Transformer in Artificial Intelligence powers over 90% of modern AI models today. Introduced by researchers at Google in 2017, the Transformer architecture changed machine learning forever. It helps ...

Alibaba released Qwen 3.5 Small models for local AI; sizes span 0.8B to 9B parameters, supporting offline use on edge devices.

This efficiency makes it viable for enterprises to move beyond generic off-the-shelf solutions and develop specialized models that are deeply aligned with their specific data domains ...

It also develops its own series of AI models, and today it announced the availability of its most capable model so far. The ...

If healthcare organizations want to make the most of what AI can offer, they must find ways to overcome significant barriers ...

Microsoft's Phi-4-reasoning-vision-15B uses careful data curation and selective reasoning to compete with models trained on five times more data, reshaping the small AI playbook.

AI analytics agents need guardrails, not bigger models. Learn why governed data, shared definitions, & semantic layers matter more than model size.

What if the future of artificial intelligence wasn’t about building ever-larger models but instead about doing more with less? In a stunning upset, the 27-million-parameter Hierarchical Reasoning ...

Why smaller, domain-trained AI models outperform general-purpose LLMs in enterprise settings.

Trained on 9 trillion DNA base pairs from every domain of life, the Evo 2 model can predict disease-causing mutations, identify genomic features and generate entirely new genetic sequences.