Recent advancements in large language models have demonstrated that extended inference--through techniques can markedly improve performance, yet these gains come with increased computational costs and the propagation of inherent biases found in natural languages. This paper explores the design of a unified AI-centric language system that addresses these challenges by offering a more concise, unambiguous, and computationally efficient alternative to traditional human languages. We analyze the limitations of natural language--such as gender bias, morphological irregularities, and contextual ambiguities--and examine how these issues are exacerbated within current Transformer architectures, where redundant attention heads and token inefficiencies prevail. Drawing on insights from emergent artificial communication systems and constructed languages like Esperanto and Lojban, we propose a framework that translates diverse natural language inputs into a streamlined AI-friendly language, enabling more efficient model training and inference while reducing memory footprints. Finally, we outline a pathway for empirical validation through controlled experiments, paving the way for a universal interchange format that could revolutionize AI-to-AI and human-to-AI interactions by enhancing clarity, fairness, and overall performance.

The field of conlang has evidenced an important growth in the last decades. This has been the product of a wide interest in the use and study of conlangs for artistic purposes. However, one important question is what it is happening with conlang in the academic world. This paper aims to have an overall understanding of the literature on conlang research. With this we aim to give a realistic picture of the field in present days. We have implemented a computational linguistic approach, combining bibliometrics and network analysis to examine all publications available in the Scopus database. Analysing over 2300 academic publications since 1927 until 2022, we have found that Esperanto is by far the most documented conlang. Three main authors have contributed to this: Garv\'ia R., Fiedler S., and Blanke D. The 1970s and 1980s have been the decades where the foundations of current research have been built. In terms of methodologies, language learning and experimental linguistics are the ones contributing to most to the preferred approaches of study in the field. We present the results and discuss our limitations and future work.

Current Grammar Error Correction (GEC) systems predominantly target major languages like English[1, 2, 3], Chinese[4, 5], German[6] and Japanese[7]. This focus is driven by the availability of comprehensive datasets and the specific linguistic characteristics inherent to these languages. Consequently, the exploration of GEC methodologies for low-resource languages has been largely overlooked, leaving a significant gap in the analysis and development of error correction strategies for these less-studied languages. Recently, Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by equipping these models with the ability to generate text that close to human language. LLMs have attracted considerable attention for their proficiency in English language tasks. Recent studies, however, reveal their potential across various languages. Despite this broad applicability, our analysis identifies a notable gap in the research landscape, particularly concerning Esperanto. As a constructed language, Esperanto presents unique challenges in terms of frequency distribution and grammar error correction that have yet to be thoroughly explored. This article delves into the word and letter frequency specific to Esperanto and embarks on a preliminary investigation into the capabilities of GPT-3.5 and GPT-4--innovations by OpenAI

At the RISC-V Summit today, Art Swift, CEO of Esperanto Technologies, announced a new, RISC-V based chip aimed at machine learning and containing nearly 1,100 low-power cores based on the open-source RISC-V architecture. Esperanto Technologies, headquartered in Mountain View, Calif., with other sites across the U.S. and Europe, was created in 2014 "with the goal of making RISC-V the architecture of choice for compute-intensive applications such as AI and machine learning." Swift traced the history of the new chip back to 2017, when Dave Ditzel – the founder and chairman of Esperanto – laid out the vision for Esperanto at the seventh RISC-V workshop. At that workshop, Ditzel set a goal of "laying down 4,000 or more cores on a single device." Ditzel called for both a simple instruction set through RISC-V and innovation in the realms of custom microarchitectures and proprietary low-power design techniques.

Over the past few weeks, we made several improvements to our transformers and tokenizers libraries, with the goal of making it way easier to train a new language model from scratch. In this post we'll demo how to train a "small" model (84 M parameters 6 layers, 768 hidden size, 12 attention heads) – that's the same number of layers & heads as DistilBERT – on Esperanto. Esperanto is a constructed language with a goal of being easy to learn. You won't need to understand Esperanto to understand this post, but if you do want to learn it, Duolingo has a nice course with 280k active learners. First, let us find a corpus of text in Esperanto.

AI applications demand new architectures to meet the need for unprecedented performance, sophisticated functionality and ultra-low power. Interconnect technology is a critical component of these architectures. "We are still in the early stages of AI maturity, but what we've learned is that sophisticated neural networks require tremendous performance," said Linley Gwennap, principal analyst at The Linley Group. "Generating this level of performance often requires a large number of cores, but these architectures can be difficult to design and test. NoC technology, such as NetSpeed's, has become an important enabling technology for AI because it automates the process of connecting CPUs and accelerators on a complex SoC." "Esperanto is putting thousands of processors and accelerators on a single chip, and our challenge was how to interconnect them. NetSpeed provided a compelling solution for interconnecting our cores with high performance and by licensing their IP we can reduce our time to market as well," said Dave Ditzel, CEO of Esperanto Technologies.

BIALYSTOK, POLAND – Instead of a hello, the head of the Esperanto association in the Polish city of Bialystok opts for "saluton," a sign that the universal language created by Ludwik Zamenhof is alive and well a century after the Jewish doctor's death. "Zamenhof created Esperanto as a counterweight to national languages, which he believed divided people and were a source of conflict," said association President Przemyslaw Wierzbowski. "Today, we know that it's economic, ethnic or religious differences that divide people, but Esperanto still has the goal of uniting us, helping us communicate," the 30-year-old added. Wierzbowski spoke from a table at Esperanto Cafe, which is located in a tower within the eastern city's market square, just steps away from where Zamenhof was born in 1859. During the 19th century, the tower was at the heart of a market packed with stalls that were manned by German, Jewish, Lithuanian and Polish merchants.