We want to explain complicated things as a combination of simpler things. You must be prepared to feel a certain sense of loss. When we break things down to their smallest parts, they may each seem as dry as dust at first, as though some essence has been lost. Where does the “knowing-how-to-build” of a Builder agent reside? It is not in any part, so it is not enough to explain what each separate agent does.

We found a way to make a tower builder out of parts. But Builder is really far from done. For example, how could Find determine which blocks are still available for use? It would have to “understand” the scene in terms of what it is trying to do. We’ll need theories both about what it means to understand and about how a machine could have a goal.

Imagine a child playing with blocks. Imagine the child’s mind contains a host of smaller minds - “agents”. Imagine an agent called Builder in control. Builder makes towers from blocks: Builder is a simple agent.

For a method, a protocol, thought about and done by you, what’s a “you”? What kinds of smaller entities may cooperate to carry out a procedure? Try this: pick up a cup of tea . Imagine that your GRASPING agents want to keep hold of the cup, your BALANCING agents want to keep the tea from spilling out, your THIRST agents want you to drink the tea, and your MOVING agents want to get the cup to your lips.

How could solid-seeming computer hardware support such a ghostly thing as research progress? The world of scientific inquiry and the world of bits appear too far apart to interact in any way. A few centuries ago it seemed impossible to explain Life - living things appeared to be so different from anything else. Last century, von Neumann helped show how cell-machines could reproduce.

To explain a FAIR research platform, we have to show how they are built from mindless stuff, smaller and simpler than anything we’d consider smart. What could these simpler particles be – the “agents” that compose the platform? There are many questions to answer: Function: How do agents work? Embodiment: What are they made of? Interaction: How do they communicate? Learning: How do we make new agents and change old ones?

“Self-describing” data products include/contain/link to any “knowledge products” (schema, ontology, etc.) they were informed by. In the sense of the data-information-knowledge-wisdom (DIKW) conceptualization, are there information products ? Perhaps self-describing information products include/contain/link to any “wisdom products” (usage context!

How do FAIR data resources work? How can you build a FAIR resource from many little parts, each non-FAIR by itself? Minsky’s The Society of Mind tries to explain how minds can work, how intelligence can emerge from non-intelligence, how you can build a mind from many little mindless parts. In the “society of mind” scheme, a mind is made of many smaller agents . Each agent plays a simple role that needs no mind at all.

Not documenting failures causes them to be repeated. This repetition is waste because no new information is generated. Only new failures generate information. There is an optimum failure rate. ¹ We can avoid oversimplifications like “embrace failures” or “avoid failures”. We maximize information gain by making outcomes equally likely. Thus, for a test with two outcomes, seek a 50% failure rate.

Inside black boxes you find programs. There are a lot of programming languages. You may not care how black box \(A\) works as a substitute for black box \(B\) if it’s observationally equivalent to black box \(B\). How do the boxes interact? What’s inside the arrows that connect the boxes? Inside arrows you find protocols. There are a lot of protocols.