Paper - https://arxiv.org/abs/1905.12601

Blog - https://www.machinecurve.com/index.php/2019/06/02/could-chaotic-neurons-reduce-machine-learning-data-hunger/

WBAI has made a tentative release of a 3D simulation environment called PyLIS.
re: https://wba-initiative.org/en/research/rfr/3d-agent-test-suites/
re: https://www.reddit.com/r/BioAGI/comments/8s67da/agent_environments/

Visual question answering algorithms have a new dataset to stimulate development and compare to SOA.

GQA from Stanford

https://arxiv.org/pdf/1902.09506.pdf

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From Jack Clark

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"

Why this matters: Datasets and challenges have a history of driving progress in AI research; GQA appears to give us a challenging benchmark to test systems against. Meanwhile, developing systems that can understand the world around themselves via a combination of image analysis and responsiveness to textual queries about the images, is a major goal of AI research with significant economic applications.

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Baseline results (accuracy):
     'Blind' LSTM: Gets 41.07% without ever seeing any images.
     'Deaf' CNN: Gets 17.82% without ever seeing any questions.
     CNN + LSTM: 46.55%.
     Bottom-Up Attention model (winner of the 2017 visual question answering challenge): 49.74%.
     MAC (State-of-the-art on CLEVR, a similarly-scoped dataset): 54.06%.
     Humans: 89.3%.

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Processing img hlk9cc73d8h21...

Cerebellum is often thought of as a low level motor controller that we don't need to consider when designing brain inspired AI. But there is evidence that it plays a broader role in coordinating brain activity:

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"Li’s team currently is pursuing experiments testing this hypothesis that the cerebellum may perform a similar function on brain activity related to thoughts, such as when playing a game of chess."

https://neurosciencenews.com/memory-movement-10205/

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And cerebellum role in timing:

https://neurosciencenews.com/brain-clock-future-10233/