# NIPS 2017 Summary

DevFeed: [NIPS 2017 Summary](<https://devfeed.tech/articles/nips-2017-summary-40109.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2017-12-11-nips-2017-summary/>)

Published: 2017-12-11T00:00:00Z

Content type: article

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [NeurIPS](<https://devfeed.tech/topics/neurips.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [ai-research](<https://devfeed.tech/tags/ai-research.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [fairness](<https://devfeed.tech/tags/fairness.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [model-interpretability](<https://devfeed.tech/tags/model-interpretability.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [papers](<https://devfeed.tech/tags/papers.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>)

## AI overview

A summary of key themes from NIPS 2017, including rapid progress in Bayesian deep learning, growing attention to model interpretability, bias, and fairness, the need for more theory in deep learning, advances in deep reinforcement learning, and ongoing questions about GANs.

## Source excerpt

Key takeaways from NeurIPS 2017: Bayesian deep learning, model interpretability, fairness, and the state of AI research.