Unleashing the Power of GPT-4 with Wolfram Alpha and Code Interpreter: A Comprehensive Test (5min read)

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The Experiment 🧪

The authors conducted a series of tests on GPT-4, integrated with Wolfram Alpha (GPT4+WA) and Code Interpreter (GPT4+CI) plug-ins, using 105 original problems in science and math. These problems were designed at high school and college levels and were conducted between June and August 2023.

The Test Sets 📚

Arbitrary Numerical Test Set: 32 problems requiring elementary science and more demanding mathematics, including three-dimensional geometry, linear algebra, probability theory, and integral calculus.

Calculation-Free Test Set: 53 questions with discrete answers, including multiple-choice questions, binary choices, and sorting problems. These questions cover topics like eclipses, distance combinations, satellites, and more.

Motivated Numerical Test Set: 20 problems with numerical answers, designed to be more natural and inherently interesting, drawing from a wide range of areas in math and physics.

Key Findings 📈

Enhanced Abilities: The plug-ins significantly enhance GPT-4's ability to solve problems. However, there are still "interface" failures where GPT-4 struggles to formulate problems in a way that elicits useful answers from the plug-ins.

Performance Level: The systems perform at the level of a middling undergraduate student. They solve some problems that even capable students find challenging, but fail on some that high school students would find easy.

Successes and Failures: Both GPT4+WA and GPT4+CI succeeded in solving problems like calculating the probability within a 100-dimensional box or determining the possibility of three earth satellites being coplanar. However, they failed in problems like calculating the angle between Sirius and the Sun as viewed from Vega or determining the Shannon entropy of a positive integer.

Room for Improvement: There's considerable room for improvement in the interfaces between GPT-4 and the plug-ins. GPT-4 often fails to take full advantage of the capacities of the plug-ins, leading to avoidable errors.

Strengths and Weaknesses: The systems are strongest on problems that can be solved by invoking a single formula. They are often weak on problems involving spatial visualization or combining several calculations of different kinds.

Historical Context: The paper also provides a historical overview of the AI systems and the testing project, detailing the rapid advancements in AI technology.

Implications and Future Directions 🌐

The integration of GPT-4 with Wolfram Alpha and Code Interpreter plug-ins represents a significant step forward in AI's ability to tackle complex math and science problems. While the current capabilities are promising, the paper highlights several areas where improvements are needed.

The findings suggest that fixing interface failures and enhancing the synergy between GPT-4 and the plug-ins could lead to a more reliable tool for college-level calculation problems. The paper serves as a valuable roadmap for researchers and developers working on AI systems, shedding light on both the potential and the challenges of integrating specialized plug-ins with large language models.

In the grand scheme of things, this integration is a glimpse into the future of AI, where collaboration between different systems and tools can lead to more robust and versatile solutions. It's a golden path towards superintelligence, and we're just getting started! 🌟

Note: The paper's full details, including specific examples of successes and failures, can be found here.

What are your thoughts on this integration? How do you see the future of AI evolving with such collaborations? Share your insights below! 🧠💭