in Language Technology, Technology

As we have explored extensively in our recent posts on advancing language AI, the most dynamic developments in areas like NLG and NLP have tended to come via large language models (LLMs) that require massive and high-quality datasets to train, which naturally favors large companies and software groups who alone manage access to these datasets. However, as the AI market continues to evolve, tech companies of all sizes continue to have a vested interest in enhancing language AI through growth that is largely propelled by the advanced research and data. Now, following the developments from an international collective research project dubbed BigScience, data scientists and researchers have collaborated with the shared aim to promote innovation throughout the broader AI community and help companies to better-address the leading challenges in the field of language AI.

Beginning in May of 2021, BigScience is a yearlong research workshop involving the collaboration of 600 researchers from multiple disciplines, representing nearly fifty nations in a collective effort to develop a massive neural network and multilingual text dataset, all on a supercomputer provided by the French government. While presented as a grassroots approach to commercializing LLMs and open-source language software, BigScience aims to not only address significant barriers to entry in the field of language AI, but to also develop new ways of utilizing datasets and new deployments for language AI technology. Specifically, finding solutions to some of the leading technological shortcomings – mainly language coverage in text datasets, and improving accessibility to training models – is one of the more important focusses in this project. Moreover, BigScience aims to explore questions like the environmental and social impacts of LLMs and supercomputers, specialized areas that require a multidisciplinary approach to research. For smaller-tech companies, especially, BigScience is enabling developments in a field that is often associated with tech giants with seemingly endless resources.

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With innovative approaches to challenges throughout the industry, projects like BigScience extend well beyond the AI community and into the very businesses that deploy AI products. As consumer preference for functionalities like chatbots becomes prevalent across global markets and languages, demand increasingly exceeds supply for high quality training data in those languages and expanding access developers in less dominant areas of the industry bodes well for improving this. Ultimately, the significance of BigScience is that it is helping drive innovation away from experimental, out-of-reach AI phenomena and toward consumer-facing products and applications that will increasingly drive the flow of technologies and services across borders. As language AI improves, translation – one of its vanguard real-world uses – will only become more important to ensuring these technologies succeed with global users.  

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