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@@ -8,7 +8,7 @@ We ❤️ reading ML papers so we have created this repo to highlight the top ML
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| 1) **Mastering Diverse Domains through World Models** -- DreamerV3 is a general algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in AI. | [Paper](https://arxiv.org/abs/2301.04104v1), [Tweet](https://twitter.com/dair_ai/status/1614676677757661185?s=20&t=3GITA7PeX7pGwrqvt97bYQ)|
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| 2) **Tracr: Compiled Transformers as a Laboratory for Interpretability** -- DeepMind proposes Tracr, a compiler for converting RASP programs into transformer weights. This way of constructing NNs weights enables the development and evaluation of new interpretability tools. | [Paper](https://arxiv.org/abs/2301.05062), [Tweet](https://twitter.com/dair_ai/status/1614676680165187584?s=20&t=3GITA7PeX7pGwrqvt97bYQ), [Code](https://github.com/deepmind/tracr) |
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-| 3) **Multimodal Deep Learning** -- Multimodal deep learning is a new book published on ArXiv. | [Paper/Book](https://arxiv.org/abs/2301.04856), [Tweet](https://twitter.com/dair_ai/status/1614676682555670528?s=20&t=3GITA7PeX7pGwrqvt97bYQ) |
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+| 3) **Multimodal Deep Learning** -- Multimodal deep learning is a new book published on ArXiv. | [Book](https://arxiv.org/abs/2301.04856), [Tweet](https://twitter.com/dair_ai/status/1614676682555670528?s=20&t=3GITA7PeX7pGwrqvt97bYQ) |
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| 4) **Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk** -- OpenAI publishes new work analyzing how generative LMs could potentially be misused for disinformation and how to mitigate these types of risks. | [Paper](https://openai.com/blog/forecasting-misuse/), [Tweet](https://twitter.com/dair_ai/status/1614676684984156160?s=20&t=3GITA7PeX7pGwrqvt97bYQ) |
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| 5) **Why do Nearest Neighbor Language Models Work?** -- Empirically identifies reasons why retrieval-augmented LMs (specifically k-nearest neighbor LMs) perform better than standard parametric LMs. | [Paper](https://arxiv.org/abs/2301.02828), [Code](https://github.com/frankxu2004/knnlm-why), [Tweet](https://twitter.com/dair_ai/status/1614676687597469696?s=20&t=3GITA7PeX7pGwrqvt97bYQ) |
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| 6) **Memory Augmented Large Language Models are Computationally Universal** -- Investigates the use of existing LMs (e.g, Flan-U-PaLM 540B) combined with associative read-write memory to simulate the execution of a universal Turing machine. | [Paper](https://arxiv.org/abs/2301.04589) , [Tweet](https://twitter.com/dair_ai/status/1614676689908277252?s=20&t=3GITA7PeX7pGwrqvt97bYQ) |
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