Dentistry is one of the oldest branches of medicine, its roots long predating agriculture. The earliest evidence of dental treatment comes from the upper paleolithic period. Like all medicine, dental healthcare today looks nothing like it did during the stone age. Machine learning, among our era’s most promising contributions, is a major factor in the difference. Dental health is among the most important areas of modern healthcare. […]
October brought amazing news for Google: the company has reached quantum supremacy. Today we’re going to look at just what that means.
ICLR (The International Conference on Learning Representations) is one of the most important international machine learning conferences. It’s popularity is growing fast, putting it on a par with such conferences as ICML, NeurIPS or CVPR.
September brought us two interesting AI-related stories, both with a surprising social context. Despite its enormous impact on our daily lives, Artificial Intelligence (AI) is often still regarded as too hermetic and obscure for ordinary people to understand.
The AI Monthly Digest is deepsense.ai’s attempt to filter out the buzz and chaos from the news ecosystem and provide readers with the news that matters.
With chatbots powering up customer service on one hand and fake news farms on the other, Natural Language Processing (NLP) is getting attention as one of the most impactful branches of Artificial Intelligence (AI).
With this edition of AI Monthly Digest, we have now for a full year been bringing readers carefully selected and curated news from the world of AI and Machine Learning (ML) that deepsense.ai’s team considers important, inspiring and entertaining.
Monitoring brand visibility is an important business problem. We describe our solution for logo detection and visibility analytics with deep learning.
AI models are skilled in Chess, Go, StarCraft and, since July, six-player Texas Hold’em Poker. But the hunt for inhuman players has begun.
Computer vision enables machines to perform once-unimaginable tasks like diagnosing diabetic retinopathy as accurately as a trained physician or supporting engineers by automating their daily work.