@A_A
@lemmy.worldSince these are homologous :
Sailor Moon' hair balls (& hair style), v.s.,
Jar jar Binks' eyes (& ears)
could some combinations be interesting ?
Supposedly Q-star is better at formal logics. Maybe it has to do with this : ...
Cette émission est animée par Marie-Louise Arsenault. Je la trouve excellente, dynamique et intéressante. Les sujets qu'elle y présente sont presque toujours intéressants et cette fois-ci, avec (notamment) le massage en question, ce fut un moment sensible, agréable et humain.
Lien dans les commentaires.
https://en.m.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate
https://lemmy.world/pictrs/image/4648e350-7d57-41ed-8d60-323399e03a52.png?format=webp&thumbnail=96
(for a total of 7 fingers per hands)
Genetic engineering will create surprising things. We could explore it in advance with ai generated images or motion pictures.
https://lemmy.world/pictrs/image/d8f2f28a-bfa9-43cc-8996-ff5b3bb277f7.png?format=webp
Breakthrough Technique: Meta-learning for Compositionality
Original :
https://www.nature.com/articles/s41586-023-06668-3
Vulgarization :
https://scitechdaily.com/the-future-of-machine-learning-a-new-breakthrough-technique/
How MLC Works
In exploring the possibility of bolstering compositional learning in neural networks, the researchers created MLC, a novel learning procedure in which a neural network is continuously updated to improve its skills over a series of episodes. In an episode, MLC receives a new word and is asked to use it compositionally—for instance, to take the word “jump” and then create new word combinations, such as “jump twice” or “jump around right twice.” MLC then receives a new episode that features a different word, and so on, each time improving the network’s compositional skills.