At the core of today’s AI revolution are neural networks—massive systems that, through repeated adjustments, learn to generate the correct output based on vast amounts of input data. While the implementation of these networks is surprisingly simple, involving simple multiplications and additions with massive parallelism, the scale of computation required to train them is extraordinary. This process, called “pre-training,” involves tweaking billions of parameters across neural networks to allow them to generate accurate results for any given input. As AI continues to evolve, researchers are uncovering new ways to refine and accelerate these systems, particularly through innovations like transformers, which allow for more efficient training, and the development of inference chains, which improve reliability and problem-solving capabilities. With breakthroughs like OpenAI’s o1 and upcoming o3 models, AI is moving closer to achieving general problem-solving abilities, marking a significant step toward a future where AI could outperform humans in a wide range of tasks, not through intelligence, but through its ability to process and analyze data more effectively than any human can.
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