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  /  Artificial Intelligence   /  It’s Time for Artificial Intelligence to Finally BLOOM and Leave GPT-3 Behind

It’s Time for Artificial Intelligence to Finally BLOOM and Leave GPT-3 Behind

BLOOM will free the language models for everyone who wants to experiment with artificial intelligence

Are you scratching your head off over finding the right kind of language model for your AI tool? Well. it’s time for no more confusion! All thanks in part to the launch of BLOOM (which is an acronym for BigScience Large Open-science Open-access Multilingual Language Model). BLOOM got its start in 2021, with development led by machine learning startup Hugging Face, which raised US$100 million in May. The main motive of BLOOM is to let the language model be free from the clutches of big tech companies and available to the commoners.

The BigScience effort benefits from a wide array of contributors including Nvidia’s Megatron and the Microsoft DeepSpeed teams, as well as receiving support from CNRS, the French National Research Agency. The BLOOM model was built and trained using the Jean Zay supercomputer that is located in France. BLOOM has an architecture that is similar to OpenAI’s GPT-3 large language model but with the key fundamental difference being that BLOOM is multilingual.

OpenAI’s GPT-3 is monolingual and BLOOM was designed from the start to be multilingual so it was trained in several languages, and also to incorporate a significant amount of programming language data. BLOOM supports 46 human languages and 13 programming languages — so that’s a very sizable difference. This is how BLOOM was trained with open-source machine learning models. The BLOOM effort involved multiple components including collecting a large dataset and then building a training model.

Experts declare that Hugging Face made use of Nvidia’s Megatron and Microsoft’s DeepSpeed open-source projects, which are both efforts designed to enable data scientists to train large language models. Both Megatron and DeepSpeed are based on the open-source PyTorch machine learning framework. For BLOOM, the researchers developed a fork of the Megatron and DeepSpeed projects that enabled the model to look at all the different languages. In terms of BLOOM itself, the project was developed in the open and makes use of its own open license that is modeled on the Responsible AI license. The researchers explain that they are trying to define what open-source means in the context of large AI models because they don’t really work as the software does. The goal of the licensing for BLOOM was to make the model as open as possible, while still retaining a degree of control over the use cases that organizations have for the model. Large language models (LLM) are a subset of the overall field of natural language processing (NLP). Any language model is like an “atomic unit” for NLP, providing the building-block components on which complex AI interactions and applications can be built. So, it doesn’t make sense for an NLP model to learn how to do summarization as well as speak a language at the same time. Le Saco said that a human doesn’t learn how to speak English and then write a full research report at the same time. Typically, it makes sense for humans to learn how to speak the language first.

To date, most AI language models have used either English or Chinese. BLOOM will now extend the use cases, notably for French, Spanish, and Arabic speakers, where there has not been an open LLM available before. In addition to providing a new foundation for multiple spoken human languages, BLOOM could enable a new era for code development as well. The use of AI for code development is a relatively nascent space, with GitHub’s Copilot, which became generally available at the end of June, being among the early leaders.