At the intersection of human communication and AI computation sits Natural Language Processing (NLP), with the potential to redefine our interaction with technology. AI researcher Rahul Ratnakar is at the vanguard of NLP advancements, pushing the envelope of machine intelligence. His groundbreaking
Google's Gemini and OpenAI's ChatGPT-4, both advanced AI platforms, have been challenged to showcase their capabilities in crafting analogies to demystify the intricate concept of stochastic gradient descent (SGD). SGD is crucial for the training of neural networks, making it an ideal subject for
AI is revolutionizing material science with the groundbreaking discovery of over 2 million new crystalline materials through sophisticated deep learning methods. This remarkable advancement opens the door to a multitude of new technological and sustainable possibilities. AI's role in material
The landscape of artificial intelligence is transforming with an emphasis on immersive interactivity. This is crucial in a world where the digital and human realms are intertwined ever so closely. AI models, particularly large language models (LLMs), have proven to be incredibly adept at
The advancement of artificial intelligence (AI) continues at a brisk pace, yet it has become increasingly clear that AI is not without its flaws. Traditional data-driven AI can falter when encountering scenarios absent from its training data, revealing the system's inherent limitations. To address
As digital demands skyrocket, there's a critical need for more powerful computing solutions. Traditional systems are overwhelmed by the immense volume of data they must process. Neuromorphic computing could be the answer, as it emulates the human brain's mechanisms to improve data management. A
