feedback – AI.RadicalShift

newsPrinceton Researchers Unveil AI Reinforcement Learning Breakthrough Without Guided Feedback

AI models, Americas, Artificial Intelligence, Education, Industrial, Machine learning, Robotics

March 12, 2025

Sheryl Rivera

A Single Goal is All You Need, AI, AI agents, Ben Eysenbach, childlike exploration, contrastive RL, emergent strategies, exploration, feedback, Grace Liu, guided feedback, International Conference on Learning Representations, Michael Tang, Princeton researchers, reinforcement learning, rewards, simulated robots, Singapore, single goal, trial and error

PRINCETON — Princeton researchers have upended conventional reinforcement learning by demonstrating that AI agents can learn faster without step-by-step feedback. In a study that challenges established training paradigms, simulated robots were assigned a single, difficult task with no guided feedback—and they succeeded, often outperforming robots trained with detailed rewards and instructions. Reinforcement learning typically relies on…
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newsAnthropic Enhances AI Development with New Features for Improved Prompting and Example Management

AI assistants, AI models, Algorithms, Americas, Analytics, Artificial Intelligence, Generative AI, Internet & Online, Prompt engineering

November 15, 2024

Sheryl Rivera

accuracy, AI applications, AI workflows, Anthropic, Anthropic Console, chain-of-thought reasoning, Claude, Claude 3.5 Sonnet, consistency, evaluations, example enrichment, example generation, example management, example standardization, feedback, Finn Bauer, ideal output, ideal outputs, JSON, Kapa.ai, Kapa.ai workflow, model responses, multi-label classification, performance, prefill addition, prompt engineering, prompt evaluator, prompt improver, rewriting, summarization task, technical knowledge base, user feedback, XML

SAN FRANCISCO — Anthropic, a leader in AI development, has unveiled new features within its Anthropic Console aimed at simplifying the process of enhancing prompts and managing examples. These advancements help developers incorporate best practices for prompt engineering, ultimately leading to more reliable AI applications. Improving Prompt Quality for More Accurate Results The effectiveness of…
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newsHitachi and DFKI Unveil AI Technology for Real-Time Evaluation of Worker Physical Load

Analytics, Artificial Intelligence, Deep learning, Europe, Healthcare, Industrial

March 20, 2019

Sheryl Rivera

AI, artificial intelligence, Automotive, deep learning, DFKI, Efficiency, Electronics, experimentation, feedback, German Research Center for Artificial Intelligence GmbH, Germany, Hannover Messe 2019, Hitachi, human activity recognition, Industrial, industrial settings, Machinery, motion data, optimization, physical load, productivity, safety, Science, security, Sensors, standardization, technology, wearable devices

KAISERSLAUTERN – Hitachi, Ltd. and the German Research Center for Artificial Intelligence GmbH (DFKI) have introduced an innovative AI technology capable of recognizing and quantifying the physical load experienced by workers in real-time. Utilizing motion data captured by wearable sensors, the AI-based system enables precise assessment of each body part’s movement, offering valuable insights to…
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What is RadicalShift AI?

RadicalShift.ai represents the paradigm shift the artificial intelligence (AI) brings upon all of us, from the way we live and work to the way we do business. To help cope with these fundamental changes across life, industries and the world in general, we are obsessively observing (30+ markets across multiple continents) and covering the AI industry while building a scalable open platform aimed at people, businesses and industry stakeholders to contribute across (benefit from) the entire spectrum of the AI industry from news, views, insights to knowledge, deployments, entities, people, products, tools, jobs, investors, pitch decks, and beyond, helping build what would potentially be a resourceful, insightful, knowledgeable and analytical source for AI related news, information and resources, ultimately becoming the AI industry graph/repository.