We Finally Know How Much It Cost to Train China’s Astonishing DeepSeek Model

DeepSeek found that it could improve the reasoning and outputs of its model simply by incentivizing it to perform a trial-and ...
Tech Xplore

Autobot platform uses machine learning to rapidly find best ways to make advanced materials

A research team led by the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) has built and ...
Tech Xplore

The AI model that teaches itself to think through problems, no humans required

Artificial intelligence is getting smarter every day, but it still has its limits. One of the biggest challenges has been ...
Harvard Business Review

How to Make Enterprise Gen AI Work

It’s time for organizations to shift from ad hoc employee experimentation with generative AI to honed, measurable, enterprise ...

Cai Cai's Machine Learning Sklearn Class: Understandable from Scratch, from Model Invocation to Practical Implementation

The design of sklearn follows the "Swiss Army Knife" principle, integrating six core modules: Data Preprocessing: Similar to ...

Ailong Applies for Deep Learning Machine Vision Patent, **AI Defect Detection** Welcomes New Breakthrough

Nanjing Ailong Automation Equipment Co., Ltd. recently announced that it has applied for a patent titled "Real-time Detection and Classification Method and System for Machine Vision Defects Based on ...
EurekAlert!

Order to disorder at the atomic scale opens possibilities for next-generation electronic devices

A new class of 2D materials known as MXenes holds the key to next-generation applications, such as consumer electronics and ...
GitHub

An interpretable machine learning framework for designing high-strength high-entropy alloys using feature selection, generative models, and ensemble learning.

There was an error while loading. Please reload this page. Data_preprocessing_and_feature_selction.py contains Python code focused on preprocessing data and selecting ...
UroToday

Harnessing Artificial Intelligence to Predict Spontaneous Stone Passage: Development and Testing of a Machine Learning-Based Calculator.

Objective: We sought to use artificial intelligence (AI) to develop and test calculators to predict spontaneous stone passage (SSP) using radiographical and clinical data. Methods: Consecutive ...
EurekAlert!

First machine learning model developed to calculate the volume of all glaciers on Earth

VENICE – A team of researchers led by Niccolò Maffezzoli, “Marie Curie” fellow at Ca’ Foscari University of Venice and the University of California, Irvine, and an associate member of the Institute of ...