dataDAO

From #simulatedHumans --> #dataSovereignty --> #monetizeYourData - Connecting the dots here

Using LLMs to simulate human behavior caught my attention last summer, with the paper from Stanford researchers titled "Generative Agents- Interactive Simulacra of human behavior" where they let loose agents with a backstory in a small simulated town, and the agents developed relationships, and planned a party !

Several more research/applied projects over the past year where LLMs have been used to simulate human behavior in NPC's or persona's to use as drop in's for behavioral research. Why bother with humans when you can simulate them 🤯 #simulatedHumans for the win(and cost savings)

Academic by day, degen by night 
@3dprintingProfs #Blockchain #KB7 
@kernel0X jokedao maxi #ai #analytics 

If you go with the flow, and assume that human generated(your) data still has some value, and LLM providers are investing billions to train better models, how about getting some of that value?

Remember the stanford folks who simulated a community? Well they outdid themselves and did deep qualitative interviews (2 hours+) with over a 1000 participants and used that transcript to train individual LLM based agents. Guess what? These agents achieved an 85% replication accuracy on the general social survey and reduced bias in responses when comparing to demographic based agents. So now you have closer replication of your behaviour/psychology/feelings etc. Imagine what is possible if you give these LLM's access to all your activity on your phone 😱

Now you need a privacy preserving mechanism to share access to your data to all these data hungry LLM's to run their algorithms on. In comes, Fully homomorphic Encryption(FHE) which allows computations on encrypted data (even on the edge), preserving the privacy of the information. And guess what? Apple enabled support a couple of weeks back. Aren't you glad there is FHE on your device?

But for true #dataSovereignty (had to spell this a few times 😥 ) you don't want to trust #bigtech .....

#inCodeWeTrust with #cryptography and #blockchains and FHE allows for #decentralized training for these data hungry models. Some experiments have already been successful, though not nearly as big as the frontier models. SMOL models with good quality data >> large models trained on the chaos of the open web.

Assuming there is value in our data, and we have a mechanism to control/secure it, and distribute it #permissionlessly for training models where we dont have to trust #bigTech, how do you #monetizeYourData?

In come #DataDAO's and a recent startup funded to incubate such Decentralized Autonomous Organizations . These are a new organizational structure that operates on blockchain technology, where rules are encoded in smart contracts, allowing for decentralized control without centralized leadership. Members participate by holding tokens that grant them voting rights on proposals concerning the organization's direction, fund allocation, or rule changes. Everything from decision-making to financial transactions is transparent and/or automatically enforced by code, aiming for greater efficiency, transparency, and community governance. All is not rosy though, and people are still figuring out best practices, but it is one solution to addressing centralization risk and individual sovereignty .

Extend this to #DataDAO’s , and one startup that I read about is @vanaxyz (https://www.vana.org/posts/accelerating-ai-data-revolution) which has incubated several Initiatives to allow users to control access to your data such as that on Reddit, Twitter and more. The Reddit DataDAO seems to have been quite successful-https://x.com/i/grok/share/S03gPzVdnZ3mspinG33a0pqAO and vana has raised a good amount of $$$. Get paid for your data … 

I believe a #LinkedinDataDAO is in the works !! 

There you have it -
 #simulatedHumans --> #dataSovereignty --> #monetizeYourData 

Follow me for more such deep dives. Mostly human, with some AI(augmented intelligence )

The LLM providers( aka OpenAI and its like) have already sucked up all human generated content on the web, and paid billions to human minions to contribute more data or clean up data but they are still hungry for more. Common approaches to solve this problem is to train models on clean(er) data and/or generate synthetic data. The jury is still out on how much further you can go with either of these two approaches.