Abstractions in programming, while hiding complexity and creating more distance to that machine code, help programmers get closer to the actual problems they’re trying to solve.

To understand where and how AI creates value, we need to understand the concept of value abstraction in two ways: the abstraction of value and the resulting value of abstraction.

In programming terms, the objects are data types and the operations are the operators that can be used in expressions and control constructs. As described in a 1975 paper by Jack Dennis [2], there are ...

Software-driven abstraction of quantum complexity away from the end users lets anyone, irrespective of their quantum expertise, leverage quantum computing for the problems that matter most to them.

Intermountain Health is deploying Layer Health’s AI engine for clinical data abstraction across several of its patient registries. The health system’s venture capital arm is also making a ...

Programming processors is becoming more complicated as more and different types of processing elements are included in the same architecture. While systems architects may revel in the number of ...

All data motion is syntactically explicit, to encourage programmers to consider the cost of communication. UPC++ encourages the use of scalable data-structures and avoids non-scalable library features ...

Data-oriented programming encourages us to model data as (immutable) data, and keep the code that embodies the business logic of how we act on that data separately.