how do journalist pick what topic to write on?

title: 'How journalists find stories and write articles'

Journalists select topics based on several factors, including audience relevance and current events. They evaluate the story's significance, asking whether it informs, educates, inspires, or entertains the audience, and consider how many people are affected by the issue at hand[2].

Additionally, insights often come from everyday conversations and social media platforms, where journalists can identify what resonates with the public. They pay attention to trends in online discussions, such as those found on Reddit, to find hot topics that can generate interest[3][2]. Ultimately, they balance the need for timely reporting with audience engagement to determine which stories to pursue[4].

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Artificial Intelligence Trends and Insights - 2025

AI's Unprecedented Growth and Impact

Artificial Intelligence (AI) is evolving at an unprecedented pace, marked by rapid advancements in user adoption, usage, and capital expenditure[1]. The confluence of accessible global internet infrastructure, ever-growing digital datasets, and breakthrough large language models (LLMs) like OpenAI’s ChatGPT has catalyzed this growth[1]. This evolution is characterized by user, usage, and revenue charts that consistently move upward, supported by corresponding increases in spending[1]. Both established tech giants and emerging AI-focused companies are aggressively pursuing innovation, product releases, investments, and acquisitions, intensifying global competition, especially between China and the USA[1].

The Competitive Landscape and Monetization Threats

The AI landscape is increasingly competitive, with rising competition, open-source momentum, and the ascent of China posing significant monetization threats[1]. Despite the potential for AI to 'do your work for you,' reminiscent of the early days of email and web search, the path to monetization is complex[1]. The intense competition and innovation, accessible compute, and global adoption of AI-infused technology create both opportunities and challenges[1]. The race to build the most capable general-purpose models may lead to commoditization and diminishing returns, as output quality converges across different providers[1].

Compute Costs, Inference, and Developer Ecosystem

AI model compute costs are high and rising, while inference costs per token are falling, leading to performance convergence and increased developer usage[1]. The cost of training frontier AI models has seen ~2,400x growth over eight years[1]. As inference becomes cheaper and more efficient, competitive pressure among LLM providers increases, focusing on latency, uptime, and cost-per-token[1]. This shift benefits users and developers with lower unit costs but raises questions about monetization and profits for model providers[1]. The AI developer ecosystem is expanding, exemplified by NVIDIA's growth to 6 million developers[1]. Computing-related patents in the USA have exploded, particularly post-ChatGPT launch, indicating heightened innovation[1].

AI Performance and Realistic Applications

AI performance has surpassed human levels of accuracy and realism in many areas[1]. In 2024, AI systems exceeded human performance on the MMLU benchmark test[1]. Conversations with AI are becoming increasingly realistic, with a significant percentage of testers mistaking AI responses for human-generated content[1]. AI is also achieving increasingly realistic image generation, as demonstrated by advancements in models like Midjourney[1]. Furthermore, AI is enabling realistic audio translation and generation, with companies like Spotify beginning to accept audiobooks AI-translated into 29 languages[1].

AI Adoption Across Industries and Sectors

AI adoption is rising across various industries and sectors, including technology, enterprise, education, government, and research[1]. Tech incumbents are prioritizing AI, with CEOs emphasizing AI's transformative potential in areas like coding, search, shopping, and healthcare[1]. Traditional enterprises are also increasing their focus on AI, targeting growth and revenue rather than just cost reduction[1]. Global CMOs are increasingly using or testing AI tools for marketing activities[1]. In the education and government sectors, there's a growing trend of announcing AI integrations, such as Arizona State University’s ‘AI Acceleration’ and the creation of ChatGPT tailored for USA federal agencies[1].

The Benefits and Risks of AI Development

The development of AI presents both significant benefits and risks[1]. The potential for AI to free humanity from repetitive work, increase production, accelerate scientific research, and provide cures for diseases is immense[1]. However, there are also risks associated with the misuse of AI, including lethal autonomous weapons, surveillance, biased decision-making, and cybersecurity threats[1]. Balancing these benefits and risks requires careful consideration and thoughtful leadership[1].

Capital Expenditure Trends in AI

CapEx spend among big technology companies has been on the rise for years, driven by increased data use and storage, and this trend has accelerated with the rise of AI[1]. Big Six tech companies in the USA have seen a +63% Y/Y increase in CapEx[1]. AI model training dataset sizes are growing exponentially, further driving the need for increased CapEx[1]. This investment is benefiting companies like NVIDIA, with data center revenue as a percentage of global data center CapEx increasing[1]. Data centers are key beneficiaries of AI CapEx spend, with construction value and capacity seeing significant growth[1]. However, data centers are also electricity guzzlers, necessitating a focus on energy efficiency and sustainable practices[1].

The Role of Open Source and China's Influence

Open-source AI is experiencing a resurgence, offering lower costs and greater accessibility for developers and enterprises[1]. China is emerging as a leader in the open-source race, with several large-scale models released[1]. While closed models dominate consumer market share, open-source models are preferred by startups, researchers, and independent developers[1]. China's advancements in AI are part of a broader effort to shift from low-cost manufacturing to high-value technology, with implications for national security and geopolitical power[1]. The competition between the USA and China in AI is intensifying, requiring strategic responses to promote innovation and maintain a competitive edge[1].

AI's Impact on the Physical World and Future

AI momentum is extending into the physical world, with intelligence embedded in vehicles, machines, and defense systems[1]. This includes the rise of self-driving fleets, AI-driven mining exploration, agricultural modernization through AI-powered weeding, and intelligent grazing systems[1]. Technologies like Starlink are expanding global internet access, enabling new users to come online with AI-native experiences[1]. As AI continues to evolve, it is expected to fundamentally reshape how work gets done, how capital is deployed, and how leadership is defined across companies and countries[1].


Where is the Bell Rock located?

The Inchcape or Bell Rock lies off 'the mouths of the Tay and the Forth, thirteen miles from Fifeness, eleven from Arbroath, and fourteen from the Red Head of Angus'[1]. The rock extends to about fourteen hundred feet[1]. At high water springs, it is buried sixteen feet[1].


How do you fix a leaky faucet yourself?

title: 'How to Fix a Leaky Faucet: Guides for Every Design and Style '

To fix a leaky faucet yourself, follow these steps:

  1. Turn Off the Water Supply: Locate the shutoff valves under the sink and turn them off. If no valves are available, shut off the main water supply to your home. After turning off the water, open the faucet to relieve any remaining pressure and ensure no water flows out[2][3].

  2. Identify the Faucet Type: Determine whether you have a compression, cartridge, ball, or ceramic disk faucet. Knowing the type will help you figure out the likely cause of the leak[2][4].

  3. Take the Faucet Apart: Cover the sink drain with a rag to prevent losing any small parts. Remove the handle by unscrewing the screws or popping off decorative caps as needed. Depending on the faucet model, you might need to detach the spout or the entire faucet from the counter to access the internal parts[2][3][4].

  4. Inspect and Replace Worn Parts: Check the internal components, looking for worn washers, O-rings, or cartridges. For traditional compression faucets, worn washers are often the issue, while O-rings might need replacing in other types. If you have a cartridge faucet, you might need to replace the entire cartridge[2][4].

  5. Clean the Components: Use distilled white vinegar and a soft scouring pad to clean any mineral deposits from the faucet parts. This cleaning may help improve the performance of your faucet[4].

  6. Reassemble the Faucet: After replacing any damaged parts, carefully reassemble the faucet in the reverse order of disassembly. Ensure all components are securely tightened, but avoid over-tightening[3][4].

  7. Turn the Water Back On: Slowly open the shutoff valves, keeping the faucet open until water flows freely and air is expelled from the pipes. Once water is flowing smoothly, check for leaks[3][4].

By following these steps, you can effectively fix a leaky faucet yourself and save on repair costs. If you're unsure about any step, consider watching instructional videos for additional guidance[5].

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What is a SAG card in acting?

title: 'What is a SAG Card? (plus, how to get one in 2024)'

A SAG card, also known as a SAG-AFTRA membership card, confirms that an individual is part of the Screen Actors Guild (SAG), which is a labor union representing actors in film, television, and radio. This card provides access to various benefits and privileges, including health and pension plans, industry discounts, and eligibility to work on union projects, which are often required for television and film roles[1][2][3].

Earning a SAG card is considered a significant milestone in an actor's career, akin to obtaining a driver's license, as it indicates professional status within the industry[2][3]. To become eligible for a SAG card, individuals must meet specific criteria, such as being hired for a speaking role in a union project or working as a background actor in SAG productions[1][4]. Once eligible, actors must submit application paperwork and pay a fee to officially join the union[3][4].


what could be a nice side dish for a carrot soup?

complementing carrot soup perfectly

A nice side dish for carrot soup could include crusty French bread, which is perfect for dipping due to its crunchy exterior and soft interior[2]. Garlic bread is also a great option, offering an aromatic and buttery flavor that complements the soup well[1]. Additionally, a Caesar salad provides a fresh and crisp contrast[1], while a mixed green salad adds vibrant elements[2]. Other excellent choices are mashed potatoes, which balance the sweetness of the soup, and cornbread, known for its subtly sweet and moist texture[3]. Grilled cheese sandwiches or baked cheese and veggie quesadillas provide a hearty contrast, making them satisfying pairings as well[3]. For a unique option, consider homemade flatbreads to mop up the soup[6], or even fried paneer cheese cubes for a crispy, vegetarian bite[4].

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What are AI’s current limits in comedic timing?

'a robot in front of a computer'

AI systems often struggle with the subtlety and timing that makes comedy effective[1]. They also lack the 'genuine touch' that comes from human creativity[2]. To be funny, AI needs cultural references, context, intuition, and spontaneity, but AI has no lived embodied experience[3].


How do you start a healthy diet?

title: 'How to Start a Healthy Diet: 14 Steps (with Pictures) - wikiHow'

To start a healthy diet, begin by making small changes rather than overwhelming yourself with drastic alterations. Select one aspect of your diet to improve, such as incorporating more fruits and vegetables or choosing wholegrain options for carbohydrates. Aim for at least 5 portions of a variety of fruits and vegetables daily, and base your meals on higher-fibre starchy foods like potatoes and whole grains[1][2][4].

Additionally, ensure you're drinking enough fluids, at least 6 to 8 glasses a day, and consider tracking your food intake to monitor your nutrition[4][5]. Focus on balanced meals that include protein, healthy fats, and limit processed foods high in sugar and salt. This gradual approach can help you adopt healthier eating habits sustainably[3][6].

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What is the Two-Minute Rule?

Man-looking-at-his-phone-at-work-what-is-the-two-minute-rule

The Two-Minute Rule is a productivity strategy proposed by David Allen in his book 'Getting Things Done.' It states that if a task can be completed in two minutes or less, you should do it immediately rather than putting it off or adding it to a to-do list. This approach helps prevent small tasks from piling up and becoming overwhelming, allowing for better mental focus on more significant projects[1][5][6].

James Clear elaborates on the Two-Minute Rule by suggesting it can also apply to habit formation. He advises scaling down new habits into actions that take less than two minutes to accomplish. This makes starting new habits less daunting and more sustainable, as the small action can lead to further engagement with the task[2][3].

The rule's effectiveness lies in its ability to reduce procrastination and encourage prompt action. For instance, tasks such as responding to a quick email, washing a dish after use, or tidying up a workspace are all suitable candidates for this rule[2][4][5]. Utilizing this technique can increase productivity and clear mental space, making it easier to focus on more complex responsibilities[6].

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Segment Anything: Advancing the Field of Image Segmentation in AI

In the realm of artificial intelligence, particularly in computer vision, segmentation tasks are crucial for a better understanding of images. Meta AI Research introduced an innovative model, the Segment Anything Model (SAM), aimed at transforming image segmentation. This blog post breaks down SAM's functionality, its deployment, and its remarkable capabilities.

Overview of Segment Anything

title: 'Figure 1: We aim to build a foundation model for segmentation by introducing three interconnected components: a promptable segmentation task, a segmentation model (SAM) that powers data annotation and enables zero-shot transfer to a range of tasks via prompt engineering, and a data engine for collecting SA-1B, our dataset of over 1 billion masks.'
title: 'Figure 1: We aim to build a foundation model for segmentation by introducing three interconnected components: a promptable segmentation task, a segmentation model (SAM) that powers data annotation and enables zero-shot transfer to a range of...Read More

The SAM project revolves around creating a foundation model specifically designed for segmentation tasks in images. SAM distinguishes itself by being able to interact with various inputs to output segmentation masks in real-time, dealing with ambiguity effectively. The core concept is to empower users with a promptable segmentation task, allowing the model to generate relevant segmentation masks based on either specified prompts or automated methods.

The team at Meta initiated this extensive project due to limitations seen in large-scale segmentation, especially concerning the need for vast annotated datasets. SAM utilizes a massive dataset dubbed SA-1B, which contains over 1 billion masks generated from 1 million images. This dataset includes high-resolution, licensed images that consider privacy concerns, ensuring ethical practices in data usage.

Architecture and Functionality

SAM is powered by a heavy-weight image encoder that enhances segmentation capabilities. It operates through three primary components: an image encoder, a prompt encoder, and a mask decoder. The image encoder processes the input image, while the prompt encoder assists the model in responding to various prompts, leading to the generation of high-quality masks. These masks allow for precise object identification and separation in images, making it invaluable for myriad applications ranging from autonomous vehicles to professional photo editing.

One of the standout features is SAM's versatility in adapting to various segmentation tasks without the need for fine-tuning. This zero-shot learning ability allows SAM to generate segmentation masks for new and unseen tasks effectively. By prompting SAM with different types of input, users can retrieve accurate segmentation masks that identify foreground objects regardless of the complexity of the image.

Training and Innovation

The training process for SAM involved unique methodologies that deviate from traditional methods. Instead of having a rigid training protocol, SAM was trained using multiple data collection methods to ensure a robust and diverse training set. These methods include assisted manual annotations, semi-automatic annotations, and fully automatic mask generation. This multifaceted approach ensures the model is exposed to a variety of tasks and real-world data.

Moreover, the team conducted extensive experiments to evaluate SAM's performance across different datasets and prompts. They compared SAM against existing state-of-the-art models in segmentation and consistently found that it significantly outperformed them. This is confirmed through empirical analysis, where SAM demonstrated superior performance in generating high-quality masks across various scenarios, proving its reliability and efficiency in different applications.

Addressing Challenges in Segmentation

Despite its capabilities, SAM acknowledges certain challenges present in the field of image segmentation. The model is built to recognize potential biases that arise during the segmentation process, particularly when handling ambiguous prompts. To address this, SAM can refine its outputs through a mechanism that focuses on additional relevant input points to enhance model accuracy.

Furthermore, SAM's design accommodates different user requirements, ensuring flexibility in various applications. It can be integrated into systems that require real-time image segmentation, proving invaluable for fields such as robotics, autonomous driving, and medical imaging.

Real-World Applications and Future Prospects

The implications of SAM extend far beyond academic research. It has significant potential in commercial applications, including e-commerce, automated inspection, and personalized content generation. As organizations increasingly depend on advanced machine learning models for image recognition and processing, SAM stands out for its practical efficiency and reliability.

Meta intends to continue improving SAM with further research, aiming to enhance its capabilities and broaden its applicability. Future iterations may include more sophisticated ways to generate segmentation masks, catering to complex use cases that demand even higher accuracy.

In conclusion, the Segment Anything model is a pioneering approach to image segmentation that has the potential to redefine how machines interpret visual data. With its groundbreaking methods, SAM not only enhances accuracy but also addresses many of the challenges in current segmentation technologies, establishing a solid foundation for future innovations in computer vision.

Table 1: Comparison of geographic and income representation. SA-1B has higher representation in Europe and Asia & Oceania as well as middle income countries. Images from Africa, Latin America & Caribbean, as well as low income countries, are underrepresented in all datasets.
Table 1: Comparison of geographic and income representation. SA-1B has higher representation in Europe and Asia & Oceania as well as middle income countries. Images from Africa, Latin America & Caribbean, as well as low income countries, are underrep...Read More