Artificial Intelligence (AI) Benchmark Task

An Artificial Intelligence (AI) Benchmark Task is an AI task that serves as a benchmark task to measure and compare the performance of different AI models or systems.

• Context:
  • It can range from being a Narrow AI Benchmark (such as language understanding benchmark) to being a General AI Benchmark.
  • It can range from being a Specialized AI Benchmark (e.g., MMLU Benchmark) to being a Multi-Modal Benchmark that integrates diverse input types (e.g., Task Me Anything Benchmark).
  • It can range from being a Static Benchmark (where inputs are fixed) to being a Dynamic Benchmark, where environments and inputs change based on context (e.g., ActPlan-1K Benchmark).
  • It can offer meaningful comparisons across various AI Models, AI Systems, and AI Techniques.
  • It can help identify strengths and weaknesses of different AI Approaches.
  • It can provide standardized Datasets and Evaluation Metrics to ensure consistent and fair comparison of AI Models.
  • It can drive advancements in AI Research by highlighting areas where current AI Models fall short and encouraging the development of more robust and capable systems.
  • ...
• Example(s):
  • Computer Vision AI Benchmarks, such as:
    • An ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which is used in the field of computer vision.
    • The COCO (Common Objects in Context) Dataset, used for object detection, segmentation, and captioning in images.
  • Natural Language Processing AI Benchmarks, such as:
    • An LLM Benchmarking Task, such as an MMLU benchmark.
    • An NLP Benchmarking Task, such as a SQuAD (Stanford Question Answering Dataset).
    • The General Language Understanding Evaluation (GLUE) benchmark, used for evaluating natural language understanding models.
    • The SuperGLUE Benchmark, an improvement over GLUE for more challenging natural language understanding tasks.
    • The Hugging Face Model Evaluations, which provide a comprehensive comparison of transformer models on a wide range of tasks.
    • HaluEval Benchmark.
  • General AI Benchmarks, such as Turing Tests, which is a measure of a machine's ability to demonstrate human-like intelligence.
  • Robustness and Performance AI Benchmarks, such as:
    • The RobustBench Benchmark, which evaluates the robustness of AI models to adversarial attacks.
    • The MLPerf Benchmark, which measures the performance of machine learning hardware, software, and services.
  • AI Agent Benchmarking Tasks.
  • Edge and Mobile AI Benchmarks, which evaluates the performance of AI models on mobile and edge devices.
  • Procedural Planning Benchmarks, such as:
    • The ActPlan-1K Benchmark, which evaluates the procedural planning abilities of visual language models (VLMs) on simulated household activities.
  • Multimodal AI Benchmarks, such as:
    • The Task Me Anything Benchmark, which programmatically generates diverse tasks combining visual, relational, and attribute-based benchmarks for multimodal AI systems.
  • Reasoning Benchmark, such as:
    • Abstraction and Reasoning Corpus (ARC) Benchmark.
  • AI Coding Benchmarks, such as:
    • SWE-bench.
    • MLE-bench.
    • ...
  • ...
• Counter-Example(s):
  • An Olympic Event, which is a competition among humans, not AI systems.
  • A Cooking Contest, which evaluates human culinary skills rather than AI capabilities.
• See: Software Benchmark, ML Benchmark, Performance Metric, Evaluation Framework, AI Agent

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References

2024

• (Chan, Chowdhury et al., 2024) ⇒ Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, and Aleksander Mądry. (2024). “MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering.”
  • NOTES:
    • The paper introduces a comprehensive framework for evaluating Autonomous AI Agents in complex Machine Learning Engineering (MLE) tasks using a benchmark of 75 curated Kaggle competitions.
    • The paper designs and implements a novel Benchmark for AI Systems, measuring agent capabilities in training, debugging, and optimizing machine learning models.

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