#AI Platforms

[jcmarchi]

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

New Post has been published on https://thedigitalinsider.com/deepfake-misuse-deepfake-detection-before-its-too-late-cybertalk/

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

Micki Boland is a global cyber security warrior and evangelist with Check Point’s Office of the CTO. Micki has over 20 years in ICT, cyber security, emerging technology, and innovation. Micki’s focus is helping customers, system integrators, and service providers reduce risk through the adoption of emerging cyber security technologies. Micki is an ISC2 CISSP and holds a Master of Science in Technology Commercialization from the University of Texas at Austin, and an MBA with a global security concentration from East Carolina University.

In this dynamic and insightful interview, Check Point expert Micki Boland discusses how deepfakes are evolving, why that matters for organizations, and how organizations can take action to protect themselves. Discover on-point analyses that could reshape your decisions, improving cyber security and business outcomes. Don’t miss this.

Can you explain how deepfake technology works? 

Deepfakes involve simulated video, audio, and images to be delivered as content via online news, mobile applications, and through social media platforms. Deepfake videos are created with Generative Adversarial Networks (GAN), a type of Artificial Neural Network that uses Deep Learning to create synthetic content.

GANs sound cool, but technical. Could you break down how they operate?

GAN are a class of machine learning systems that have two neural network models; a generator and discriminator which game each other. Training data in the form of video, still images, and audio is fed to the generator, which then seeks to recreate it. The discriminator then tries to discern the training data from the recreated data produced by the generator.

The two artificial intelligence engines repeatedly game each other, getting iteratively better. The result is convincing, high quality synthetic video, images, or audio. A good example of GAN at work is NVIDIA GAN. Navigate to the website https://thispersondoesnotexist.com/ and you will see a composite image of a human face that was created by the NVIDIA GAN using faces on the internet. Refreshing the internet browser yields a new synthetic image of a human that does not exist.

What are some notable examples of deepfake tech’s misuse?

Most people are not even aware of deepfake technologies, although these have now been infamously utilized to conduct major financial fraud. Politicians have also used the technology against their political adversaries. Early in the war between Russia and Ukraine, Russia created and disseminated a deepfake video of Ukrainian President Volodymyr Zelenskyy advising Ukrainian soldiers to “lay down their arms” and surrender to Russia.

How was the crisis involving the Zelenskyy deepfake video managed?

The deepfake quality was poor and it was immediately identified as a deepfake video attributable to Russia. However, the technology is becoming so convincing and so real that soon it will be impossible for the regular human being to discern GenAI at work. And detection technologies, while have a tremendous amount of funding and support by big technology corporations, are lagging way behind.

What are some lesser-known uses of deepfake technology and what risks do they pose to organizations, if any?

Hollywood is using deepfake technologies in motion picture creation to recreate actor personas. One such example is Bruce Willis, who sold his persona to be used in movies without his acting due to his debilitating health issues. Voicefake technology (another type of deepfake) enabled an autistic college valedictorian to address her class at her graduation.

Yet, deepfakes pose a significant threat. Deepfakes are used to lure people to “click bait” for launching malware (bots, ransomware, malware), and to conduct financial fraud through CEO and CFO impersonation. More recently, deepfakes have been used by nation-state adversaries to infiltrate organizations via impersonation or fake jobs interviews over Zoom.

How are law enforcement agencies addressing the challenges posed by deepfake technology?

Europol has really been a leader in identifying GenAI and deepfake as a major issue. Europol supports the global law enforcement community in the Europol Innovation Lab, which aims to develop innovative solutions for EU Member States’ operational work. Already in Europe, there are laws against deepfake usage for non-consensual pornography and cyber criminal gangs’ use of deepfakes in financial fraud.

What should organizations consider when adopting Generative AI technologies, as these technologies have such incredible power and potential?

Every organization is seeking to adopt GenAI to help improve customer satisfaction, deliver new and innovative services, reduce administrative overhead and costs, scale rapidly, do more with less and do it more efficiently. In consideration of adopting GenAI, organizations should first understand the risks, rewards, and tradeoffs associated with adopting this technology. Additionally, organizations must be concerned with privacy and data protection, as well as potential copyright challenges.

What role do frameworks and guidelines, such as those from NIST and OWASP, play in the responsible adoption of AI technologies?

On January 26th, 2023, NIST released its forty-two page Artificial Intelligence Risk Management Framework (AI RMF 1.0) and AI Risk Management Playbook (NIST 2023). For any organization, this is a good place to start.

The primary goal of the NIST AI Risk Management Framework is to help organizations create AI-focused risk management programs, leading to the responsible development and adoption of AI platforms and systems.

The NIST AI Risk Management Framework will help any organization align organizational goals for and use cases for AI. Most importantly, this risk management framework is human centered. It includes social responsibility information, sustainability information and helps organizations closely focus on the potential or unintended consequences and impact of AI use.

Another immense help for organizations that wish to further understand risk associated with GenAI Large Language Model adoption is the OWASP Top 10 LLM Risks list. OWASP released version 1.1 on October 16th, 2023. Through this list, organizations can better understand risks such as inject and data poisoning. These risks are especially critical to know about when bringing an LLM in house.

As organizations adopt GenAI, they need a solid framework through which to assess, monitor, and identify GenAI-centric attacks. MITRE has recently introduced ATLAS, a robust framework developed specifically for artificial intelligence and aligned to the MITRE ATT&CK framework.

For more of Check Point expert Micki Boland’s insights into deepfakes, please see CyberTalk.org’s past coverage. Lastly, to receive cyber security thought leadership articles, groundbreaking research and emerging threat analyses each week, subscribe to the CyberTalk.org newsletter.

[techdriveplay]

What Is the Best Way to Use AI in Content Creation?

Artificial Intelligence (AI) has transformed various industries, and content creation is no exception. By understanding what is the best way to use AI in content creation, creators can leverage this technology to enhance productivity, quality, and creativity. From automated writing tools to data analysis, AI offers diverse applications that can streamline the content production process, ensuring…

[cbrownjc]

I hope this is true. Because it would just be so in line with deciding to be so cartoonishly evil in public right before SAG was set to go on strike as well. A union that really has the power to shut down the whole industry and make them lose astronomical amounts of money per day.

All that Deadline article probably did was make SAG more emboldened to get everything they're asking for and not extend the deadline again.

[everythingwaschocolateandsugar]

Revolutionizing Industries with AI: An Overview of the Market Landscape

Artificial Intelligence (AI) is revolutionizing the way businesses operate across various industries. The market landscape for AI solutions is growing at an unprecedented pace, and it is estimated to reach a value of $190 billion by 2025. In this article, we will provide an overview of how AI is revolutionizing industries and the market landscape for AI solutions.

Healthcare

AI is playing a critical role in the healthcare industry, from diagnostics to drug development. With AI, healthcare professionals can analyze patient data more accurately, identify patterns, and develop personalized treatment plans. AI-powered devices can also monitor patients remotely, reducing the burden on healthcare professionals and improving patient outcomes. AI-powered drug discovery solutions can also accelerate the drug development process and improve the accuracy of clinical trials.

Finance

AI is transforming the finance industry, from fraud detection to customer service. AI-powered solutions can analyze large amounts of data in real-time, enabling financial institutions to detect and prevent fraud quickly. AI-powered chatbots and virtual assistants can also improve customer experience by providing personalized financial advice and support. AI-powered trading algorithms can also help financial institutions make more informed investment decisions.

Request for Sample PDF: https://www.nextmsc.com/artificial-intelligence-market/request-sample

Retail

AI is revolutionizing the retail industry by providing personalized recommendations, improving inventory management, and enhancing the customer experience. With AI, retailers can analyze customer data to understand their preferences and shopping behavior, providing personalized recommendations and improving customer satisfaction. AI-powered inventory management systems can also help retailers optimize their inventory levels, reducing waste and increasing profitability. AI-powered chatbots and virtual assistants can also improve the customer experience by providing personalized support and assistance.

Manufacturing

AI is transforming the manufacturing industry by improving production efficiency, reducing downtime, and enhancing product quality. With AI, manufacturers can analyze production data in real-time, identifying inefficiencies and areas for improvement. AI-powered predictive maintenance solutions can also identify potential equipment failures before they occur, reducing downtime and improving production efficiency. AI-powered quality control solutions can also improve product quality by identifying defects and anomalies in real-time.

Market Landscape for AI Solutions

The market landscape for AI solutions is vast and diverse, with various players offering AI solutions across industries. The market is dominated by tech giants such as Google, Microsoft, and Amazon, who offer a wide range of AI-powered solutions across various industries. However, there are also numerous startups and smaller players offering specialized AI solutions for specific industries and use cases.

Conclusion

AI is revolutionizing industries across the board, from healthcare to finance to retail to manufacturing. The market landscape for AI solutions is growing at an unprecedented pace, with numerous players offering AI solutions across various industries. As AI technology continues to evolve, it is important for businesses to stay informed and adapt to the changing landscape. With the right AI solutions, businesses can improve efficiency, reduce costs, and enhance the customer experience, leading to increased profitability and growth.

[pillowfort-social]

fuck generative ai

[lilbittymonster]

I've seen several people lament over the past week that they aren't good at gpose lighting, so between that and recent discussions of a certain prominent pro-AI gposer, I thought I'd throw something together about how I do my lighting in gpose. Hopefully this'll be helpful to some of you!

My go to setup for lighting is two rim lights sandwiching my subject. This is a bit of an extreme example, but you can clearly see where I dropped the red and the blue lights versus how they frame her in the actual shot that I wanted.

For something a little less extreme I still try to get a good profile light on the side of her face/body that's the furthest away from the camera, usually the side that's going to be in the most shadow. Just enough light to brush up and reflect off of her to make the line between her and the background a little clearer.

And this is especially necessary when hair colour matches the background colour, like when I want to take pictures of Vaisha with their pitch black hair at night.

This one is a bit more on the extreme side, but I was also making a themed shot to go with an ask. The important part is highlighting the part of their face that's deeper in shadow so the colour can reflect off of it and define it against the dark background.

Even with brightly lit subjects and brightly lit areas, you're still going to get some weird shadows, so having a couple rim lights to fill in the gaps makes a shot look more rounded. Here's a recent glamtober shot taken in broad daylight, before and after I dropped a yellow and pink light on either side of her.

While the shot on the left is fine on it's own, the shadow of the chair behind her, the shadow of the skirt, and the shadow cast on the right side of her hair pull away from the rest of the daylight. The bright yellow of the chair cushion is a little jarring against the duller purple of the dress. By adding the pink light off to her right, and a golden light behind her to the left, the rest of the picture warms back up and is tinted a little more to the pink side and makes everything look a little more unified.

I also will use lighting to either amplify preexisting light sources or mimic natural light sources.

For example, this alpine lamp is not actually very bright. So what I did was swing the camera around until the lamp filled my field of view and then put a gold light right on top of it to cast a soft backlight over the two of them on the couch.

Or mimicking and enhancing the light of the setting sun here. I dropped a red-gold light off some distance opposite of the camera, where the sun would be coming in, to better reflect off of Kitali's face.

Another example is putting down a very light pink light on the sand to mimic the reflected sunlight. You can just barely see it on the outline of their pants, but it was enough to light the underside of Estinien's face for the final shot.

A more subtle example is placing a pale blue glow to her right to match the glow of the Fae spear, and a soft orange light to her left to match the glowing tree mushrooms. This one even comes with free complementary colours!

A couple more examples of enhancing natural and object light.

A bright blue light dropped at the tip of the spear to enhance it's glow.

Red reflecting off of Nidhogg's Eye.

A strong orange to her right to mimic the setting sun.

A cool blue from above and to her left to strengthen the moonlight.

And sometimes I just start throwing in colours for fun, or to play with colour theory for some extra pop.

And if you want to get funky you can throw down a pride flag!

TL;DR is: wrapping your subjects with light rather than aiming a single point directly at them will help round out the shot, strengthening the natural lighting can help enhance a shot without making it look overproduced, and adding points of complementary colour adds interest and breaks up monochromatic colour palettes.

Most importantly, have fun with it!

(And if you found this helpful I'd appreciate it if you could reblog!)

[bixels]

After the enormous boost in users and popularity Tumblr received post-Twitter scare, Staff decided it wasn't healthy to stay at the top for so long (altitude sickness) and have been hard at work blowing their own feet up with sticks of dynamite to return to the good ol' days of post-NSFW ban. Godspeed, Staff. May you beat Elon and his blasting crew at reducing your respective sites to a smoldering crater.

[lanlishiba]

Equestria Twilight pls give me strength

[jcmarchi]

Matthew Ikle, Chief Science Officer at SingularityNet – Interview Series

New Post has been published on https://thedigitalinsider.com/matthew-ikle-chief-science-officer-at-singularitynet-interview-series/

Matthew Ikle, Chief Science Officer at SingularityNet – Interview Series

Matthew Ikle is the  Chief Science Officer at SingularityNET, a company founded with the mission of creating a decentralized, democratic, inclusive and beneficial Artificial General Intelligence. An ‘AGI’ that is not dependent on any central entity, that is open for anyone and not restricted to the narrow goals of a single corporation or even a single country.

SingularityNET team includes seasoned engineers, scientists, researchers, entrepreneurs, and marketers. Our core platform and AI teams are further complemented by specialized teams devoted to application areas such as finance, robotics, biomedical AI, media, arts and entertainment.

Given your extensive experience and role at SingularityNET, how confident are you that we will achieve AGI by 2029 or sooner, as predicted by Dr. Ben Goertzel?

I am going to answer this question in a bit of a roundabout way. 2029 is roughly five years from now. Many years ago (early-mid 2010s), I was extremely optimistic about AGI progress. My optimism at the time was founded on the level of detailed thought and convergence of ideas I witnessed in AGI research at the time. While most of the big ideas from that era, I believe, still hold promise, the difficulty, as is often the case, comes from fleshing out the details of such broad-stroke visions.

With that caveat in mind, there is now a plethora of new information, from numerous disciplines – neuroscience, mathematics, computer science, psychology, sociology, you name it – that provides not just the mechanisms for finishing those details, but also conceptually supports the foundations of that earlier work. I am seeing patterns, and in quite divergent fields, that all seem to me to be converging at an accelerating rate toward analogous sorts of behaviors. In many ways, this convergence reminds me of the period of time prior to the release of the first iPhone. To paraphrase Greg Meredith, who is working on our RhoLang infrastructure for safe concurrent processing, the patterns I see these days are related to origin stories – how did the first life/cell begin on earth? How and when did mind form? And related questions regarding phase transitions for example.

For example, there is quite a bit of new experimental research that tends to support the ideas underlying a complex dynamical systems viewpoint. EEG patterns of human subjects, for example, display remarkable behavior in alignment with such system dynamics. These results harken back to some much earlier work in consciousness theories. Now there appears to be the beginnings of experimental backup for those theoretical ideas.

At SingularityNET, I am thinking a lot about the self-similar structures that generate such dynamics. This is quite different, I would argue, than what is happening in much of the DNN/GPT community, though there is certainly recognition among certain more fundamental researchers of those ideas. I would point to the paper “Consciousness in Artificial Intelligence: Insights from the Science of Consciousness” released by 19 researchers in August of 2023, for example. The researchers spanned a variety of disciplines including consciousness studies, AI safety research, brain science, mathematics, computer science, psychology, neuroscience and neuroimaging, and mind and cognition research. What those researchers have in common is bigger than a simple quest for the next incremental architectural improvement in DNNs, but instead they are focused on scientifically understanding the big philosophical ideas underpinning human cognition and how to bring them to bear to implement real AGI systems.

What do you see as the biggest technological or philosophical hurdles to achieving AGI within this decade?

Understanding and answering big philosophical and scientific questions including:

What is life? We may think the answer is clear, but biological definitions have proven problematic. Are viruses “alive” for example.

What is mind?

What is intelligence?

How did life emerge from a few base chemicals in specific environmental conditions? How could we replicate this?

How did the first “mind” emerge? What ingredients and conditions enabled this?

How do we implement what we learn when investigating the above five questions?

Is our current technology up to the task of implementing our solutions? If not, what do we need to invent and develop?

How much time and personnel do we need to implement our solutions?

SingularityNET views neuro-symbolic AI as a promising solution to overcome the current limitations of generative AI. Could you explain what neuro-symbolic AI is and how SingularityNET plans to leverage this approach to accelerate the development of AGI?

Historically, there have been two main camps of AGI researchers, along with a third camp blending the ideas of the other two. There have been researchers who believe solely in a sub-symbolic approach. These days, this primarily means using deep neural networks (DNNs) such as Transformer models including the current crop of large language models (LLMs). Due to the use of artificial neural networks, sub-symbolic approaches are also called neural methods. In sub-symbolic systems processing is run across identical and unlabeled nodes (neurons) and links (synapses). Symbolic proponents use higher-order logic and symbolic reasoning, in which nodes and links are labeled with conceptual and semantic meaning. SingularityNET follows a third approach which would be most accurately described as a neuro-symbolic hybrid, leveraging the strengths of symbolic and sub-symbolic methods.

Yet it is a specific sort of hybrid largely based on Ben Goertzels’ patternist philosophy of mind and detailed in, among many other documents, his screed “The General Theory of General Intelligence: A Pragmatic Patternist Perspective”.

While much of current DNN and LLM research is based upon simplistic neural models and algorithms, the use of mammoth datasets (e.g. the entire internet), and correct settings of billions of parameters in the hopes of achieving AGI, SingularityNET’s PRIMUS strategy is based upon foundational understandings of dynamic processes at multiple spatio-temporal scales and how best to align such processes to prompt desired properties to emerge at different scales. Such understandings enable us to proceed to guide AGI research and development in a human understandable manner.

What frameworks do you believe are critical to ensure that AGI development benefits all of humanity? How can decentralized AI platforms like SingularityNET promote a more equitable and transparent process compared to centralized AI models?

All kinds of ideas here:

Transparency — While nothing is perfect, ensuring complete transparency of the decision-making process can help everyone involved (researchers, developers, users, and non-users alike) align, guide, understand, and better handle AGI development for the benefit of humanity. This is similar to the problem of bias which I will touch on below.

Decentralization – While decentralization can be messy, it can help ensure that power is shared more broadly. It is not, in itself, a panacea, but a tool that, if used correctly, can help create more equitable processes and results.

Consensus-based decision-making – decentralization and consensus-based decision making can work together in the pursuit of more equitable processes and results. Again, they don’t always guarantee equity. There are also complexities that need to be addressed here in terms of reputation and areas of expertise. For example, how can we best balance conflicting desired characteristics? I view transparency, decentralization, and consensus-based decision-making, as just three critically important tools that can be used to guide AGI development for the benefit of humanity.

Spatiotemporal alignment of emergent phenomena across multiple scales from the extraordinarily small to the inordinately large. In developing AGI, I believe it is important to not just rely on a single “black-box” approach in which one hopes to get everything correct at the outset. Instead, I believe designing AGI with fundamental understandings at various development stages and at multiple scales can not only make it more likely to achieve AGI, but more importantly to guide such development in alignment with human values.

SingularityNET is a decentralized AI platform. How do you envision the intersection of blockchain technology and AGI evolving, particularly regarding security, governance, and decentralized control?

Blockchain certainly has a role to play in AI control, security, and governance. One of blockchain’s biggest strengths is its ability to foster transparency. The question of bias is a great example of this. I would argue that every person and every dataset is biased. I have my own personal biases, for example, when it comes to what I believe is required to achieve truly safe, beneficial, and benevolent AGI. These biases were forged by my studies and background and they guide my own work.

At the same time, I try to be completely open to ideas that conflict with my biases and am willing to adjust my biases based upon new evidence. Regardless, I try my best to be open and transparent with respect to my biases, and to then condition my ideas and decisions based upon a self-reflective understanding of those biases. It is tricky, it is difficult but, I believe, better than not acknowledging one’s own biases. By its nature, blockchain allows for better and transparent tracking, tracing, and verification of processes and events. In a similar manner as I described previously, transparency is a necessary, but not always sufficient, component for security, governance, and decentralized control.

How blockchain and AGI co-evolve is an interesting question. In order that the two technologies interact toward a positive singularity, it seems clear that the fundamental characteristics I keep pointing at (transparency, decentralization, consensus, and values alignment), are central and critical and must be kept in mind at all stages of their co-evolution.

As a leader who has been closely involved in both AI and blockchain, what do you believe are the most important factors for fostering collaboration between these two fields, and how can that drive innovation in AGI?

I come from the AI/AGI side of that pair. As is often the case when integrating cross-disciplinary ideas, much comes down to matters of language and communication. All groups need to listen to each other in order to better understand how the technologies can help one another. In my job at SingularityNET, this has been a constant struggle. High-end researchers, which it would be an understatement to say that SingularityNET has in abundance, often have clear mental conceptions of big ideas. When working across disciplinary boundaries, the difficult part is realizing that not everyone is “in your head”. What one takes for granted, will not be so clearly observed from those in other fields. Even words used in common can be used differently across different fields of study. There was a recent case in our BioAI work, in which biologists were using a mathematical term, but not entirely correctly in terms of its mathematical definition. Once those sorts of situations are clearly understood, the team can move forward with common purpose so that the integration truly proves the whole greater than the sum of its parts.

How do you see the AI and blockchain industries working towards greater diversity and inclusion, and what role does SingularityNET play in promoting these values?

AI and blockchain can both play major roles in improving diversification and inclusion efforts. Although I believe it is impossible to remove all bias – many biases form simply through life experiences – one can be open and transparent about one’s biases. This is something I actively strive to do in my own work which is biased by my academic background so that I see problems through a lens of complex system dynamics. Yet I still strive to be open to and understand ideas and analogies from other perspectives. AI can be harnessed to aid in this self-reflection process, and blockchain can certainly aid with transparency. SingularityNET can play a huge role by hosting tools for detecting, measuring, and removing, as much as is possible, biases in datasets.

How does SingularityNET’s work in decentralized AI ecosystems contribute to solving global challenges such as sustainability, education, and job creation, especially in regions like Africa, where you have a special interest?

 Sustainability:

Applying AI and system models to solve complex ecosystem problems at massive scale.

Monitoring such solutions at scale.

Using blockchain to track, trace, and verify such solutions.

Using a combination of AI, ecosystem models, hyper-local data, and blockchain, we have ideated complete solutions to artisanal mining in Africa, and agricultural carbon sequestration at scale.

Education:

As a former tenured full professor of mathematics and computer science, education is extremely important to me, especially as it provides opportunities to underserved student populations. It is important to:

Enhance accessibility by developing hybrid courses to reach students who may face geographical, financial, or time constraints.

Promote diversity and Inclusion by Increasing the participation of underserved populations in AI, blockchain, and other advanced technologies.

Foster interdisciplinary knowledge by creatin courses that bridge academic and professional fields.

Support career advancement by providing skills and certifications that are directly applicable to the job market.

I view both AGI and blockchain, and their synergies, as playing critical roles addressing the above objectives within “apprenticeship to mastery” style programs centered upon hands-on project-based learning.

Job Creation:

By fostering the four educational objectives above, it seems to me AGI, blockchain, and other advanced technologies, coupled with positive collaborations among teachers and learners, could encourage and spawn entire new technologies and businesses.

As someone committed to achieving a positive singularity, what specific milestones or breakthroughs in AI technology do you believe will be necessary to ensure that AGI develops in a beneficial way for society?

Ability to align emergent phenomena in human interpretable manners across multiple spatiotemporal scales.

Ability to understand at a deeper level the concepts underlying “spontaneous” phase transitions.

Ability to overcome multiple hard problems at a fine detail to enable true multi-processing through state superpositions.

Transparency at all stages.

Decentralized decision-making based upon consensus building.

Thank you for the great interview, readers who wish to learn more should visit SingularityNET.

[dutchs-blog]

American Dad Ai Genarated

[burnyourtrains]

Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art Stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art stop stealing art

This means reposting without permission. Feeding into ai. Taking as your own. Not giving credit to the artist. Posting things that aren't yours in the first place. Etc.

This goes for fanart, original pieces from the artist, writing, gifsets - anything someone took the time to make.

Stop stealing art

[thatgirlonstage]

The thing about tumblr is that there’s a panic about how the site is dying and falling apart literally every other week so eventually if you’ve been here long enough you just get zen about it. Like if anything specific is your breaking point do whatever you want but personally they’re gonna have to pry me out of the vents like a feral raccoon before I leave. Anyway if you’re new here and you see people talk about how something is the end of tumblr and you’re afraid they’re correct I just want you to know I’ve been here through probably like 300 ends of tumblr. I’m not saying it will never happen for real but statistically I remain skeptical.

[screecherofthenight]

Guys. GUYS. I was thinking about a Wolf 359 social media au and I came to an earth shattering realization. Kepler is a story time youtuber. he’s a fucking STORYTIME YOUTUBER. I’m having a moment.

[milunessence]

Seya O

[pillowfort-social]