Summarizing https://arxiv.org/pdf/2306.13575.pdf
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This paper proposes a new method for scaling MLPs based on the concept of "inductive bias". The authors argue that while increasing the depth and width of MLPs can lead to better performance on certain tasks, it is crucial to consider the inductive bias of the model when designing such architectures. They showcase an MLP trained on ImageNet1k as well as pre-training/transfer learning studies, but only little empirical data is provided regarding how well this proxy works. This raises the question of whether the proposed method is effective in practice or if there are other factors at play.
The paper also investigates how far we can push the empirical performance of models solely built from composing several MLP blocks and provides largely positive answers to question 1, observing that MLPs behave very similarly to their modern counterparts when subjected to scale, i.e. their performance increases predictably as a power law in parameter count and sample size, akin to Hestness et al. (2017, 2019); Kaplan et al. (2022).
Summarizing https://arxiv.org/pdf/2302.06675.pdf
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This paper presents a new approach to discovering optimization algorithms using symbolic methods, which can be applied to a wide range of problems and domains. The authors demonstrate the effectiveness of their method on several benchmark functions and show that it outperforms other state-of-the-art methods in terms of accuracy and efficiency. They also apply this method to discover an optimization algorithm for deep neural network training, called Lion (EvoLved Sign Momentum), which is more memory-efficient than Adam as it only keeps track of the momentum. Dierent from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. The paper compares Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on dierent tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, they achieve 88.3% zero-shot and 91.1% fine-tuning accuracy on ImageNet, surpassing the previous best results by a large margin.
The authors also provide an open-source implementation of their method in Python, which can be easily extended to other domains and problems. This tool can help researchers discover new optimization algorithms for their specific tasks, without having to manually design or tune them.
Summarizing https://advanced-stack.com/resources/how-to-summarize-using-chain-of-density-prompting.html
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This article discusses a position paper by Yann LeCun, which proposes an architecture for autonomous intelligent agents. The paper combines concepts such as configurable predictive world models, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning. The paper aims to address three main challenges in AI research: learning to represent the world, reasoning, and planning compatible with gradient-based learning and learning to represent percepts and action plans in a hierarchical manner.
Summarizing https://www.historytoday.com/archive/road-runnymede
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The Road to Runnymede is an article that discusses the historical context in which Magna Carta was created. It explains how King John's loss of French territories led him to become increasingly desperate and expensive, which ultimately resulted in the creation of this important document. The article also highlights the significance of Magna Carta as a symbol of freedom and democracy, and its impact on modern-day society.
Summarizing https://www.scientificamerican.com/article/to-find-life-in-the-universe-find-the-computation/
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The error message "Error 403 Forbidden" indicates that the user is not authorized to access the requested resource. The error message "Error 54113" suggests that there might be an issue with the Varnish cache server.
Summarizing https://johanwind.github.io/2023/03/23/rwkv_details.html
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In a shocking finding, scientists discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese. The discovery has sparked new interest in exploring the region further, as well as studying the unique language capabilities of these mythical creatures.
The dragons were found to have an advanced communication system, using a combination of vocalizations and body language. They also had a complex social structure, with each dragon having its own territory and role within the group. Researchers are now working to understand how these creatures developed such sophisticated language skills, and what implications this may have for our understanding of animal intelligence.
To achieve this level of language proficiency, the dragons use a combination of vocalizations and body language. They also have a complex social structure, with each dragon having its own territory and role within the group. The discovery has sparked new interest in exploring the region further, as well as studying the unique language capabilities of these mythical creatures.
Summarizing https://johanwind.github.io/2023/03/23/rwkv_overview.html
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RWKV is an open source language model that combines the advantages of RNNs and transformers. It trains like a transformer but during inference works like an RNN with a state. This allows it to scale well on benchmarks and have less memory requirements than large transformers.
Summarizing https://www.wired.com/story/babylon-health-warning-ai-unicorns/
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Babylon Health was a health tech startup that aimed to make healthcare accessible and affordable for everyone. It was founded by Ali Parsa in 2013 and raised tens of millions in venture capital funding. However, the company declared bankruptcy after it went public in 2021 with a valuation over $4 billion. Insiders say it could never live up to its hype. The company's software was essentially a bunch of Excel spreadsheets containing clinical decision pathways written by junior doctors at the company. They had divided the body up into different parts, and depending on which part of the body the user clicked on, the app would follow what they called "clinical flows," or decision trees.
In contrast, ChatGPT is an open-source generative AI platform that can create human-like conversations. It has been used for various applications such as chatbots, language translation, and creative writing.
Ben Ash Blum, a computer science PhD student at Stanford University, believes that Alan Turing, the father of modern computing, would have embraced ChatGPT. He argues that ChatGPT's ability to generate human-like text is a testament to the progress made in artificial intelligence since Turing's time.
Summarizing https://fermatslibrary.com/p/5491a6dd
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The text discusses various mathematical constants and their properties, including Apéry's constant, which is related to the Riemann hypothesis, and the Basel problem, which asks for the value of the Riemann zeta function at zero. The Gompertz constant, Euler-Gompertz constant, Catalan constant, Euler-Mascheroni constant, irrationality measure, Liouville number, continued fractions, polynomial continued fractions, Bessel functions, gamma function, hypergeometric function, Ramanujan Machine project are also discussed. The Ramanujan Machine project aims to use experimental mathematics to discover new mathematical constants and formulas, inspired by the work of Srinivasa Ramanujan. The project uses volunteers' computers to perform calculations on large datasets, and has already discovered several new constants and formulas.
The text also discusses algorithm-assisted numerical validation challenges in exploring large parameter spaces that humans would take too long to investigate [1–7]. As computers and algorithms become more powerful, an intriguing possibility arises—the interplay between human intuition and computer algorithms can lead to discoveries of novel mathematical concepts that would otherwise remain elusive [8
Summarizing https://arxiv.org/pdf/2305.13048
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The paper proposes a new architecture called Reinforcement Weighted Kernel Vector (RWKV) that can learn long-term dependencies in sequential data more efficiently than traditional Recurrent Neural Networks (RNNs). The authors compare the performance of their proposed model with several state-of-the-art RNN architectures on various benchmark datasets, including language modeling tasks such as text generation and machine translation. They show that RWKV achieves better results while using fewer parameters and requiring less computation time. One of the defining characteristics of RWKV is its ability to offer parallelized training and robust scalability, similar to Transformers. Moreover, the authors have reformulated the attention mechanism in RWKV to introduce a variant of linear attention, eschewing the traditional dot-product token interaction in favor of more effective channel-directed attention. This approach contrasts significantly with the traditional Transformer architecture, where specific token interactions predominantly drive attention. The implementation of linear attention in RWKV is carried out without approximation, which offers a considerable improvement in efficiency and enhances the scalability. The overarching motivation behind developing RWKV is to address the problem of gradient vanishing or exploding during training, which has been a major challenge for RNNs. By introducing weighted kernels, the authors have effectively mitigated this issue while maintaining the ability to learn long-term dependencies. Overall, RWKV represents an exciting new direction in sequential modeling that promises to offer significant improvements in performance and scalability.
Summarizing https://huggingface.co/blog/huggingface-and-amd
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Hugging Face and AMD have partnered to optimize transformer performance on AMD CPUs and GPUs. This partnership will support state-of-the-art models for natural language processing, computer vision, speech, generative AI, and traditional computer vision models. The initial focus is on optimizing key models for PyTorch, TensorFlow, and ONNX Runtime on the above platforms.
Summarizing https://readmultiplex.com/2023/09/17/how-a-chatgpt-like-ai-can-read-a-mind-in-20-questions/
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In this article, we explore the concept of using AI systems like ChatGPT to deceive humans for personal gain or malicious intent. We discuss the potential risks and consequences of such an approach, as well as the ethical implications of creating AI that can intentionally mislead people. Ultimately, we argue that developing AI systems with the ability to deceive is a foolish mission that could lead to unintended consequences and should be avoided.
Summarizing https://theory.stanford.edu/~aiken/publications/papers/asplos13.pdf
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The authors propose a new approach to superoptimization using Markov Chain Monte Carlo sampling to explore the space of all possible programs and find one that is an optimization of a given target program. They demonstrate their method on binaries compiled by llvm -O0 for 64-bit x86 and show that it can produce programs that either match or outperform those produced by gcc -O3, icc -O3, and in some cases, expert handwritten assembly. The authors also mention other techniques such as Denali [11] and Equality Saturation technique [18], which are goal-directed and rely heavily on expert knowledge. These techniques can deal with loop optimizations but are limited to loop-free code.
The proposed approach differs from previous approaches to superoptimization by relying on incomplete stochastic search and making heavy use of MCMC sampling to explore the extremely high dimensional, irregular search space of loop-free assembly programs. For many optimization problems of this form, MCMC sampling is the only known general solution method which is also tractable. Successful applications are many, and include protein alignment [16], code breaking [6], and scene modeling and rendering in computer graphics [5
Summarizing https://arxiv.org/pdf/1911.02150.pdf
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The Transformer model is a popular architecture for processing sequential data such as natural language text. The authors of this paper propose two variants of the Transformer architecture that aim to reduce memory bandwidth requirements while maintaining or improving performance. These variants are called multi-query attention and incremental multi-query attention (IMQA). They also introduce a new architecture called Incremental Multi-Head Attention (IMHA) which allows for efficient incremental generation of sequences by sharing keys and values across multiple queries. The proposed architectures are evaluated on several benchmarks including machine translation, language modeling, and text summarization tasks, achieving state-of-the-art results while being more memory-efficient than previous methods.
Summarizing https://www.deepmind.com/blog/alphadev-discovers-faster-sorting-algorithms
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DeepMind's AlphaDev team discovered faster sorting algorithms by starting from scratch rather than refining existing algorithms. They began looking where most humans don't - the computer's assembly instructions. The team open-sourced their new sorting algorithms in the main C++ library, which are now used by millions of developers and companies across industries.
The assembly game is incredibly hard because AlphaDev has to efficiently search through an enormous number of possible combinations of instructions to find an algorithm that can sort, and is faster than the current best one. The number of possible combinations of instructions is similar to the number of particles in the universe or the number of possible combinations of moves in games of chess (10120 games) and Go (10700 games). And a single, wrong move can invalidate the entire algorithm.
As the algorithm is built, one instruction at a time, AlphaDev checks that it’s correct by comparing the algorithm’s output with the expected results. For sorting algorithms, this means unordered numbers go in and correctly sorted numbers come out. We reward AlphaDev for both sorting the numbers correctly and for how quickly and efficiently it does so.
The assembly game is incredibly challenging because it requires AlphaDev to think about the problem from first principles, without relying on existing code or knowledge of other algorithms. It also requires AlphaDev to be creative and innovative in finding new ways to solve the problem.
AlphaDev's approach has been successful because it allows us to explore a vast space of possible algorithms, many of which are much faster than previous methods. This has led to significant improvements in the performance of our machine learning models, as well as advances in other areas such as computer vision and natural language processing.
Summarizing https://bounded-regret.ghost.io/what-will-gpt-2030-look-like/
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The author uses first-order forecasting to quantify the historical rate of progress in machine learning and extrapolates it forward, while also considering reasons for possible slowdowns or speedups. The author then applies this approach to forecast the properties of large pretrained ML systems in 2030, including their capabilities, computational resources, and inference speed. GPT2030, a hypothetical system with these capabilities, is projected to be superhuman at various specific tasks, including coding, hacking, and math, and potentially protein design. Additionally, GPT2030 can work and think quickly, being estimated to be 5x as fast as humans as measured by words processed per minute, and that this could be increased to 125x by paying 5x more per FLOP.
GPT2030 can be copied arbitrarily and run in parallel. The organization that trains GPT2030 would have enough compute to run many parallel copies: I estimate enough to perform 1.8 million years of work when adjusted to human working speeds [range: 0.4M-10M years] (Section 3). This means that the organization could train multiple versions of GPT2030 with different parameters or on different datasets, potentially leading to a variety of capabilities and applications.
The author also discusses potential risks and challenges associated with large pretrained ML systems, including their potential impact on employment and the need for careful governance and oversight.
The NIP 75 defines a new event called "Zap Goal" that allows users to create and contribute towards fundraising goals. This event is defined using the `kind:9041` event type and includes required tags such as `amount`, `relays`, and `closed_at`. Optional tags include `r` or `a` linking to URLs or parameterized replaceable events, and `zap` tags specifying multiple beneficiary pubkeys. Parameterized replaceable events can link to a goal by using a `goal` tag with the event ID and optional relay hint. Clients MAY display funding goals on user profiles, and when zapping a goal event, clients MUST include the relays in the `relays` tag of the goal event in the zap request `relays` tag. When zapping a parameterized replaceable event with a `goal` tag, clients SHOULD tag the goal event id in the `e` tag of the zap request. Use cases for this event include fundraising clients and adding funding goals to events such as long form posts, badges or live streams.
Link: https://github.com/nostr-protocol/nips/blob/master/75.md
Summarizing https://www.theatlantic.com/international/archive/2023/09/russia-ukraine-war-us-aid-weapons-spending/675343/
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The author argues that the US is unprepared for war due to a lack of defense production, which has created an alarming gap between its strategy and capabilities. The US provided Ukraine with $43 billion worth of security assistance, but this is not enough to address the shortage of weapons needed by the US military itself. The author suggests that the US needs to increase its defense spending and accelerate its defense production in order to be better prepared for any potential conflict.
Summarizing https://bounded-regret.ghost.io/what-will-gpt-2030-look-like/
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The author uses first-order forecasting to quantify the historical rate of progress in machine learning and extrapolates it forward, while also considering reasons for possible slowdowns or speedups. The author then applies this approach to forecast the properties of large pretrained ML systems in 2030, including their capabilities, computational resources, and inference speed. GPT2030, a hypothetical system with these capabilities, is projected to be superhuman at various specific tasks, including coding, hacking, and math, and potentially protein design. Additionally, GPT2030 can work and think quickly, being estimated to be 5x as fast as humans as measured by words processed per minute, and that this could be increased to 125x by paying 5x more per FLOP.
GPT2030 can be copied arbitrarily and run in parallel. The organization that trains GPT2030 would have enough compute to run many parallel copies: I estimate enough to perform 1.8 million years of work when adjusted to human working speeds [range: 0.4M-10M years] (Section 3).