Đề thi thử tốt nghiệp THPT môn Tiếng Anh Sở GD&ĐT Lạng Sơn năm 2026 - Mã đề 01

[I] Go is a game that has been around for 3000 years. It is widely accepted as the most challenging strategy game that exists. [II] In other words, even though it has simple rules, it is not a simple game to excel at. Surprise! DeepMind managed to create a machine that could master the game, without being programmed with explicit rules and without being taught by a professional Go player. [III] AlphaGo mainly played against itself and learned from this self-play. At its core, it learned like a human learns, by looking at the board, evaluating the options, making moves, and learning from mistakes – it just did it a lot faster than any human can. [IV]

This is extremely exciting because, at its core, what it means is that computer scientists have had all the tools they needed to do this for years. Neural networks have been known about and discussed since the middle of the last century. All it really took was simply applying them in new ways. AlphaGo beating the world’s best Go player proves that AI has the potential to do anything. It can learn and understand anything, and from that learning and understanding, it can accomplish what humans can accomplish in a much shorter period of time.

So, what does this mean for the future? It suggests we are closer than ever to advanced AI systems that can learn and evolve in ways similar to humans. AlphaGo can learn the most complex, intuition and creativity-based logic game known to man and it didn’t do so through a finite database or search trees alone. It learned by practicing and developing new strategies. This ability hints at a new form of digital creativity – machines capable of discovering solutions beyond traditional programming.

Unlike earlier game-playing systems, AlphaGo represents a shift toward intelligent, intuitive machines that can be applied beyond board games. From practical real-world uses to more personal applications, it marks the beginning of a new generation of self-learning AI. These systems go beyond the limited calculating abilities of earlier technologies, signaling that the long-feared “AI winter” may finally be over.

The fashion industry is undergoing a profound transformation as biotech startups introduce lab-grown alternatives to traditional textiles. For decades, materials like leather and cotton have been criticized for their environmental impact, as their production requires vast amounts of water and often involves harmful chemicals. In response, scientists are turning to mycelium – the root structure of mushrooms – to grow leather in specialized laboratories. This innovative process produces materials that closely resemble animal hides in texture and durability, yet takes only weeks instead of years, significantly reducing environmental damage and carbon emissions.

Within these controlled environments, researchers can fine-tune the properties of the material by adjusting the nutrients supplied to the fungi. This flexibility allows them to create fabrics of varying thickness, from lightweight materials for clothing to sturdier ones for footwear and accessories. Moreover, these bio-based materials can be grown into precise shapes, minimizing the waste typically generated during cutting and trimming in conventional textile manufacturing.

In addition to material innovation, bio-glues and natural dyes are transforming garment production. Traditional dyeing processes are among the most polluting stages of the fashion supply chain, often releasing toxic substances into waterways. By contrast, engineered bacteria can now produce vibrant pigments that naturally bond to fabrics without harsh chemicals. Because these dyes are biodegradable, the water used during production can be safely recycled, supporting a more sustainable and circular system.

Although bio-fabricated textiles are currently more expensive than synthetic alternatives, ongoing technological advancements and stricter environmental regulations are expected to drive wider adoption. In the future, biology-based manufacturing may redefine fashion by offering sustainable solutions that balance innovation, functionality, and environmental responsibility.

Global Dining: More Than Just Food

Food is a universal language, but the way we eat can differ greatly from one country to another. If you are traveling abroad, understanding table manners is a great way to show respect (19) ____ the local culture.

• Research whether it is polite to finish everything (20) ____ is on your plate or to leave a small amount of food to show you are full.

• Use traditional utensils such as chopsticks or bread to scoop up (21) ____ of rice and stews.

• Invite locals and (22) ____ experienced travelers to give you advice, and try (23) ____ yourself in the local dining customs to avoid accidental offense.

• Learning even a few basic phrases in the local language works (24) ____ to make your dining experience more enjoyable and authentic.

Clever Crows – the most intelligent birds?

Crows have yet again confounded (30) ____ with their remarkable problem-solving skills. A group of New Caledonian crows was presented with a problem in which food was placed in a passageway too narrow for their beaks to access. However, four out of eight birds (31) ____ put together two short sticks located nearby to make a longer fishing rod-type device to enable them to reach the food. In the wild, crows have been known to also use leaves, rocks and even their own feathers to (32) ____ otherwise impossible tasks. They even craft and use hooks to snare insects.

How intelligence evolves was studied in one cleverly-designed experiment when birds were prompted to use a kind of vending machine to (33) ____ rewards. By studying the birds’ cognitive abilities, the researchers hope that they might help development of Artificial Intelligence (AI) technology. Presented with these tasks, (34) ____ anything crows would (35) ____ in nature, scientists showed how the birds adapted their own instinctual behaviour to complete the fabricated tests.

The integrity of online examinations has been significantly bolstered by the introduction of AI-powered proctoring systems. These platforms use advanced algorithms to monitor students via webcams, (36) ____ . By analyzing eye movements and background noise, the software can detect suspicious behavior that might escape a human invigilator’s notice. Many educational institutions have already adopted these high-tech solutions, (37) ____ .

However, the reliance on automated surveillance has sparked a heated debate regarding student privacy. Some critics argue that constant monitoring creates an atmosphere of distrust. (38) ____ . For instance, students from underprivileged backgrounds may lack a private, quiet space, leading the AI to flag them unfairly for “suspicious” environmental factors.

Finally, the effectiveness of AI depends heavily on the quality of its training data. (39) ____ . While it is true that technology reduces the workload for teachers, the risk of “algorithmic bias” remains a serious concern. (40) ____ , requiring developers to refine these systems to ensure they are both secure and equitable for all candidates, regardless of their physical or social circumstances.