Create a no-code AI researcher with two research modes and verifiable links, so you get quick answers and deeper findings ...

Early-2026 explainer reframes transformer attention: tokenized text becomes Q/K/V self-attention maps, not linear prediction.

Abstract: The object point clouds acquired by the original LiDAR are inherently sparse and incomplete, resulting in suboptimal single object tracking (SOT) precision for 3D bounding boxes, especially ...

America's AI boom requires a lot of power. NPR's Scott Detrow speaks with Wall Street Journal reporter Jennifer Hiller about the workers who are building the electric grid one transformer at a time.

Siddhesh Surve is an accomplished Engineering leader with topics of interest including AI, ML, DS, DE, Cloud compute.

A new AI developed at Duke University can uncover simple, readable rules behind extremely complex systems. It studies how systems evolve over time and reduces thousands of variables into compact ...

The industrial sector is becoming a proxy for high-growth AI infrastructure as the calendar switches over to 2026. Tech experts and Wall Street analysts are pointing to power as the biggest bottleneck ...

We dive deep into the concept of Self Attention in Transformers! Self attention is a key mechanism that allows models like BERT and GPT to capture long-range dependencies within text, making them ...

Transformers have revolutionized deep learning, but have you ever wondered how the decoder in a transformer actually works? In this video, we break down Decoder Architecture in Transformers step by ...