What’s next for Google Bard, and how will it lead in advancing natural language processing?

Here are my thoughts on some possible directions, or “next steps” for Google Bard and the way it can continue to push the boundaries of natural language processing:

Continued Improvement of Model Quality: This remains the foundational concern with large language models like Bard. The team would likely continuously work on improving the accuracy, fluency, and relevance of language used by Bard to generate even more insightful, factual, and accurate, as well as comprehensive and user-friendly responses.

Expanding to Larger and More Diverse Training Sets: This approach would expand the variety of input sources to improve learning. Training on bigger datasets, for example, more books, articles, and digital content (text and videos), along with a wide range of human conversation recordings is likely to allow Bard to have a deep understanding of how sentences are formulated to better generate text, translate languages, and be more informed overall.

Fostering Cross-Language NLP Improvements: Bard was built through the power of transformers, a method of pre-training machine learning models through large amounts of language data. One way for Google Bard to truly “lead” is to further push improvements on these underlying machine learning tools. Doing so will pave the way for improvements on many other important cross-language NLP technologies such as machine translation and automatic text summarization.

Addressing Ethical Concerns and Societal Responsibilities: Large language models come with concerns of misrepresentation, bias, and offensive outputs. As a leader in this field, Google Bard and the teams working on its development would do well to constantly investigate and implement approaches to be more proactive in identifying and mitigating any such issues throughout the development, training, and usage stages of the model.

Moreover, a leading-edge NLP model ought to consider not just language and human interactions but also the ethical impacts of its uses: how to make the technology better serve its users, society, and the world—including developing the ability to recognize human emotions and social contexts; to avoid offensive or potentially harmful responses; and to be used only for positive and responsible purposes.