Machine Transliteration provides the ability to transliterate a basic language into different languages in a computational way. Transliteration is an important technical process that has caught the attention most recently. The Sinhala transliteration has many constraints because of the insufficiency of resources in the Sinhala language. Due to these limitations, Sinhala Transliteration is highly complex and time-consuming. Therefore, the majority of the Sri Lankans uses non-formal texting language named 'Singlish' to make that process simple. This study has focused on the transliteration of the Singlish language at the word level by reducing the complication in the transliteration. A new approach of coding system has invented with the rule-based approach that can map the matching Sinhala words even without the vowels. Various typing patterns were collected by different communities for this. The collected data have analyzed with every Sinhala character and unique Singlish patterns related to them were generated. The system has introduced a newly initiated numeric coding system to use with the Singlish letters by matching with the recognized typing patterns. For the mapping process, fuzzy logic-based implementation has used. A codified dictionary has also implemented including unique numeric values. In this system, Each Romanized English letter was assigned with a unique numeric code that can construct a unique pattern for each word. The system can identify the most relevant Sinhala word that matches with the pattern of the Singlish word or it gives the most related word suggestions. For example, the word 'kiyanna,kianna, kynna, kynn, kiynna' have mapped with the accurate Sinhala word "kiyanna". These results revealed that the 'Swa Bhasha' transliteration system has the ability to enhance the Sinhala users' experience while conducting the texting in Singlish to Sinhala.

The evaluation of natural language processing (NLP) systems is crucial for advancing the field, but current benchmarking approaches often assume that all systems have scores available for all tasks, which is not always practical. In reality, several factors such as the cost of running baseline, private systems, computational limitations, or incomplete data may prevent some systems from being evaluated on entire tasks. This paper formalize an existing problem in NLP research: benchmarking when some systems scores are missing on the task, and proposes a novel approach to address it. Our method utilizes a compatible partial ranking approach to impute missing data, which is then aggregated using the Borda count method. It includes two refinements designed specifically for scenarios where either task-level or instance-level scores are available. We also introduce an extended benchmark, which contains over 131 million scores, an order of magnitude larger than existing benchmarks. We validate our methods and demonstrate their effectiveness in addressing the challenge of missing system evaluation on an entire task. This work highlights the need for more comprehensive benchmarking approaches that can handle real-world scenarios where not all systems are evaluated on the entire task.

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Assessing the quality of natural language generation systems through human annotation is very expensive. Additionally, human annotation campaigns are time-consuming and include non-reusable human labour. In practice, researchers rely on automatic metrics as a proxy of quality. In the last decade, many string-based metrics (e.g., BLEU) have been introduced. However, such metrics usually rely on exact matches and thus, do not robustly handle synonyms. In this paper, we introduce InfoLM a family of untrained metrics that can be viewed as a string-based metric that addresses the aforementioned flaws thanks to a pre-trained masked language model. This family of metrics also makes use of information measures allowing the adaptation of InfoLM to various evaluation criteria. Using direct assessment, we demonstrate that InfoLM achieves statistically significant improvement and over $10$ points of correlation gains in many configurations on both summarization and data2text generation.

In a concert hall in Switzerland, an audience sat in anticipation to listen to a version of Beethoven's Tenth Symphony for the first time. The symphony, which the German composer never finished, was only scraps of notes when he died. Some composers have tried to string together a version of the symphony since then. But now, artificial intelligence has given it a shot. The four-minute extract created has been called BeethovANN Symphony 10.1.

Colombo is talking about artificial intelligence. Millennium IT Founder and other companies Tony Weeresinghe delivered an oration on AI to the alumni of the University of Colombo. Vallibel One Group Founder Chairman and power behind many others Dhammika Perera delivered a two-hour monologue on the subject at an event organised by the Computer Society of Sri Lanka a few weeks back. To become a knowledge-based economy we have to understand and ride the waves of technology that keep rolling into this island. Beyond that, we must contribute to the world's knowledge.

The business community and the corporate sector should understand that Artificial Intelligence (AI) and data science are new, rapidly developing fields offering novel business opportunities. Collaborating with the IT industry and start-ups will yield rich dividends for both, Chairman, Sri Lanka Association for Software and Services Companies (SLASSCOM), Jeevan Gnanam said. SLASSCOM organised the first-ever AI Asia Summit in Colombo last week which exclusively discussed the topics of Data Science and Machine Learning with many acclaimed international and local speakers outlining the latest developments in AI across application, research and adoption. Sri Lanka's IT/BPM industry for the past two decades has predominantly focussed on two key areas for export earnings; that is software development and finance and accounting. Over the years the industry has grown positively with some of the major global partners, making Sri Lanka one of the key destinations for IT/BPM business.

The "deep artificial composer", or "DAC" for short, generates brand-new melodies that imitate traditional folk music of Irish or Klezmer origin. It does so without plagiarizing already existing ones, since melodies it writes are as original as those produced by a human composer. The results were presented in April at this year's edition of the Evostar conference. The DAC actually produces musical scores of melodies, symbolic music written using notation, and does not generate audio files. The deep artificial composer can produce complete melodies, with a beginning and an end, that are completely novel and that share features that we relate to style," says Swiss Federal Institute of Technology in Lausanne (EPFL) scientists Florian Colombo who developed the artificial intelligence under the guidance of Wulfram Gerstner, director of the Computational Neuroscience Laboratory. "To my knowledge, this is the first time that an artificial neural network model has produced entire and convincing melodies.

Now artificial intelligence is set to conquer the world of music. Swiss researchers say they have developed a computer algorithm that can generate new tunes in various musical genres after listening to more than 2,000 songs. And while it isn't quite at the level of Justin Bieber or Adele, future versions of the system may be fine-tuned to create songs that rival those by today's musicians. The deep artificial composer, or DAC, produces original melodies that are'quite agreeable to listen to', according to its developers at EPFL research university in Lausanne, Switzerland The DAC system is trained to'listen' to existing tunes to learn what works. It then teaches itself to predict the pitch and duration of every note following another.