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Amoeba-inspired method could enable energy-efficient hardware for complex optimization

Combinatorial optimization problems are ubiquitous, underpinning decision-making in a wide range of fields, including ...
The Tech Edvocate

Unbelievable: Fintech Jobs See 35% AI Demand Surge – Are You Missing Out?

Spread the loveIf you’ve been paying any attention to the financial sector lately, you’ve probably heard the whispers, seen ...
Quanta Magazine

‘Huge Breakthrough’ in the Math of Imbalance

For the first time in 30 years, computer scientists have found a better way to allocate objects evenly between two groups.
Communications of the ACM

The Code Optimization Flywheel Won’t Spin Itself

Jatin Ganhotra is a Senior Software engineer in the AI for Code group at IBM’s Thomas J. Watson Research Center, Yorktown ...

Beckhoff's TwinCAT 3 Machine Learning Creator Simplifies AI Training

TwinCAT 3 Machine Learning Creator facilitates automated training of AI models for industrial applications and includes model ...
Hosted on MSN

The Steam Machine still solves PC gaming's biggest problem despite its terrible value

Steam Machine removes PC friction: plug into your TV, sign into Steam, and start playing. One company, one warranty: Valve support simplifies troubleshooting vs custom PC headaches. Price ...
IEEE

A Review on Bilevel Optimization Using Evolutionary Algorithms and Machine Learning Approaches

Bilevel Optimization (BLO) is a hierarchical problem structure involving two interdependent levels of optimization tasks. While traditionally approached through evolutionary algorithms (EAs), recent ...
PNAS

Demonstrating real advantage of machine learning–enhanced Monte Carlo for combinatorial optimization

In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...
Eos

Collinearity is Not Always a Problem in Machine Learning

The workflow used in the paper is illustrated here, showing how synthetic geological models were constructed and how their geophysical properties were derived via forward simulation. Then, different ...
Phys.org

Machine learning helps solve a central problem of quantum chemistry

Within the STRUCTURES Cluster of Excellence, two research teams at the Interdisciplinary Center for Scientific Computing (IWR) have refined a computing process, long held to be unreliable, such that ...
Frontiers

Hybrid optimization of interpretable ensemble machine learning for petrophysical property prediction from well logs

Unconventional reservoirs, including shale gas, tight oil, and coalbed methane formations, have emerged as vital contributors to global energy security, accounting for a substantial portion of the ...