A persistent reliability gap exists between AI’s theoretical capabilities and real-world performance. Human-in-the-loop (HITL) systems offer a powerful solution by integrating human expertise directly into AI processes, creating a collaborative framework that enhances accuracy, reduces bias, and builds trust
Discover comprehensive strategies to detect and mitigate bias in NLP models. Learn how diverse data collection, algorithmic fairness techniques, and human oversight create more ethical and equitable AI language systems. Practical insights for developers and businesses
Artificial intelligence (AI) is fast becoming a daily tool in our daily lives, not only transforming the way we live and work, but also how we humans interface with machines and with each other. We are offering this AI Training Data Guide because as AI continues to advance, it’s crucial
Artificial Intelligence’s application in Automatic Speech Recognition (ASR) has become indispensable, with its numerous applications ranging from voice assistants, call center services, to assistive tools for the deaf and elderly. The accuracy of ASR systems is heavily dependent on substantial training data. This data can be speech, simulated dialogues involving
Parallel data, also known as parallel corpora, refers to collections of translation pairs comprising sentences and their corresponding translations. These datasets are utilized in the training and evaluation of machine translation models. Creation of parallel data can be accomplished through manual, automatic, or synthetic means using monolingual data. It can
Artificial intelligence has transformed how we access knowledge and connect across languages. But for smaller or under-resourced languages, the digital shift has brought new risks. Instead of preservation, poorly trained AI systems often accelerate decline. Recent analyses from MIT, including cases from Greenlandic, Fulfulde, and Inuktitut Wikipedias, show how error-filled
This is a guest post by one of our most esteemed clients, Manuel Herranz, CEO of Pangeanic. We collect, classify and supply data for AI training at NLP Consultancy: and working with data allows us to test models and understand what the market wants. Our close relationship with Pangeanic as
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Discover why off-the-shelf AI data fails and how NLPConsultancy’s custom speech data solutions provide the quality, precision, and scalability enterprises need for high-performance AI systems.
A Watershed Moment for AI Accountability China and Spain are setting new global benchmarks in AI regulation, demanding clear labelling of AI-generated content both visibly and invisibly by 2025. This regulatory shift marks the beginning of a new era: transparency-by-design in the age of generative AI. At NLP CONSULTANCY, where
New architectures and cheaper energy are required to achieve Ubiquitous AI In recent years, we’ve witnessed a remarkable phenomenon in the technology world: a diverse set of disciplines—Computer Science, Pattern Recognition, Machine Learning, Computational Linguistics, and Natural Language Processing—have all collapsed under the singular banner of “AI.” This convergence isn’t
In an ever-more-complex and competitive landscape dominated by titans like ChatGPT-4 and Anthropic’s Claude, DeepSeek-R1 has emerged as a surprising frontrunner. Although it has become clear that DeepSeek wasn’t built on $5M budget, this new language model not only competes with industry giants but also outperforms them in critical benchmarks.
The demand for high-quality datasets has never been more critical. Among these datasets, long-form parallel corpora are standing out as indispensable resources for advancing multilingual communication and linguistic automation. This is due to the new fluency by LLMs we have grown used to since late 2022 with the advent of
LLMs or Large Language Models (LLM) are advanced deep learning algorithms capable of performing a wide range of tasks related to natural language processing (NLP). Language Models have been around for a while. Short History Language models have been around in various forms for several decades, evolving significantly with advancements
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Founded in 2022, our mission is to make AI more multilingual and accurate thanks to expert data annotation and data labeling services for the creators of the future.
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