#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.

[nitor-infotech]

Watch our video to discover the trends of cloud computing.

[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.

[jcmarchi]

Centralised AI is dangerous: how can we stop it? - AI News

New Post has been published on https://thedigitalinsider.com/centralised-ai-is-dangerous-how-can-we-stop-it-ai-news/

Centralised AI is dangerous: how can we stop it? - AI News

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The intelligence displayed by generative AI chatbots like OpenAI’s ChatGPT has captured the imagination of individuals and corporations, and artificial intelligence has suddenly become the most exciting area of technology innovation.

AI has been recognised as a game changer, with potential to transform many aspects of our lives. From personalised medicine to autonomous vehicles, automated investments to digital assets, the possibilities enabled by AI seem endless.

But as transformational as AI will be, there are a lot of risks posed by this new technology. While fears about a malicious, Skynet-style AI system going rogue are misplaced, the dangers of AI centralisation are not. As companies like Microsoft, Google and Nvidia forge ahead in their pursuit of AI, fears about the concentration of power in the hands of just a few centralised players are becoming more pronounced.

Why should we worry about decentralised AI?

Monopoly power

The most pressing issue arising from centralised AI is the prospect of a few tech giants achieving monopolistic control over the industry. The big tech giants have already accumulated a very significant market share in AI, giving them possession of vast amounts of data. They also control the infrastructure that AI systems run on, enabling them to stifle their competitors, hobble innovation, and perpetuate economic inequality.

By achieving a monopoly over the development of AI, these companies are more likely to have an unfair influence on regulatory frameworks, which they can manipulate to their advantage. It will mean that smaller startups, which lack the enormous resources of big tech giants, will struggle to keep up with the pace of innovation. Those that do survive and look like they might thrive will almost certainly end up being acquired, further concentrating power in the hands of the few. The result will be less diversity in terms of AI development, fewer choices for consumers, and less favourable terms, limiting the use-cases and economic opportunities promised by AI.

Bias and Discrimination

Aside from monopolistic control, there are genuine fears around the bias of AI systems, and these concerns will take on more importance as society increasingly relies on AI.

The risk stems from the fact that organisations are becoming more reliant on automated systems to make decisions in many areas. It’s not unusual for a company to employ AI algorithms to filter job applicants, for example, and the risk is that a biased system could unfairly exclude a subset of candidates based on their ethnicity, age or location. AI is also used by insurance companies to set policy rates, by financial services firms to determine if someone qualifies for a loan and the amount of interest they’ll need to pay, and by law enforcement to determine which areas are more likely to see higher crime. In all of these use-cases, the potential implications of biased AI systems are extremely worrying.

Whether it’s law enforcement targeting minority communities, discriminatory lending practices or something else, centralised AI can potentially exacerbate social inequality and enable systemic discrimination.

Privacy and surveillance

Another risk posed by centralised AI systems is the lack of privacy protections. When just a few big companies control the vast majority of data generated by AI, they gain the ability to carry out unprecedented surveillance on their users. The data accumulated by the most dominant AI platforms can be used to monitor, analyse and predict an individual’s behaviour with incredible accuracy, eroding privacy and increasing the potential for the information to be misused.

It’s of particular concern in countries with authoritarian governments, where data can be weaponised to create more sophisticated tools for monitoring citizens. But even in democratic societies, there is a threat posed by increased surveillance, as exemplified by the revelations of Edward Snowden about the US National Security Agency’s Prism program.

Corporations can also potentially misuse consumer’s data to increase their profits. In addition, when centralised entities accumulate vast amounts of sensitive data, this makes them more lucrative targets for hackers, increasing the risk of data leaks.

Security risks

Issues of national security can also arise due to centralised AI. For instance, there are justified fears that AI systems can be weaponised by nations, used to conduct cyberwarfare, engage in espionage, and develop new weapons systems. AI could become a key tool in future wars, raising the stakes in geopolitical conflicts.

AI systems themselves can also be targeted. As nations increase their reliance on AI, such systems will make for enticing targets, as they are obvious single points of failure. Take out an AI system and you could disrupt the entire traffic flow of cities, take down electrical grids, and more.

Ethics

The other major concern of centralised AI is about ethics. That’s because the handful of companies that control AI systems would gain substantial influence over a society’s cultural norms and values, and might often prioritise profit, creating further ethical concerns.

For example, AI algorithms are already being used widely by social media platforms to moderate content, in an attempt to identify and filter out offensive posts. The worry is that algorithms, either by accident or design, might end up suppressing free speech. 

There is already controversy about the effectiveness of AI-powered moderation systems, with numerous seemingly innocuous posts being blocked or taken down by automated algorithms. This leads to speculation that such systems are not broken but being manipulated behind the scenes based on the political narrative the platform is trying to promote.

The alternative? Decentralised AI

The only logical counterweight to centralised AI is the development of decentralised AI systems that ensure that control of the technology remains in the hands of the majority, rather than the few. By doing this, we can ensure that no single company or entity gains a significant influence over the direction of AI’s development.

When the development and governance of AI is shared by thousands or millions of entities, its progress will be more equitable, with greater alignment to the needs of the individual. The result will be more diverse AI applications, with an almost endless selection of models used by different systems, instead of a few models that dominate the industry.

Decentralised AI systems will also mean checks and balances against the risk of mass surveillance and manipulation of data. Whereas centralised AI can be weaponised and used in a way that’s contrary to the interests of the many, decentralised AI hedges against this kind of oppression.

The main advantage of decentralised AI is that everyone is in control over the technology’s evolution, preventing any single entity from gaining an outsized influence over its development.

How to decentralise AI

Decentralised AI involves a rethink of the layers that make up the AI technology stack, including elements like the infrastructure (compute and networking resources), the data, models, training, inference, and fine-tuning processes.

We can’t just put our hopes in open-source models if the underlying infrastructure remains fully centralised by cloud computing giants like Amazon, Microsoft and Google, for instance. We need to ensure that every aspect of AI is decentralised

The best way to decentralise the AI stack is to break it down into modular components and create markets around them based on supply and demand. One such example of how this can work is Spheron, which has created a Decentralised Physical Infrastructure Network (DePIN) that anyone can participate in.

With Spheron’s DePIN, everyone is free to share their underutilised computing resources, essentially renting them out to those who need infrastructure to host their AI applications. So, a graphic designer who uses a powerful laptop with a GPU can donate processing power to the DePIN when they’re not using it for their own work, and be rewarded with token incentives.

What this means is that the AI infrastructure layer becomes widely distributed and decentralised, with no single provider in control. It’s enabled by blockchain technology and smart contracts, which provide transparency, immutability and automation.

DePIN can also work for open-source models and underlying data. For instance, it’s possible to share training datasets on a decentralised network like Qubic, which will make sure the provider of that data is rewarded each time their information is accessed by an AI system.

To ensure access and permissions are decentralised, every part of the technology stack is distributed in this way. However, the AI industry currently struggles to provide such a level of decentralisation. Although open-source models have become extremely popular among AI developers, most people continue to rely on proprietary cloud networks, meaning the training and inference processes are heavily centralised.

But there are strong incentives for decentralisation to win out. One of the primary advantages of DePIN networks, for example, is that they help to reduce overheads. Because networks like Spheron don’t rely on intermediaires, participants don’t need to make any payments or share revenue with third-parties. Moreover, they can afford to be more competitive in terms of pricing than corporations that are under pressure to grow profitability.

Decentralisation must win

The future of AI holds a lot of potential, but it’s also perilous. While the capabilities of AI systems have improved dramatically in the last few years, most of the advances have been made by all-powerful companies and that has resulted in an increase in their influence over the industry. There’s a price to pay for this, not just in monetary terms.

The only reasonable alternative is to promote the greater adoption of decentralised AI, which can enhance accessibility and ensure a greater flexibility of AI. By allowing everyone to participate in the development of AI on an equal footing, we’ll see more diverse, interesting, and useful applications that can benefit everyone equally, as well as putting their users first.

Building a decentralised AI future will involve a great deal of coordination and collaboration across every layer of the AI stack. Fortunately, there are strong incentives for participants to do just that. And again, the incentives are not just monetary.

[lanlishiba]

Equestria Twilight pls give me strength

[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.

[pillowfort-social]