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CatalyzeX grabs $1.64M seed to help developers find right machine learning model

TechCrunch

Machine learning is exploding, and so are the number of models out there for developers to choose from. The company co-founders, brothers Gaurav Ragtah and Himanshu Ragtah, saw that there was so much research being done and wanted to build a tool to make it easier for developers to find the most applicable models for their use case.

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Snorkel AI scores $35M Series B to automate data labeling in machine learning

TechCrunch

One of the more tedious aspects of machine learning is providing a set of labels to teach the machine learning model what it needs to know. It also announced a new tool called Application Studio that provides a way to build common machine learning applications using templates and predefined components.

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QuantrolOx uses machine learning to control qubits

TechCrunch

QuantrolOx , a new startup that was spun out of Oxford University last year, wants to use machine learning to control qubits inside of quantum computers. As with all machine learning problems, QuantrolOx needs to gather enough data to build effective machine learning models. million (or about $1.9

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Stability AI backs effort to bring machine learning to biomed

TechCrunch

Called OpenBioML , the endeavor’s first projects will focus on machine learning-based approaches to DNA sequencing, protein folding and computational biochemistry. “OpenBioML is one of the independent research communities that Stability supports,” Mostaque told TechCrunch in an email interview.

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From Machine Learning to AI: Simplifying the Path to Enterprise Intelligence

Cloudera

Thats why were moving from Cloudera Machine Learning to Cloudera AI. From Science Fiction Dreams to Boardroom Reality The term Artificial Intelligence once belonged to the realm of sci-fi and academic research. Why AI Matters More Than ML Machine learning (ML) is a crucial piece of the puzzle, but its just one piece.

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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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The AI Future According to Google Cloud Next ’25: My Interesting Finds

Xebia

ADK powers the newly announced Agentspace, Google’s research agent and Google customer support agents. Expert Google-Built Agents : Agentspace includes Deep Research and Idea Generation agents, in addition to NotebookLM for Enterprise. offers a scikit-learn-like API for ML. BigFrames 2.0