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  1. 6. pro 2019.

    You'd think a generative model should answer different queries but deep learning models like VAEs are trained to answer just one type of query! We introduce a method called query-training that creates an inference network that can answer novel query types.

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  2. 6. pro 2019.

    We show that a singular principle of variable-order sequence learning in a structured model can explain cognitive maps phenom: spatial maps, transitive inference, temporal order, hierarchical planning, remapping etc. -- all important for AI & neuroscience.

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  3. 27. kol 2019.

    Our paper proposes a new model for cognitive maps in the hippocampus which involves learning higher-order graphs of event sequences. It can explain flexible planning, route-dependence of place cells, and subsumes successor representations.

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  4. 6. lip 2019.

    Deep learning is contributing to climate change: one more reason to develop data-efficient 'green' learning algorithms as we do at Vicarious! Small data for the win!

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  5. 6. svi 2019.

    If yuor laugnage moedl cnnaot raed tihs, it mgiht not be vrey dnagorues 😜. See our paper on learning graphs that can model higher-order sequences and deal with uncertainty.

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  6. proslijedio/la je Tweet
    31. sij 2019.

    ICYMI: This gripper processes data from a schematic visual, predicts the effect of imagined movements and writes those results onto an “ blackboard,” and finally, transforms its imagination into action via from :

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  7. proslijedio/la je Tweet

    “Even when you try to teach robots by demonstration, they’re just repeating the exact same motions you show them, not the concept underlying them,” Dileep George with

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  8. 22. sij 2019.

    A thought is a program! Check out our blog to see how we are building a cognitive architecture towards AGI. Also included: code, data, and a free link to our recent Science Robotics paper on concept learning.

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  9. 16. sij 2019.

    Our concept-learning work is now published in Science Robotics! Checkout this very nice video that Science made to explain the paper.

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  10. 10. pro 2018.

    Our new paper learns concepts as programs on a 'visual cognitive computer', showing zero-shot task-transfer on robots, and strong generalization. We bring cogsci ideas about perceptual symbol systems and image schemas into the realm of machine learning.

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    27. stu 2018.

    “AI has a common sense problem.” Getting from narrow AI to general AI will involve flipping the paradigm and efficiently learning from a small dataset, the way a human does, says co-founder (who has the coolest AI Twitter handle)

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  12. 13. stu 2018.

    Some common sense about common sense. Very much worth the read and not just because we are in it.

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  13. 6. stu 2018.

    Great shoutout to our SchemaNets paper in Melanie Mitchell's thoughtful article on the current state of AI.

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  14. 7. kol 2018.

    Here are the links to our CNN-18 papers: Cortical Microcircuits from a Generative Vision Model: Explaining Visual Cortex Phenomena using Recursive Cortical Network:

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  15. 7. kol 2018.

    Checkout our CCN-18 papers! (1) RCN-derived circuits suggest precise functional roles for feed-forward, feedback, and lateral connections, and for the thalamus. (2) RCN can explain multiple visual phenomena including two well-known illusions.

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  16. 6. lip 2018.

    Mathew Hutson's 's wonderfully researched feature in Science is worth a read. The article explores how AI can learn more like humans and highlights research at Vicarious!

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    “Regulation has always a part in society to ensure that companies do not externalise their costs. Governments have a critical role in this context.” Dileep George

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  18. proslijedio/la je Tweet
    30. tra 2018.

    Missed Bill Gates and Scott Phoenix's interviews on last Friday's episode of In Principle? Catch up and stream it now!

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  19. 26. tra 2018.

    Keep an eye out for Scott on the newest installment of PBS's In Principle. The episode also features Bill Gates (!!!) and airs tomorrow night at 8:30.

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  20. 7. velj 2018.

    Learning concepts through sensorimotor interactions is important for general AI. Check out our AAAI18 paper and blog on this topic:

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