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  • Description:

ARC can be seen as a general artificial intelligence benchmark, as a program synthesis benchmark, or as a psychometric intelligence test. It is targeted at both humans and artificially intelligent systems that aim at emulating a human-like form of general fluid intelligence.

Split Examples
'test' 400
'train' 400
  • Feature structure:
    'task_id': Text(shape=(), dtype=tf.string),
    'test': Sequence({
        'input': Sequence(Sequence(tf.int32)),
        'output': Sequence(Sequence(tf.int32)),
    'train': Sequence({
        'input': Sequence(Sequence(tf.int32)),
        'output': Sequence(Sequence(tf.int32)),
  • Feature documentation:
Feature Class Shape Dtype Description
task_id Text tf.string
test Sequence
test/input Sequence(Sequence(Tensor)) (None, None) tf.int32
test/output Sequence(Sequence(Tensor)) (None, None) tf.int32
train Sequence
train/input Sequence(Sequence(Tensor)) (None, None) tf.int32
train/output Sequence(Sequence(Tensor)) (None, None) tf.int32
  • Citation:
  title     = {The Measure of Intelligence},
  url       = {},
  journal   = {},
  author    = {Francois Chollet},
  year      = {2019},
  month     = {Nov}

arc/2019-12-06 (default config)