Stepping from the pages of science fiction into the real-world factory floors and logistics centers, humanoid robotics is on the verge of a dramatic transformation. What were once captivating tech demos are now becoming a realistic solution for a new era of automation and human-robot collaboration
The introduction of Google's AI Overviews marks a turning point in the evolution of the internet, catalyzing a paradigm shift from a referral-based web to an answer-centric ecosystem. This transformation, driven by generative artificial intelligence, is fundamentally changing the long-standing symbiotic relationship between search engines, content creators, and users.
Shenzhen, China's premier technology hub, is spearheading the autonomous vehicle revolution. But this isn't just about futuristic robotaxis. The city is aggressively deploying autonomous technology to boost core industries and fundamentally redesign urban services, from logistics to public sanitation.
How many friends do you really have? The number of your Facebook connections might run into the hundreds or even thousands, but with how many people do you maintain a truly deep and meaningful relationship?
Tesla's Optimus robot can now fold laundry. Figure AI's Figure 01 can brew a cup of coffee after a simple verbal request. These are not scenes from a science fiction movie; they are the reality of 2024. The humanoid robotics revolution is at our doorstep, poised to fundamentally reshape our understanding of work, productivity, and technology itself.
Why is it so difficult to predict the weather more than a week in advance? Why can a tiny change in a system lead to a dramatically different outcome? These are the kinds of questions that led to the development of one of modern science's most fascinating fields: chaos theory.
One of the greatest paradoxes of the 21st century is that while humanity has access to virtually infinite energy sources in the form of sun and wind, one of its most pressing challenges is ensuring the security of its energy supply.
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.