The Politics of Early Programming Languages

There probably has never been such a controversial programming language as Algol. In the early 1960s the disciplinary success of the so-called Algol project in helping to forge the discipline of computer science was not matched by a significant adoption of the Algol language, in any of its three versions. This contrast is even more striking when considering the contemporary success of IBM’s Fortran, a language that, like Algol, was also conceived for scientific computation, but unlike Algol, initially only available for IBM computers. Through extensive archival research, this article shows how the relentless pursuit of a still better language that came to dominate the agenda of the Algol project brought to the fore the tension between the research-driven dimension of the project and the goal of developing a reliable programming language. Such a strong research-oriented agenda increased IBM’s doubts about a project that the firm already felt little urge to support. Yet IBM did not want to appear as obstructing the development of either Algol or Cobol, even if these “common languages” posed a clear risk to the firm’s marketing model. The US Department of Defense’s endorsement of Cobol and the rising popularity of Algol in Europe convinced IBM to push for the use of Fortran in Western Europe in order to protect the domestic market. IBM’s action in support of Fortran reminds us of the power imbalances that have shaped computer science.

Difficulty Scaling in Proof of Work for Decentralized Problem Solving

We propose DIPS (Difficulty-based Incentives for Problem Solving), a simple modification of the Bitcoin proof-of-work algorithm that rewards blockchain miners for solving optimization problems of scientific interest. The result is a blockchain which redirects some of the computational resources invested in hash-based mining towards scientific computation, effectively reducing the amount of energy ‘wasted’ on mining. DIPS builds the solving incentive directly in the proof-of-work by providing a reduction in block hashing difficulty when optimization improvements are found. A key advantage of this scheme is that decentralization is not greatly compromised while maintaining a simple blockchain design. We study two incentivization schemes and provide simulation results showing that DIPS is able to reduce the amount of hash-power used in the network while generating solutions to optimization problems.

Secure Multiparty Computation

The Secure Multiparty computation is characterized by computation by a set of multiple parties each participating using the private input they have. There are different types of models for Secure Multiparty computation based on assumption about the type of adversaries each model is assumed to protect against including Malicious and Covert Adversaries. The model may also assume a trusted setup with either using a Public Key Infrastructure or a using a Common Reference String. Secure Multiparty Computation has a number of applications including Scientific Computation, Database Querying and Data Mining.