Records found: 9
Google's new AI agents show promise in digital collaboration but face challenges like unreliable outputs and coordination issues. Clear definitions and protocols are essential for their future success.
The AI Hype Index sheds light on the current realities of AI agents and the emergence of AI-powered toys through the OpenAI and Mattel partnership, highlighting reliability and safety concerns.
This tutorial demonstrates how to create a groundedness verification tool using Upstage API and LangChain, featuring single and batch checks, multi-domain testing, and result analysis.
AI benchmarks are increasingly outdated as models optimize for tests rather than true intelligence. New evaluation methods like LiveCodeBench Pro and Xbench aim to provide more meaningful measures of AI abilities.
As AI becomes more widespread, uncertainty quantification emerges as a vital tool to build trust in AI outputs by highlighting prediction confidence and risks. Advances in computation are making this approach faster and easier to implement.
Discover how explainable AI addresses unpredictability in AI systems, fostering trust and accountability while enabling businesses to transform operations with transparent, reliable processes.
Traditional AI benchmarks often fail to reflect real-world complexities and human expectations. New evaluation methods emphasize human feedback, robustness, and domain-specific testing for more reliable AI.
Atla's detailed τ-Bench analysis and EvalToolbox introduce real-time diagnosis and correction of LLM agent failures, enhancing performance beyond traditional evaluation methods.
Discover why structured, component-driven workflows are critical to transitioning generative AI applications from impressive demos to reliable production systems with measurable business impact.