I have long been frustrated with all the different text mark up languages and word processors that I have used. There are many reasons for this; but the biggest issue is that markups (including very powerful ones like TeX) are not targeted at the kind of stuff I write.
Nowadays, it seems archaic to still be thinking in terms of sections and chapters. The world is linked and that applies to the kind of technical writing that I do.
I believe that the issue is fundamental. A concept like "section" is inherently about the structure of a document. But, what I want to focus on are concepts like "example", "definition", and "function type".
A second problem is that, in a complex environment, the range of documentation that is available to an individual reader is actually composed of multiple sources. Javadoc exemplifies this: an individual library may be documented using Javadoc into a single HTML tree. However, most programmers require access to multiple…
Generic types, as can now be seen in all the major programming languages have a flip side that has yet to be widely appreciated: existential types.
Variables whose types are generic may not be modified within a generic function (or class): they can be kept in variables, they can be passed to other functions (provided they too have been supplied to the generic function), but other than that they are opaque. Again, when a generic function (or class) is used, then the actual type binding for the generic must be provided – although that type may also be generic, in which case the enclosing entity must also be generic.
Existential types are often motivated by modules. A module can be seen to be equivalent to a record with its included functions: except that modules also typically encapsulate types too. Abstract data types are a closely related topic that also naturally connect to existential types (there is an old but still very relevant and readable article on the topic Abstract types have …
It seems to me that one of the basic questions that haunt AI researchers is 'what have we missed?' Assuming that the goal of AI is to create intelligence with similar performance to natural intelligence; what are the key ingredients to such a capability?
There is an old saw It takes 10,000 hours to master a skill There is a lot of truth to that; it effectively amounts to 10 years of more-or-less full-time focus. This has been demonstrated for many fields of activity from learning an instrument, learning a language or learning to program.
But it does not take 10,000 hours to figure out if it is raining outside, and to decide to carry an umbrella. What is the difference?
One informal way of distinguishing the two forms of learning is to categorize one as `muscle memory' and the other as 'declarative memory'. Typically, skills take a lot of practice to acquire, whereas declarative learning is instant. Skills are more permanent too: you tend not to forget a skill; but it is…