{"query": "So, which AI model did the best on the MMMU benchmark according to Yue and his team back in 2023?", "pos": "MMMU (val) Gemini Ultra (0-shot) GPT-4V (0-shot)\nMaj@32 pass@1 pass@1\nArt & Design 74.2 70.0 65.8\nBusiness 62.7 56.7 59.3\nScience 49.3 48.0 54.7\nHealth & Medicine 71.3 67.3 64.7\nHumanities & Social Science 78.3 78.3 72.5\nTechnology & Engineering 53.0 47.1 36.7\nOverall 62.4 59.4 56.8\nTable 8|Gemini Ultra performance on the MMMU benchmark (Yue et al., 2023) per discipline."}
{"query": "The GSPMD partitioner, part of the XLA compiler, is responsible for dividing the training step calculation.", "pos": "The GSPMD partitioner (Xu et al., 2021) in the XLA compiler\npartitions the training step computation, and the MegaScale XLA compiler (XLA, 2019) pass statically\nschedules appropriate collectives so that they maximally overlap with the computation with very little\nvariation in step time.\nMaintaining a high goodput2at this scale would have been impossible using the conventional\napproach of periodic checkpointing of weights to persistent cluster storage. For Gemini models, we\ninstead made use of redundant in-memory copies of the model state, and on any unplanned hardware\nfailures, we rapidly recover directly from an intact model replica."}
{"query": "What's the impact of where you live and your social status on how well AI image labeling tech works?", "pos": "Thoughwedo\nnot see large discrepancies across different groups, we note that this metric is imperfect as the human\nreference captions could be inherently biased. Additionally, we perform a zero-shot classification style\nevaluation with the Dollarstreet dataset (Rojas et al., 2022) to measure discrepancies in performance\nacross images which come from different geographic locations. As is seen in previous work, we find\nthat models work less effectively for images from lower socioeconomic regions and regions outside\nNorth America and Europe. This is an area where we need further research and work to improve in\nfuture iterations of our models.\nIn addition to comparing performance on tasks across groups, we also consider how people are\ndescribed in captions."}
