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DeepSeek R2 is coming fast: Can the West keep up?

Dataconomy

Liang, who began his career in smart imaging and later managed a research team, was praised for hiring top algorithm engineers and fostering a collaborative environment. The firm allocated 70% of its revenue towards AI research, building two supercomputing AI clusters, including one consisting of 10,000 Nvidia A100 chips during 2020 and 2021.

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Satellite Data, Bushfires and AI: Safeguarding Wine Industry Amidst Climate Challenges

Towards AI

Detecting drought in January 2020 (on the left) using the EVI vegetation index Yellow means very healthy vegetation while dark green means unhealthy. Clustering similar fields using unsupervised K-means clustering The outcome of K-means clustering is cluster labels that assign each data point to one of the K clusters.

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Ending an Ugly Chapter in Chip Design

Flipboard

The crux of the clash was whether Google’s AI solution to one of chip design’s thornier problems was really better than humans or state-of-the-art algorithms. In Circuit Training and Morpheus, a separate algorithm fills in the gaps with the smaller parts, called standard cells. The agent places one block at a time on the chip canvas.

EDA 177
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Introducing Multimodal Clustering

DataRobot

Yes, data created over the next three years will far exceed the amount created over the past 30 years ( Source : IDC Worldwide Global DataSphere Forecast, 2020-2024). Clustering is a technique that can be used to get a sense of the data while allowing to tell a powerful story. Introducing Multimodal Clustering. Name Clusters.

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Anthropic’s $5B, 4-year plan to take on OpenAI

Flipboard

A pitch deck for Anthropic’s Series C fundraising round discloses these and other long-term goals for the company, which was founded in 2020 by former OpenAI researchers. The deck confirms that target number, though only half was raised at the time of the document’s creation from a “confidential investor.”

AI 178
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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning Blog

As a result, machine learning practitioners must spend weeks of preparation to scale their LLM workloads to large clusters of GPUs. Aligning SMP with open source PyTorch Since its launch in 2020, SMP has enabled high-performance, large-scale training on SageMaker compute instances. To mitigate this problem, SMP v2.0

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Get Maximum Value from Your Visual Data

DataRobot

it’s possible to build a robust image recognition algorithm with high accuracy. In 2020, our team launched DataRobot Visual AI. Multimodal Clustering. Multimodal Clustering provides users with a one-click, one line-of-code experience to build and deploy clustering models on any data, including images.