all the other components of a computer combined, highlighting just how significant their price has become.

Mixture of Experts (MoE) is a machine learning architecture that follows the "divide and conquer" principle. The basic idea is to break down a large model into several smaller, specialized sub-models – called "experts" – each specializing in a specific task or subset of the data.

When using Large Language Models (LLMs) like GPT-4o or Claude Sonnet, a common question arises, particularly for the vast number of users worldwide who interact with these tools in languages other than English: which language should one use to achieve the most effective results? While the multilingual capabilities of these models allow for effective communication in numerous languages, their performance often seems diminished compared to interactions conducted purely in English. This exploration delves into why that might be the case and when switching to English could be beneficial.

The human brain, a complex biological system perfected over millions of years of evolution, stands in contrast to Large Language Models (LLMs), the latest achievements in artificial intelligence. Although LLMs demonstrate impressive capabilities in language processing, can they ever surpass the complexity and abilities of the human brain?