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Artificial intelligence
fromTheregister
1 day ago

LLMs fail in 8 out of 10 early differential diagnosis cases

AI models fail at early differential diagnosis in over 80% of cases, highlighting significant limitations for patient self-diagnosis.
Data science
fromMedium
1 week ago

15 Datasets for Training and Evaluating AI Agents

Datasets for training and evaluating AI agents are essential for building reliable agentic systems and preventing execution failures.
Science
fromNature
3 weeks ago

Drowning in data sets? Here's how to cut them down to size

The Square Kilometre Array Observatory will generate massive data, but storage and retention pose significant challenges for researchers.
Productivity
fromEntrepreneur
1 month ago

How AI Clears the Path to Faster, Better Executive Decisions

Decision slowdowns stem from disorganized inputs forcing leaders to decode information rather than decide, which AI can resolve by standardizing briefs, surfacing tradeoffs, and documenting rationale.
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more#brainiac
Information security
fromSecuritymagazine
2 months ago

Product Spotlight on Analytics

Taelor Sutherland is Associate Editor at Security magazine covering enterprise security, coordinating digital content, and holding a BA in English Literature from Agnes Scott College.
fromNature
2 months ago

Multimodal learning with next-token prediction for large multimodal models - Nature

Since AlexNet5, deep learning has replaced heuristic hand-crafted features by unifying feature learning with deep neural networks. Later, Transformers6 and GPT-3 (ref. 1) further advanced sequence learning at scale, unifying structured tasks such as natural language processing. However, multimodal learning, spanning modalities such as images, video and text, has remained fragmented, relying on separate diffusion-based generation or compositional vision-language pipelines with many hand-crafted designs.
Artificial intelligence
Data science
fromCIO
2 months ago

5 perspectives on modern data analytics

Data/business analytics is the top IT investment priority, yet analytics projects often fail due to poor data, vague objectives, and one-size-fits-all solutions.
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