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Invincible season 2 ending explained: is [SPOILER] dead, mid-credits scene, Spider-Man cameo rumor, and your biggest questions answered

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Full spoilers follow for Invincible season 2 and its source material.

Invincible season 2 part 2 has ended with a bang. Exactly one month after its return, the hit Prime Video show is leaving our screens once more – and boy, did it leave us with a lot of unanswered questions.



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The big Apple lawsuit explained: why Apple’s getting sued and what it means for the iPhone

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It’s a move that’s sent shockwaves through the entire tech industry: the US government, through the Department of Justice (DOJ), is suing Apple for what it sees as unfairly and illegally building a monopoly around the iPhone.

You can read the full filing here, but we’re going to break down the key points for you here – why Apple is being sued, what it might mean for the iPhone and the tech industry in the future, and what the arguments are on both sides.

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What is Suno? The viral AI song generator explained – and how to use it for free

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Since ChatGPT burst onto the scene in November 2022 we’ve seen generative AI make some some startlingly human-like artistic creations – and the latest tool to go viral is Suno, an AI-powered song generator.

We’ve seen AI music generators before, from Adobe’s Project Music GenAI to YouTube’s Dream Track and Voicify AI (now Jammable). But the difference with Suno is that it can create everything, from song lyrics to vocals and instrumentation, from a simple prompt. You can even steer it towards the precise genre you want, from Delta Blues to electronic chillwave.

A laptop screen on an orange background showing the Suno AI tool

(Image credit: Suno)

In Suno’s new V3 model, you can now create full two-minute songs with a free account. The results can be varied, depending on which genre you choose, but Suno is capable of some seriously impressive results.

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Divas, captains, ghosts, ants and bumble-bees: collaborator attitudes explained

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Two cubic and sphere shaped avatars made out of multi-coloured fabric against a grey background.

It takes all sorts: different collaborators approach projects in different ways, and managing relationships between them is a crucial challenge.Credit: Andriy Onufriyenko/Getty

As a psychologist, I’m equipped with a theoretical understanding of emotions, attitudes, beliefs and behaviour – and, because of this, I might reasonably be expected to manage relationships with collaborators effectively. Some of my best memories of collaboration involve teamwork in which we have made space to speak explicitly about our emotions, attitudes, beliefs and behaviours with empathy and compassion. But it would be naive of me to think that all my collaborations will flow smoothly and easily.

Some time ago, I found myself venting about a difficult collaboration by capturing my emotions on paper. I created personas with whom to have imaginary dialogues, and used humour to detach myself from the situation and gain perspective. I then reflected on how everyone in the team had contributed to the mess we were in, and I made an effort to take responsibility for my own reactions in the situation. Finally, I reflected on what I could do differently in future.

My research looks at the meaning we give to the use of silence in our everyday lives, and to the promotion of mental health and well-being through writing groups. Therefore, taking the time to write about the challenges I faced in one of my projects felt natural.

The personas I created fitted five collaborator attitudes. The list is far from comprehensive and often veers into stereotype, but I’ve found it helpful, and sometimes funny, to think about academic collaborations in this way, so as to better manage interpersonal relationships.

Five collaborator attitudes

Which of these personas have you encountered in your collaborations?

• The ‘diva’ brings visibility to a project because they have already published on the topic, or are on the cover of magazines. However, they often expect to be a co-author by default, because they are enrolled in the project, present themselves as immensely busy, expect others to adapt to their calendars, show little room for compromise or rush into sketching bullet points that others need to decipher and elaborate on. When they do share their knowledge, they can quickly help the team become unstuck.

• The ‘captain’ gives a sense of direction to a manuscript, and can bring the whole team with them when at their best. Their authoritative style fits the conventional supervisor–supervisee dynamic, in which the supervisee receives a to-do list of corrections. And if the manuscript contains a typo, the captain comments on it rather than correcting it themselves.

• The ‘ghost’ appears and disappears. Sometimes they’re available and committed, but occasionally they’re hard to find, slowing decision-making and confusing the rest of the team. Getting this person on a call or to a meeting might be difficult. They do eventually attend to their tasks, even if delayed. If there is active conflict, their quietness might inspire others to pause and reflect.

• The ‘ant’ is reliable and available. Even when busy, they find time for a short call or to answer a crucial question by e-mail. Their egos are small, and both their contributions and their feedback are constructive. They are also conciliatory when conflict arises. But their neutrality can be frustrating, and sometimes it doesn’t help to resolve a conflict.

• The ‘bumble bee’ is hard-working, humble and efficient. They reply quickly and compromise on dilemmas around deadlines, schedules and tasks. They tend to feel more weighed down than others when conflict arises. If they end up taking on more responsibilities than necessary to keep the boat afloat, they risk overreacting to missed deadlines or misunderstandings.

That reciprocal feeling

We don’t always have the freedom to choose who we work with, so count yourself lucky if your team includes ants, bumble bees or both. Aim to collaborate with people who actively reflect on the potential biases of their scientific thinking, and who can compromise after a discussion, or even admit they were wrong. Pay attention to the words they use to refer to younger scholars, and whether they prefer to give commands than to propose shared responsibilities. Do you feel reciprocity when you approach them, or do you sit with the gut feeling that communication goes only one way, because they sit above you in a certain hierarchy?

We all risk showing attitudes typical of divas, captains and ghosts when we are stressed, demotivated or busy. In addition, burn-out can be around the corner for ants and bumble bees.

This is why, if you want to submit a grant proposal or an article within a given deadline and survive the process, you should make a cooperation agreement with co-authors as soon as possible. Here’s how to do it.

Five people in a conference room co-creating a problem statement at Design Thinking Bootcamp, March 2024, Amsterdam, Netherlands.

Olga Lehmann (back, centre) works with her team on a cooperation agreement.Credit: Design Thinkers Academy, Netherlands

Personalize cooperation agreements. Cooperation agreements are contracts between collaborators that lay out some general rules of behaviour. They should be a team effort, and not only the priority of a principal investigator. Make clear at a meeting what you all expect from each other as collaborators. This could, for example, include a commitment that every author reads entire drafts, and not only sections of it, or set out what would happen if the product of the project is commercialized. What seems obvious to you might not be a given for your colleague. Clarify how to deliver and receive feedback, such as pointing to what others have done well, and try to honour and understand that there might be cultural differences in how people express their points of view.

Decide what should happen when. Agree when co-authors are to put their hands on the keyboard (to correct a typo, for example), and when they should make side comments for others to work on (to clarify the meaning of an idea, for instance, or the significance of results in a previous study).

Build meetings into the schedule. I regret the times I did not allocate enough time for meetings. This led to e-mails being the main means of communication, and a fast-track for misunderstandings. The collaborations that have worked best for me included regular check-in meetings, in person or remotely — with actions sent to those who could not attend, along with a short video or written summary. I often returned to these minutes when in doubt, which helped me to feel effective in my communication. Scheduling periodic check-ins to discuss the collaboration process is a worthwhile investment, even if it takes some effort to make calendars coincide.

Make a conflict-management plan. Agreements that focus only on the distribution of tasks are naive. What happens, for example, if co-authors disagree on the interpretation of data, the theories around it, or how tasks are allocated? Don’t wait until conflict jumps into your office uninvited.

Expect conflict to emerge in one way or another, and be prepared for it with a plan of action. Will the entire team be on board to make decisions if disagreements occur? When will an external adviser be contacted? What should be kept in e-mail format, and when should people have a call? Ask all your team members the same questions, and write the answers in a common document. We all have blind spots, and we need one another to gain insight, which is difficult when running against the clock or dealing with chaotic group dynamics when divas, captains and ghosts are on board.

Give people the benefit of the doubt. Show empathy to others, while holding them accountable. Trust that most of the co-authors want to submit a clear, structured and promising manuscript to a journal or funding agency. Perhaps a co-author is going through the break-up of a relationship, or a bereavement, or is closing a book deal. Maybe they are not as familiar as you are with the features of the writing platform you are using. Be kind, rather than officious, when redirecting people to what stands in the cooperation agreement.

Have an emergency exit available. “Don’t take it personally” is often good advice, but sometimes things do get personal in academia. As a young scholar, I have been afraid to be direct when people have undermined my competence or will. Power dynamics are a part of most early-career researchers’ daily lives, and you cannot force someone listen to you if they are committed to misinterpreting your intentions or have a rigid mindset that obstructs working collaboratively.

As passionate as you might be about your science, you do not need to bear disrespect to be published. If you feel that is happening, consider telling someone else at your workplace, arrange to postpone deadlines until conflict is sorted, talk to a counsellor or even report the situation to your institution’s ethics committee or funding agency, if necessary.

Fair’s fair

We need to break free from impractical and unfair co-authoring attitudes that cost us money and time, and threaten our mental health. To do so, we must be more intentional about the relational process that writing a scientific article or application entails. Whether the first author of an article or a grant application is a junior or a senior scholar, all co-authors should honour what writing collaboratively is about. It is fair to expect the actual work that someone has put into a manuscript to be a central criterion for co-authorship status.

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iPhone Privacy Settings Explained (Video)

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iPhone Privacy Settings

In today’s digital era, where data privacy has become a paramount concern, understanding the intricacies of your iPhone privacy settings is crucial. For iPhone users, navigating through its privacy configurations can seem daunting at first glance. However, you will be pleased to know that with a bit of guidance, mastering these settings is within reach. This video below from Stephen Robles gives us a rundown of the iPhone’s privacy features, offering a clear, engaging, and comprehensive overview to ensure your information remains secure.

First and foremost, the iPhone privacy settings are designed to give you control over your personal information. Whether you’re a tech enthusiast or someone who uses their phone for basic needs, knowing how to adjust these settings can make a significant difference in your digital footprint.

  1. Location Services: At the heart of privacy concerns for many users is the management of location services. Your iPhone allows you to customize which apps have access to your location and when. If you are wondering how to restrict this access, it’s quite straightforward. Navigate to Settings > Privacy > Location Services. Here, you can adjust permissions for each app individually, choosing between options like ‘Never’, ‘Ask Next Time’, ‘While Using the App’, or ‘Always’.
  2. App Permissions: Beyond location, apps request access to various other data types, such as your contacts, calendars, photos, microphone, and camera. To review and manage these permissions, head to Settings > Privacy, where you will find a list of features. Tapping on any of these allows you to see which apps have requested access and modify their permissions accordingly.
  3. Tracking: With increasing concern over apps tracking our activities for advertising purposes, Apple has introduced a feature that requires apps to ask for your permission to track you across other apps and websites. You can control this by going to Settings > Privacy > Tracking, where you can enable or disable the permission for apps to request to track you.
  4. Analytics and Improvements: If you prefer not to share device analytics with Apple, you can opt-out easily. These analytics help Apple improve products and services but may contain information about how you use your device. To disable this, go to Settings > Privacy > Analytics & Improvements and toggle off the options you prefer not to participate in.
  5. Significant Locations: A lesser-known feature is Significant Locations, which tracks places you frequently visit to provide personalized services, such as predictive traffic routing. If this feels too intrusive, you can clear your history and turn off this feature by navigating to Settings > Privacy > Location Services > System Services > Significant Locations.

Empowering Your Privacy

Understanding and configuring your iPhone’s privacy settings empowers you to take charge of your personal information. While the options might seem overwhelming at first, taking the time to familiarize yourself with them can greatly enhance your privacy and security. Remember, it’s not just about preventing external threats; it’s also about understanding what you’re sharing and with whom.

By adjusting your location services, managing app permissions, controlling ad tracking, opting out of analytics sharing, and being mindful of significant locations, you can significantly reduce your digital footprint. It’s about finding the right balance that works for you, ensuring that your privacy preferences are respected.

The journey to mastering the iPhone’s privacy settings is an ongoing one, as features and options evolve with each software update. Staying informed and regularly reviewing your settings can make all the difference in safeguarding your privacy in the digital age. Your iPhone is a powerful tool, and with the right configurations, you can enjoy its myriad benefits while maintaining control over your personal information.

Source & Image Credit: Stephen Robles

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Google Gemini Ultra 1.0 features and capabilities explained

Google Gemini Ultra 1.0 features and capabilities explained

Google has unveiled a new artificial intelligence model, the Gemini Ultra 1.0, which promises to transform how we interact with digital technology. This advanced AI model is part of the Google Gemini suite and comes with a host of features designed to enhance your digital experience. For those who are always on the lookout for the latest tech tools, understanding what the Gemini Ultra 1.0 offers is essential.

The Gemini Ultra 1.0 stands out with its superior reasoning abilities, surpassing the capabilities of the original Gemini model. With a monthly subscription fee of $20, in addition to a Google One membership, users gain access to an AI that can solve complex puzzles and answer questions with remarkable accuracy. This development has the potential to change the way we solve problems and access information.

Although the AI’s ability to understand and generate images is a significant step forward, it may not be as advanced as some competing products, suggesting room for improvement. However, Gemini Ultra shines when it comes to processing text. It can summarize large amounts of data or expand on brief notes with ease, making it an invaluable tool for professionals who handle a lot of information.

Google Gemini Ultra 1.0

The integration with Google Workspace and the AI’s ability to summarize emails are areas where Gemini Ultra particularly excels, surpassing the original Gemini and enhancing productivity. The review also touches on the ethical aspects of AI, noting that both Gemini versions provide justifications for their conclusions, albeit with different levels of detail. This transparency is crucial as AI becomes increasingly integrated into our daily lives.

For developers, the code analysis feature of Gemini Ultra shows promise when dealing with short snippets of code but may struggle with more complex sequences. This is an area that could benefit from additional development.

One of the strong points of Gemini Ultra is its ability to generate email content based on web searches, leveraging its current understanding of real-world data. This feature is particularly useful for creating relevant and timely communications.

Despite its progress in complex reasoning and writing tasks, Gemini Ultra has limitations. Further improvements in image generation and better integration with other Google services, such as Gmail and Google Drive, could enhance the overall user experience.

The Gemini Ultra 1.0 is a powerful AI model that excels in complex reasoning, text summarization, and email composition. It represents a significant improvement over the standard Gemini model, particularly for professionals who require these advanced functions. However, there is always room for improvement. With the tech community keen to see how it compares to competitors like GPT-4 and the possibility of more comprehensive Gmail and Google Drive integration, the development of Gemini Ultra is likely to continue.

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Generative AI explained in simple terms

Generative AI explained in simple terms

This is the time of generative AI, a sophisticated branch of technology that is rapidly altering the landscape of content creation. It’s a field where the lines between human ingenuity and machine efficiency are blurring, giving rise to a new era of innovation. Generative AI is distinct from the AI most people are familiar with. Instead of merely processing information, it has the remarkable ability to produce new content that was once considered the sole province of human creativity. Imagine a tool that could offer you intelligent solutions on demand, much like having a digital genius at your fingertips. This is the essence of what generative AI brings to the table.

Generative AI refers to a subset of artificial intelligence technologies that can generate new content, such as text, images, music, and even code, based on the patterns and data they have learned from. Unlike traditional AI, which focuses on understanding or interpreting existing information, generative AI takes this a step further by creating original output that can mimic human-like creativity. The foundation of generative AI involves complex algorithms and models that learn from vast amounts of data, identifying underlying patterns, structures, and relationships within this data.

Generative AI explained in simple terms

The key to unlocking the full potential of generative AI lies in prompt engineering—the art of crafting the right instructions to guide the AI towards generating the desired outcome. As AI becomes more integrated into our everyday tasks, mastering this skill is becoming increasingly important. It ensures that the AI’s output aligns with our goals and expectations.

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Generative AI is a step above its predecessors in its ability to create. While traditional AI systems are adept at organizing and classifying existing data, generative AI can write essays, create music, or produce realistic images from a simple text description. This is made possible by Large Language Models (LLMs) like the Generative Pre-trained Transformer (GPT). These models are trained on vast amounts of data, enabling them to generate text that is not only coherent but also contextually relevant. They are powered by complex algorithms that allow them to improve their performance continuously.

The capabilities of generative AI are not limited to text. It can turn rough sketches into detailed, lifelike images, provide elaborate descriptions of visuals, convert speech to text, and even create spoken content or video clips from written descriptions. Multimodal AI products push these boundaries even further by blending different forms of media, thereby enriching the user experience and expanding the functionality of AI. Application Programming Interfaces (APIs) play a pivotal role in the integration of AI into various products. They act as the bridge that allows different software components to communicate with each other, making it possible for AI to become a seamless part of our digital tools.

Summary explanation of Generative AI

To understand generative AI, it’s crucial to grasp two key concepts: machine learning and neural networks. Machine learning is a method of teaching computers to learn from data, improve through experience, and make predictions or decisions. Neural networks, inspired by the human brain’s architecture, are a series of algorithms that recognize underlying relationships in a set of data through a process that mimics the way a human brain operates.

Generative AI operates primarily through two models: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

  1. Generative Adversarial Networks (GANs): GANs consist of two parts, a generator and a discriminator. The generator creates new data instances, while the discriminator evaluates them against real data. The generator’s goal is to produce data so authentic that the discriminator cannot distinguish it from real data. This process continues until the generator achieves a high level of proficiency. An example of GANs in action is the creation of realistic human faces that do not belong to any real person.
  2. Variational Autoencoders (VAEs): VAEs are also used to generate data. They work by compressing data (encoding) into a smaller, dense representation and then reconstructing it (decoding) back into its original form. VAEs are particularly useful in generating complex data like images and music by learning the probability distribution of the input data.

Examples of Generative AI Applications:

  • Text Generation: Tools like OpenAI’s GPT (Generative Pre-trained Transformer) can produce coherent and contextually relevant text based on a given prompt. For instance, if you ask it to write a story about a lost kitten, GPT can generate a complete narrative that feels surprisingly human-like.
  • Image Creation: DeepArt and DALL·E are examples of AI that can generate art and images from textual descriptions. You could describe a scene, such as a sunset over a mountain range, and these tools can create a visual representation of that description.
  • Music Composition: AI like OpenAI’s Jukebox can generate new music in various styles by learning from a large dataset of songs. It can produce compositions in the style of specific artists or genres, even singing with generated lyrics.
  • Code Generation: GitHub’s Copilot uses AI to suggest code and functions to developers as they type, effectively generating coding content based on the context of the existing code and comments.

As we observe the swift progress of generative AI, it’s important to maintain a balanced perspective. We must embrace the possibilities that AI offers while acknowledging its current limitations. Human insight remains irreplaceable, providing the domain expertise and ethical guidance that AI is not equipped to handle.

Generative AI is reshaping the boundaries of what we consider achievable. It presents us with tools that enhance human productivity and creativity. By gaining an understanding of AI models, becoming proficient in prompt engineering, and preparing for the advent of more autonomous systems, we position ourselves not just as spectators but as active contributors to the unfolding future of technology.

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Quantum computing Hype vs Reality explained

Quantum Computing Hype vs Reality explained

Quantum computing is a term that has been generating a lot of excitement in the tech world. This cutting-edge field is different from the computing most of us are familiar with, which uses bits to process information. Quantum computers use something called qubits, which allow them to perform complex calculations much faster than current computers. While quantum computing is still in its early stages and not yet part of our everyday lives, it’s showing great potential for specialized uses.

One of the leaders in this field is Google Quantum AI, which has developed one of the most sophisticated quantum processors so far. Their work is a testament to it’s researchers commitment to advancing the industry. However, quantum computing is still largely in the research phase, and it will likely be several years before it becomes more mainstream.

Experts in the industry believe that it could take a decade or more before we have quantum computers that are fully functional and error-free, capable of handling practical tasks. This timeline is similar to the development of classical computers, which gradually became more powerful and useful over time.

Google Research Quantum Computing

Learn more about quantum computing as Google Research explains more about the hype and reality of the cutting-edge computer technology that is still under development. As quantum computing continues to develop, we’re starting to see more applications for this technology. It’s expected that quantum systems will enhance, rather than replace, traditional computers, increasing our overall computing capabilities.

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The potential for quantum computing to transform various industries is immense. It could greatly improve research in fusion energy by making simulations more efficient and reducing the amount of computation needed. In healthcare, it could speed up the process of modeling new drugs. Quantum computing might also lead to better battery technology by optimizing electrochemical simulations, which could result in more effective energy storage solutions and help produce more environmentally friendly fertilizers.

Hype vs Reality

History has shown us that new technologies often lead to applications that we didn’t anticipate. As quantum computing technology continues to evolve, its full potential will become clearer. Quantum computing represents a significant shift in computational capabilities, promising to solve problems intractable for classical computers. However, the field is in its nascent stages, and there’s often a gap between public perception (hype) and the current state of technology (reality). Here’s a comprehensive explanation, distinguishing between the hype and reality of quantum computing:

Quantum Computer Hype :

  • Instant Problem Solving: A common misconception is that quantum computers can instantly solve extremely complex problems, like breaking encryption or solving intricate scientific issues, which traditional computers cannot.
  • Universal Application: There’s a belief that quantum computers will replace classical computers for all tasks, offering superior performance in every computing aspect.
  • Imminent Revolution: The public often perceives that quantum computing is just around the corner, ready to revolutionize industries in the immediate future.
  • Unlimited Capabilities: The hype often implies that there are no theoretical or practical limits to what quantum computing can achieve.

Quantum Computing Reality :

  • Specialized Problem Solving: Quantum computers excel at specific types of problems, such as factorization (useful in cryptography) or simulation of quantum systems. They are not universally superior for all computational tasks.
  • Niche Applications: Currently, quantum computers are suited for particular niches where they can leverage quantum mechanics to outperform classical computers. This includes areas like cryptography, materials science, and complex system modeling.
  • Developmental Stage: As of now, quantum computing is in a developmental phase. Key challenges like error correction, coherence time, and qubit scalability need to be addressed before widespread practical application.
  • Physical and Theoretical Limits: Quantum computers face significant physical and engineering challenges. These include maintaining qubit stability (decoherence) and managing error rates, which grow with the number of qubits and operations.
  • Quantum Supremacy vs. Quantum Advantage: While quantum supremacy (a quantum computer solving a problem faster than a classical computer could, regardless of practical utility) has been claimed, the more crucial milestone of quantum advantage (practical and significant computational improvements in real-world problems) is still a work in progress.
  • Hybrid Systems: The foreseeable future likely involves hybrid systems where quantum and classical computers work in tandem, leveraging the strengths of each for different components of complex problems.
  • Investment and Research: Significant investment and research are ongoing, with breakthroughs happening at a steady pace. However, it’s a field marked by incremental progress rather than sudden leaps.
  • Ethical and Security Implications: The rise of quantum computing brings ethical considerations, particularly in cybersecurity (e.g., breaking current encryption methods) and data privacy. It necessitates the development of new cryptographic methods (quantum cryptography).

The excitement around quantum computing is not without merit. Each new discovery moves us closer to what once seemed like the stuff of science fiction. The progress made by Google Quantum AI and others in this field is a strong sign of the transformative power of quantum computing.

Quantum computing is still in its infancy, but the advancements made by Google and other pioneers are steadily paving the way for a future that includes quantum computation. Although the current state of quantum computing may not live up to the high expectations some have for it, the potential applications and ongoing research suggest that it could indeed live up to its promise in the years to come.

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Synology NAS Home vs Homes folders explained

The difference between Synology NAS Home vs Homes folders explained

If you have just purchased a Synology NAS you may have already encountered two folders that might cause a bit of confusion: “Home” and “Homes.” Understanding the purpose and function of these folders on your Synology NAS is crucial for anyone looking to manage their files effectively and ensure that privacy is maintained.

The “Home” folder is essentially your personal space on the Synology NAS. It’s where you can keep your own files and data, away from the prying eyes of other users on the network. Think of it as your private drawer in a shared office; it’s yours, and no one else should be rummaging through it without your permission. This privacy is not just a matter of convenience but a fundamental aspect of how the Synology NAS ensures the security of your personal data.

On the other hand, the “Homes” folder serves a different purpose. It’s an administrative tool that provides a collective view of all individual “Home” folders. This is particularly useful for network administrators who need to oversee the entire system. However, it’s important to note that while the “Homes” folder allows for this oversight, it’s not intended for direct file manipulation. In fact, making changes to the “Homes” folder can lead to complications, such as access issues or confusion among users.

When it comes to sharing files, you might be tempted to just pass them directly from your “Home” folder. However, Synology has a better solution: Synology Drive. This tool is specifically designed for sharing files and gives you the ability to set precise access permissions. By using Synology Drive, you can ensure that your “Home” folder remains a secure and private space, while still being able to share files as needed.

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One of the features of Synology NAS devices that users appreciate is the ability to take snapshots, which are essentially pictures of your data at a specific point in time. These snapshots can be lifesavers when it comes to file recovery. However, there are certain limitations when it comes to snapshots within the “Home” and “Homes” folders. Users need to be aware of these restrictions to avoid surprises during the recovery process.

For those who are more technically inclined, the replication and failover processes for the “Homes” folder can present some challenges. Because each user’s data and permissions are unique, replicating and managing failover requires careful planning. The goal is to ensure that users can still access their data and that the integrity of the data is not compromised in any way.

Synology NAS devices are not just about storing files; they also integrate with various applications. For instance, Synology Drive and Synology Photos use the “Home” folder to store user-specific data. This means that any changes to the structure of the “Home” folder could disrupt these applications. Therefore, it’s crucial to maintain the integrity of the “Home” folder to ensure that these applications function correctly.

For network administrators, there are certain best practices to follow when it comes to the “Homes” folder. One key recommendation is to avoid modifying the “Homes” folder directly. This can help prevent access issues for users. Additionally, hiding the “Homes” folder from network places can be a wise move, as it helps to prevent accidental changes and keeps the user interface clean and straightforward.

The personalized nature of the “Home” folder means that tasks like replication, creating snapshots, and migrating data can be more complex than with shared folders. It’s important to adhere to best practices and consider alternative strategies to ensure that data remains intact and accessible.

For those new to Synology NAS, getting to grips with the “Home” and “Homes” folders is an important step. Recognizing their distinct roles, maintaining appropriate privacy settings, and using the right tools for file sharing and backup will help you avoid common pitfalls. With this knowledge, you can make the most of your Synology NAS device, keeping your data secure and your system running smoothly.

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New York Times and OpenAI GPT lawsuit explained

New York Times and OpenAI GPT lawsuit explained

The esteemed publication, The New York Times, has taken a bold step by filing a lawsuit against OpenAI, the creators of the sophisticated AI model known as GPT-4. This legal challenge, which also involves tech giant Microsoft due to its association with OpenAI, is centered around claims of copyright infringement. The New York Times is seeking significant financial compensation, alleging that its copyrighted content was used without permission to train the AI system.

At the heart of this dispute is the demand for the complete removal of GPT-4 and any other models that may have utilized The New York Times’ copyrighted material during their training. This case is critical as it could set a new legal standard that might affect the future of AI development and the use of copyrighted materials in machine learning.

The New York Times argues that OpenAI’s models, which have consumed its content, now pose a threat to its business by offering similar journalistic services. The publication claims that GPT-4 can generate summaries and even reproduce exact excerpts from its articles, essentially redistributing its content without authorization.

A key point in the lawsuit is whether AI systems like GPT-4 retain exact copies of copyrighted texts or whether they simply learn patterns and generate similar content independently. This distinction is crucial and could determine the outcome of the case.

New York Times OpenAI lawsuit

In the past, U.S. courts have been reluctant to hold AI systems accountable for the data on which they are trained, often dismissing lawsuits related to such issues. However, this case could break that pattern, particularly if it is proven that GPT-4 can recall and reproduce copyrighted material.

The implications of this legal battle are far-reaching. Should The New York Times emerge victorious, it could reshape the AI industry, especially regarding how AI models are trained and the necessity of securing permissions for copyrighted content. Such a shift could fundamentally change how AI companies acquire and use training data.

Quick summary of the New York Times and OpenAI GPT lawsuit :

  • The New York Times is suing OpenAI and its affiliates for copyright infringement.
  • The lawsuit seeks significant financial damages and the removal of GPT-4 and related models.
  • Previous lawsuits against AI models for similar reasons have generally not succeeded.
  • U.S. courts have typically rejected claims against the training data used by AI models.
  • The lawsuit argues that OpenAI’s models, using data from the New York Times, compete with the newspaper’s ability to deliver news.
  • The New York Times alleges that OpenAI’s models can generate detailed summaries and verbatim excerpts from its articles without authorization.
  • The case may hinge on whether OpenAI’s models store actual copies of copyrighted material.
  • The outcome of the lawsuit could have implications for the future of AI models and their interaction with copyrighted content.

As the situation unfolds, it is crucial to consider the balance between encouraging AI innovation and protecting intellectual property rights. The outcome of this lawsuit will likely have significant consequences not only for OpenAI and its affiliates but also for the wider AI community and its interaction with copyrighted materials.

The confrontation between The New York Times and OpenAI is not just a legal matter; it is a pivotal moment that could influence the direction of technological advancement and the protection of creative works. The resolution of this case is eagerly anticipated, as it will set a precedent for how AI entities and content creators coexist and collaborate in the rapidly evolving digital landscape.

Filed Under: Technology News, Top News





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