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As a fundamental technology of artificial intelligence, existing machine learning (ML) methods often rely on extensive human intervention and manually presetting, like manually collecting, selecting, ...

The power of machine learning comes at a price. Once you have the skills, the toolkit, the hardware, and the data, there is still the complexity involved in creating and fine-tuning a machine learning ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

The flexibility, agility and ultimate cost of machine learning projects can be significantly impacted by data logistics and dependencies, according to Jim Scott, VP, Enterprise Architecture, at MapR.

Quality data is at the heart of the success of enterprise artificial intelligence (AI). And accordingly, it remains the main source of challenges for companies that want to apply machine learning (ML) ...

New theoretical physics research introduces a simulation method of machine-learning-based effective Hamiltonian for super-large-scale atomic structures. This effective Hamiltonian method could ...

A new machine learning method has slashed battery testing times -- a key barrier to longer-lasting, faster-charging batteries for electric vehicles -- by nearly fifteenfold. Battery performance can ...