Web search is expected to evolve dramatically over the next decade, as it combines recent breakthroughs in artificial intelligence, particularly those relating to massive neural language models such as GPT-3.
We may think of replacing the entire system with a black-box model that directly answers any query you ask in one futuristic vision. There’s no need to sift through search results, click on links, and then scroll through pages anymore. This may appear enticing at first, but it does pose significant issues in terms of reliability and trust. Is the information gathered by the system accurate, and where does it come from?
Instead, Stanford researchers propose a solution that blends the greatest features of current search technology with recent AI advancements. This field of study is known as Neural Information Retrieval (IR), and they are working on a method based on the Colbert model.
Neural IR is an innovative new field of research at the intersection between NLP and IR. It has already produced impressive results ahead of its time, with both Google and Microsoft announcing that Neural IR will be used in their search engines this year to improve functionality for users across different platforms. The best part? This cutting-edge technology only continues to progress as more organizations explore ways it can help them grow!
Their suggestion is modest in that it preserves vital components of today’s web search user experience. However, there is a lot of evidence that it can lead to far better systems while maintaining reliability and trust.
Researchers are working on cutting-edge technology at the intersection of Natural Language Processing and Information Retrieval. These innovations have incredible potential for changing our lives in all aspects, from tech to society. The brain behind these advancements is neural language models which Stanford researchers believe should be applied with caution – as they know that even if it seems like a perfect answer when you ask them questions, language models will never be infallible oracles. The more advanced neural IR methods, like ColBERT and Baleen, use these models to improve search results while also preserving trustfulness.
Supporting Paper: https://arxiv.org/pdf/2105.02274.pdf
Source: https://hai.stanford.edu/news/moderate-proposal-radically-better-ai-powered-web-search
Sanskriti is currently pursuing her bachelor’s in Journalism, Psychology, and English and is enthusiastic about getting to know new people, uncovering their stories, and engaging with the atmosphere. She has an inclination towards news affairs, writing and teaching.
