AI For Everyone: Super-Smart Systems That Reward Data Creators

As AI rapidly pervades every sector of the economy, there are few questions more urgent than who owns, controls and guides the data used to train AI systems, and the models and conclusions that AI learns from this data.

Data about our bodies and our genomes are stored in pharma company databases and used for proprietary R&D, without our explicit consent and without us receiving any reward for therapies discovered.

This may in the end be the most critical application of blockchain technology, and associated methods like homomorphic encryption and multiparty computation, which allow sophisticated AI data processing to occur while still respecting data privacy.

Suppose AI #2 - let's call it the Data Connector - is good at finding biological and medical datasets relevant to a certain problem, and preparing these datasets for AI analysis.

The Data Connector then comes to the rescue, feeding the Analogy Master with the data about mouse cancer it needs to drive its creative brainstorming, supporting the Disease Analyst to solve its problem.

The Analogy Master and Data Connector don't necessarily need to see the Disease Analyst's proprietary data, because using multiparty computation or homomorphic encryption, AI analytics can take place on an encrypted version of a dataset without violating data privacy.

What if the three AI agents in this example scenario are owned by different parties? What if the data about human prostate cancer utilized by the Disease Analyst is owned and controlled by the individuals with prostate cancer, from whom the data has been collected? This is not the way the medical establishment works right now.

Centralization of AI data analytics and decision-making, in medicine as in other areas, is prevalent at this point due to political and industry structure reasons and inertia, rather than because it's the only way to make the tech work.

It will exist in the overall network, including a combination of generalization-oriented AI agents like the Analogy Master and special purpose AI agents like the Disease Analyst and "Connector" AI agents like the Data Connector above.

We will see work toward more general forms of AI that are owned and guided by the individuals feeding the AI with the data they need to learn and grow.