AI systems will learn bad behavior to meet performance goals, suggest researchers

Two researchers at Stanford University suggest in a new preprint research paper that repeatedly optimizing large language ...

Large Language Models Get All the Hype, but Small Models Do the Real Work

For many tasks in corporate America, it’s not the biggest and smartest AI models, but the smaller, more simplistic ones that are winning the day.

Why AI Costs Are Spiraling—And How Smarter Workloads Can Fix It

At the heart of every AI workload lies a pipeline—the process of ingesting, transforming, training, and serving data. These ...
TMCnet

Cognizant's AI Lab Announces Breakthrough Research for Fine-Tuning LLMs and Records its 61st U.S. Patent Issuance

Cognizant (Nasdaq: CTSH) today announced a breakthrough from its AI Lab that introduces a novel, efficiency-focused method ...
Devdiscourse

New AI forecasting model promises greater stability for Kenya’s volatile food prices

Kenya’s food markets are known for extreme volatility influenced by weather shocks, inflation, currency fluctuations, and ...
Scientific Research Publishing

Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A. (2020) Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Journal of Machine ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...
Scientific Research Publishing

Mohammadagha, M. (2025) Hyperparameter Optimization Strategies for Tree-Based Machine Learning Models Prediction: A Comparative Study of Ada-Boost, Decision Trees, and Random ...

ABSTRACT: This study presents a comprehensive and interpretable machine learning pipeline for predicting treatment resistance in psychiatric disorders using synthetically generated, multimodal data.
IEEE

Optimizing Fairness in Machine Learning: A Hyperparameter Tuning Approach

Abstract: Machine learning models are increasingly utilized in critical areas such as finance, hiring, and criminal justice, yet they often inherit or amplify societal biases, leading to unfair ...
marktechpost

Prefix-RFT: A Unified Machine Learning Framework to blend Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT)

Large language models are typically refined after pretraining using either supervised fine-tuning (SFT) or reinforcement fine-tuning (RFT), each with distinct strengths and limitations. SFT is ...
IEEE

Federated Learning With Automated Dual-Level Hyperparameter Tuning

Abstract: Federated Learning (FL) is a decentralized machine learning (ML) approach where multiple clients collaboratively train a shared model over several update rounds without exchanging local data ...