AI Benchmarking: Measuring Progress Toward Smarter AI
We need to know how various models perform relative to each other, in order to track our progress
This is a high-level, non-technical overview of benchmarking in AI. ChatGPT and assisted with research and phrasing certain concepts. All errors are of course mine but that won’t stop me from blaming the AI.
Which AI Model is the GOAT?
A perennial debate rages among sports fans: who is the greatest basketball player of all time, Michael Jordan or LeBron James?1 Each side marshals its statistics—championship rings, MVP awards, points scored over their careers, average points per game per season, and countless others—to argue their case. This is a classic example of benchmarking: a structured way of measuring, comparing, and debating excellence. In sports, benchmarking allows us to contextualize individual performance within the broader history of the game, providing a framework for meaningful comparison.
This concept is not limited to sports. In AI, benchmarking serves a similarly pivotal role. Just as fans debate the GOAT status in basketball, researchers and industry leaders constantly evaluate which AI model is the best. However, instead of rings and points, AI benchmarking relies on datasets, tasks, and metrics to assess models’ performance, efficiency, and robustness. Whether it’s determining how closely a model approaches human-level intelligence or identifying its suitability for a specific application, benchmarking is central to the field’s progress.
We’ll explore the principles of AI benchmarking: its components, importance, challenges, and future trends. Like the endless debate over MJ2 vs King James, benchmarking AI models is as much about understanding where we are as it is about envisioning where we will go.
Key Components of AI Benchmarking
Tasks or Domains. Just as Jordan’s and LeBron’s scoring titles and championship rings define their benchmarks, AI tasks such as image recognition or natural language understanding serve as domains where models are tested and compared. For instance, image classification can be evaluated using benchmarks like ImageNet, while general knowledge and reasoning might be assessed with datasets such as Big-bench. Each task defines a particular domain that tests a unique aspect of an AI model’s abilities.
Datasets. Datasets serve as the input for benchmarking tests, often curated to be representative, unbiased, and challenging. Examples include CIFAR-10 for image recognition, GLUE for natural language understanding, and SQuAD for question-answering tasks. These datasets provide standardized environments in which AI models can be evaluated and compared.
Metrics. The performance of AI models is quantifies using specific metrics. Common metrics include accuracy, precision, recall, and F1 score for classification tasks, while metrics like BLEU evaluate the quality of machine translations. Today, new metrics like energy usage and memory footprints are increasingly relevant as efficiency becomes as important as raw performance.
Scenarios. Benchmarks may use real-world or simulated environments to test how models perform under different conditions. For example, adversarial robustness tests how well a model handles deliberately misleading data, helping guage its reliability when faced with malicious inputs.
Baselines. In basketball, comparing Jordan or LeBron to the average NBA player provides essential context. Similarly, baseline models provide context for understanding the performance of newer AI systems. These might be older AI models, human-level performance baselines, or even simpler algorithms that set a performance floor. These baselines help establish how much progress has been made and what constitutes a meaningful advancement.
Importance of AI Benchmarking
The easy answer here is that we need some way to determine how performant this AI model is relative to that AI model. It’s the same reason we compare cars, or baseball players, or countries’ economies, to one another. We want to know where one model stands relative to others. Over time we should be able to infer which are the most capable models, and extrapolate capabilities for next-generation models.
Specific things that benchmarking allows us to do include:
Progress Tracking: AI benchmarks allow us to measure how the technology advances over time, providing a roadmap for how capabilities grow.
Fair Comparison: With standardized conditions, different models and methods can be compared on a level playing field.
Model Selection: For businesses, benchmarks are a valuable tool to select the best models for specific applications. For example, choosing the most efficient model might be critical in a low-resource environment. This will become especially pertinent as edge computing devices are imbued with AI capabilities.
Identifying Weaknesses: Benchmarks can highlight areas where AI systems underperform. This helps guide researchers towards the next breakthrough.
Setting Standards: Benchmarks create standards that industries adopt.
Real-World Example: In healthcare, benchmarking has helped advance diagnostics. For instance, AI models are now capable of detecting diseases in medical imaging with greater accuracy than human radiologists. Similarly, in autonomous vehicles, benchmarks like KITTI allow us to track improvements in obejct detection and pedestrian recognition, directly contributing to safer streets.
Challenges in AI Benchmarking
Despite their importance, AI benchmarks come with significant challenges:
Bias in Datasets: If datsets used in benchmarks are not representative of the real world, the performance scores can be misleading. For instance, the COMPAS dataset, used for predicting criminal recidivism, has been criticized for racial bias, which led to flawed AI predictions with real-world impacts.
Evolving Tasks: AI capabilities advance rapidly, and older benchmarks can quickly become irrelevant. Consider MNIST, a simple dataset of handwritten digits. Today, state-of-the-art AI achieves nearly perfect results on MNIST, rendering it an ineffective tool for measuring AI progress.
Overfitting to Benchmarks: Sometimes, models are optimized specifically to perform well on popular benchmarks rather than achieving true generalizability. For example, language models trained on benchmarks like MMLU can excel in structured tasks but struggle in casual, human-like interactions.
Lack of Contextual Understanding: Many benchmarks fail to capture higher-level reasoning or common-sense understanding, making it hard to evaluate how models would perform in everyday situations.
Computational Costs: Running benchmarks, especially for state-of-the-art models, requires significant computational resources. This makes the benchmarking process expensive and limits access to only well-funded research institutions.
Notable Benchmarking Platforms and Initiatives
OpenAI Gym: This platform provides environments for reinforcement learning, helping researchers assess models in simulated settings that mimic real-life complexity.
MLPerf: MLPerf has become an industry standard for evaluating both AI training and inference performance. It helps businesses compare how different models and hardware configurations stack up against one another.
SuperGLUE: Designed as an improvement over the original GLUE benchmark, SuperGLUE assesses advanced natural language understanding.
DynaBench: Unlike static benchmarks, DynaBench offers a dynamic approach, continuously adapting its tasks as models improve. This ensures that models aren’t just getting better at static datasets but are learning to adapt and generalize effectively.
Case Study: Recently, Epoch AI released a benchmarking study comparing large language models like GPT-4 and Claude. Their findings showed that GPT-4 excelled in logical reasoning but lagged slightly in creative writing tasks. Such benchmarking studies give researchers and developers valuable insight into where models shine and where they need improvement.
The Future of AI Benchmarking
As AI becomes more advanced, benchmarks will also need to evolve. Here are some emerging trends:
Ethical Benchmarks: Evaluating AI based on fairness and bias is becoming more critical. Future benchmarks may include a ‘Bias-Score’ to assess ethical considerations across different demographics.
Sustainability Metrics: As AI models grow, they demand more energy. Efficiency and sustainability will likely become important measures, rewarding models that achieve more with less computational power.
Dynamic Benchmarks: Static benchmarks will gradually be replaced or complemented by adaptive challenges, like DynaBench, which can grow alongside advancements in AI capabilities.
Conclusion
AI benchmarking is more than just a comparison of numbers. It’s an essential component that drives progress across fields ranging from healthcare to finance to autonomous systems. Benchmarks help us understand where we are on the path to AGI, illuminate the strengths and weaknesses of existing models, and set clear goals for the future. The road to AGI will not only be defined by breakthroughs in model architecture but also by our ability to develop meaningful ways to evaluate, text, and compare these models.
Here’s a great video about this debate, which makes extensive use of benchmarking.
There are no fewer than three celebrities who might be referred to as MJ: Michaels Jordan & Jackson, and Earvin “Magic” Johnson. And there’s even a Michael Johnson, hero of the 1996 Atlanta Olympics. To say nothing of the actor Michael B Jordan. Or Spiderman’s paramour Mary Jane Watson. Here, of course, MJ means Michael Jordan.