AIs Improving Themselves? Automated Design of Agentic Systems

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

35:12

Sidhu Moose Wala explodes onto the Canadian music scene. His sound is a fusion of two worlds - hip-hop with the poetic language of rural Punjab, where he is from. After years of struggle he’s making it. But with the spotlight comes a dark side. As his fame grows, so do the threats. "We will kill you." Presented by broadcaster and DJ Bobby Friction and investigative journalist Ishleen Kaur. Season 8 of World of Secrets, The Killing Call, is a BBC Eye investigation for the BBC World Service. Archive audio credits: Lovepreet Waraich, Malwa TV, BritAsia TV, MPHONE Canteeni Mandeer, GK Digital, Thakur Media, Capital Extra, Famous Punjab TV, ModernSings, Dheeth.jeha, RealRohitBlogs, Mirror Now, India Today. Here’s a link to the BBC Eye two-part documentary films, which we recommend you watch after listening to this podcast: https://bit.ly/thekillingcall If you are in the UK, you can watch on iPlayer: https://www.bbc.co.uk/programmes/m002f18y…

about a year ago 7:37

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The paper proposes a new research area called Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful AI systems, including inventing new components and combining them in novel ways. The authors introduce Meta Agent Search, an algorithm that uses a meta agent to iteratively program increasingly sophisticated agents by learning from a growing archive of previously discovered agents. The paper demonstrates that Meta Agent Search can discover agents that outperform hand-designed systems across various domains, including coding, science, and math, showing promise for efficiently developing increasingly capable AI systems. Importantly, the discovered agents demonstrate robustness and generalizability, exhibiting strong performance even when transferred across domains and models.

Read the paper here: https://arxiv.org/pdf/2408.08435

71 episodes

AIs Improving Themselves? Automated Design of Agentic Systems

published about a year ago

MP3•Episode home

Archived series ("Inactive feed" status)

When? This feed was archived on May 02, 2025 14:13 (2M ago). Last successful fetch was on November 09, 2024 13:09 (7M ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

Read the paper here: https://arxiv.org/pdf/2408.08435

71 episodes

All episodes

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

1
Does the DIFF Transformer make a Diff? 8:03

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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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Automating Scientific Discovery: ScienceAgentBench 9:49

32 weeks ago9:49

9:49

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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Prune This! PyTorch and Efficient AI 8:04

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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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Predicting the Future from the Past: Sequential RNN Stuff 9:47

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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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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LLM Tokenizers, from HFs LNP Course 12:23

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OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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PyTorch vs Tensorflow: Who Wins in CNN? 11:49

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OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Google's 43 Rules for Machine Learning 9:28

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OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Do we Need the Mamba Mindset when LLMs Fail? MoE Mamba and SSMs 11:57

33 weeks ago11:57

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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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Agentic Retrieval Augmented Generation (RAG) systems 7:39

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OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Reviewing Stanford on Linear Regression and Gradient Descent 8:25

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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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

1
Where'd My Gradient Go? It Vanished! 8:39

34 weeks ago8:39

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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…

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

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Automating Scientific Discovery: ScienceAgentBench 7:38

34 weeks ago7:38