#Generative AI technologies

[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.

[meanya]

I saw a post before about how hackers are now feeding Google false phone numbers for major companies so that the AI Overview will suggest scam phone numbers, but in case you haven't heard,

PLEASE don't call ANY phone number recommended by AI Overview

unless you can follow a link back to the OFFICIAL website and verify that that number comes from the OFFICIAL domain.

My friend just got scammed by calling a phone number that was SUPPOSED to be a number for Microsoft tech support according to the AI Overview

It was not, in fact, Microsoft. It was a scammer. Don't fall victim to these scams. Don't trust AI generated phone numbers ever.

[ahex-technologies]

Empowering Developers: A Beginner's Guide to Generative AI Development

Introduction:

The field of Generative Artificial Intelligence (AI) is experiencing rapid growth, presenting developers with ample opportunities to innovate across various domains. Whether it's generating images, music, or natural language, generative AI opens up a plethora of creative possibilities and solutions. In this guide, we'll offer developers a comprehensive overview to kickstart their journey into generative AI software, covering crucial concepts, tools, and available resources.

Understanding Generative AI:

Generative AI encompasses algorithms and models aimed at creating new data resembling existing samples. Unlike traditional AI systems focused on classification or prediction, generative AI endeavors to generate novel content by learning underlying patterns and structures from training data. This includes techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, each with unique strengths and applications.

Getting Started:

To embark on generative AI development, a strong foundation in machine learning and deep learning is essential. Proficiency in neural networks, optimization algorithms, and popular frameworks such as TensorFlow, PyTorch, or Keras is crucial. Additionally, grasping probability theory, statistics, and linear algebra aids in understanding the mathematical principles behind generative AI models.

Choosing a Framework:

After mastering the basics, selecting a deep learning framework aligned with project requirements is paramount. TensorFlow and PyTorch stand out as the most commonly used frameworks for generative AI development, offering extensive libraries and community support. Experimenting with both frameworks will help identify the best fit for workflow and coding preferences.

Exploring Generative AI Models:

Delve into the realm of generative models to understand various architectures and techniques. Starting with simpler models like Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs) before progressing to advanced architectures like Transformers for natural language processing tasks is advisable. Implementing these models from scratch enhances understanding of their workings and effective training methodologies.

Accessing Pretrained Models:

In addition to building models from scratch, leveraging pretrained generative models from open-source repositories and model zoos is recommended. These pretrained models serve as robust starting points for projects, facilitating fine-tuning on specific datasets or transfer learning tasks. Notable pretrained models include StyleGAN for image generation and GPT for natural language generation.

Experimenting with Creative Projects:

Once proficient with fundamentals, unleash creativity by experimenting with generative AI in personal projects. Whether it's generating art, music, stories, or virtual environments, there are boundless avenues to explore. Starting small with simple projects and gradually tackling more ambitious ideas fosters confidence and expertise.

Joining the Community:

Remember, you're not alone in this journey. Engage with online communities, forums, and meetups dedicated to generative AI development to connect with peers, exchange ideas, and learn from shared experiences. Collaborating with mentors and peers accelerates learning and inspires pushing the boundaries of generative AI.

Conclusion:

Generative AI development presents developers with a unique blend of creativity and technology. By mastering core concepts, selecting appropriate tools and frameworks, and embarking on creative projects, developers can unlock generative AI's full potential to innovate in the digital realm. So, dive in, get creative, and let generative AI propel your imagination to new heights! For more details, visits Ahex Technologies

[prokopetz]

I suppose the thin silver lining to the drive to replace human customer service workers with generative AI is that the predatory robocall scammers no longer seem to have enough human operators to close the deal, so even if you play along, the robot just claims it's "transferring" you and then hangs up.

[incognitopolls]

Anon's explanation:

I’m curious because I see a lot of people claiming to be anti-AI, and in the same post advocating for the use of Glaze and Artshield, which use DiffusionBee and Stable Diffusion, respectively. Glaze creates a noise filter using DiffusionBee; Artshield runs your image through Stable Diffusion and edits it so that it reads as AI-generated. You don’t have to take my work for it. Search for DiffusionBee and Glaze yourself if you have doubts. I’m also curious about machine translation, since Google Translate is trained on the same kinds of data as ChatGPT (social media, etc) and translation work is also skilled creative labor, but people seem to have no qualms about using it. The same goes for text to speech—a lot of the voices people use for it were trained on professional audiobook narration, and voice acting/narration is also skilled creative labor. Basically, I’m curious because people seem to regard these types of gen AI differently than text gen and image gen. Is it because they don’t know? Is it because they don’t think the work it replaces is creative? Is it because of accessibility? (and, if so, why are other types of gen AI not also regarded as accessibility? And even then, it wouldn’t explain the use of Glaze/Artshield)

Additional comments from anon:

I did some digging by infiltrating (lurking in) pro-AI spaces to see how much damage Glaze and other such programs were doing. Unfortunately, it turns out none of those programs deter people from using the ‘protected’ art. In fact, because of how AI training works, they may actually result in better output? Something about adversarial training. It was super disappointing. Nobody in those spaces considers them even a mild deterrent anywhere I looked. Hopefully people can shed some light on the contradictions for me. Even just knowing how widespread their use is would be informative. (I’m not asking about environmental impact as a factor because I read the study everybody cited, and it wasn’t even anti-AI? It was about figuring out the best time of day to train a model to balance solar power vs water use and consumption. And the way they estimated the impact of AI was super weird? They just went with 2020’s data center growth rate as the ‘normal’ growth rate and then any ‘extra’ growth was considered AI. Maybe that’s why it didn’t pass peer review... But since people are still quoting it, that’s another reason for me to wonder why they would use Glaze and Artshield and everything. That’s why running them locally has such heavy GPU requirements and why it takes so long to process an image if you don’t meet the requirements. It’s the same electricity/water cost as generating any other AI image.)

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We ask your questions anonymously so you don’t have to! Submissions are open on the 1st and 15th of the month.

[shutter-pan]

[warakami-vaporwave]

BSOD v2 Ocean Waves

[tea-knight]

[aq2003]

nobody understands the robot revolution like i do it's not about "AI bad" it's about how AI has no inherent morality to it as a technology and it reflects human bias/greed/entitlement i am sorry

[mad-girlslove-song]

the shittiest human art will always be leagues better than the best ai "art". a child's finger paintings and macaroni crafts will always be better than a computer's subpar attempt at recreating the starry night. your stick figures and smiley faces will always surpass an algorithm's bastardized boticelli painting. the most mediocre hallmark movie will always be better than whatever bullshit sora churns out. the most cringeworthy "i'm 14 and this is deep" notes app poetry will always be better than whatever chatgpt can come up with. always

[plasmalink]

I just... Really hate all the techbros who are saying shit like "hey artists you better watch your days, we'll automate creativity and then its over for you guys!", not just for the obvious reason of how fucked up that world view is, but because it's heavily souring my genuine childlike wonder at how cool this technology is.

Like, take a step back, forget all the discourse we've been forced to have, and just think. We can tell a robot "gender reveal 9/11" and it can make an image of blue and pink exploding towers. That on its own is fucking insane?

And the actual like, algorithms and math that went into it too! We make robots that teach robots to teach themselves how to do stuff. Then just stick that shit in the microwave for a while and boom. Or just tell it "Here's the sum of all knowledge on earth, figure it out".

And I especially love those early AI generated images where any time you try and focus on something it falls apart, but it almost makes sense when you see if from the corner of your eye. That type of image, I don't know how to describe it fully, but it makes me think of when I'm trying to remember a dream as it's fading away in my head.

And these FUCKING chucklefucks decide "hey, let's use this cool as fuck technology in the worst possible way. let's get rid of those pesky people making a living doing what they love and get those shmucks back in an office building so they can get a real job. i'm going to be an asshole about it online and also pay $8 a month so maybe elon musk will notice me"

I'm so fucking tired, man. I want to be excited about new technology and shit but I just can't at this stage.

[morerichka8]

[naturewondr]

[incognitopolls]

For the purposes of this poll, research is defined as reading multiple non-opinion articles from different credible sources, a class on the matter, etc.– do not include reading social media or pure opinion pieces.

Fun topics to research:

Can AI images be copyrighted in your country? If yes, what criteria does it need to meet?

Which companies are using AI in your country? In what kinds of projects? How big are the companies?

What is considered fair use of copyrighted images in your country? What is considered a transformative work? (Important for fandom blogs!)

What legislation is being proposed to ‘combat AI’ in your country? Who does it benefit? How does it affect non-AI art, if at all?

How much data do generators store? Divide by the number of images in the data set. How much information is each image, proportionally? How many pixels is that?

What ways are there to remove yourself from AI datasets if you want to opt out? Which of these are effective (ie, are there workarounds in AI communities to circumvent dataset poisoning, are the test sample sizes realistic, which generators allow opting out or respect the no-ai tag, etc)

–

We ask your questions so you don’t have to! Submit your questions to have them posted anonymously as polls.

[shutter-pan]

[warakami-vaporwave]

BSOD v2 MtFuji v2