[the notes from the book were retrieved with kobogarden, with the purpose of aiding to create a map of the ideas the book left me. The full list of book highlights can be found here. Reading is still in progress.]

why this book

I had come across the author on the Mindscape podcast, and I was curious to delve further, with the expectation on possibly expanding my knowledge on the field of cognition and adjacent topics.

I haven't yet finished it, and currently stand somewhere at its midpoint. After the introducing chapters on what is educability and some other basic concepts, it springboards from the information processing perspective to superimpose our process of learning with processes from machine learning. It is a very computational-heavy perspective (an interesting one, too).

With time, I've come to find this idea in many other pieces of writing. Something seems to have changed with the advent of the computer and the information age.

the definining characteristic of humanity

The mark of humanity is that a single individual can acquire the knowledge created by so many other individuals. It is this ability to absorb theories at scale, rather than the ability to contribute to their creation, that I identify as humanity’s most characteristic trait.

It starts by stating educability as a conceptual alternative to intelligence, as the latter is poorly defined (and consequently poorly measured); not only that, there is an argument for educability as the defining cognitive capability in our evolution, and what let us progress towards civilization (the author seems to have also coined the term civilization enabler.

Educability is, then,

[...] the capability to learn and acquire belief systems from one’s own experience and from others, and to apply these to new situations.

As we are able to build on the knowledge of our predecessors, we go farther.

educability can be modelled as information processing

Since we take the perspective of acting upon information, the author considers that the subject matter of educability is information processing: this will bring us closer to Computer Science and adjacent notions of computability, even as far as to argue that just as the field of physics is concerned with describing and modeling the real-world, an analogous match can be made between the study of the brain and the field of information processing.

While the twentieth century saw unparalleled developments in the classical sciences, equally important and particularly in the work of Alan Turing in the 1930s, it saw the birth of the science of information processing. By that time, it was commonplace not to marvel that physical concepts that are not visible, such as energy or electric charge, could have useful meaning. The fact that the same held for notions of information processing and computation, terms that I shall use synonymously, was startling news. The import of this news was well understood by the early pioneers of computing, namely, Turing himself and John von Neumann. They sought immediately to use computation to study biological phenomena, such as the brain, cognition, and genetics. Each of these phenomena involves the transformation of information. Focusing on the information processing rather than the physical realization became a viable and necessary approach toward understanding these once a scientific approach to information processing had come into view. I, and many others, consider it self-evident that if we are to understand how the brain works, we will need to understand it in terms of information processing.

Later on, an experiment with sea snails, in which their behaviour is altered upon stimuli that makes them closer to an evolutionary need, is analyzed through the lens of a learning algorithm, and it serves as an example of how the learning process — educability — can concretely be taken as information processing.

the seven capabilities that constitute the educability model

In each of the next seven sections, I shall focus on a separate capability that will be used as a foundation stone for the model. These capabilities are learning from examples, generalization, large memory, symbolic names, teaching, chaining, and a Mind’s Eye.

Each one is computational, in the sense [that] information is being processed to meet a specification, and this specification can be realized with feasible resources. The physical substrate on which the processing takes place, whether biological or silicon, is immaterial.

generalization and the definition of PAC learnability

When introducing the capability of generalization, Valiant supports the definition of the first with a concept he himself coined:

In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant.

In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions. The goal is that, with high probability (the "probably" part), the selected function will have low generalization error (the "approximately correct" part). The learner must be able to learn the concept given any arbitrary approximation ratio, probability of success, or distribution of the samples.

from Wikipedia

the role of a large memory in educability

This is probably the easiest one to recognize.

Do large brains have any useful purpose beyond the memorization of large numbers of individual memories, such as the hiding places of nuts, landmarks for navigation, or where you left your keys? The answer must be yes.

We use our memory also to store general learned information. This information may be both what we have learned from personal experience as well as what we have been taught by instruction. There is an analogy here with general-purpose computers. Computers, including the ones we have in our cell phones, now have very large memories. Computers use memory for storing both programs and data. In human terms, these correspond, respectively, to the general rules we know and memories of individual instances. For example, a general rule is that to find my keys, I search in my pockets. An individual instance would be the memory of having put them in a specific unusual place, say, on a chair.

As already mentioned, a most spectacular development in human evolution has been the tripling in brain size since our ancestors and those of the other living apes parted company. Educability offers a suggestion for why this might be useful. We do not need it for memorizing more hiding places for nuts. We use it for storing information to help decide what to do or think next in our lives.