Welcome to today's podcast, where we dive into the recent achievement of Google's Gemini in completing Pokémon Blue, a feat that has drawn considerable attention but also raises important questions about the benchmark comparisons in AI models. While Gemini's success is impressive, it required significant external help, making it difficult to assess its true capabilities.

Developer JoelZ, who is behind Gemini plays Pokémon, cautioned against using this achievement as a measure of comparative performance. He stated, "please don’t consider this a benchmark for how well an LLM can play Pokémon." This caution stems from the fact that Gemini operates with a custom "agent harness" that provides advanced information about the game, aiding its navigation and problem-solving tasks.

As Julian Bradshaw pointed out, "the difference in tools and frameworks between models like Gemini and Claude can skew the results." In essence, while both models play the game, Gemini's harness gives it an edge by offering insights that Claude lacks.

As we consider these developments in AI, it's clear that while there are glimmers of potential, "we're still a long way from the kind of envisioned future where an Artificial General Intelligence can figure out a way to beat Pokémon just because you asked it to." Join us next time as we continue to explore the fascinating world of AI and gaming.
Link to Article