artificial intelligence

OpenAI reveals who’s winning with AI at work

Good morning, AI enthusiasts. OpenAI's first-ever enterprise report is out, and one stat stands out: 75% of staff say they're now handling tasks they literally couldn't do before.With AI unlocking entirely latest capabilities across...

Optimizing PyTorch Model Inference on CPU

grows, so does the criticality of optimizing their runtime performance. While the degree to which AI models will outperform human intelligence stays a heated topic of debate, their need for powerful and expensive...

The Machine Learning “Advent Calendar” Day 8: Isolation Forest in Excel

with Decision Trees, each for Regression and Classification, we are going to proceed to make use of the principle of Decision Trees today. And this time, we're in unsupervised learning, so there aren't any...

The AI Bubble Will Pop — And Why That Doesn’t Matter

“AI is all hype!” “AI will transform all the things!” of labor constructing AI systems for businesses, I’ve learned that everybody appears to be in one in all these two camps. The reality, as history shows,...

Poetiq cracks major reasoning benchmark

Good morning, AI enthusiasts. Six months ago, the most effective AI models could barely hit 5% on the ARC-AGI-2 reasoning benchmark. Today, a tiny startup just crossed 50% — and beat Google using its...

The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

, we explored how a Decision Tree Regressor chooses its optimal split by minimizing the Mean Squared Error (MSE). Today for Day 7 of the Machine Learning “Advent Calendar”, we proceed the identical approach but...

Multi-Agent Arena: Insights from London Great Agent Hack 2025

Persons are going to make use of increasingly more AI. Acceleration goes to be the trail forward for computing. These fundamental trends, I completely imagine in them. Jensen Huang. Nvidia CEO I had the amazing...

The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel

working with k-NN (k-NN regressor and k-NN classifier), we all know that the k-NN approach could be very naive. It keeps your entire training dataset in memory, relies on raw distances, and doesn't...

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