Artificial Intelligence (AI) System Benchmark Task

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

• AKA: AI System Evaluation Task, AI Performance Assessment.
• Context:
  • Input(s): AI Model, AI Test Dataset, AI System Configuration
    • optional: AI Training Dataset.
  • Output(s): AI Benchmark System Assessment Report.
  • Performance Measure(s): AI System Accuracy, AI System Latency, AI System Cost.
  • ...
  • It can range from being a Narrow AI Benchmark to being a General AI Benchmark, depending on its scope.
  • It can range from being a Specialized AI Benchmark to being a Multi-Modal Benchmark, depending on its input type.
  • It can range from being a Static AI Benchmark to being a Dynamic Benchmark, depending on its environment type.
  • It can range from being a Simple AI Benchmark to being a Complex AI Benchmark, depending on its complexity level.
  • It can range from being a Single-Domain AI Benchmark to being a Cross-Domain AI Benchmark, depending on its domain coverage.
  • ...
  • It can measure AI System Capability.
  • It can assess AI System Robustness and AI System Reliability.
  • It can offer meaningful comparisons across various AI Models, AI Systems, and AI Techniques.
  • It can be part of AI Development Processes.
  • It can support AI System Selection.
  • It can guide AI Research Directions.
  • ...
• Examples:
  • Computer Vision AI Benchmarks, such as:
    • ImageNet Large Scale Visual Recognition Challenge (ILSVRC), evaluating computer vision capabilities.
    • COCO Dataset (2024), testing object detection and segmentation.
    • Visual Language Model Benchmark (2024), assessing multimodal understanding.
    • ...
  • Natural Language Processing AI Benchmarks, such as:
    • LLM Benchmarking Tasks, such as MMLU Benchmark (2024)
    • NLP Benchmarking Tasks, such as SQuAD (2024)
    • General Language Understanding Evaluation (GLUE), for language understanding
    • SuperGLUE Benchmark, for advanced NLP capability
    • Hugging Face Model Evaluations, for transformer models
    • HaluEval Benchmark, for hallucination detection
    • ...
  • General AI Benchmarks, such as:
    • Turing Tests, measuring human-like intelligence.
    • ActPlan-1K Benchmark, for procedural planning.
    • ...
  • Robustness and Performance AI Benchmarks, such as:
    • RobustBench Benchmark, for adversarial robustness.
    • MLPerf Benchmark, for system performance.
    • ...
  • AI Agent Benchmarking Tasks.
  • Multimodal AI Benchmarks, such as:
    • Task Me Anything Benchmark, for diverse capability testing.
  • Reasoning Benchmarks, such as:
    • Abstraction and Reasoning Corpus (ARC) Benchmark.
  • AI Coding Benchmarks, such as:
    • SWE-bench, for software engineering.
    • MLE-bench, for machine learning engineering.
    • ...
  • ML Benchmark Task.
  • ...
• Counter-Examples:
  • AI Case Study.
• See: Software Benchmark, Evaluation Framework, AI Agent, Data Science Case Study.

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References

2024-11-20

• Perplexity.ai
  • Question: What are AI benchmarks and how are they used to evaluate artificial intelligence systems? Please include information about different types of benchmarks (like task-specific vs general benchmarks), key evaluation metrics used, major benchmark categories (such as computer vision and NLP benchmarks), and examples of popular benchmarking frameworks. Also explain how benchmarking helps in comparing AI models and measuring their performance, accuracy, and efficiency. Include information about challenges in AI benchmarking and how benchmarks contribute to AI Development and AI Research.
  • Answer: AI benchmarks are standardized tests designed to measure and evaluate the performance of artificial intelligence systems across various tasks and domains.
    • AI Benchmark Types:
      • Task-Specific AI Benchmarks:
        • Natural Language Processing benchmarks evaluate language understanding and generation capabilities
        • Computer Vision benchmarks assess image and video analysis performance
        • Reasoning and Problem-Solving benchmarks test cognitive abilities
      • General AI Benchmarks:
        • ImageNet for image recognition systems
        • GLUE (General Language Understanding Evaluation) for Natural Language Processing
        • MATH dataset for mathematical reasoning capabilities
    • AI Performance Metrics:
      • Core AI Metrics:
        • Accuracy: Ratio of correctly predicted instances to total instances
        • Precision: Quality of positive predictions
        • Recall: Model's ability to identify relevant instances
        • F1 Score: Harmonic mean of precision and recall
        • ROC-AUC: Performance measurement for classification problems
    • Major Benchmark Categories:
      • Natural Language Processing:
        • GLUE and SQuAD for Language Understanding
        • Winograd Schema Challenge for AI Reasoning
      • Computer Vision:
        • ImageNet for Object Recognition
        • MS COCO for Object Detection
        • Cityscapes for Semantic Segmentation
    • AI Development Frameworks:
      • Development Frameworks:
        • PyTorch: Favored by researchers for prototyping and experimentation
        • TensorFlow: Known for flexibility and scalability
        • Scikit-Learn: Suitable for traditional machine learning tasks
        • Keras: User-friendly framework for Deep Learning
    • Challenges in AI Benchmarking:
      • Current AI Benchmark Limitations:
        • Lack of standardization across different applications
        • Narrow scope of individual benchmarks
        • High costs and resource requirements for evaluation
        • Risk of benchmark overfitting
      • Emerging AI Benchmark Issues:
        • Difficulty in evaluating real-world performance
        • Challenge of assessing multimodal capabilities
        • Need for more comprehensive evaluation methods
    • Impact on AI Development:
      • AI Research Benefits:
        • Enables quantitative comparison between different AI systems
        • Promotes reproducibility and transparency
        • Drives innovation and competition in the field
      • AI Industry Applications:
        • Helps organizations select appropriate AI solutions
        • Guides development and improvement of AI models
        • Facilitates performance monitoring and optimization
    • Future Directions:
      • Emerging AI Benchmark Trends:
        • Development of more comprehensive evaluation frameworks
        • Integration of human evaluation alongside automated benchmarks
        • Focus on real-world application testing
        • Emphasis on multimodal assessment capabilities
  • Citations:

[1] https://claude3.us/analyzing-claude-3-benchmarks/
[2] https://www.assemblyai.com/blog/objective-benchmarks-how-to-evaluate-ai-models/
[3] https://www.nownextlater.ai/Insights/post/ai-benchmarks-misleading-measures-of-progress-towards-general-intelligence
[4] https://www.restack.io/p/ai-model-evaluation-answer-benchmark-metrics-cat-ai
[5] https://www.datacamp.com/blog/top-ai-frameworks-and-libraries
[6] https://www.restack.io/p/ai-benchmarking-answer-how-to-benchmark-ai-models-cat-ai
[7] https://venturebeat.com/ai/rethinking-ai-benchmarks-a-new-paper-challenges-the-status-quo-of-evaluating-artificial-intelligence/
[8] https://www.larksuite.com/en_us/topics/ai-glossary/benchmarking
[9] https://themarkup.org/artificial-intelligence/2024/07/17/everyone-is-judging-ai-by-these-tests-but-experts-say-theyre-close-to-meaningless
[10] https://cset.georgetown.edu/publication/measuring-ai-development/
[11] https://www.spiceworks.com/tech/artificial-intelligence/articles/are-ai-benchmarks-reliable/

2024-11-08

• https://x.com/karpathy/status/1855659091877937385
  • Moravec's paradox in LLM evals
  • I was reacting to this new benchmark of frontier math where LLMs only solve 2%. It was introduced because LLMs are increasingly crushing existing math benchmarks. The interesting issue is that even though by many accounts (/evals), LLMs are inching well into top expert territory (e.g., in math and coding, etc.), you wouldn't hire them over a person for the most menial jobs. They can solve complex closed problems if you serve them the problem description neatly on a platter in the prompt, but they struggle to coherently string together long, autonomous problem-solving sequences in a way that a human would find very easy.
  • This is Moravec's paradox in disguise, who observed 30+ years ago that what is easy/hard for humans can be non-intuitively very different to what is easy/hard for computers. E.g., humans are very impressed by computers playing chess, but chess is easy for computers as it is a closed, deterministic system with a discrete action space, full observability, etc. Vice versa, humans can tie a shoe or fold a shirt and don't think much of it at all, but this is an extremely complex sensorimotor task that challenges the state of the art in both hardware and software. It's like that Rubik's Cube release from OpenAI a while back where most people fixated on the solving itself (which is trivial) instead of the actually incredibly difficult task of just turning one face of the cube with a robot hand.
  • So I really like this FrontierMath benchmark and we should make more. But I also think it's an interesting challenge how we can create evals for all the "easy" stuff that is secretly hard. Very long context windows, coherence, autonomy, common sense, multimodal I/O that works, ... How do we build good menial job evals? The kinds of things you'd expect from any entry-level intern on your team.

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.

2023

• (Mialon et al., 2023) ⇒ Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom. (2023). "GAIA: A Benchmark For General AI Assistants." In: arXiv preprint arXiv:2311.12983. doi:arXiv:2311.12983
  • NOTE: It proposes a comprehensive evaluation methodology for general AI systems, emphasizing their performance in multi-domain tasks.

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