The convolutional semantic mapper or "Convolv" is a universal translation algorithm that learns and operates in real time, simultaneously training a lattice of partial models representing the speaker and the social and cultural context of the interaction in addition to the core language model. This framework allowed people for the first time to have natural, nuanced, and free-flowing conversations across languages and cultures. Although "Convolv" originally was only the name of the computational specifications, it is now often used to refer to any devices and interfaces that implement the algorithm.
Theory / Overview
The Convolv algorithm has its origins in early approaches to natural language processing, such as deep learning, hybrid model integration, convolutional neural networks, generative adversarial algorithms, and latent-state transition models. However, it was not until around -335 GE that improvements in massively parallel neural computation hardware made it possible to expore complex, model-based approaches to realtime language translation.
The Convolv algorithm differed from previous automatic translation algorithms in two key respects. First, it couples its core language model with a lattice of models representing aspects of the speak, cultural context, and interaction environment. These models learn in tandem with one another and allow the model to build up an understanding some of the cultural "deep structure" of idioms and linguistic conventions rather than simply memorizing expressions. Second, it takes in parallel channels of both linguistic and non-linguistic input, so that it can make use of contextual signals the humans have available but that are often excluded from machine learning such as a speaker's body language or even the situational or cultural context of the interaction. The inspiration for this came from studies of language learning in children, who need to experience language as part of a fully immersive environment with a full set of non-linguistic cues in order to learn a new language quickly and effectively.
Although Convolv devices are always updating and learning in the background, they can operate in either "sure" or "unsure" mode. In "sure" mode it simply presents the highest probability translation of any incoming language signal. In "unsure" mode it presents a rank-ordered set of possible interpretations, along with options for user intervention to increase or decrease the weight of one or more of the options, or even to assign a brand new meaning to a signal pattern based on their own prior knowledge.
Discovery
The first version of the Convolutional Semantic Mapping algorithm was created in -320 GE by the R&D facility of Apex Infosys in Mumbai, India. The first hardware systems implementing the Convolv algorithm were standard dedicated rack machines with configurable input channels and display port options. Apex Infosys patented the algorithm and kept it proprietary, but began leasing access to the system over the global network as a networked service.
The algorithm was so successful that it quickly became a standard used for all communication in the business world. Because of the algorithm's ability to translate idioms and even "smooth out" language carrying culturally charged connotations, many companies started using the Convolv for
all business communication, even when all parties (ostensibly) spoke the same language.
By this time the United States government was on the verge of collapse and ideology-driven political violence was on the rise in Europe. Radical and sometimes violent rhetoric began coming out of some European countries demanding that the source for the Convolv algorithm be "opened up" as a global resource for humanity. Such demand would likely have been simply ignored a few decades earlier, but flooding in coastal cities such as Mumbai (where Apex Infosys was headquartered) and fresh water scarcity throughout the region was threatening to destabilize the entire region. In order to appease large investors in Europe, Apex Infosys made the source for the Convolv algorithm public (without giving up patent rights) in -302 GE.
Global Spread
Over the decade that followed, the main customers purchasing Convolv hardware or the rights to implement the algorithm were governments in Europe. Political leaders hoped that technology for universal translation would be able to stem the tide of rising isolationism and political radicalization brought on by the collapse of the United States government. Some governments even passed laws requiring all news and entertainment streaming devices to integrate access to Convolv functionality, whether on the local device or networked through a shared service.
When the
Transeŭropa Demokratia Reto was established in -292 GE, the Convolv had already become so pervasive that the technology was seen by many to be a key and critical component of in the operation of this new transnational system. Specifically, the Convolv finally erased the last of the barriers to economic and informational transactions between countries in Europe. There was no longer any need to learn the languages, idioms, customs, or cultural habits of other people in order to carry out business transactions with them: eveything was mediated through the Convolv.
Freed from the burden of even trying to absorb or appreciate other cultures, the constituent nation-states of the TDR drifted even further into ethnonationalism and isolationism. The Convolv became the primary channel through which all economic transactions were conducted. Meanwhile, environmental instability was causing increasing problems in South Asia. When the governments of Pakistan, India, and Bangladesh all collapsed in rapid succession in -263 GE, Apex Infosys ceased to operate and no governmental system was capable of, or even interested in, enforcing the patent on the Convolv algorithm. The Convolutional Semantic Mapper had defaulted into the public domain.
Intersteller Exploration
Although the Convolv was ubiquitous already at the start of the Galactic Era, there didn't seem to be any need to include the technology on the first round of uncrewed exploration vessels created by the
Ianus Autofactory. It wasn't until the launch of
Project Expedition that language translation became a priority in space exploration. Convolv rack machines were standard hardware in
Witten vessels starting with model QS and in all
Hořavas vessels starting around 123 GE, and became standard to offer a HUD interface for for Convolv display in most space suits.
The Convolv even gained some popular recognition in 239 GE, because it played a crucial role in our ability to communicate with the natives of
Planetagua. The algorithm was able to learn their language "from scratch" by finding complex correlations between their language use and other behaviors.
