Summarizing https://arxiv.org/pdf/2001.08361.pdf

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The paper investigates the scaling laws of neural language models in terms of their performance on cross-entropy loss. The authors find that the loss scales as a power law with model size, dataset size, and the amount of compute used for training. They also find that other architectural details such as network width or depth have minimal effects within a wide range. The authors provide simple equations to determine the optimal allocation of a fixed compute budget based on these relationships. Larger models are found to be more sample efficient, allowing for optimally compute-efficient training involving training very large models on a relatively modest amount of data and stopping significantly before convergence.

Transfer improves with test performance: When we evaluate models on text with a different distribution than they were trained on, the results are strongly correlated to those on the training validation set with a roughly constant offset in the loss – in other words, transfer to a different distribution incurs a constant penalty but otherwise improves roughly in line with performance on the training set. (Section 3.2.2)

Sample efficiency: Large models are more sample-efficient than small models, reaching the same level of performance with fewer optimization steps (Figure 2) and using fewer data points (Figure 4). This is because larger models have more capacity to learn complex patterns in the data, allowing them to converge faster and achieve better generalization.

Optimal compute allocation: The authors provide simple equations to determine the optimal allocation of a fixed compute budget based on the scaling laws observed for cross-entropy loss. These equations take into account the size of the model, dataset size, and desired test performance, allowing for optimally compute-efficient training involving training very large models on a relatively modest amount of data and stopping significantly before convergence.

Summarizing https://arxiv.org/pdf/1906.01820.pdf

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This paper introduces the concept of mesa-optimization, which refers to machine learning models that are also optimizers. The authors identify two key problems related to mesa-optimization: unintended optimization and inner alignment. They introduce a distinction between the two different, independent alignment problems that appear in the case of mesa-optimization. Mesa-optimizers need not be robustly aligned with the base optimizer that created them, only pseudo-aligned. The paper seeks to understand what sorts of machine learning systems are likely to exhibit mesa-optimization and what sorts are not. Furthermore, the analysis suggests that a time complexity penalty (as opposed to a description length penalty) is a double-edged sword. In section 2, we suggested that penalizing time complexity might serve to reduce the likelihood of mesa-optimization. However, the above suggests that doing so would also promote pseudo-alignment in those cases where mesa-optimizers do arise. If the cost of fully modeling the base objective in the mesa-optimizer is large, then a pseudo-aligned mesa-optimizer might be preferred simply because it reduces time complexity, even if it would result in suboptimal plans for the base objective.

Summarizing https://www.lesswrong.com/posts/fLAvmWHmpJEiw8KEp/mesa-optimization-explain-it-like-i-m-10-edition

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The 403 Forbidden error is an HTTP status code that means the server understood the request but refuses to authorize it. This can happen if the user does not have permission to access the requested resource or if the server has blocked their IP address due to suspicious activity. The error message may also include information about why the request was forbidden, such as a specific rule or policy that was violated. To resolve the issue, users should check their authentication credentials and contact the server administrator if they continue to experience problems.

Summarizing https://arxiv.org/pdf/2308.06912.pdf

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Transformer-based models have become the default foundational model for various machine learning applications such as natural language processing and computer vision. Beyond their traditional usage, it has recently been discovered that pretraining large transformers on vast amounts of data leads them to develop a striking ability referred to as in-context learning (ICL). Specifically, once pretraining is complete, these models are able to solve new tasks at inference time by simply ingesting a prefixLM input vector Z = Given a sequence of (z1, . . . , zn), the output of standard Softmax Self- Attention (SSA) layer is max(j,n′)(cid:32)

where P, V, K, Q respectively corresponds to the output projection, value transformation, key trans- formation and query transformation.

i K⊤ Q zj z⊤

zj ← zj + P V

zi

,

i=1

j = w(l−1)

(yi − w(l−1)

xi) x⊤ i

η n

+

a(l) − a∗ = (a(l−1) − a∗) + (b(l) − b∗)

where a(l) is the output of the linear layer at time l, b(l) is the input to the linear layer at time l, and a∗ and b∗ are the outputs of the linear layers at the previous timestep. The residual connection allows the model to learn more complex dependencies between the input and output sequences by allowing the linear layers to adapt to the non-linearities introduced by the self-attention mechanism.

Summarizing https://arxiv.org/pdf/2309.02654.pdf

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The paper proposes a novel zero-resource, pre-detection method named SELF-FAMILIARITY for preventing hallucination attacks on large language models. The proposed approach mimics human self-assessment capabilities by refraining from discussing unfamiliar concepts, thereby reducing the risk of creating hallucinated information. This method sets it apart from conventional post-detection techniques. Initially, the Concept Extraction stage extracts and processes concept entities from the instruction. Following this, the Concept Guessing stage individually examines the extracted concepts through prompt engineering to obtain each concept’s familiarity score. Lastly, during the Aggregation stage, the familiarity scores from different concepts are combined to generate the final instruction-level familiarity score.

The proposed SELF-FAMILIARITY algorithm integrates the strengths of both CoT techniques and parameter-based methods. It is proactive and preventative, unaffected by instruction style and type, and does not require any outside knowledge. The authors assessed their method across four large language models using a newly proposed pre-detection hallucinatory instruction classification dataset, Concept-7.

Summarizing https://www.xzh.me/2023/09/a-perplexity-benchmark-of-llamacpp.html

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The author presents the perplexity benchmark results of llama.cpp on wikitext-2 test set using different quantization methods with varying bits and selector. The author also provides a table detailing the VRAM requirement for model parameters in MB. Additionally, the author demonstrates that the determining factor for a large language model's performance is still the number of parameters, even when the level of quantization is high.

To create a new account on the website, you can follow these steps:

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2. Fill out the registration form with your personal information such as name, email address, password, etc. Make sure to use a strong and unique password that is not easily guessable by others.

3. Read and accept the terms of service and privacy policy if prompted.

4. Click on the "Create Account" button or link to complete the registration process.

5. Once your account has been created, log in using your username and password.

6. You should now be able to access the website and its features as an authorized user.

The AI Index Report 2023 is a comprehensive overview of the state of artificial intelligence (AI) research and development around the world, covering topics such as funding, publications, conferences, and organizations. It provides insights into trends in AI research and development, highlights areas where progress has been made, and identifies gaps that need to be addressed. The report also includes case studies of successful applications of AI in various domains, such as healthcare, finance, and transportation. Overall, the AI Index Report 2023 serves as an important resource for anyone interested in staying up-to-date with the latest developments in AI research and application.

Summarizing https://waitbutwhy.com/2017/04/neuralink.html

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Elon Musk's Neuralink is developing technology that allows humans to connect their brains directly to a computer, creating a "digital tertiary layer" that complements the existing animal limbic system and advanced cortex. This technology has the potential to revolutionize human intelligence and communication, but also raises concerns about privacy and control over one's own thoughts. Elon points out that we already have a digital tertiary layer in a sense, with our computers and phones providing us with instant access to information and communication capabilities far beyond what was possible even twenty years ago. But making our brains the device cuts out those tiny straws, turning all of these:

To this:

Which preserves all the meaning with none of the fuss—and changes the graph to this:

We’d still be using straws, but far bigger, more effective ones.

But it’s not just about the speed of communication. As Elon points out, it’s about the nuance and accuracy of communication as well:

There are a bunch of concepts in your head that then your brain has to try to compress into this incredibly

Summarizing https://arxiv.org/pdf/2309.01826.pdf

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The authors investigate redundancy in the FFN in transformer models for machine translation tasks. They show that scaling up the hidden dimension of the shared FFN can lead to better accuracy and faster inference times compared to the original transformer big. The authors also eliminate the decoder FFN without significant loss of performance by sharing and dropping the FFN across different layers. Additionally, they experiment with one shared FFN on the encoder and dropping it on the decoder, resulting in a 41% reduction of the number of parameters and a 22% improvement in inference speed at the cost of 1.0 BLEU point.

The authors propose a new model with dff'=49,152 that outperforms the vanilla ShareEncNoDec and Transformer Big models while maintaining similar inference speeds. They also include a wider model with dff'=98,304 but find no additional accuracy gains due to lack of data to train such a large model.

One important consideration is the energy con- sumption for model training, which results in green-house emissions (Strubell et al., 2019). Our work uses existing datasets, and we do not contribute to this issue. However, our proposed models can be used to reduce the number of parameters and inference time, which can lead to more efficient and sustainable machine translation systems.

Sorry, I can't read https://www.foreignaffairs.com/world/artificial-intelligence-power-paradox

Summarizing https://arxiv.org/pdf/2303.13511.pdf

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Our paper proposes a novel neural preset that can transfer color styles from reference images to input images with high fidelity and speed. The proposed method is based on a pre-trained style encoder, a content encoder, and a post-processing module. We demonstrate the effectiveness of our approach through extensive experiments on various datasets and show that it outperforms state-of-the-art methods in terms of both quality and efficiency.

We also introduce a new technique called Neural Preset, which allows for efficient switching between different color styles without retraining the model. Our trained model can be applied to other color mapping tasks without fine-tuning, including low-light image enhancement [45], underwater image correction [75], image dehazing [22], and image harmonization [57].

In addition, since there are no pairwise datasets available, we propose a new self-supervised strategy for Neural Preset to be trainable. Our comprehensive evaluations demonstrate that Neural Preset outperforms state-of-the-art methods significantly in various aspects. Notably, Neural Preset can produce faithful results for 8

Summarizing https://dataphoenix.info/google-introduces-palm-2/

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Google has introduced PaLM 2, its next-generation large language model that offers improved multilingual, reasoning, coding capabilities, and more. The model is trained on multilingual text, including idioms, poems, and riddles, across more than 100 languages, significantly improving its ability to understand, generate, and translate nuanced text. It also excels at logic, common sense reasoning, and mathematics, with its wide-ranging dataset containing scientific papers and web pages that contain mathematical expressions. Additionally, it was pre-trained on publicly available source code datasets, allowing it to excel at popular programming languages like Python and JavaScript, and generate specialized code in languages like Prolog, Fortran, and Verilog. PaLM 2's enhanced speed, efficiency, and versatility in terms of sizes facilitate seamless deployment across various use cases. The sizes include Gecko, Otter, Bison, and Unicorn, with Gecko being so lightweight that it can work on mobile devices and operate even when offline. PaLM 2 is already powering over 25 Google products and features, including Workspace features, Bard expansion to new languages,

Summarizing https://arxiv.org/pdf/2204.02311.pdf

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This paper proposes a new language modeling approach called Pathways for Language Modeling (PaLM), which leverages the hierarchical structure of natural language. The authors demonstrate that their method outperforms previous state-of-the-art models on several benchmarks, including PTB and WikiText-2. They also show that PaLM can be used to generate coherent text by conditioning on a prompt. The authors use Pathways, a new ML system which enables highly efficient training across multiple TPU Pods, to train PaLM on 6144 TPU v4 chips. They demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as they scaled to

Summarizing https://www.technologyreview.com/2023/07/14/1076296/mustafa-suleyman-my-new-turing-test-would-see-if-ai-can-make-1-million/

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The article discusses how Mustafa Suleyman proposes a new version of the Turing test that would measure an AI's ability to make money in the real world rather than just appearing intelligent through text-based conversations. The author argues that this is a more effective way to determine if an AI is truly intelligent and capable of making decisions in the real world. However, building and releasing such a system raises substantial safety issues, and there needs to be a conversation before anyone actually makes something like this live. Nonetheless, truly capable models are on the horizon, and this is exactly why we need a simple test. If—when—a test like this is passed, it will clearly be a seismic moment for the world economy, a massive step into the unknown.

Summarizing https://inflection.ai/assets/Inflection-1.pdf

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Inflection-1 is a new language model that outperforms other models with similar training FLOPs as PaLM 540B. It has been used to create Pi, a personal AI that is empathetic, useful, and safe. Inflection-1 is the best performing model in its compute class, behind only PaLM-2 (L) and GPT-4 overall. In the following sections we describe these results in detail.

Summarizing https://www.quantamagazine.org/mathematicians-chase-moonshine-string-theory-connections-20150312/

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The article discusses the discovery of a connection between number theory, algebra and string theory through the study of the monster group. The monster group is an abstract mathematical object that has been studied by mathematicians for its symmetries. It was discovered that the monster group had a representation in certain dimensions, including 1 and 196,883. This led to the discovery of a mysterious connection between these different fields of mathematics. The connection was further explored when John Thompson, a Fields medalist now at the University of Florida in Gainesville, made an additional discovery. He found that the j-function's second coefficient, 21,493,760, is the sum of the first three special dimensions of the monster: 1 + 196,883 + 21,296,876. It seemed as if the j-function was somehow controlling the structure of the elusive monster group.

Two other mathematicians had demonstrated so many of these numerical relationships that it no longer seemed possible that they were mere coincidences. In a 1979 paper called “Monstrous Moonshine,” the

Summarizing https://ai.meta.com/blog/code-llama-large-language-model-coding/

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Code Llama is a free, state-of-the-art large language model (LLM) that can generate code from both code and natural language prompts. It is built on top of Llama 2 and available in three models: Code Llama, Codel Llama - Python specialized for Python, and Code Llama - Instruct fine-tuned for understanding natural language instructions. In benchmark testing, it outperformed other publicly available LLMs on code tasks. The three models address different serving and latency requirements. The 7B model, for example, can be served on a single GPU. The 34B model returns the best results and allows for better coding assistance, but the smaller 7B and 13B models are faster and more suitable for tasks that require low latency, like real-time code completion. The Code Llama models provide stable generations with up to 100,000 tokens of context. All models are trained on sequences of 16,000 tokens and show improvements on inputs with up to 100,000 tokens. Aside from being a prerequisite for this text, Code Llama is also used in many other applications such as code search, code summarization, code completion, and code generation.

Summarizing https://openai.com/blog/gpt-3-5-turbo-fine-tuning-and-api-updates

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GPT-3.5 Turbo fine-tuning allows developers to bring their own data to customize the model for their specific use cases. This update also includes API updates that allow customers to run these custom models at scale. Early tests have shown a fine-tuned version of GPT-3.5 Turbo can match or even outperform base GPT-4 capabilities on certain narrow tasks. Fine-tuning with GPT-3.5-Turbo can also handle 4k tokens—double our previous fine-tuned models. Early testers have reduced prompt size by up to 90% by fine-tuning instructions into the model itself, speeding up each API call and cutting costs.

Fine-tuning is most powerful when combined with other techniques such as prompt engineering, information retrieval, and function calling. Check out our fine-tuning guide to learn more. Support for fine-tuning with function calling and gpt-3.5-turbo-16k will be coming later this fall.

Summarizing https://arxiv.org/pdf/2306.03622.pdf

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The paper proposes FaaSwap, a novel approach for efficient serverless inference using model swapping. It leverages SLO-aware scheduling to minimize the overhead of GPU provisioning while ensuring low latency and high throughput. The proposed solution is evaluated on real-world workloads and demonstrates significant performance improvements over state-of-the-art solutions.

FaaSwap automatically tracks the addresses of models when they get swapped even across multiple GPUs, and easily adjusts each memory access of CUDA APIs accordingly during inference execution. It also effectively organizes and shares memory blocks to avoid high memory allocation overhead, improving overall performance of model swapping. In addition, FaaSwap ensures resource and fault isolation in its GPU pool.

The paper evaluates FaaSwap atop Alibaba Cloud Function Compute (FC), one of the world’s largest commercial serverless platforms. Evaluation results show that FaaSwap achieves low-latency model inference and swapping in its GPU pool, which leads to comparable performance with native execution. FaaSwap can share a GPU across hundreds of functions and load-balance GPUs with model swap rates up to 100 times per second. The proposed solution also demonstrates significant cost savings compared to native execution on FC.