Both students and working professionals frequently rely on search engines to find solutions to intricate queries. Google, in its efforts to maintain its edge as the world’s leading search engine, has recently upgraded its features to better serve those in STEM fields. As of recently, if you’re grappling with a challenging math or physics problem, Google’s Search and Lens might just be your new best friends.
Google has introducednew features in both Search and Lens to assist users in visualizing STEM-related concepts and determining the right equations for their problems. Whether you’re trying to decipher a complicated physics concept or a perplexing geometry problem, these tools aim to make the process more intuitive.
For instance, if you’re stuck on a calculus problem, you can now simply type your equation into the Search bar or snap a photo with Lens toreceive a step-by-step explanationand solution. This feature is not limited to just equations; it extends toword problems, especially those from high-school physics topics.
The integration of Lens, in particular, addresses a unique challenge in geometry. Describing visual problems using words can be cumbersome. For example, if you’re given a diagram of a triangle with measurements of two sides and need to find its area, Lens can now interpret both the visual and text components of the problem, offering a comprehensive step-by-step guide on how to solve it.
This advancement isn’t just about problem-solving. Google has also introduced new interactive 3D models on Search, allowing users to visually explore almost 1,000 topics from biology, chemistry, physics, and astronomy — an example given by the company was a search for the term “mitochondria.” This feature can be particularly useful for those who wish to gain a deeper visual understanding of complex STEM concepts.
The underlying technology that powers these features can be traced back to Google’s efforts in enhancing its AI capabilities. A notable mention on this front is the integration ofPaLM into Bard. Initially, Bard, based on Google’s Language Model for Dialogue Application (LaMDA), was more adept at holding conversations than logical reasoning. However, with the fusion of PaLM into its code base, Bard’s capabilities expanded to include arithmetic, code completion, semantic parsing, logical inference, and more. We could be seeing a similar implementation of machine learning here with the new tools in Search and Lens.
While AI tools like the features Google is introducing today with Search and Lens are becoming increasingly sophisticated, it’s essential for users to approach them as supplementary resources, complementing traditional learning and problem-solving methods. As technology continues to evolve, the line between human and machine capabilities might blur, but the essence of learning and understanding will always remain inherently human.
