Let’s make it Real Good

They made AI

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Real Good AI is a 501(c)3 research nonprofit created by content creators and PhD scientists coming together to improve transparency and safety of AI for a better world.

“Mandy…has founded [Real Good AI] to try to fight back at what is going wrong with AI. And no, this isn't something that I just joined. This is something that I believe in”
-Mark Fischbach [Influencer, Filmmaker, Real Good Board Vice President]

Current AI sucks

No one really understands AI. Really, no one.

Companies leading the AI field are purposefully obscuring the truth about AI and its limitations while acting irresponsibly to try to make money.

The math (Neural Network) models current AI uses are:

  • Unexplainable

  • Rely on too much (stolen) data

  • Destroy the environment through inefficiencies

  • Are just plain wrong too often

AI generated videos, voice, pictures, music, and art are dangerous to humanity through our livelihoods, our art, our psychology, and so much more. We deserve to know what is real and where things came from.

All of these issues have a common solution: if we could really understand AI, it could get better.

Let’s be that change.

  • Make a Difference NOW

    Instead of saying “yes” or “no” to AI by using the REAL rating, we all have the specific language to talk about the nuanced involvement of AI in everything.

  • Fix AI FOREVER

    As a crowdfunded research organization, we pursue research that is important to all of us. We are not looking to make a profit so our information is completely free, open, and unbiased.

  • Make Sure EVERYONE Knows

    And that’s where YOU come in! Knowledge, science, or discoveries only mean something when they are shared with everyone so our outreach programs are designed to get everyone in!

We are NOT doing:

  • Building or using data centers

  • Stealing data to train on it

  • Making an AI product

  • Lying about what our results mean to sell you something

What We ARE doing:

  • Imparting statistical models in AI to improve transparency, uncertainty quantification, and computational efficiency

  • Developing proof of concept AI methodologies for data attribution, meaning we know want to know where something came from

  • Using our methodologies to solve unprofitable but vital problems to helping the world

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