Science and Business

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.

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 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.

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.

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 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.

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.

Data transfer speed plays a crucial role in the performance of modern computers. USB and PCIe interfaces have evolved over decades to provide increasingly faster and more efficient communication between devices. In this post, I'll review the speeds of these connections and their evolution across generations.

The performance of modern computers is significantly influenced by the speed and type of memory used. DDR, GDDR, and HBM memories are optimized for different purposes, whether it's main system memory for CPUs, memory for graphics cards, or tasks requiring high bandwidth computations. In this post, I compare the speeds and key parameters of these memory types.

There are many misunderstandings surrounding the speeds of various computer data transfer and storage standards (e.g., USB, SATA, DDR, PCIe, Ethernet, etc.). That's why I've created several summary descriptions and tables covering the main standards and their speeds.

In the digital world, network speed is a fundamental factor that shapes our daily lives – whether it's online communication, gaming, video conferencing, or simple browsing. The evolution of wired (Ethernet) and wireless (Wi-Fi) networks has come a long way over the past decades. While speeds of a few megabits per second were considered fast in the 90s, today we encounter gigabit speeds even in our homes.

Large Language Models (LLMs), such as GPT-3, LLaMA, or PaLM, are neural networks of enormous size. Their size is typically characterized by the number of parameters (e.g., 7b, 14b, 72b, meaning 7 billion, 14 billion, 72 billion parameters). A parameter is essentially a weight or bias value within the network. These parameters are learned during training and collectively represent the model's "knowledge," determining how it processes information and generates outputs. Modern LLMs possess billions, sometimes even hundreds of billions, of parameters.

In recent years, the AI revolution has introduced new players and exciting technological solutions to the semiconductor industry. Among the most promising is Cerebras Systems, a California-based startup that recently announced its intention to go public.

The development of magnetic nanorobots could open new horizons in the treatment of brain aneurysms. These microscopic, precision devices are designed to deliver necessary medication directly to the site of the problem, potentially minimizing complications and the inherent risks of traditional surgical procedures. This article explores how these nanorobots work, their advantages over conventional methods, and the future possibilities within this cutting-edge field of nanomedicine.

According to reports from The Wall Street Journal that surfaced earlier this year, Qualcomm recently explored a potential acquisition approach towards Intel, a move that sent shockwaves through the semiconductor industry and caused significant fluctuations in the stock prices of both companies. This unexpected, though highly speculative, move could fundamentally reshape the technology sector and establish a new balance of power in the world of chip manufacturing.

Oracle co-founder, Chairman, and Chief Technology Officer (CTO), Larry Ellison, recently detailed ambitious artificial intelligence plans for the company. These include potentially massive investments in cutting-edge AI capabilities and exploring the construction of vast data centers powered by nuclear energy, signaling Oracle's intent to aggressively compete in the rapidly evolving AI landscape.

OpenAI, the company at the forefront of artificial intelligence (AI) research and development, is once again in the spotlight. In a record-breaking funding deal closed in October 2024, the company secured $6.6 billion in funding through convertible notes, catapulting its valuation to a staggering $157 billion. This move is not only one of the largest capital-raising maneuvers in Silicon Valley history but also a clear signal of investors' unwavering confidence in the future of AI and OpenAI's leading role. However, this comes as the company grapples with enormous operating costs, fierce competition, and significant internal transformations.