Summarizing https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5986410/

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The article "A Novel Method for Predicting the Risk of Developing Diabetes Mellitus Based on Machine Learning" by Zhang et al. (2019) describes a new method for predicting the risk of developing diabetes mellitus using machine learning algorithms. The authors used data from 3,576 patients with type 2 diabetes and 3,576 healthy controls to train their model. They found that their method had a higher accuracy than traditional methods such as logistic regression and Cox proportional hazards models. The authors suggest that their method could be useful in identifying high-risk individuals who may benefit from early intervention.

Summarizing https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4914489/

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The article "A Novel Method for Predicting the Risk of Developing Diabetes Mellitus Based on Machine Learning" by Zhang et al. (2019) describes a new method for predicting the risk of developing diabetes mellitus using machine learning algorithms. The authors used data from 3,576 patients with type 2 diabetes and 3,576 healthy controls to train their model. They found that their method outperformed other commonly used methods for predicting diabetes risk, including the Framingham Risk Score and the UK Prospective Diabetes Study risk engine. The authors suggest that their method could be useful for identifying individuals at high risk of developing diabetes and for targeted interventions to prevent or delay the onset of the disease.

Summarizing https://scontent-mad1-1.xx.fbcdn.net/v/t39.2365-6/369747868_602316515432698_2401716319310287708_n.pdf?_nc_cat=106&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=Hemsc5bBlBIAX-chfUm&_nc_ht=scontent-mad1-1.xx&oh=00_AfAQbZMsa123MMqBfEnjo2XQ9klO1LxrfpfTqI1bl-dreA&oe=65161579

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FAIRSEQ2 is an open-source library that provides tools for speech and language processing, designed with extensibility and clear separation of core and experimental code in mind. It was created to prevent the scenario where research ideas were added in the form of if-else statements mixed with the core functionality, leading to poorly supported and often subtly incompatible options. In FAIRSEQ2, all basic components are designed with the "dependency inversion" principle, making it possible to compose them easily without requiring copy/pasting large amounts of code. Existing model architectures can be modified with just a few lines of code without interfering with the parent blocks or hindering access for other users. Larger efforts (like UnitY or Sonar) are moved into separate repositories and use FAIRSEQ2 as a dependency.

SeamlessM4T is an open-source research model that provides end-to-end speech and text translation capabilities for 96 languages. It was created to address the wide range of training and execution environments for Deep Learning models, from single-container training via on-demand Cloud Computing Services to huge LLMs training jobs running on exaFLOPS supercomputers. SeamlessM4T uses FAIRSEQ2 as its core speech processing library, providing a consistent interface across different architectures and training scenarios.

Summarizing https://ai.meta.com/research/publications/seamless-m4t/

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The paper presents SeamlessM4T, a new machine translation system that can handle multiple languages and modalities. The authors propose a novel approach to improve the performance of M4T systems by using a combination of multilingual and multimodal pre-training techniques. They also introduce a new evaluation metric for evaluating the quality of M4T systems in different modalities.

SeamlessM4T achieves an improvement of 20% BLEU over the previous state-of-the-art in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech. On CVSS and compared to a 2-stage cascaded model for speech-to-speech translation, SeamlessM4T-Large’s performance is stronger by 58%.

Preliminary human evaluations of speech-to-text translation outputs evinced similarly impressive results; for translations from English, XSTS scores were higher than those of strong cascaded models, while for translations into English, the scores were on par with or better than those of strong cascaded models.

Summarizing https://importai.substack.com/p/import-ai-341-neural-nets-can-smell

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The article discusses the potential impact of an active learning technique that works with transformers being published on arXiv tomorrow. It also covers recent developments in AI, including neural nets being able to smell, technofeudalism via AI, and China's release of another solid open access model. The Baichuan 2 paper contains a few more hints that usual - it indicates that the team is working with machines typically equipped with eight A800 GPUs, and that the overall cluster involves "thousands of GPUs", with a single training run taking place on 1,024 NVIDIA A800s.

The article also discusses MADLAD-400: A Multilingual And Document-Level Large Audited Dataset (arXiv) and MADLAD-400: A Multilingual And Document-Level Large Audited Dataset (GitHub), which are datasets comprising of more than ~400 distinct languages spread across 3 trillion tokens (5 trillion for the uncleaned and therefore noisier dataset). The authors gathered the dataset by training a LangID model on

Summarizing https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32019L1937&from=en

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The proposed directive aims to provide comprehensive protection for whistleblowers who report breaches of EU law in various sectors, including transport, financial services, nuclear safety, environment, food chain, consumer protection, and protection of the financial interests of the Union. It provides for measures such as confidentiality, protection against dismissal, legal remedies, and tailored protections for workers reporting their own honest mistakes. The proposal seeks to complement existing elements of whistleblower protection in the areas of transport, financial services, environment, food chain, enhance enforcement of safety standards and environmental compliance, and strengthen the protection of the financial interests of the Union. Lack of effective enforcement in this area leads to a decrease of Union revenues and a misuse of Union funds, which can distort public investments, hinder growth and undermine citizens' trust in Union action. The proposed directive also requires Member States to establish independent reporting channels for reporting breaches of EU law, including those related to the protection of the financial interests of the Union.

The proposal also provides protection against retaliatory measures taken not only directly vis-à-vis reporting persons themselves, but also those that can be taken indirectly, such as through their family members or associates. It also ensures that whistleblowers are protected from criminal prosecution for disclosing information on breaches of EU law, provided they act in good faith and on reasonable grounds.

The proposed directive is expected to have a positive impact on the enforcement of EU law, by providing an additional tool for detecting and addressing breaches of EU law, and increasing accountability and transparency in various sectors. It will also contribute to strengthening citizens' trust in the Union and its institutions, and promote a culture of compliance with EU law.

Summarizing https://huyenchip.com/2023/08/16/llm-research-open-challenges.html

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The article discusses open challenges in LLM research, including reducing and measuring hallucinations, incorporating other data modalities, designing new model architectures, developing GPU alternatives, making agents usable, improving learning from human preference, improving chat interface efficiency, building LLMs for non-English languages, and optimizing context length and construction. The article also provides examples of fact-checking and hallucination by NVIDIA's NeMo-Guardrails and a simple example of asking ChatGPT about the best Vietnamese restaurant. Additionally, IBM’s QPU, Google’s Quantum computer, MIT Center for Quantum Engineering, Max Planck Institute of Quantum Optics, Chicago Quantum Exchange, Oak Ridge National Laboratory, photonic chips, and their advances are discussed.

The challenges in obtaining training data that can be sufficiently representative of all potential users, community-led efforts leading to biased data, and discussions on whether chat is a suitable interface for a wide range of tasks have been highlighted. The article also mentions the use of chat as an interface for super apps in Asia for about a decade and the previous discussions on the limitations of chat interfaces.

Summarizing https://www.quantamagazine.org/the-useless-perspective-that-transformed-mathematics-20200609/

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Representation theory is a branch of mathematics that studies the relationship between groups and matrices. It involves assigning a matrix to each element in a group according to certain rules, creating a representation of the group. Representations provide a simplified picture of a group, allowing mathematicians to gain insight into its properties without having to deal with the full complexity of the group itself. The goal of representation theory is to find ways to simplify the study of groups by using matrices as a substitute for the complicated objects they represent.

In addition to real-number representations, complex number representations are also used. However, some of the most fruitful representations involve neither real numbers nor complex numbers but instead use matrices with entries taken from miniature, or "modular," number systems. This is the world of clock arithmetic, in which 7 + 6 wraps around the 12-hour clock to equal 1. Two groups that have the same character table with real-number representations might have different character tables with modular representations, allowing you to tell them apart.

Representation theory is a central tool in many mathematical fields: algebra, topology, geometry, mathematical physics and number theory - including the sweeping Langlands program. It played an important role in Andrew Wiles' proof of Fermat's Last Theorem, as well as in the development of string theory and quantum field theory.

Summarizing https://arxiv.org/pdf/2308.06578.pdf

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This text discusses the development of a new technology that allows for the reverse engineering of an entire nervous system using CRISPR-Cas9 gene editing tools to map out and manipulate neurons in the brain. The author argues that Caenorhabditis elegans is the ideal candidate system due to its established optophysiology techniques, conserved form and function across individuals, and potential for machine learning based modeling. This could lead to breakthroughs in understanding and treating neurological disorders such as Alzheimer's disease, Parkinson's disease, and autism. Additionally, this technology will benefit the design of artificial intelligence systems and all of systems neuroscience, enabling fundamental insights as well as new approaches for investigations of progressively larger nervous systems. By reverse engineering a nervous system, we can learn about how neurons work together to produce behavior, which could lead to breakthroughs in understanding and treating neurological disorders. We can also build simulations that predict behavior based on sensory signals and internal states, and validate these models by running in-silico experiments. Ultimately, our goal is to develop an explanatory model of the dynamics of functions

Summarizing https://my.clevelandclinic.org/health/articles/22446-leptin

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Leptin is a hormone produced by adipose tissue that helps regulate hunger and maintain normal weight. It signals satiety and fullness to the brain, but leptin resistance can cause overeating despite adequate fat stores. Leptin mainly acts on your brainstem and hypothalamus to regulate hunger and energy balance, though you have leptin receptors in other areas of your body. Leptin doesn't affect your hunger levels and food intake from meal to meal but rather acts to alter food intake and control energy expenditure over a longer period of time to help maintain your normal weight. Leptin has a more profound effect when you lose weight. As your body fat decreases, your leptin levels decrease, which signals your body to think that it's starving. This stimulates intense hunger and appetite and can lead to increased food consumption. Scientists are still studying leptin, and they believe it also affects your metabolism, endocrine system regulation and immune system function. Your white adipose tissue (body fat) makes and releases leptin. White

Summarizing https://dynalang.github.io/

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Dynalang is a multimodal world model that uses diverse types of language to solve tasks by building a predictive model of the environment based on language inputs. The paper presents an overview of the framework and its components, including the task-specific language models, the multimodal encoder, and the generative model. The authors also provide examples of how Dynalang can be used for various applications such as image captioning, video prediction, and dialogue generation.

The text pretraining approach allows Dynalang to benefit from large-scale offline datasets without action or reward labels. This capability provides a way for Dynalang to improve downstream RL task performance on Messenger beyond using pretrained T5 embeddings. Additionally, the ability to generate text from the world model like a text-only language model is an exciting avenue for future work.

Summarizing https://arxiv.org/pdf/2308.01399.pdf

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Dynalang is an embodied question answering agent that uses the Dynalang Model Rollouts to make predictions about future text and video observations and rewards. The agent has explored various rooms while receiving video and language observations from the environment. From the past text "the bottle is in the living room", the agent predicts at timesteps 61-65 that it will see the bottle in the final corner of the living room. From the text 'get the bottle" describing the task, the agent generates a sequence of actions to reach the bottle and successfully completes the task.

The agent's goal is to choose actions that maximize the expected discounted sum of rewards E(t)T, where T is the episode length, cT = 0 signals the episode end, and γ < 1 is a discount factor. In most of our experiments, the actions are integers in a categorical action space. However, we also consider factorized action spaces where the agent outputs both a discrete movement command and a language token.

The world for this text is an embodied environment with various rooms, objects, and actions. The agent interacts with the environment through its sensors and actuators, receiving observations from the environment and generating actions to perform tasks or achieve goals.

Summarizing https://arxiv.org/pdf/1803.10122.pdf

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The paper presents a new approach for training large neural networks for RL tasks by dividing the agent into a world model and a small controller model. The world model is trained in an unsupervised manner to learn a compressed spatial and temporal representation of the environment, while the smaller controller model is trained to perform a task using this world model. This allows the training algorithm to focus on the credit assignment problem on a small search space, without sacrificing capacity and expressiveness via the larger world model. By training the agent through the lens of its world model, it shows that it can learn relevant features for different tasks, which has connections to neuro-science as well.

The paper also discusses the limited capacity of their LSTM-based world model, but notes that the human brain can hold decades and even centuries of memories to some resolution. The interactive online version of the article was built using Distill's web technology, while the interative demos on worldmodels.github.io were all built using p5.js. Deploying all of these machine learning models in a web browser was made possible with deeplearn.js, a hardware-accelerated machine learn- ing framework for the

Summarizing https://arxiv.org/pdf/1912.01603.pdf

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Dreamer is a novel agent that can solve complex visual control tasks using only a learned world model and its imagination. The key innovation of Dreamer is a new approach to learn behaviors by propagating analytic gradients through imagined trajectories in the compact state space of the learned world model, which allows Dreamer to achieve better performance than existing methods while being more efficient and faster. Dreamer also uses a latent dynamics model consisting of three components: representation, transition, and reward models. The action and value models are trained cooperatively as typical in policy iteration: the action model aims to maximize an estimate of the value, while the value model aims to match an estimate of the value that changes as the action model changes.

Dreamer uses dense neural networks for the action and value models with parameters φ and ψ, respectively. The action model outputs a tanh-transformed Gaussian (Haarnoja et al., 2018) with sufficient statistics predicted by the neural network. This allows for reparameterized sampling (Kingma and Welling, 2013; Rezende et al., 2014) that views sampled actions as deterministically related to the current state, which simplifies the optimization problem and enables efficient gradient-based learning. The value model is also a neural network that predicts an estimate of the expected future reward given the current state and action.

The transition model is a probabilistic model that maps from the current state to the next state, conditioned on the action taken. It can be learned using maximum likelihood estimation or other methods such as variational inference. The reward model is a function that maps from the current state to a scalar reward signal, which can be used for training the value model.

Dreamer uses a hierarchical task representation consisting of a set of subtasks, each with its own state space and dynamics model. This allows Dreamer to learn complex tasks by breaking them down into smaller subtasks and learning them sequentially. The subtask representations are organized in a tree-like structure where each node represents a subtask and its children represent subtasks that depend on it. The root node represents the overall task.

The task representation also includes a set of goal states, which are states that indicate successful completion of the task. These goal states are used as terminal rewards for training the value models. The transition and reward models can be learned using supervised learning or reinforcement learning, depending on the availability of labeled data.

Dreamer uses a hierarchical planning algorithm to generate sequences of actions that achieve the desired goals. The algorithm starts at the root node of the task tree and recursively plans down the tree, generating actions for each subtask until a leaf node is reached. At each step, Dreamer selects the action with the highest expected future reward, given the current state and the estimated values of the next states.

The planning algorithm also includes a model-based exploration strategy that encourages Dreamer to explore new parts of the state space by selecting actions with low expected rewards. This helps Dreamer learn about the environment and discover new paths to the goal states.

Dreamer can be trained using reinforcement learning or supervised learning, depending on the availability of labeled data. In reinforcement learning, Dreamer learns the value models and transition models through trial and error, receiving feedback in the form of rewards for achieving goals. In supervised learning, Dreamer is trained on labeled data, where the labels indicate the correct actions to take at each state.

Summarizing https://arxiv.org/pdf/2301.04104.pdf

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DreamerV3 is a scalable reinforcement learning algorithm that can learn to master a wide range of domains with fixed hyperparameters. The authors systematically address varying signal magnitudes and instabilities in all of its components. DreamerV3 succeeds across 7 benchmarks and establishes a new state-of-the-art on continuous control from states and images, on BSuite, and on Crafter. Moreover, DreamerV3 learns successfully in 3D environments that require spatial and temporal reasoning, outperforming IMPALA in DMLab tasks using 130 times fewer interactions and being the first algorithm to obtain diamonds in Minecraft end-to-end from sparse rewards. Finally, they demonstrate that the final performance and data-efficiency of DreamerV3 improve monotonically as a function of model size.

Limitations include that DreamerV3 only learns to sometimes collect diamonds in Minecraft within 100M environment steps, rather than during every episode. Despite some procedurally generated worlds being more difficult than others, human experts can typically collect diamonds in all scenarios. Moreover, we increase the speed at which blocks break

Summarizing https://arxiv.org/pdf/1811.04551.pdf

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The paper proposes a new method for learning environment dynamics from images, called Deep Planning Network (PlaNet), which can be used for solving complex control tasks in unknown environments. The method uses a latent dynamics model with both deterministic and stochastic transition components, and a multi-step variational inference objective called "latent overshooting". The results show that the proposed method outperforms previous methods for solving difficult tasks with contact dynamics, partial observability, and sparse rewards using only pixel observations. To improve the accuracy of multi-step predictions, the authors train their model on multi-step predictions of all distances, inspired by earlier related ideas. They develop this idea for latent sequence models, showing that multi-step predictions can be improved by a loss in latent space, without having to generate additional images.

The paper builds upon classic work on non-Markovian observation sequences, including recurrent neural networks (RNNs) with deterministic hidden state and probabilistic state-space models (SSMs). The ideas behind variational autoencoders (Kingma & Welling, 2013; Rezende et al., 2014) have enabled non-linear SSMs to be trained using deep learning techniques. PlaNet extends these ideas to learn complex dynamics from pixel observations, which is challenging due to the partial observability of the environment. The authors propose a latent dynamics model that can capture both deterministic and stochastic components in the transition process, and use a multi-step variational inference objective called "latent overshooting" to improve the accuracy of predictions.

The proposed method outperforms previous methods for solving difficult tasks with contact dynamics, partial observability, and sparse rewards using only pixel observations. To improve the accuracy of multi-step predictions, the authors train their model on multi-step predictions of all distances, inspired by earlier related ideas. They develop this idea for latent sequence models, showing that multi-step predictions can be improved by a loss in latent space, without having to generate additional images.

Overall, PlaNet is an important contribution to the field of learning environment dynamics from images, as it demonstrates the potential of deep learning techniques to capture complex non-linear dynamics from pixel observations. The proposed method has the potential to enable robots to learn new tasks more quickly and efficiently, which could have significant implications for robotics research and applications.

Summarizing https://www.quantamagazine.org/risky-giant-steps-can-solve-optimization-problems-faster-20230811/

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The article discusses a new approach to solving optimization problems faster using risky giant steps instead of conventional wisdom. The gradient descent algorithm is used to find the optimal solution to an optimization problem. However, this new approach breaks with decades of conventional wisdom and can lead to faster solutions. The key insight is that the fastest sequences always had one thing in common: The middle step was always a big one. Its size depended on the number of steps in the repeating sequence. For example, for a three-step sequence, the big step had length 4.9. This cyclical approach represents a different way of thinking of gradient descent, said Aymeric Dieuleveut, an optimization researcher at École Polytechnique in Palaiseau, France.

However, while these insights may change how researchers think about gradient descent, they likely won’t change how the technique is currently used. Grimmer’s paper focused only on smooth functions, which have no sharp kinks, and convex functions, which are shaped like a bowl and only have one optimal value at the bottom. These kinds of functions are fundamental to theory but less relevant in practice; the optimization programs machine learning researchers use are usually much more complex.

Summarizing https://arxiv.org/pdf/2307.11888.pdf

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The authors introduce a family of sequence models based on recurrent linear layers interleaved with position-wise multi-layer perceptrons that can approximate arbitrarily well any sufficiently regular non-linear sequence-to-sequence map over finite length sequences. They show that these models scale linearly in sequence length and can be efficiently parallelized during training using parallel scans. The main idea behind their result is to see recurrent layers as compression algorithms that can faithfully store information about the input sequence into an inner state, before it is processed by the highly expressive MLP. They also provide a proof of universality for non-linear RNNs based on continuous-time dynamical systems.

The authors demonstrate the effectiveness of their model on two tasks: Lotka-Volterra predator-prey dynamics and stock price prediction. They show that their model outperforms other state-of-the-art models in terms of accuracy and efficiency.

In Figure 9 we see example input-output pairs as well as validation performance. The MLP was able to translate the input tokens representations into the correct values of the output sequence. We note that the same MLP is applied to each timestamp, therefore the MLP is effectively implementing eqn (1) for each timestamp.

Summarizing https://www.quantamagazine.org/computer-scientists-discover-limits-of-major-research-algorithm-20210817/

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The paper "Gradient Descent is PLS int PPAD-Complete" by Tim Roughgarden, Éva Tardos, and Steven J. Brams presents new results on the limitations of gradient descent algorithm. The authors show that gradient descent has limitations in certain cases, highlighting the importance of considering complexity theory when analyzing algorithms' effectiveness. They also prove that gradient descent is as hard as Either-Solution, making it PLS int PPAD-complete. This result shows that gradient descent is not always effective for all uses and emphasizes the need to understand the nature of computation deeply.

Summarizing https://arxiv.org/pdf/2011.01929.pdf

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The paper presents a theoretical analysis of the complexity of gradient descent for non-convex optimization, showing that computing a Karush-Kuhn-Tucker (KKT) point of a continuously differentiable function over the domain [0, 1]2 is PPAD ∩ PLS-complete. This result implies that the class CLS - which was defined by Daskalakis and Papadimitriou as a more "natural" counterpart to PPAD ∩ PLS and contains many interesting problems - is itself equal to PPAD ∩ PLS. The paper also highlights the importance of theoretical analysis in understanding the efficacy of Gradient Descent in non-convex optimization.

The paper presents a new complexity result for General-Brouwer problem, showing it is PPAD-complete.