Windows 10: Authenticator nom longer generating codes for Facebook / Meta

I have recently upgraded my iPhone and on my new device I was logged out of the authenticator app. When I have logged back in my Facebook account was no longer there and as such I can no longer gain access to Facebook via 2-factor-authentication. Is there anyway I can generate a new code through Authenticator for Facebook / Meta?

:)

 

Meta Labeling AI-Generated Images on Facebook, Instagram and Threads

Meta Newsroom release: As a company thats been at the cutting edge of AI development for more than a decade, its been hugely encouraging to witness the explosion of creativity from people using our new generative AI tools, like our Meta AI image generator which helps people create pictures with simple text prompts. As the difference between human and synthetic content gets blurred, people want to know where the boundary lies. People are often coming across AI-generated content for the first time and our users have told us they appreciate transparency around this new technology. So its important that we help people know when photorealistic content theyre seeing has been created using AI. We do that by applying Imagined with AI labels to photorealistic images created using our Meta AI feature, but we want to be able to do this with content created with other companies tools too. Thats why weve been working with industry partners to align on common technical standards that signal when a piece of content has been created using AI. Being able to detect these signals will make it possible for us to label AI-generated images that users post to Facebook, Instagram and Threads. Were building this capability now, and in the coming months well start applying labels in all languages supported by each app. Were taking this approach through the next year, during which a number of important elections are taking place around the world. During this time, we expect to learn much more about how people are creating and sharing AI content, what sort of transparency people find most valuable, and how these technologies evolve. What we learn will inform industry best practices and our own approach going forward. A New Approach to Identifying and Labeling AI-Generated Content When photorealistic images are created using our Meta AI feature, we do several things to make sure people know AI is involved, including putting visible markers that you can see on the images, and both invisible watermarks and metadata embedded within image files. Using both invisible watermarking and metadata in this way improves both the robustness of these invisible markers and helps other platforms identify them. This is an important part of the responsible approach we’re taking to building generative AI features. Since AI-generated content appears across the internet, weve been working with other companies in our industry to develop common standards for identifying it through forums like the Partnership on AI (PAI). The invisible markers we use for Meta AI images IPTC metadata and invisible watermarks are in line with PAIs best practices. Were building industry-leading tools that can identify invisible markers at scale specifically, the “AI generated” information in the C2PA and IPTC technical standards so we can label images from Google, OpenAI, Microsoft, Adobe, Midjourney, and Shutterstock as they implement their plans for adding metadata to images created by their tools.

While companies are starting to include signals in their image generators, they havent started including them in AI tools that generate audio and video at the same scale, so we cant yet detect those signals and label this content from other companies. While the industry works towards this capability, were adding a feature for people to disclose when they share AI-generated video or audio so we can add a label to it. Well require people to use this disclosure and label tool when they post organic content with a photorealistic video or realistic-sounding audio that was digitally created or altered, and we may apply penalties if they fail to do so. If we determine that digitally created or altered image, video or audio content creates a particularly high risk of materially deceiving the public on a matter of importance, we may add a more prominent label if appropriate, so people have more information and context. This approach represents the cutting edge of whats technically possible right now. But its not yet possible to identify all AI-generated content, and there are ways that people can strip out invisible markers. So were pursuing a range of options. Were working hard to develop classifiers that can help us to automatically detect AI-generated content, even if the content lacks invisible markers. At the same time, were looking for ways to make it more difficult to remove or alter invisible watermarks. For example, Metas AI Research lab FAIR recently shared research on an invisible watermarking technology were developing called Stable Signature. This integrates the watermarking mechanism directly into the image generation process for some types of image generators, which could be valuable for open source models so the watermarking cant be disabled. This work is especially important as this is likely to become an increasingly adversarial space in the years ahead. People and organizations that actively want to deceive people with AI-generated content will look for ways around safeguards that are put in place to detect it. Across our industry and society more generally, well need to keep looking for ways to stay one step ahead. In the meantime, its important people consider several things when determining if content has been created by AI, like checking whether the account sharing the content is trustworthy or looking for details that might look or sound unnatural. These are early days for the spread of AI-generated content. As it becomes more common in the years ahead, there will be debates across society about what should and shouldnt be done to identify both synthetic and non-synthetic content. Industry and regulators may move towards ways of authenticating content that hasnt been created using AI as well content that has. What were setting out today are the steps we think are appropriate for content shared on our platforms right now. But well continue to watch and learn, and well keep our approach under review as we do. Well keep collaborating with our industry peers. And well remain in a dialogue with governments and civil society. AI Is Both a Sword and a Shield Our Community Standards apply to all content posted on our platforms regardless of how it is created. When it comes to harmful content, the most important thing is that we are able to catch it and take action regardless of whether or not it has been generated using AI. And the use of AI in our integrity systems is a big part of what makes it possible for us to catch it. Weve used AI systems to help protect our users for a number of years. For example, we use AI to help us detect and address hate speech and other content that violates our policies. This is a big part of the reason why weve been able to cut the prevalence of hate speech on Facebook to just 0.01-0.02% (as of Q3 2023). In other words, for every 10,000 content views, we estimate just one or two will contain hate speech. While we use AI technology to help enforce our policies, our use of generative AI tools for this purpose has been limited. But were optimistic that generative AI could help us take down harmful content faster and more accurately. It could also be useful in enforcing our policies during moments of heightened risk, like elections. Weve started testing Large Language Models (LLMs) by training them on our Community Standards to help determine whether a piece of content violates our policies. These initial tests suggest the LLMs can perform better than existing machine learning models. Were also using LLMs to remove content from review queues in certain circumstances when were highly confident it doesnt violate our policies. This frees up capacity for our reviewers to focus on content thats more likely to break our rules. AI-generated content is also eligible to be fact-checked by our independent fact-checking partners and we label debunked content so people have accurate information when they encounter similar content across the internet. Meta has been a pioneer in AI development for more than a decade. We know that progress and responsibility can and must go hand in hand. Generative AI tools offer huge opportunities, and we believe that it is both possible and necessary for these technologies to be developed in a transparent and accountable way. Thats why we want to help people know when photorealistic images have been created using AI, and why we are being open about the limits of whats possible too. Well continue to learn from how people use our tools in order to improve them. And well continue to work collaboratively with others through forums like PAI to develop common standards and guardrails. Source: Labeling AI-Generated Images on Facebook, Instagram and Threads | Meta

 

Need to find my authentication code. Facebook says I previously set one, but honestly I don't remember it.

I know two-step authentication is now the thing. Recently, my Facebook account was hacked, and I was asked to reset my password. Next, Facebook asked for my six digit Authenication code. I don't ever recall setting such a code. I sent Facebook an email in fact several, but no response. Can you suggest how I can fix this. They asked for my Driver's license, and I sent them a copy, but that didn't work.

* Moved from Community Center

 

Need to find my authentication code. Facebook says I previously set one, but honestly I don't remember it.

https://www.facebook.com/help/search?helpref=search&query=authentication code

Sounds like Facebook will send you this code when you click on something during the login/forgotten password/reset password process.

https://www.facebook.com/help/13269...sion_id=9ee1d670acacc9361f785b30f4d7c189&sr=0

Don