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On December 20, 2024, the ARC Prize Foundation reported that OpenAI's o3 scored 75.7% on the semi-private ARC-AGI-1 evaluation set, and 87.5% with a high-compute configuration — a jump far beyond earlier models on a benchmark built to resist memorization.
On September 12, 2024, OpenAI introduced o1 and reported that it averaged 83% on AIME 2024 when 64 samples were combined, versus 12% for GPT-4o, plus a Codeforces Elo of 1673.
On 30 November 2020 DeepMind announced that the organisers of CASP14 had recognised its AlphaFold system as a solution to the 50-year-old protein-folding problem, with accuracy approaching that of experimental methods.
The 2015 ImageNet classification contest was won by ResNet, a network up to 152 layers deep whose layers learn residual functions relative to their inputs; it reached 3.57% error on the test set and, by one account, exceeded human performance.
The DARPA Robotics Challenge Finals, held on 5 and 6 June 2015 at the Fairplex in Pomona, California, put 23 teams and their robots through eight tasks; Team KAIST's DRC-Hubo took first place and the $2 million prize.
At ILSVRC 2014, GoogLeNet won the classification track with 6.67% top-5 error, while VGG's network won localisation and took second in classification; more than 50 institutions took part.
A two-day DARPA competition in December 2013 in which 16 teams put prototype disaster-response robots through eight simulated tasks; SCHAFT of Tokyo scored highest with 27 points out of a possible 32.
In 2013 the ILSVRC classification task was won by Clarifai with an ensemble of convolutional networks at 11.7% top-5 error, while OverFeat won localisation. Zeiler and Fergus's visualization paper, also reporting ImageNet results, appeared the same month.
In October 2012 AlexNet, a deep neural network built by a University of Toronto team, won the ImageNet image-classification challenge by a wide margin. Its result showed that large neural networks trained on GPUs could outperform hand-designed vision methods.
A Kaggle competition run in August–October 2012 in which competitors predicted molecules' biological activities from numerical descriptors of their chemical structures; a deep-learning team led by Geoffrey Hinton took first place.
On 13 April 2010, Fei-Fei Li announced the first ImageNet Large Scale Visual Recognition Challenge, a 1,000-category image categorization benchmark. It ran for eight editions, from 2010 to 2017.
At ICDAR 2009, Alex Graves won three connected handwriting competitions — French, Farsi and Arabic — with LSTM networks trained using Connectionist Temporal Classification, the first time a recurrent neural network won an international contest.
An open competition run by Netflix from 2006 to 2009 that offered $1,000,000 to any team able to improve its movie recommendation algorithm by 10%, and released 100 million anonymous ratings for contestants to work with.
