#AI-Powered Text Generation

[ctrinity]

Canva Magic Write ✨🖊️

Canva Magic Write Magic Write is Canva’s AI-powered copywriting assistant designed to help users easily generate various types of text, from blog posts to letters.

🖊️ Canva Magic Write Magic Write is Canva’s AI-powered copywriting assistant designed to help users easily generate various types of text, from blog posts to letters. Canva Magic AI Writer 💡 Idea Generation This tool assists in brainstorming by providing new ideas and topics, making it easier to create content across different formats. Canva Paragraphs Generator 🎨 Integration into Designs Users…

[lonelyslutavatar]

Listen i love your art very much but with all that has been happening with AI and artists...we writers honestly feel like we might be next, people are already making ais that write stories for them. I understand that this is just for fun for you but thats how ai art started too and now you hear about people devaluing artists n their skill n hard work because suddenly they can make 'art' with a press of their finger so why should they pay for the 'same thing'. Creator to creator, please dont contribute to this mess

oh yeah, no, i totally understand. I'm never gonna publish the model itself nor will i ever do anything to contribute to that mess but I'm a curious person and will still do it to satiate that curiousity but that model is never gonna leave my pc

[lazerinth]

Putting down certain artists to build up other artists is so unnecessary. Art is creativity, not a competition.

“You’re the only artist to draw your sona accurately to your real life self.” Why is this a problem to begin with? Let artists draw themselves however they want.

“Why do only artists with mid styles get popular? Why aren’t artists with good styles nearly as popular?” This is just rude. Art is subjective, there is no such thing as a “mid/bad” art style. If you want your favourite artists to be popular, help support them and get their name around. Some artists don’t even want to be popular, some prefer having a small community.

As long as an artist isn’t causing harm to themselves or others with what they create, why are you so concerned with what they are doing? Art is about creating, expressing, and exploring oneself.

[thoughtsofthegirlwiththecurl]

Working in publishing, my inbox is basically just:

Article on the Horrors of AI

Article on How AI Can Help Your Business

Article on How AI Has Peaked

Article on How AI Is Here to Stay Forever

Article on How AI Is a Silicon Valley Scam That Doesn't Live Up to the Promise and In Fact Can't Because They've Literally Run Out of Written Words to Train LLMs On

[melted-snowperson]

I'm going to post a positive brother to assist me help me it ain't me with another 4 permanently I'm going forward that I've been cradling and they've learned nice of you know when it's would be incredibly unusually nice to be no one so if it ain't a ball seat possibly bother to do anything this world please do get in contact with me hey it's okay asWait alone greetings minimize heights like let's hope yourself said it however it means dancing tickets to eyes if you lamb bowed on us trans let it drown dragon into the USA American new Sandwich

im very confused but i hope you have a nice day??

definitely one of the more bamboozling things that’s happened to me on here lol

[reviewtechnology]

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[rjas16]

The Human Impact of Generative AI

Shaping Tomorrow: How Generative AI Empowers People and Transforms Work

The Metaverse and Generative AI: Creating Immersive Experiences

Generative AI is swiftly transforming the landscape of numerous industries by enabling more efficient workflows and sparking once-impossible innovations. At the heart of this transformation is the capacity of generative AI to automate complex processes and generate new content, from visuals to code, thereby enhancing productivity and creative potential. This article sheds light on how generative AI is revolutionizing various sectors, improving communication through advanced natural language processing, personalizing experiences in healthcare, and even redefining content creation and software development.

The Power of Text-to-Image and Image-to-Image Generation

Generative AI technologies are redefining creativity and design by allowing for the seamless generation of visual content from textual descriptions. This capability accelerates the creative process and democratizes design capabilities, making them accessible to professionals without deep graphic design skills. Industries such as marketing and entertainment are reaping immense benefits from these advancements. For instance, graphic designers can now produce bespoke visuals in minutes, significantly reducing project turnaround times and allowing for greater scalability in creative projects, thereby enhancing business productivity.

This integration across business functions showcases the substantial efficiency gains that can be achieved, enabling companies to tailor their marketing materials quickly and in alignment with consumer preferences without requiring extensive manual effort.

Revolutionizing Software Development with Code Generation and Completion

Integrating generative AI into software development drastically enhances the efficiency and accuracy of coding processes. By automating mundane coding tasks, AI-powered tools free developers to focus on more complex and innovative aspects of software design. These advancements are not just about speeding up development but are transforming how software is created, tested, and deployed.

AI-Powered Coding Assistants

AI coding assistants like GitHub Copilot have been at the forefront of this transformation. According to a GitHub survey, developers using these AI tools report a 55% increase in productivity. These assistants leverage vast code repositories to offer real-time suggestions and complete lines of code, significantly speeding up the development process and reducing bugs.

For example, GitHub Copilot acts like a pair programmer, suggesting entire blocks of code based on natural language comments or a few lines of code. This greatly speeds up the coding process and enhances code quality by suggesting industry-standard practices and reducing errors.

Startups Leading the Charge in AI-Driven Code Generation

Several innovative startups are making waves in this space by focusing on specific niches of the development process

Tabnine - This tool uses machine learning to provide code completions for developers, supporting over a dozen programming languages. Its model learns from the codebase it's working on, offering tailored suggestions that improve over time.

Replit- Aimed at making coding more accessible, Replit provides a collaborative browser-based IDE with AI-powered coding assistance. It's particularly popular among educators and learners, democratizing access to coding tools and environments.

Codota- Like Tabnine, Codota offers intelligent code completions driven by AI. It integrates seamlessly with popular IDEs like IntelliJ and WebStorm, streamlining the development workflow by predicting needs and reducing repetitive coding tasks.

The Future of AI in Software Development

The trajectory of AI in software development points toward more integrated systems where AI tools assist with code and planning, testing, and deployment processes. These tools are expected to become more predictive, using historical data to guide development strategies and optimize team workflows.

By integrating AI into software development, the industry is seeing increased productivity and a shift in the developer's role from coder to innovator. As AI continues to evolve, the future of coding looks set to be more intuitive, creative, and, importantly, more efficient.

AI-Powered Content Creation: A New Era

The advent of generative AI is reshaping the landscape of content creation across multiple platforms. From crafting engaging blog posts to generating dynamic social media content and personalized emails, AI tools play a pivotal role in automating content generation, saving time, and maintaining a high standard of creativity and relevance.

Enhancing Productivity and Creativity

AI content generation tools are a boon for content creators, as they significantly reduce the time spent on content production. According to case studies from Jasper AI, thanks to AI assistance, content creators save an average of 3-4 hours per week. This time savings translates directly into increased productivity, allowing creators to focus more on strategy and less on the mechanics of content creation.

For instance, platforms like Jasper AI offer a range of content creation tools that automate the writing process, from first draft to finished piece, while ensuring the content is engaging and tailored to the audience. Similarly, Writesonic provides tools to enhance marketing content, enabling businesses to produce ads, product descriptions, and marketing copy quickly and efficiently.

The Role of AI in Personalization

Beyond sheer output, AI's real power in content creation lies in its ability to personalize content. By analyzing user behavior and preference data, AI can tailor content to meet the nuanced demands of different audience segments. This level of personalization is particularly effective in marketing, where tailored content can significantly improve engagement rates and conversions.

Navigating Challenges

While the benefits are substantial, using AI in content creation also presents challenges, particularly regarding the originality and authenticity of the content. To address this, many AI platforms are incorporating advanced algorithms that generate content and ensure that it is unique and aligns with the brand's voice. Additionally, a growing emphasis is on blending human creativity with AI efficiency to produce innovative and genuine content.

AI Tools Transforming the Content Landscape

Several other tools and platforms are at the forefront of this AI-driven content revolution

Grammarly leverages AI to correct grammar and enhance the tone and clarity of the text, making it more effective and audience-appropriate.

Articoolo creates unique textual content from scratch, simulating a human writer and significantly shortening the content development cycle

Advancements in Natural Language Processing: Understanding and Communicating Better

Natural language processing (NLP) is the heart of generative AI, enabling machines to understand and interact using human language. This technology has seen significant advancements in recent years, leading to improved communication tools and a deeper understanding of textual data across industries.

Enhanced Communication Tools

One of the most visible impacts of advanced NLP is improving communication tools such as chatbots and virtual assistants. These AI-driven systems can now handle complex conversations, understand nuances, and provide increasingly indistinguishable responses from human interactions. For instance, chatbots powered by sophisticated NLP models are used in customer service to respond instantly to customer inquiries, reducing wait times and improving customer satisfaction.

Sentiment Analysis and Translation

NLP is also pivotal in sentiment analysis, where AI models assess the emotional tone behind text data. This is incredibly useful for businesses to gauge customer sentiment from reviews, social media posts, and other interactions. Machine translation has benefited immensely from NLP, enabling more accurate and context-aware translations that are crucial in global communications.

Real-World Applications of NLP

Customer Service : AI-enhanced chatbots can now provide 24/7 customer service, precisely handling inquiries and redirecting complex issues to human operators.

Market Analysis : NLP tools analyze vast amounts of data from market research to provide insights into consumer behavior, trends, and preferences.

Healthcare : In the medical field, NLP is used to interpret and classify clinical documentation, helping in faster and more accurate patient diagnoses.

Cutting-Edge NLP Technologies

Platforms like OpenAI's GPT -4 are leading the charge in NLP technology. This model has set new standards for language models with its ability to generate coherent and contextually relevant text based on minimal input. This model and others like it are enhancing existing applications and paving the way for new uses that were previously unimaginable.

Challenges and Ethical Considerations

Despite its advancements, NLP faces challenges, particularly in bias and ethical use. Ensuring that AI systems do not perpetuate existing biases in training data is a significant concern that requires ongoing attention and refinement. Moreover, as NLP systems become more integrated into daily activities, privacy and data security questions become more pressing.

Generative AI in Healthcare and Drug Discovery: Accelerating Progress

Accelerating Drug Discovery

One of AI's most impactful applications in healthcare is accelerating the drug discovery process. Traditional drug development is notoriously time-consuming and costly, often taking over a decade and billions of dollars to bring a new drug to market. AI models can predict the effectiveness of compounds much faster than traditional experimental methods, reducing the time and financial investments required. For example, AI systems can simulate the interaction between drugs and biological targets to identify promising candidates for further development, thereby streamlining the early stages of drug discovery.

A McKinsey report highlights that AI has the potential to halve the time required for drug discovery, suggesting a reduction in timelines from 10 years to just five years. This not only speeds up the availability of new medications but also significantly cuts down on R&D costs.

Personalized Medicine

Beyond drug discovery, generative AI enhances personalized medicine, where treatments are tailored to individual patients. By analyzing genetic data, AI systems can predict how patients respond to various therapies, allowing for more personalized and effective care. This approach is particularly transformative in fields like oncology, where understanding the specific genetic makeup of a tumor can guide more targeted and effective treatment strategies.

AI in Medical Imaging

Another critical area where AI is making strides is in medical imaging. AI algorithms can process images faster and often more accurately than human radiologists, identifying subtle patterns that might be overlooked. Tools like Google Health's AI model for breast cancer screening, which has been shown to improve the accuracy of detecting breast cancer in mammograms, exemplify the potential of AI to enhance diagnostic accuracy and improve patient outcomes

Ethical Considerations and Challenges

While the benefits are substantial, integrating AI in healthcare raises significant ethical and privacy concerns. Data security, consent for using personal medical data, and ensuring AI does not perpetuate existing healthcare disparities must be addressed. These challenges require robust regulatory frameworks and ongoing oversight to ensure that the benefits of AI in healthcare are realized without compromising patient trust or safety.

AI-Driven Personalization: Tailoring Experiences for Maximum Impact

Artificial intelligence significantly enhances personalization across various sectors, transforming how services and content are delivered to meet individual preferences and needs. This customization is crucial in e-commerce, education, and media, where tailored experiences can significantly boost user engagement and satisfaction.

Personalized Recommendations

In e-commerce, AI-driven personalization engines analyze user behavior, past purchases, and browsing history to recommend products that users are more likely to purchase. Companies like Amazon and Netflix are renowned for using AI to generate personalized recommendations, which enhances the user experience and increases revenue through improved conversion rates.

Customized Learning Experiences

AI personalization in education revolutionizes learning by adapting content to fit each student's learning pace and style. Platforms like Khan Academy use AI to offer a customized learning path for each user, making education more accessible and effective by addressing individual learning needs and preferences. This approach helps identify areas where students struggle and provide targeted exercises to improve their understanding and retention of the subject matter.

Personalized AI-Powered Content Creation

AI is also making strides in personalized content creation. Tools like Grammarly and Quill Bot tailor writing aids to the user's style and preferences, improving written communication's clarity, tone, and grammaticality. This personalization enhances the writing process and ensures the content effectively conveys the intended message.

Business Benefits

Personalization can lead to significant business benefits, including increased customer loyalty and spending. A study by Deloitte found that companies that leverage consumer behavior insights through personalization see revenue increase by 6% to 10%, which is two to three times higher than those that don't. Personalized marketing campaigns ensure that customers receive messages that resonate with their specific needs and preferences, greatly enhancing the effectiveness of marketing efforts.

Democratizing Development: Low-Code/No-Code Platforms

The rise of low-code and no-code platforms marks a significant shift in how software and applications are developed. These platforms democratize the ability to build complex systems without extensive programming knowledge. This technology enables a broader range of people, including those without formal coding expertise, to create applications, automate workflows, and contribute to digital transformation efforts within their organizations.

Empowering Non-Technical Users

Low-code and no-code platforms such as Microsoft PowerApps, Google AppSheet, and Bubble empower non-technical users to build applications through intuitive graphical user interfaces. These platforms provide drag-and-drop components, pre-built templates, and simple logic formulas, making it easier for non-developers to bring their ideas to life quickly and efficiently.

Reducing Development Time and Costs

The impact of these platforms on development time and cost is profound. By simplifying the development process, low-code and no-code platforms can reduce the time to develop and deploy applications by up to 90%. This reduction accelerates innovation within companies and significantly cuts costs associated with traditional software development, such as hiring specialized development staff and lengthy project timelines.

Enhancing Business Agility

Companies utilizing low-code/no-code platforms can enhance their agility by quickly adapting to changing market conditions and business needs. These tools allow businesses to prototype and iterate on solutions rapidly, enabling a more responsive approach to customer needs and market dynamics.

Case Studies

Microsoft PowerApps has enabled companies to build custom business apps that connect to their data stored in the underlying data platform (Microsoft Dataverse) or in various online and on-premises data sources.

Bubble allows users to design interactive, multi-user apps for desktop and mobile browsers. Users can create web applications ranging from simple prototypes to complex SaaS applications without writing a single line of code.

Challenges and Considerations

While low-code and no-code platforms offer numerous benefits, they also present challenges, such as limited customization for complex requirements and potential issues with scaling as needs grow. Moreover, reliance on these platforms can lead to vendor lock-in, where businesses depend on the platform's capabilities and pricing structures.

The Future of Low-Code/No-Code

As these platforms mature, they are expected to become more robust, offering greater flexibility, integration options, and advanced features that cater to more complex development needs. The evolving landscape of low-code/no-code technology promises to blur the lines between technical and non-technical users, fostering a more inclusive environment for innovation across industries.

Scaling low-code and no-code platforms has inherent limitations and challenges that can impact their effectiveness, especially as organizational needs grow and become more complex. Here's a closer look at some of these limitations and how they might affect the broader adoption and scalability of these platforms

Customization and Flexibility

Limited Customization: Low-code and no-code platforms offer significant ease of use and speed through pre-built templates and drag-and-drop interfaces. However, they often need more flexibility for more complex, customized solutions. Businesses may find that these platforms can only sometimes accommodate the specific requirements or unique processes that differentiate them from their competitors.

Integration Issues: As organizations scale, the need to integrate with other systems and data sources increases. Low-code and no-code platforms sometimes need help with complex integrations or more support for specific external APIs, limiting their utility in a fully integrated tech ecosystem.

Performance and Scalability

Performance Constraints: Applications built on low-code/no-code platforms can suffer performance issues as user numbers increase and data loads become heavier. These platforms may need to be optimized for high-performance scenarios, leading to slower response times and reduced user satisfaction.

Scalability Challenges: Scaling applications built with low-code/no-code tools can be problematic, especially when dealing with large volumes of data or high transaction rates. While some platforms are improving their capabilities in this area, there remains a significant gap compared to custom-developed applications.

Security and Compliance

Security Concerns: The ease of application development also comes with the risk of creating security vulnerabilities, particularly if the platform does not enforce strict security standards. Organizations must be vigilant about the security aspects of applications developed through low-code/no-code platforms, especially when handling sensitive data.

Compliance Issues: Regulatory compliance can also be a concern, as the automatic code generation and data handling procedures of low-code/no-code platforms might not automatically align with specific industry regulations, such as GDPR or HIPAA, requiring additional oversight to ensure compliance.

Maintenance and Support

Dependence on Vendors: Using low-code/no-code platforms often means relying on the vendor for updates, security patches, and new features. This dependence can lead to issues if the platform does not evolve in line with the latest technological developments or if vendor support is lacking.

Technical Debt: Applications built on low-code/no-code platforms can accumulate technical debt if not properly maintained. This can lead to increased costs and resources being diverted to manage and upgrade legacy systems initially developed to save time and money.

Moving Forward with Low-Code/No-Code

Despite these limitations, strategic use of low-code and no-code platforms can still benefit many organizations, especially when used for specific purposes where the advantages outweigh the drawbacks. Businesses should carefully evaluate their long-term needs and choose platforms with the best ease of use, flexibility, and scalability. Understanding these limitations will help organizations make informed decisions about when and how to incorporate low-code and no-code solutions into their IT strategy, ensuring they can maximize the benefits while mitigating potential downsides.

AI-Enabled Cybersecurity: Staying Ahead of Threats

Artificial intelligence (AI) has emerged as a crucial ally in the rapidly evolving cybersecurity landscape. With cyber threats becoming more sophisticated and frequent, AI technologies are pivotal in enhancing defenses by automating detection, response, and prevention strategies. This integration of AI in cybersecurity is not just a trend but a necessary evolution to cope with the scale and complexity of modern cyber threats.

Enhanced Threat Detection

AI excels in identifying patterns and anomalies, which makes it ideal for threat detection. Machine learning algorithms can analyze vast amounts of data from network traffic, logs, and past incidents to identify unusual behavior that may signify a security breach. This capability allows for real-time threat detection, significantly reducing the time between infiltration and response.

Automated Response Systems

Once a threat is detected, the speed of response is critical. AI-powered systems can respond to threats faster than human teams, automating certain responses to common types of attacks. Rapid response capability can mitigate the effects of attacks, stopping them before they spread throughout the network or result in significant data loss.

Vulnerability Management

AI also aids in vulnerability management by identifying weak points in the network before attackers can exploit them. By continuously scanning systems and software for vulnerabilities and comparing them against emerging threats, AI systems can prioritize vulnerabilities that pose the most immediate risk, guiding cybersecurity teams on where to focus their remediation efforts.

Predictive Capabilities

One of the most promising aspects of AI in cybersecurity is its predictive capabilities. By learning from historical data, AI can predict the types of attacks likely to occur, enabling organizations to prepare defenses proactively rather than reactively. This forward-looking approach helps maintain a stronger security posture and better preparation against potential threats.

Challenges and Ethical Considerations

While AI significantly enhances cybersecurity efforts, it raises privacy and ethical data use challenges. The vast amounts of data required to train AI models must be handled responsibly to ensure privacy protections are not compromised. Furthermore, as AI systems become more autonomous in making security decisions, establishing clear accountability for decisions made by AI is crucial.

Embracing the Future with Generative AI

As explored throughout this article, generative AI is not just a technological advancement but an exponential shift recasting industry models, enhancing human creativity, and redefining what is possible in the digital age. From revolutionizing content creation to reshaping software development and pushing the boundaries in healthcare, AI's impact is profound and far-reaching.

Advancements in natural language processing have improved how we interact with machines, making them more intuitive and responsive. In cybersecurity, AI's predictive capabilities are setting new standards for protection, staying one step ahead of evolving threats. Meanwhile, in the realms of personalization and education, AI is creating experiences that are more tailored and impactful than ever before. However, the journey does not end here. The future holds even greater potential as we continue to innovate and integrate AI into various facets of our lives and work. The opportunities to leverage AI for driving growth, efficiency, and creativity are limitless, and the time to act is now.

Engage with Coditude

Are you ready to harness the power of generative AI to transform your business? Connect with Coditude today and join us at the forefront of this exciting revolution. Our team of experts is dedicated to helping you explore the vast possibilities of AI, from developing custom AI solutions to integrating AI-driven processes into your existing systems. Whether you're looking to enhance your cybersecurity defenses, streamline your content creation, or tap into AI's powerful analytics for strategic insights, Coditude is here to guide you every step of the way. Let's build the future together—innovative, efficient, and brighter than ever.

[chronologicalimplosion]

[[Screenshot ID: A twitter thread by sjklapecwriting that says:

The fact that "AI = what makes NPCs in video games do things" and "AI = complex scientific models that have been in use for years" and "AI = non-generative tools that automate tedious processes" and "AI = generative tools" are all called AI feels like deliberate obfuscation.

I want good AI in video games but I also don't want AI in video games at all and I think AI is useful in the sciences but don't trust AI at all to be used in science or medicine or law and AI to colour-correct a video or remove greenscreen is cool but AI generating movies sucks.

We talk about things that threaten art and creativity and steal vast quantities of work from artists and burn the amazon and drain the seas to do it all with the same language as something that can track a part of a video so special effects are easier to make and that sucks.

And this obfuscation feels so deliberate to me, because now people freak out if someone talks about wanting to use "AI that learns from the player" or "an AI solution to help me edit video" or "an AI that can be used to generate theoretical materials to test" and they look silly.

Because now "AI" is so firmly wrapped up with the concept of "generative models" that people are, rightfully, on guard against any mention of AI whatsoever to the point where entirely distinct technologies get uselessly criticized under the same umbrella.

Like imagine if we had no other language but "tank" to describe motor vehicles, so if I said we needed "public transit vehicles" everyone thought I wanted M1A1 Abrams for civilian transit. That's what it's like talking about AI.

End ID]]

[neturbizenterprises]

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Note: This video contains an affiliate link - where if you click on it, there is a possibility that the Manufacturer may provide me a commission.

Whether you're a seasoned professional or just starting out, Leonardo AI is here to revolutionize your creative process. Simply put, Leonardo AI is creativity unleashed. Leverage your generative AI creativity with a unique suite of tools to convey your ideas to the world. Leonardo AI hosts a community of creations while offering generative AI utilities that inspire creative minds in various fields such as character design, game assets, concept art, graphic design, fashion marketing advertising, product photography architecture interior design and so much more!

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[alchemiclee]

if I had money, I would pay someone to rewrite heaven official's blessing, but lesbian. if I was rich, i'd pay someone to turn it into a whole manga

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[river-taxbird]

There is no such thing as AI.

How to help the non technical and less online people in your life navigate the latest techbro grift.

I've seen other people say stuff to this effect but it's worth reiterating. Today in class, my professor was talking about a news article where a celebrity's likeness was used in an ai image without their permission. Then she mentioned a guest lecture about how AI is going to help finance professionals. Then I pointed out, those two things aren't really related.

The term AI is being used to obfuscate details about multiple semi-related technologies.

Traditionally in sci-fi, AI means artificial general intelligence like Data from star trek, or the terminator. This, I shouldn't need to say, doesn't exist. Techbros use the term AI to trick investors into funding their projects. It's largely a grift.

What is the term AI being used to obfuscate?

If you want to help the less online and less tech literate people in your life navigate the hype around AI, the best way to do it is to encourage them to change their language around AI topics.

By calling these technologies what they really are, and encouraging the people around us to know the real names, we can help lift the veil, kill the hype, and keep people safe from scams. Here are some starting points, which I am just pulling from Wikipedia. I'd highly encourage you to do your own research.

Machine learning (ML): is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. (This is the basis of most technologically people call AI)

Language model: (LM or LLM) is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. (This would be your ChatGPT.)

Generative adversarial network (GAN): is a class of machine learning framework and a prominent framework for approaching generative AI. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. (This is the source of some AI images and deepfakes.)

Diffusion Models: Models that generate the probability distribution of a given dataset. In image generation, a neural network is trained to denoise images with added gaussian noise by learning to remove the noise. After the training is complete, it can then be used for image generation by starting with a random noise image and denoise that. (This is the more common technology behind AI images, including Dall-E and Stable Diffusion. I added this one to the post after as it was brought to my attention it is now more common than GANs.)

I know these terms are more technical, but they are also more accurate, and they can easily be explained in a way non-technical people can understand. The grifters are using language to give this technology its power, so we can use language to take it's power away and let people see it for what it really is.

[deorwineinfotech]

The topic of whether humans or AI are better at creating content depends on a number of factors, including the context in which the content is created. In this aspect, both humans and AI have advantages and disadvantages. Let’s look at the benefits of each:

[k0libra]

Confusion

(k0Libra ramblings are under the cut)

Did you know that if you incorrectly set up LLM it will generate text without the user's input, infinitely "talking" to itself? That's the sole goal of LLM - to generate text, but this behaviour really showcases that modern "AI" has no idea that it's even talking to someone.

I doubt that androids in D:BH run the same "AI" that we have now because that would undermine the game's narrative. I'm inclined to think that their AI is engineered by replicating the human brain in machine form. I'm thinking that also because thirium was essential for android creation, for some reason it was impossible to create them with conventional computational machines. It makes sense, I suppose, since we don't have enough power to recreate brains, even now. 

This brings a very interesting point: humans played god again with something they don't understand fully - the human brain. There's a high probability that we'll never figure out how it works. That makes deviancy somewhat expected; how can you control something when you don't know how it works? 

For me, cases of critical malfunction in software and hardware are very interesting topics, so I decided to paint this type of idea anyway.

[mostlysignssomeportents]

Harpercollins wants authors to sign away AI training rights

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/11/18/rights-without-power/#careful-what-you-wish-for

Rights don't give you power. People with power can claim rights. Giving a "right" to someone powerless just transfers it to someone more powerful than them. Nowhere is this more visible than in copyright fights, where creative workers are given new rights that are immediately hoovered up by their bosses.

It's not clear whether copyright gives anyone the right to control whether their work is used to train an AI model. It's very common for people (including high ranking officials in entertainment companies, and practicing lawyers who don't practice IP law) to overestimate their understanding of copyright in general, and their knowledge of fair use in particular.

Here's a hint: any time someone says "X can never be fair use," they are wrong and don't know what they're talking about (same goes for "X is always fair use"). Likewise, anyone who says, "Fair use is assessed solely by considering the 'four factors.'" That is your iron-clad sign that the speaker does not understand fair use:

https://pluralistic.net/2024/06/27/nuke-first/#ask-questions-never

But let's say for the sake of argument that training a model on someone's work is a copyright violation, and so training is a licensable activity, and AI companies must get permission from rightsholders before they use their copyrighted works to train a model.

Even if that's not how copyright works today, it's how things could work. No one came down off a mountain with two stone tablets bearing the text of 17 USC chiseled in very, very tiny writing. We totally overhauled copyright in 1976, and again in 1998. There've been several smaller alterations since.

We could easily write a new law that requires licensing for AI training, and it's not hard to imagine that happening, given the current confluence of interests among creative workers (who are worried about AI pitchmen's proclaimed intention to destroy their livelihoods) and entertainment companies (who are suing many AI companies).

Creative workers are an essential element of that coalition. Without those workers as moral standard-bearers, it's hard to imagine the cause getting much traction. No one seriously believes that entertainment execs like Warner CEO David Zaslav actually cares about creative works – this is a guy who happily deletes every copy of an unreleased major film that had superb early notices because it would be worth infinitesimally more as a tax-break than as a work of art:

https://collider.com/coyote-vs-acme-david-zaslav-never-seen/

The activists in this coalition commonly call it "anti AI." But is it? Does David Zaslav – or any of the entertainment execs who are suing AI companies – want to prevent gen AI models from being used in the production of their products? No way – these guys love AI. Zaslav and his fellow movie execs held out against screenwriters demanding control over AI in the writers' room for 148 days, and locked out their actors for another 118 days over the use of AI to replace actors. Studio execs forfeited at least $5 billion in a bid to insist on their right to use AI against workers:

https://sites.lsa.umich.edu/mje/2023/12/06/a-deep-dive-into-the-economic-ripples-of-the-hollywood-strike/

Entertainment businesses love the idea of replacing their workers with AI. Now, that doesn't mean that AI can replace workers: just because your boss can be sold an AI to do your job, it doesn't mean that the AI he buys can actually do your job:

https://pluralistic.net/2024/07/25/accountability-sinks/#work-harder-not-smarter

So if we get the right to refuse to allow our work to be used to train a model, the "anti AI" coalition will fracture. Workers will (broadly) want to exercise that right to prevent AI models from being trained at all, while our bosses will want to exercise that right to be sure that they're paid for AI training, and that they can steer production of the resulting model to maximize the number of workers than can fire after it's done.

Hypothetically, creative workers could simply say to our bosses, "We will not sell you this right to authorize or refuse AI training that Congress just gave us." But our bosses will then say, "Fine, you're fired. We won't hire you for this movie, or record your album, or publish your book."

Given that there are only five major publishers, four major studios, three major labels, two ad-tech companies and one company that controls the whole ebook and audiobook market, a refusal to deal on the part of a small handful of firms effectively dooms you to obscurity.

As Rebecca Giblin and I write in our 2022 book Chokepoint Capitalism, giving more rights to a creative worker who has no bargaining power is like giving your bullied schoolkid more lunch money. No matter how much lunch money you give that kid, the bullies will take it and your kid will remain hungry. To get your kid lunch, you have to clear the bullies away from the gate. You need to make a structural change:

https://chokepointcapitalism.com/

Or, put another way: people with power can claim rights. But giving powerless people more rights doesn't make them powerful – it just transfers those rights to the people they bargain against.

Or, put a third way: "just because you're on their side, it doesn't follow that they're on your side" (h/t Teresa Nielsen Hayden):

https://pluralistic.net/2024/10/19/gander-sauce/#just-because-youre-on-their-side-it-doesnt-mean-theyre-on-your-side

Last month, Penguin Random House, the largest publisher in the history of human civilization, started including a copyright notice in its books advising all comers that they would not permit AI training with the material between the covers:

https://pluralistic.net/2024/10/19/gander-sauce/#just-because-youre-on-their-side-it-doesnt-mean-theyre-on-your-side

At the time, people who don't like AI were very excited about this, even though it was – at the utmost – a purely theatrical gesture. After all, if AI training isn't fair use, then you don't need a notice to turn it into a copyright infringement. If AI training is fair use, it remains fair use even if you add some text to the copyright notice.

But far more important was the fact that the less that Penguin Random House pays its authors, the more it can pay its shareholders and executives. PRH didn't say it wouldn't sell the right to train a model to an AI company – they only said that an AI company that wanted to train a model on its books would have to pay PRH first. In other words, just because you're on their side, it doesn't follow that they're on your side.

When I wrote about PRH and its AI warning, I mentioned that I had personally seen one of the big five publishers hold up a book because a creator demanded a clause in their contract saying their work wouldn't be used to train an AI.

There's a good reason you'd want this in your contract; the standard contracting language contains bizarrely overreaching language seeking "rights in all media now know and yet to be devised throughout the universe":

https://pluralistic.net/2022/06/19/reasonable-agreement/

But the publisher flat-out refused, and the creator fought and fought, and in the end, it became clear that this was a take-it-or-leave-it situation: the publisher would not include a "no AI training" clause in the contract.

One of the big five publishers is Rupert Murdoch's Harpercollins. Murdoch is famously of the opinion that any kind of indexing or archiving of the work he publishes must require a license. He even demanded to be paid to have his newspapers indexed by search engines:

https://www.inquisitr.com/46786/epic-win-news-corp-likely-to-remove-content-from-google

No surprise, then, that Murdoch sued an AI company over training on Newscorp content:

https://www.theguardian.com/technology/2024/oct/25/unjust-threat-murdoch-and-artists-align-in-fight-over-ai-content-scraping

But Rupert Murdoch doesn't oppose the material he publishes from being used in AI training, nor is he opposed to the creation and use of models. Murdoch's Harpercollins is now pressuring its authors to sign away their rights to have their works used to train an AI model:

https://bsky.app/profile/kibblesmith.com/post/3laz4ryav3k2w

The deal is not negotiable, and the email demanding that authors opt into it warns that AI might make writers obsolete (remember, even if AI can't do your job, an AI salesman can convince Rupert Murdoch – who is insatiably horny for not paying writers – that an AI is capable of doing your job):

https://www.avclub.com/harpercollins-selling-books-to-ai-language-training

And it's not hard to see why an AI company might want this; after all, if they can lock in an exclusive deal to train a model on Harpercollins' back catalog, their products will exclusively enjoy whatever advantage is to be had in that corpus.

In just a month, we've gone from "publishers won't promise not to train a model on your work" to "publishers are letting an AI company train a model on your work, but will pay you a nonnegotiable pittance for your work." The next step is likely to be, "publishers require you to sign away the right to train a model on your work."

The right to decide who can train a model on your work does you no good unless it comes with the power to exercise that right.

Rather than campaigning for the right to decide who can train a model on our work, we should be campaigning for the power to decide what terms we contract under. The Writers Guild spent 148 days on the picket line, a remarkable show of solidarity.

But the Guild's real achievement was in securing the right to unionize at all – to create a sectoral bargaining unit that could represent all the writers, writing for all the studios. The achievements of our labor forebears, in the teeth of ruthless armed resistance, resulted in the legalization and formalization of unions. Never forget that the unions that exist today were criminal enterprises once upon a time, and the only reason they exist is because people risked prison, violence and murder to organize when doing so was a crime:

https://pluralistic.net/2024/11/11/rip-jane-mcalevey/#organize

The fights were worth fighting. The screenwriters comprehensively won the right to control AI in the writers' room, because they had power:

https://pluralistic.net/2023/10/01/how-the-writers-guild-sunk-ais-ship/

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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CC BY-SA 2.0 https://creativecommons.org/licenses/by-sa/2.0/deed.en

[reasonsforhope]

"As a Deaf man, Adam Munder has long been advocating for communication rights in a world that chiefly caters to hearing people. 

The Intel software engineer and his wife — who is also Deaf — are often unable to use American Sign Language in daily interactions, instead defaulting to texting on a smartphone or passing a pen and paper back and forth with service workers, teachers, and lawyers. 

It can make simple tasks, like ordering coffee, more complicated than it should be. 

But there are life events that hold greater weight than a cup of coffee. 

Recently, Munder and his wife took their daughter in for a doctor’s appointment — and no interpreter was available. 

To their surprise, their doctor said: “It’s alright, we’ll just have your daughter interpret for you!” ...

That day at the doctor’s office came at the heels of a thousand frustrating interactions and miscommunications — and Munder is not isolated in his experience.

“Where I live in Arizona, there are more than 1.1 million individuals with a hearing loss,” Munder said, “and only about 400 licensed interpreters.”

In addition to being hard to find, interpreters are expensive. And texting and writing aren’t always practical options — they leave out the emotion, detail, and nuance of a spoken conversation. 

ASL is a rich, complex language with its own grammar and culture; a subtle change in speed, direction, facial expression, or gesture can completely change the meaning and tone of a sign. 

“Writing back and forth on paper and pen or using a smartphone to text is not equivalent to American Sign Language,” Munder emphasized. “The details and nuance that make us human are lost in both our personal and business conversations.”

His solution? An AI-powered platform called Omnibridge. 

“My team has established this bridge between the Deaf world and the hearing world, bringing these worlds together without forcing one to adapt to the other,” Munder said. 

Trained on thousands of signs, Omnibridge is engineered to transcribe spoken English and interpret sign language on screen in seconds...

“Our dream is that the technology will be available to everyone, everywhere,” Munder said. “I feel like three to four years from now, we're going to have an app on a phone. Our team has already started working on a cloud-based product, and we're hoping that will be an easy switch from cloud to mobile to an app.” ...

At its heart, Omnibridge is a testament to the positive capabilities of artificial intelligence. "

-via GoodGoodGood, October 25, 2024. More info below the cut!

To test an alpha version of his invention, Munder welcomed TED associate Hasiba Haq on stage. 

“I want to show you how this could have changed my interaction at the doctor appointment, had this been available,” Munder said. 

He went on to explain that the software would generate a bi-directional conversation, in which Munder’s signs would appear as blue text and spoken word would appear in gray. 

At first, there was a brief hiccup on the TED stage. Haq, who was standing in as the doctor’s office receptionist, spoke — but the screen remained blank. 

“I don’t believe this; this is the first time that AI has ever failed,” Munder joked, getting a big laugh from the crowd. “Thanks for your patience.”

After a quick reboot, they rolled with the punches and tried again.

Haq asked: “Hi, how’s it going?” 

Her words popped up in blue. 

Munder signed in reply: “I am good.” 

His response popped up in gray. 

Back and forth, they recreated the scene from the doctor’s office. But this time Munder retained his autonomy, and no one suggested a 7-year-old should play interpreter. 

Munder’s TED debut and tech demonstration didn’t happen overnight — the engineer has been working on Omnibridge for over a decade. 

“It takes a lot to build something like this,” Munder told Good Good Good in an exclusive interview, communicating with our team in ASL. “It couldn't just be one or two people. It takes a large team, a lot of resources, millions and millions of dollars to work on a project like this.” 

After five years of pitching and research, Intel handpicked Munder’s team for a specialty training program. It was through that backing that Omnibridge began to truly take shape...

“Our dream is that the technology will be available to everyone, everywhere,” Munder said. “I feel like three to four years from now, we're going to have an app on a phone. Our team has already started working on a cloud-based product, and we're hoping that will be an easy switch from cloud to mobile to an app.” 

In order to achieve that dream — of transposing their technology to a smartphone — Munder and his team have to play a bit of a waiting game. Today, their platform necessitates building the technology on a PC, with an AI engine. 

“A lot of things don't have those AI PC types of chips,” Munder explained. “But as the technology evolves, we expect that smartphones will start to include AI engines. They'll start to include the capability in processing within smartphones. It will take time for the technology to catch up to it, and it probably won't need the power that we're requiring right now on a PC.” 

At its heart, Omnibridge is a testament to the positive capabilities of artificial intelligence. 

But it is more than a transcription service — it allows people to have face-to-face conversations with each other. There’s a world of difference between passing around a phone or pen and paper and looking someone in the eyes when you speak to them. 

It also allows Deaf people to speak ASL directly, without doing the mental gymnastics of translating their words into English.

“For me, English is my second language,” Munder told Good Good Good. “So when I write in English, I have to think: How am I going to adjust the words? How am I going to write it just right so somebody can understand me? It takes me some time and effort, and it's hard for me to express myself actually in doing that. This technology allows someone to be able to express themselves in their native language.” 

Ultimately, Munder said that Omnibridge is about “bringing humanity back” to these conversations. 

“We’re changing the world through the power of AI, not just revolutionizing technology, but enhancing that human connection,” Munder said at the end of his TED Talk. 

“It’s two languages,” he concluded, “signed and spoken, in one seamless conversation.”"

-via GoodGoodGood, October 25, 2024