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Marketing tech
fromInfoQ
1 day ago

Reimagining Platform Engagement with Graph Neural Networks

Graph neural networks can enhance recommender systems by personalizing content and optimizing for long-term user engagement.
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.
DevOps
fromInfoWorld
3 weeks ago

An architecture for engineering AI context

AI systems must intelligently manage context to ensure accuracy and reliability in real applications.
Artificial intelligence
fromMedium
3 weeks ago

Less Compute, More Impact: How Model Quantization Fuels the Next Wave of Agentic AI

Model quantization and architectural optimization can outperform larger models, challenging the belief that more GPUs equal greater intelligence.
Artificial intelligence
fromwww.scientificamerican.com
4 weeks ago

As AI keeps improving, mathematicians struggle to foretell their own future

First Proof, a benchmarking initiative, is launching its second round to evaluate large language models' ability to contribute to research-level mathematics, now requiring transparency and access from participating AI companies.
Python
fromPyImageSearch
1 month ago

DeepSeek-V3 Model: Theory, Config, and Rotary Positional Embeddings - PyImageSearch

DeepSeek-V3 introduces revolutionary architectural innovations including Multihead Latent Attention that reduces KV cache memory by 75% while maintaining model quality, addressing critical challenges in inference efficiency, training cost, and long-range dependency capture.
fromMedium
2 months ago

From Graphs to Generative AI: Building Context That Pays-Part 1

Every year, poor communication and siloed data bleed companies of productivity and profit. Research shows U.S. businesses lose up to $1.2 trillion annually to ineffective communication, that's about $12,506 per employee per year. This stems from breakdowns that waste an average of 7.47 hours per employee each week on miscommunications. The damage isn't only interpersonal; it's structural. Disconnected and fragmented data systems mean that employees spend around 12 hours per week just searching for information trapped in those silos.
Data science
Python
fromPyImageSearch
2 months ago

TF-IDF vs. Embeddings: From Keywords to Semantic Search - PyImageSearch

Vector databases and embeddings enable semantic search and retrieval-augmented generation by mapping text meaning into geometric vectors for similarity-based retrieval.
fromPyImageSearch
1 month ago

Vector Search with FAISS: Approximate Nearest Neighbor (ANN) Explained - PyImageSearch

In the previous lesson, you learned how to turn text into embeddings - compact, high-dimensional vectors that capture semantic meaning. By computing cosine similarity between these vectors, you could find which sentences or paragraphs were most alike. That worked beautifully for a small handcrafted corpus of 30-40 paragraphs. But what if your dataset grows to millions of documents or billions of image embeddings? Suddenly, your brute-force search breaks down - and that's where Approximate Nearest Neighbor (ANN) methods come to the rescue.
Python
fromInfoQ
2 months ago

Building Embedding Models for Large-Scale Real-World Applications

What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.
Artificial intelligence
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