Abstract The literature on programming languages contains an abundance of informal claims on the relative expressive power of programming languages, but there is no framework for formalizing such statements nor for deriving interesting consequences. As a first step in this direction, we develop a formal notion of expressiveness and investigate its properties. To validate the theory, we analyze some widely held beliefs about the expressive power of several extensions of functional languages. Based on these results, we believe that our system correctly captures many of the informal ideas on expressiveness, and that it constitutes a foundation for further research in this direction.

“While Silicon Valley is investing tens of billions of dollars chasing the AGI dream, academic computing research in the U.S. is facing a severe drought.”

This really adds to some of the anecdotes I’ve heard regarding the competitiveness of the CS academic track now.

Challenge related to universal machines/VMs

An educational website featuring a collection of innovations by women in the fields of computer science and technology.

An educational website featuring a collection of innovations by women in the fields of computer science and technology.

Pikchr (pronounced “picture”) is a PIC-like markup language for diagrams in technical documentation.

is is a guide for people who want to self-study the basics of Computer Science, plus a some extras hot stuff.

hea selgitus 16bit-32bit olemusest.

Denotational semantics is a methodology for giving precise meaning to a programming language. While the syntax of a language is always formally specified in a variant of BNF, the more important part of definining its semantics is mostly left to natural language. which is ambiguous and leaves many questions open. Especially to construct a compiler for this language, we need a much deeper understanding than can be provided by informal descriptions.

Broadly speaking, denotational semantics is concerned with finding mathematical objects called domains that represent what programs do.

When a condition is found to be TRUE, the engine executes the THEN clause, which results in new information being added to its dataset. In other words, the engine starts with a number of facts and applies rules to derive all possible conclusions from those facts. This is where the name “forward chaining” comes from – the fact that the inference engine starts with the data and reasons its way forward to the answer, as opposed to backward chaining, which works the other way around.How about backward chaining?In backward chaining, the system works from conclusions backwards towards the facts, an approach called goal driven. Compared to forward chaining, few data are asked, but many rules are searched. Backward-chaining rules engines are not suited for dynamic situations and are mostly only used as expert systems in decision making

We work closely with the most progressive support organizations in the world at companies like GoFundMe, Harry’s, Monzo, and Stripe. Learn why they love us.