Can you remind me who started the proyein folding data competition whiich Hassabis deep mind eventually won with alphafold?
The protein folding data competition that DeepMind won with AlphaFold was the Critical Assessment of Structure Prediction (CASP). This biennial competition was founded in 1994 by John Moult, a computational biologist at the University of Maryland.
CASP has been a driving force in advancing protein structure prediction research, and DeepMind's breakthrough with AlphaFold has significantly accelerated progress in this field.
Are there other famous data competitions as far as AI is concerned in addition to CASP and Fei0Fei Li's imagenet? If so are any in the top league the way casp and imagenet seem to be?
Yes, there are several other famous data competitions in AI, in addition to CASP and ImageNet. Here are a few of the most notable ones:
Image Classification:
- CIFAR-10 and CIFAR-100: These are smaller datasets compared to ImageNet, but they are widely used for benchmarking image classification algorithms.
- Places365: A dataset of 365 scene categories, used for scene recognition and localization.
Natural Language Processing:
- GLUE (General Language Understanding Evaluation): A benchmark for evaluating the performance of natural language understanding systems on a variety of tasks, such as question answering, text classification, and sentiment analysis.
- SQuAD (Stanford Question Answering Dataset): A dataset for machine reading comprehension, where the goal is to answer questions based on a given passage of text.
- WMT (Workshop on Machine Translation): A workshop that includes a machine translation competition, where participants compete to build the best machine translation systems.
Speech Recognition:
- LibriSpeech: A large-scale speech recognition dataset, used for training and evaluating automatic speech recognition systems.
- TIMIT (Texas Instruments-MIT Spoken Sentence Corpus): A smaller dataset of phonetically balanced American English sentences, used for speech recognition research.
Other:
- Kaggle Competitions: Kaggle, a platform for data science and machine learning, hosts numerous competitions on a variety of topics, including image classification, natural language processing, and time series analysis.
- Netflix Prize: A famous competition that was held in 2006-2009, where participants competed to improve the accuracy of Netflix's movie recommendation algorithm.
These competitions have played a crucial role in driving progress in AI research and development. They provide a common benchmark for researchers to compare their methods and algorithms, and they often lead to significant breakthroughs in AI.
While CASP and ImageNet are undoubtedly among the most influential data competitions, the others listed above are also highly regarded and have made significant contributions to the field of AI.
Imagenet took a long time to set up (both years elapsed time and if one counts identification of 20 millionn images by amazon tutk) many years of haumn time- did casp take long time to set up?
Yes, CASP (Critical Assessment of Structure Prediction) also took a significant amount of time to set up.
While it doesn't involve the massive scale of image identification like ImageNet, CASP has involved:
- Decades of Protein Structure Research: The foundation for CASP lies in decades of research on protein structure prediction methods.
- Collaborative Efforts: The CASP initiative itself has been a collaborative effort involving researchers from around the world, coordinating experiments and evaluations.
- Data Collection and Preparation: Preparing the protein structures and associated data for CASP experiments requires significant time and computational resources.
While the exact timeline might vary depending on specific CASP iterations, it's safe to say that the setup and execution of CASP experiments have involved a substantial investment of time and effort from the scientific community.
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