The Gradient

@gradientpub

The Gradient cuts through the hype and the cynicism to provide accessible, sophisticated reporting on the latest AI research.

Vrijeme pridruživanja: siječanj 2018.

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  1. 25. sij

    GPT-2 can generate some impressive looking sentences, but claims that "anything that looks like genuine understanding is just an illusion."

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  2. 18. sij

    From to , many worry about the significant economic future impacts of AI. Economist predicts that instead, “the future of AI in the economy will resemble the Internet more than Skynet.”

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  3. 23. pro 2019.

    Gradient editor reflects on his 2019 NeurIPS experience

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  4. 30. stu 2019.

    Overhyped claims about AI have contributed to past AI winters.  fears that we could be headed down that same path again.  Here's what we can do to stop it.

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    25. stu 2019.

    Artificial Life for AI People A fantastic intro to the ALife field by , with a good list of resources to connect with the ALife research community! Happy to see more articles like this (that don't follow the gradient) published in

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  6. 25. stu 2019.

    AI asks fundamental questions about the nature of “intelligence”, but what about understanding life itself? gives an overview of Artificial Life for AI people.

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  7. 23. stu 2019.

    Do you enjoy reading Gradient articles? Would you like to help make them a reality? The Gradient editorial team is seeking a few additional editors. Shoot us an email at editor@thegradient.pub if you are interested!

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  8. 17. stu 2019.

    Most surviving documents from humanity's ancient history lie dormant in storage, unreadable to all but a handful of expert scholars. discusses how researchers from are using ML to solve this tragedy.

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  9. 13. stu 2019.

    Have you ever heard someone mention Gaussian Processes and not known what they were talking about? gives an intuitive overview of this important, but often underlooked method in ML.

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  10. 19. lis 2019.

    Evaluation metrics are crucial to progress in machine learning. In light of recent interest in transfer learning in NLP, gives a comprehensive overview of some of the most important evaluation metrics in language modeling.

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  11. 18. lis 2019.
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    PyTorch long ago became the tool of choice for AI research at fb. It’s now the tool of choice for production as well.

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  13. 10. lis 2019.

    The war between ML frameworks has raged on since the rebirth of deep learning. Who is winning? 's data analysis shows clear trends: PyTorch is winning dramatically among researchers, while Tensorflow still dominates industry.

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    15. ruj 2019.

    The : On Naming the Languages We Study and Why It Matters Great article by on why English is neither synonymous with nor representative of natural language. Anyone doing NLP should read this.

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  15. 15. ruj 2019.

    English is not the world’s only language. As such, encourages NLP researchers to also work on other languages, or at the very least, follow the : name the languages you use.

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  16. 27. kol 2019.

    Do neural networks learn what we think they learn? reviews research that suggests that they often instead fall prey to the so-called Clever Hans effect and discusses its implications for NLP.

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    21. kol 2019.

    “The ultimate goal of Retrospectives is to make research more human. That means getting researchers to write papers like how they’d talk to a friend. It means making research transparent & accessible for everyone. It means making an environment where it’s okay to make mistakes.”

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  18. 21. kol 2019.

    Researchers currently don't have a forum for sharing updated thoughts on their past papers. introduces ML Retrospectives, a new NeurIPS workshop that wants to change that.

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  19. 19. kol 2019.

    Aseem Saxena gives an overview of the latest advances in robotics presented at the 2019 Robotics Science and Systems conference.

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  20. 6. kol 2019.

    Many predict that healthcare will undergo an AI revolution. discusses its impacts thus far.

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