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on system performance; as well as new features like LoRA, Speculative Decoding, and better Quantization Support. Additionally, we are collaborating with hardware vendors like AMD, AWS Inferenetia, and Intel Habana to bring LLM to the broadest community. Appendix: Feature Comparison DeepSpeed-FastGen currently offers basic functionalities, supporting only three model types and lacking popular features like stop strings and parallel sampling (e.g., beam search). We do expect the DeepSpeed-FastGen

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. This indicates a potential problem with using LLM to evaluate its own performance on domains that requires deep expertise. The lack of expertise may cause LLMs not knowing its flaws and thus cannot well judge the correctness of task results. Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code,

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w era of heightened material prosperity across the planet; Scientific breakthroughs and new technologies and medicines will dramatically expand, as AI helps us further decode the laws of nature and harvest them for our benefit; The creative arts will enter a golden age, as AI-augmented artists, musicians, writers, and filmmakers gain the ability to realize their visions far faster and at greater scale than ever before; I even think AI is going to improve warfare, when it has to happen, by reduci

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paper (see below), this post gets to the heart of the big question in LLM scaling: are we running out of data? This builds on the post above and gives a refreshed view on scaling laws; A survey of large language models: Comprehensive breakdown of current LLMs, including development timeline, size, training strategies, training data, hardware, and more; Sparks of artificial general intelligence: Early experiments with GPT-4: Early analysis from Microsoft Research on the capabilities of GPT-4, the

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ual settings of the parameters are confident (low $H(y \vert \mathbf{x}, \boldsymbol{\theta})$).” In other words, each individual posterior draw is confident but a collection of draws carry diverse opinions. BALD was originally proposed for an individual sample and Kirsch et al. (2019) extended it to work in batch mode. Forgetting Events To investigate whether neural networks have a tendency to forget previously learned information, Mariya Toneva et al. (2019) designed an experiment: They track

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$ up to time $t$ and $\hat{U} t(a)$ is a function reversely proportional to how many times action $a$ has been taken. See here for more details; Boltzmann exploration: The agent draws actions from a boltzmann distribution (softmax) over the learned Q values, regulated by a temperature parameter $\tau$; Thompson sampling: The agent keeps track of a belief over the probability of optimal actions and samples from this distribution. See here for more details The following strategies could be used fo

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wer and run the AI on it, and it figures out how to build even better chips. This probably cannot be stopped. As we have learned, scientific advancement eventually happens if the laws of physics do not prevent it. More important than that, unless we destroy ourselves first, superhuman AI is going to happen, genetic enhancement is going to happen, and brain-machine interfaces are going to happen. It is a failure of human imagination and human arrogance to assume that we will never build things sm

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the corresponding interpretable data representation $x b$, let us sample instances around $x b$ by drawing nonzero elements of $x b$ uniformly at random where the number of such draws is also uniformly sampled. This process generates a perturbed sample $z b$ which contains a fraction of nonzero elements of $x b$. Then we recover $z b$ back into the original input $z$ and get a prediction score $f(z)$ by the target model. Use many such sampled data points $z b \in \mathcal{Z} b$ and their model p

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