Opinion
CIOOpinion

AI isn’t failing, people are failing with AI

AI isn’t the problem — rushing it into the wrong tasks without the right data, expertise or guardrails is what makes projects fall apart.

Specialised SLMs better suited for India needs than LLMs, says Gnani AI CTO

In an interview at the India AI Impact Summit, Ananth Nagaraj discusses Gnani.ai's shift from speech systems to voice-to-voice models, sector-specific AI and its plans under the IndiaAI Mission ...
InfoQ

Building Embedding Models for Large-Scale Real-World Applications

Sahil Dua discusses the critical role of embedding models in powering search and RAG applications at scale. He explains the ...
Hosted on MSN

Transformer encoder architecture explained simply

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...
Frontiers

Robust automated preclinical fMRI preprocessing via a multi-stage dilated convolutional Swin Transformer affine registration

Accurate preprocessing of functional magnetic resonance imaging (fMRI) data is crucial for effective analysis in preclinical studies. Key steps such as denoising, skull-stripping, and affine ...
ascopubs.org

SmokeBERT: A Bidirectional Encoder Representations From Transformers–Based Model for Quantitative Smoking History Extraction From Clinical Narratives to Improve Lung Cancer ...

We cross-validated four pretrained Bidirectional Encoder Representations from Transformers (BERT)–based models—BERT, BioBERT, ClinicalBERT, and MedBERT—by fine-tuning them on 90% of 3,261 sentences ...
IEEE

SPGFormer: Structure Perception Graph Transformer With Laplacian Position Encoding for Hyperspectral Image Classification

Abstract: With the integration of graph structure representation and self-attention mechanism, the graph Transformer (GT) demonstrates remarkable effectiveness in hyperspectral image (HSI) ...