If I could rewrite the ending of a famous novel, I would choose “1984” by George Orwell. The original ending is bleak, with Winston Smith ultimately getting broken by the oppressive regime of Big Brother, leading to his complete submission and loss of individual thought.

Original Ending

In the original ending, Winston is tortured into betraying his lover Julia and finally comes to love Big Brother, signifying the regime’s absolute power and the futility of resistance. The novel closes with a sense of hopelessness, as Winston’s spirit is utterly crushed.

Revised Ending

In my version, the ending would offer a glimmer of hope. Instead of fully succumbing to the regime, Winston manages to retain a small part of his rebellious spirit. Here’s how I would change it:

After enduring intense torture, Winston is released, seemingly broken. However, instead of a complete transformation, he retains a hidden spark of resistance. This is revealed through subtle actions and thoughts. For instance, while he publicly conforms and expresses love for Big Brother, he secretly begins to write a journal again, documenting the truth as he sees it. This time, he uses a more secure method, such as coded language or a hidden location.

Julia, too, is shown to have retained some of her rebellious nature. The novel ends with a clandestine meeting between Winston and Julia, where they exchange a fleeting, meaningful glance, suggesting a mutual understanding and a continued, albeit covert, resistance.

Impact

This revised ending would still acknowledge the tremendous power and reach of totalitarian regimes, but it would also emphasise the resilience of the human spirit and the enduring possibility of resistance. It would leave readers with a sense of hope and the idea that, even in the darkest of times, the fight for freedom and truth can continue in small, seemingly insignificant ways. This subtle act of defiance would suggest that while the regime can control actions, it cannot completely extinguish thoughts and the innate desire for freedom.

When interviewing programmer candidates, it’s essential to ask questions that evaluate their technical skills, problem-solving abilities, and cultural fit. Here are five important questions to consider:

1. Can you describe a challenging project you worked on? What was your role, and how did you handle the challenges?

• Purpose: This question assesses the candidate’s experience, problem-solving skills, and ability to handle complex tasks. It also provides insight into their role within a team and their approach to overcoming obstacles.

2. How do you stay updated with the latest trends and technologies in programming?

• Purpose: This evaluates the candidate’s commitment to continuous learning and staying current in a rapidly evolving field. It also gives you a sense of their passion for technology and self-improvement.

3. Can you explain a time when you had to debug a difficult issue? What tools and methods did you use?

• Purpose: This question helps gauge the candidate’s debugging skills, their ability to troubleshoot problems, and their familiarity with debugging tools. It also reveals their persistence and attention to detail.

4. How do you prioritise and manage your tasks when working on multiple projects simultaneously?

• Purpose: This assesses the candidate’s organisational and time management skills, which are crucial for productivity and meeting deadlines. It also gives insight into their ability to handle stress and multitask effectively.

5. Can you describe a situation where you had to work closely with a team member who had a different opinion or approach? How did you handle it?

• Purpose: This question evaluates the candidate’s communication and teamwork skills. It reveals their ability to handle conflicts, collaborate with others, and adapt to different working styles.

These questions are designed to uncover the candidate’s technical abilities, problem-solving skills, learning mindset, organisational capabilities, and interpersonal skills, providing a comprehensive view of their potential fit for the role.

Supervised learning and unsupervised learning are two main types of machine learning techniques, and they work differently.

In supervised learning, you teach the model using a dataset that includes both inputs and the correct outputs (labels). Imagine you have a bunch of emails, and each email is marked as either “spam” or “not spam.” The model learns from this labeled data to understand what features (like certain words or phrases) are associated with spam. Once trained, it can then predict whether new, unseen emails are spam or not. This approach is like a teacher guiding students, providing the right answers during the learning process. Examples of supervised learning tasks are classification (sorting things into categories) and regression (predicting numerical values).

Unsupervised learning is different because the data used to train the model doesn’t come with labels. Instead, the model tries to find patterns and structures in the data independently. For instance, if you have a bunch of customer data with no labels, unsupervised learning can help group customers into segments with similar behaviours. It’s like exploring a new place without a map – you’re trying to figure out the layout based on what you observe. Common tasks include clustering (grouping similar items) and dimensionality reduction (simplifying data while keeping important parts).

In short, supervised learning uses labelled data to make predictions, while unsupervised learning finds patterns in unlabeled data.