Đề ôn thi Đề thi vào lớp 10 Hà Tĩnh môn Tiếng Anh - Mã đề HTI-100

Before the 1970s, calculators were large, expensive machines that only big businesses could afford. The invention of the integrated circuit changed everything, allowing for much smaller devices. The first handheld calculators appeared and quickly became popular. (24) __________, their high price meant that not everyone could buy one. If we didn't have pocket calculators today, life (25) __________ be much more difficult for many students and professionals.

Early models could only perform basic functions, and engineers had to (26) __________ complex problems like memory limitations and short battery life. Now, (27) __________ student in a science class probably owns a smartphone with a calculator app far more powerful than those early devices. The development of the pocket calculator had a huge (28) __________ on education and business, making complex calculations accessible to everyone. The journey from a room-sized machine (29) __________ a pocket-sized gadget is a perfect example of technological progress.

Have you ever finished watching a great 'phim bộ' on Netflix and immediately seen a perfect suggestion for your next show? It can feel like the service knows you personally. The recommendations often seem so accurate that they could not possibly be random. This isn't magic; it's the result of a very powerful and complex system designed to understand your viewing habits and keep you engaged.

This system is driven by a recommendation algorithm, a type of artificial intelligence. It doesn't just track which movies or series you watch. It collects much more detailed data about your behavior. For example, the algorithm knows when you pause, rewind to re-watch a scene, or abandon a movie halfway through. It also notes what time of day you usually watch and on which device. All this information helps create a unique profile of your preferences.

Streaming services use this data to build a detailed profile of your tastes. Based on this profile, they can predict what else you might like with impressive accuracy. The system categorizes content into thousands of specific 'micro-genres', like 'suspenseful sci-fi movies from the 1980s with a strong female lead'. When your viewing history shows you like several pieces of content in a micro-genre, the algorithm will recommend more of them to you.

So, the next time Netflix suggests a new show that becomes your favorite, you'll know why. It's not a lucky guess but a calculated prediction based on data. Understanding this process can empower you as a user. You can consciously 'train' the algorithm by rating shows or trying different genres, helping the service to fine-tune its suggestions and help you discover content you'll truly love.