Artificial Intelligence Surpasses Human Capabilities
Introduction to Gemini
At the ICPC, only correct solutions earn points, and the time it takes to come up with the solution affects the final score. Gemini, an AI model, reached the upper rankings quickly, completing eight problems correctly in just 45 minutes. After 677 minutes, Gemini 2.5 Deep Think had 10 correct answers, securing a second-place finish among the university teams.
Gemini’s Impressive Solutions
You can take a look at all of Gemini’s solutions on GitHub, but Google points to Problem C as especially impressive. This question, a multi-dimensional optimization problem revolving around fictitious “flubber” storage and drainage rates, stumped every human team. But not Gemini.
According to Google, there are an infinite number of possible configurations for the flubber reservoirs, making it challenging to find the optimal setup. Gemini tackled the problem by assuming that each reservoir had a priority value, which allowed the model to find the most efficient configuration using a dynamic programming algorithm. After 30 minutes of churning on this problem, Deep Think used nested ternary search to pin down the correct values.
Gemini’s Problem-Solving Process
Gemini’s solutions for this year’s ICPC were scored by the event coordinators, but Google also turned Gemini 2.5 loose on previous ICPC problems. The company reports that its internal analysis showed Gemini also reached gold medal status for the 2023 and 2024 question sets.
An example of Gemini’s problem-solving process can be seen in the figure below.
Future Implications of Gemini’s Capabilities
Google believes Gemini’s ability to perform well in these kinds of advanced academic competitions portends AI’s future in industries like semiconductor engineering and biotechnology. The ability to tackle a complex problem with multi-step logic could make AI models like Gemini 2.5 invaluable to the people working in those fields. The company points out that if you combine the intelligence of the top-ranking university teams and Gemini, you get correct answers to all 12 ICPC problems.
Of course, five hours of screaming-fast inference processing doesn’t come cheap. Google isn’t saying how much power it took for an AI model to compete in the ICPC, but we can safely assume it was a lot. Even simpler consumer-facing models are too expensive to turn a profit right now, but AI that can solve previously unsolvable problems could justify the technology’s high cost.
Conclusion
Gemini’s impressive performance at the ICPC demonstrates the potential of AI to surpass human capabilities in complex problem-solving. As AI technology continues to advance, we can expect to see more applications of AI in various industries, leading to breakthroughs and innovations that were previously unimaginable.
Frequently Asked Questions
What is Gemini, and how does it work?
Gemini is an AI model developed by Google that uses advanced algorithms and machine learning techniques to solve complex problems. Its exact workings are not publicly disclosed, but it is known to use dynamic programming and nested ternary search to find optimal solutions.
What are the potential applications of Gemini’s capabilities?
Gemini’s abilities have potential applications in various industries, including semiconductor engineering, biotechnology, and other fields that require complex problem-solving. Its ability to tackle multi-step logic problems could make it an invaluable tool for researchers and engineers.
How expensive is it to run an AI model like Gemini?
The exact cost of running an AI model like Gemini is not publicly disclosed, but it is likely to be very high. However, the potential benefits of using AI to solve previously unsolvable problems could justify the high cost, especially in industries where breakthroughs could lead to significant advancements and innovations.
