Chris Nowinski is a former football player at Harvard University and professional wrestler with WWE, World Wrestling Entertainment. After enduring a career-ending head injury, Chris has dedicated his professional life to serving patients and families affected by brain trauma, particularly Chronic traumatic encephalopathy, or CTE, a progressive neurodegenerative disease that develops after repeated head injuries. Jay and Chris discuss the state of head injuries in American athletics, the difference between advocating for head safety at youth and professional levels, Chris’ newest research, and much more. Episode Chapters (00:00) Intro (00:50) changes in the culture around concussions in the past two decades (02:39) padded helmet technology (03:55) concussion reporting in the NFL (10:35) Chris’ career path and concussion history (14:52) connecting with activists who haven’t themselves suffered a traumatic brain injury (17:42) SHAAKE - a new sign to identify concussions (20:53) Unions can help players advocate for safety policies (23:10) final thoughts and goodbye For video episodes, watch on www.youtube.com/@therudermanfamilyfoundation Stay in touch: X: @JayRuderman | @RudermanFdn LinkedIn: Jay Ruderman | Ruderman Family Foundation Instagram: All About Change Podcast | Ruderman Family Foundation To learn more about the podcast, visit https://allaboutchangepodcast.com/…
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Accidents, Traffic, and Efficiency: AI for Transportation and Logistics
Manage episode 446500682 series 3605861
Вміст надано Brian Carter. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Brian Carter або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
This paper provides a comprehensive overview of deep generative models (DGMs) and their applications within transportation research. It begins by outlining the fundamental principles and concepts of DGMs, focusing on various model types such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and Diffusion Models. The paper then examines the effectiveness of DGMs in diverse transportation research areas, including data generation, estimation and prediction, and unsupervised representation learning. It also includes practical tutorial codes to guide researchers in implementing these models for real-world transportation applications, highlighting current challenges and opportunities for future development.
Read the paper: https://arxiv.org/abs/2410.07066
71 епізодів
Accidents, Traffic, and Efficiency: AI for Transportation and Logistics
OVERFIT: AI, Machine Learning, and Deep Learning Made Simple
ПоширитиManage episode 446500682 series 3605861
Вміст надано Brian Carter. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Brian Carter або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
This paper provides a comprehensive overview of deep generative models (DGMs) and their applications within transportation research. It begins by outlining the fundamental principles and concepts of DGMs, focusing on various model types such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and Diffusion Models. The paper then examines the effectiveness of DGMs in diverse transportation research areas, including data generation, estimation and prediction, and unsupervised representation learning. It also includes practical tutorial codes to guide researchers in implementing these models for real-world transportation applications, highlighting current challenges and opportunities for future development.
Read the paper: https://arxiv.org/abs/2410.07066
71 епізодів
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Introducing a novel transformer architecture, Differential Transformer, designed to improve the performance of large language models. The key innovation lies in its differential attention mechanism, which calculates attention scores as the difference between two separate softmax attention maps. This subtraction effectively cancels out irrelevant context (attention noise), enabling the model to focus on crucial information. The authors demonstrate that Differential Transformer outperforms traditional transformers in various tasks, including long-context modeling, key information retrieval, and hallucination mitigation. Furthermore, Differential Transformer exhibits greater robustness to order permutations in in-context learning and reduces activation outliers, paving the way for more efficient quantization. These advantages position Differential Transformer as a promising foundation architecture for future large language model development. Read the research here: https://arxiv.org/pdf/2410.05258…
Introducing, ScienceAgentBench, a new benchmark for evaluating language agents designed to automate scientific discovery. The benchmark comprises 102 tasks extracted from 44 peer-reviewed publications across four disciplines, encompassing essential tasks in a data-driven scientific workflow such as model development, data analysis, and visualization. To ensure scientific authenticity and real-world relevance, the tasks were validated by nine subject matter experts. The paper presents an array of evaluation metrics for assessing program execution, results, and costs, including a rubric-based approach for fine-grained evaluation. Through comprehensive experiments on five LLMs and three frameworks, the study found that the best-performing agent, Claude-3.5-Sonnet with self-debug, could only solve 34.3% of the tasks using expert-provided knowledge. These findings highlight the limitations of current language agents in fully automating scientific discovery, emphasizing the need for more rigorous assessment and future research on improving their capabilities for data processing and utilizing expert knowledge. Read the paper: https://arxiv.org/pdf/2410.05080…
Both sources explain neural network pruning techniques in PyTorch. The first source, "How to Prune Neural Networks with PyTorch," provides a general overview of the pruning concept and its various methods, along with practical examples of how to implement different pruning techniques using PyTorch's built-in functions. The second source, "Pruning Tutorial," focuses on a more in-depth explanation of pruning functionalities within PyTorch, demonstrating how to prune individual modules, apply iterative pruning, serialize pruned models, and even extend PyTorch with custom pruning methods. Read this: https://towardsdatascience.com/how-to-prune-neural-networks-with-pytorch-ebef60316b91 And the PyTorch tutorial: https://pytorch.org/tutorials/intermediate/pruning_tutorial.html…
The source is a chapter from the book "Dive into Deep Learning" that explores the historical development of deep convolutional neural networks (CNNs), focusing on the foundational AlexNet architecture. The authors explain the challenges faced in training CNNs before the advent of AlexNet, including limited computing power, small datasets, and lack of crucial training techniques. They discuss how AlexNet overcame these obstacles by leveraging powerful GPUs, large-scale datasets like ImageNet, and innovative training strategies. The chapter also delves into the architecture of AlexNet, highlighting its similarities to LeNet, and comparing its advantages in terms of depth, activation function, and model complexity control. Finally, the authors emphasize the importance of AlexNet as a crucial step towards the development of the deep networks used today, showcasing its impact on the field of computer vision and deep learning. Read more: https://d2l.ai/chapter_convolutional-modern/alexnet.html…
This text is an excerpt from the "Dive into Deep Learning" book, specifically focusing on the processing of sequential data. The authors introduce the challenges of working with data that occurs in a specific order, like time series or text, and how these sequences cannot be treated as independent observations. They delve into autoregressive models, where future values are predicted based on past values, and highlight the common problem of error accumulation when predicting further into the future. The text discusses the concept of Markov models, where only a limited history is needed to predict future events, as well as the importance of understanding the causal structure of the data. The excerpt then provides a practical example of using linear regression for autoregressive modeling on synthetic time series data and demonstrates the limitations of simple models for long-term prediction. Read more: https://d2l.ai/chapter_recurrent-neural-networks/sequence.html…
This excerpt from "Mental Models," a chapter in the "People + AI Guidebook," focuses on the importance of understanding and managing user mental models when designing AI-powered products. The authors discuss how to set expectations for adaptation, onboard users in stages, plan for co-learning, and account for user expectations of human-like interaction. By carefully considering these factors, product designers can ensure that users form accurate mental models and have a positive experience with AI-powered products. Read more here: https://pair.withgoogle.com/chapter/mental-models/…
This excerpt from Hugging Face's NLP course provides a comprehensive overview of tokenization techniques used in natural language processing. Tokenizers are essential tools for transforming raw text into numerical data that machine learning models can understand. The text explores various tokenization methods, including word-based, character-based, and subword tokenization, highlighting their advantages and disadvantages. It then focuses on the encoding process, where text is first split into tokens and then converted to input IDs. Finally, the text demonstrates how to decode input IDs back into human-readable text. Read more: https://huggingface.co/learn/nlp-course/en/chapter2/4…
This research paper examines the efficiency of two popular deep learning libraries, TensorFlow and PyTorch, in developing convolutional neural networks. The authors aim to determine if the choice of library impacts the overall performance of the system during training and design. They evaluate both libraries using six criteria: user-friendliness, available documentation, ease of integration, overall training time, overall accuracy, and execution time during evaluation. The paper proposes a novel methodology for comparing these libraries by eliminating external factors that could influence the comparison and focusing solely on the six chosen criteria. The study finds that while both libraries offer similar capabilities, PyTorch is better suited for tasks that prioritize speed and ease of use, while TensorFlow excels in tasks demanding accuracy and flexibility. The authors conclude that the choice of library has a significant impact on both design and performance and that the presented criteria can assist users in selecting the most appropriate library for their specific needs. Read more: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699128/pdf/sensors-22-08872.pdf…
This document provides a comprehensive set of rules for building and deploying machine learning systems, focusing on best practices gleaned from Google’s extensive experience. The document is divided into sections that cover the key stages of the machine learning process, including launching a product without ML, designing and implementing metrics, creating a first pipeline, feature engineering, human analysis, and addressing the challenges of training-serving skew. The rules cover a wide range of topics, from choosing the right objective function to detecting silent failures, and from creating human-understandable features to avoiding feedback loops. The document also offers guidance for navigating the transition from simple to more complex models as a system matures and performance plateaus. Go deeper here: https://developers.google.com/machine-learning/guides/rules-of-ml…
The research paper "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" explores a novel approach to language modeling by combining State Space Models (SSMs), which offer linear-time inference and strong performance in long-context tasks, with Mixture of Experts (MoE), a technique that scales model parameters while minimizing computational demands. The authors introduce MoE-Mamba, a model that interleaves Mamba, a recent SSM-based model, with MoE layers, resulting in significant performance gains and training efficiency. They demonstrate that MoE-Mamba outperforms both Mamba and standard Transformer-MoE architectures. The paper also explores different design choices for integrating MoE within Mamba, showcasing promising directions for future research in scaling language models beyond tens of billions of parameters. Read it: https://arxiv.org/abs/2401.04081…
We discuss how to build Agentic Retrieval Augmented Generation (RAG) systems, which use AI agents to retrieve information from various sources to answer user queries. The author details the challenges he faced when building an Agentic RAG system to answer customer support questions, and provides insights into techniques like prompt engineering and structured responses that helped him achieve better results. He also discusses best practices for building effective Agentic RAG systems and touches on the future of AI agents, including multi-agent systems and autonomous agents. Read more: https://arxiv.org/pdf/2408.08435…
Let's get RE(a)L, U! This research paper explores the impact of different activation functions, specifically ReLU and L-ReLU, on the performance of deep learning models. The authors investigate how the choice of activation function, along with factors like the number of parameters and the shape of the model architecture, influence model accuracy across various data domains (continuous, categorical with and without transfer learning). The study concludes that L-ReLU is more effective than ReLU when the number of parameters is relatively small, while ReLU generally performs better with larger models. The paper also highlights the importance of considering the specific data domain and the use of pre-trained models for transfer learning when selecting the most suitable activation function. Read more: https://github.com/christianversloot/machine-learning-articles/blob/main/why-nonlinear-activation-functions-improve-ml-performance-with-tensorflow-example.md…
This lecture from Stanford University's CS229 course, "Machine Learning," focuses on the theory and practice of linear regression and gradient descent, two fundamental machine learning algorithms. The lecture begins by motivating linear regression as a simple supervised learning algorithm for regression problems where the goal is to predict a continuous output based on a set of input features. The lecture then introduces the cost function used in linear regression, which measures the squared error between the predicted output and the true output. Gradient descent, an iterative algorithm, is then explained as a method to find the parameters that minimize the cost function. Two variants of gradient descent, batch gradient descent and stochastic gradient descent, are discussed with their respective strengths and weaknesses. The lecture concludes with a derivation of the normal equations, an alternative approach to finding the optimal parameters in linear regression that involves solving a system of equations rather than iteratively updating parameters. Watch Andrew Ng teach it at Stanford: https://www.youtube.com/watch?v=4b4MUYve_U8&t=1086s&pp=ygUSdmFuaXNoaW5nIGdyYWRpZW50…
This video discusses the vanishing gradient problem, a significant challenge in training deep neural networks. The speaker explains how, as a neural network becomes deeper, gradients—measures of how changes in network parameters affect the loss function—can decrease exponentially, leading to a situation where early layers of the network are effectively frozen and unable to learn. This problem arises because common activation functions like the sigmoid function can produce very small derivatives, which compound during backpropagation. The video then explores solutions like using different activation functions (like ReLU) and architectural changes (like residual networks and LSTMs) to mitigate this issue. Watch the video: https://www.youtube.com/watch?v=ncTHBi8a9uA&pp=ygUSdmFuaXNoaW5nIGdyYWRpZW50…
A scientific paper exploring the development and evaluation of language agents for automating data-driven scientific discovery. The authors introduce a new benchmark called ScienceAgentBench, which consists of 102 diverse tasks extracted from peer-reviewed publications across four disciplines: Bioinformatics, Computational Chemistry, Geographical Information Science, and Psychology & Cognitive Neuroscience. The benchmark evaluates the performance of language agents on individual tasks within a scientific workflow, aiming to provide a more rigorous assessment of their capabilities than solely focusing on end-to-end automation. The paper's experiments test five language models across three frameworks: direct prompting, OpenHands CodeAct, and self-debug, revealing that even the best-performing agent, Claude-3.5-Sonnet with self-debug, can only independently solve 32.4% of the tasks and 34.3% with expert-provided knowledge. The results highlight the limited capacities of current language agents in automating scientific tasks and underscore the need for further development to improve their ability to process scientific data, utilize expert knowledge, and handle complex tasks. Read more: https://arxiv.org/pdf/2410.05080…
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Daily Deals
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