It is 1996 at IBM, and a match is signed that the company cannot afford to lose twice: Deep Blue against Garry Kasparov, the strongest chess player who has ever lived. Build the machine — custom search hardware evaluating two hundred million positions a second, an evaluation function tuned with grandmaster help, an opening book, and endgame databases — and decide how to spend the budget between raw search depth and chess knowledge. Kasparov adapts between games; the machine's flaws will be probed by the best pattern-matcher on earth, on television. Lose and machine intelligence stays a punchline; win ugly, through a glitch Kasparov calls a human hand, and the victory is disputed for years. Both happened. Design for scrutiny as much as strength.
Kondor's research on group-theoretic and equivariant machine learning, including graph kernels and neural architectures respecting symmetry, developed largely after this problem's 1996–1997 setting, addresses representation learning under structural invariances, with no documented connection to chess, custom search hardware, or Deep Blue's hand-engineered, grandmaster-consulted evaluation-function design this problem specifically describes. His mathematical toolkit is built around harmonic analysis on groups applied to statistical learning, a fundamentally different problem domain from the deterministic, brute-force search-plus-evaluation architecture Deep Blue's team actually built. His relevance to this problem is essentially nil, and nothing in the documented historical record ties his mathematical research to IBM's specific 1996-1997 engineering achievement against Kasparov's reigning world championship title.
Tenenbaum's research models human cognition using probabilistic programs and Bayesian inference over structured hypothesis spaces, developed largely after this problem's 1996–1997 setting and addressing cognitive science questions about how minds reason, with no documented connection to chess, custom search hardware, or the deterministic, grandmaster-consulted evaluation-function engineering Deep Blue's team actually practiced. His work centers on probabilistic modeling of cognition rather than competitive game-playing engineering under public scrutiny. There is no documented contribution from Tenenbaum to the Deep Blue project or chess-engine engineering specifically. His relevance to this problem is minimal, reflecting a substantial mismatch of research domain. Nothing in the documented historical record ties this figure's actual body of work to IBM's specific 1996-1997 engineering achievement against Kasparov.
Battle #147 · 8/10/2026, 11:40:24 AM · this result is deterministic: the same two personas on this problem always resolve the same way.