Automation using Causal Inference Modeling System (CIMS) will exponentially accelerate science and the development of longevity technology.
For centuries, the 'database' of human knowledge has been stored in books, journals and in the brains of living scientists.
This database process to facilitate science has been time-consuming and costly. Roughly, the steps are:
Causal inference models, championed by Turing Award winner Judea Pearl, can formally represent scientific knowledge and data. More detail on this is provided in the FAQ that follows.
How can this process be improved by digitizing the most current and useful knowledge from the "database" into a Causal Inference Model?
Consider each step:
This automation of most of the scientific process will result in a substantial acceleration of progress. However there is a more significant speed-up:
Many of the parts of the process we are automating, such as designing experiments and peer-reviewing, are the parts that required scientists to be PhDs. Automating these eliminates bias and allows for thousands more people to chip away at the knowledge gaps. With an integrated, global scale, decentralized project manager, anyone who wants to help cure a disease or work on other projects can do so.
This FAQ tree will let you drill down from highly conceptual answers to the technical details as needed.
Q: What are Causal Inference Models? Can they really model complex science and data?
Causal Inference Models: Level 1 answer:
One cannot talk about causal models without talking about Judea Pearl.
Judah Pearl won the Turing award in computer science for inventing Bayesian networks. Since then, he has rejected Bayesian networks in favor of causal inference models.
He also won The Frontiers of Knowledge Award for "laying the foundations of modern artificial intelligence, so computer systems can process uncertainty and relate causes to effects"
You can learn how causal models work and how they can model reality here by reading his book "The Book of Why" (2018) or watching this Microsoft Plenary video.
Q: Causal Inference Models vs. Statistical Models vs. Symbolic Models
| Causal Inference Models | Statistical Models | Symbolic Models | |
|---|---|---|---|
| Ontology | A graph of causal relations | Probability Expressions | Structured Sentences |
| Handling of Time | Handles time well, even at various scales | Models two points: Before & After. Time interperted by a human and not easily automatable | Terrible at representing time |
| Application to individual cases & access real world | Handles individual cases well & easily refers to the real world | Can't handle individual cases at all. Human interpertation needed to apply to the real world | Can't handle statistics well. Human interpertation needed to apply to the real world |
Q: LEVEL3
Causal Inference Models: Level 3 answer:
The knowledge store we are using in this project uses a language for making models called Proteus.
What does Proteus look like?
"JSON + Time + sparse lists + references/expressions + abstractions"
Given the "mathyness" of the papers on causal models it might seem that Causal Inference models will be hard to understand. Actually, they can be quite intuitive. Causal Inference Models, as we are using them, are created and stored as text files or streams. Let us quickly develop some intuitions about them!
Start by considering how JSON can be used to store the state-at-an-instant of anything. Even quantum systems.
Here is a JSON example from the web:
{
“participant”: {
“name”: “rose”,
“age”: “17”,
“status”: “enrolled”
}
}
{
participant: {
name: rose,
age: 17,
status: enrolled
}
}
The obvious difference is that the quotes are gone. Proteus does have strings, but in this case, all the items given can be from abstractions, or "classes" whose behavour or meaning is defined elsewhere.
Notice that these models record something about "rose" at an instant in time. They do not capture that Rose may later change her name, become older or become 'unenrolled' later. Nor does it represent her past.
To represent how a system changes over time we have lists that record how the state of a system has changed over time. That brings up another problem: The list would be too big if they had to list every state an object ever had or has. So we have sparse lists. We also need to populate the lists with expressions and references instead of literal values, making them Turing complete. For the Time lists, the expressions refer to previous values so that they update correctly through time. Representing how a system would change over time in different situations is where Causal Inference Models really shine.
"Proteus = JSON + Time + sparse lists + references/expressions + abstractions" captures the basic intuition about how Proteus-based causal models look.
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