Beste Deep Learning podcaster (2024)

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Finally? Super-Accurate Weather Forecasts with AI 8:04

9h ago8:04

8:04

This research paper examines a new deep-learning approach to optimizing weather forecasts by adjusting initial conditions. The authors test their method on the 2021 Pacific Northwest heatwave, finding that small changes to initial conditions can significantly improve the accuracy of 10-day forecasts using both the GraphCast and Pangu-Weather deep-l…

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Poles Apart in the Church? 21:47

4d ago21:47

21:47

The church is not immune to polarity that all too often leads to contention and division—the opposite of biblical unity. This study in Ephesians aims to transform your understanding of the nature of biblical unity and its priority in the life of each Christian.Av Michael Gray

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052: Speech Synthesis - The anatomy of a voice assistant #4 28:46

4d ago28:46

28:46

Send us a text In this concluding episode of the 'Anatomy of a Voice Assistant' series, CTO Shawn Wen discusses the intricacies of speech synthesis in voice assistants, emphasizing the importance of authentic, human-like voices in improving user engagement and containment rates. Kylie and Shawn chat about the evolution of AI voices from the early d…

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Как войти в IT и создать коммьюнити с с Михаилом Васильевым 1:33:28

6d ago1:33:28

1:33:28

В этом подкасте мы беседуем с выпускником DLS, который делится своим опытом поиска работы в области машинного обучения. Узнаем о трудностях, с которыми он столкнулся на пути к карьере в ML после 35 лет, и обсудим, как можно начать успешный путь в этой сфере в любом возрасте.

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Let's Descend: Why Calculus is Important in Deep Learning 11:00

19h ago11:00

11:00

An introduction to the fundamental concepts of calculus, explaining how they are essential for understanding deep learning. It begins by illustrating the concept of a limit using the calculation of a circle's area, before introducing the concept of a derivative, which describes a function's rate of change. It then extends these concepts to multivar…

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OMG OpenAI's o1 is a LogicQuake! The AI Reasoning Layer, Sequoia Capital 6:42

21h ago6:42

6:42

The source, "Generative AI's Act o1: The Reasoning Era Begins | Sequoia Capital," discusses the evolution of AI models from simply mimicking patterns to engaging in more deliberate reasoning. The authors argue that the next frontier in AI is the development of "System 2" thinking, where models can reason through complex problems and make decisions …

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Assemble Your Team! OpenAI's Swarm 8:43

1d ago8:43

8:43

Swarm is an experimental, educational framework from OpenAI that explores ergonomic interfaces for multi-agent systems. It is not intended for production use, but serves as a learning tool for developers interested in multi-agent orchestration. Swarm uses two main concepts: Agents and handoffs. Agents are entities that encapsulate instructions and …

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Bio-Origami: The 2024 Nobel Prize in Chemistry for AI Protein Folding (DeepMind) 10:49

1d ago10:49

10:49

The provided sources detail the groundbreaking work of three scientists who were awarded the 2024 Nobel Prize in Chemistry for their contributions to protein structure prediction using artificial intelligence. David Baker, a biochemist, developed a computer program to create entirely new proteins, while Demis Hassabis and John Jumper, from Google D…

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Anthropic Controversy: Dario Amodei's Machines of Loving Grace Post 13:40

2d ago13:40

13:40

Dario Amodei, CEO of Anthropic, argues that powerful AI could revolutionize various fields, including healthcare, neuroscience, economics, and governance, within 5-10 years. He envisions a future where AI could cure most diseases, eradicate poverty, and even promote democracy. However, this optimistic vision is met with skepticism from Reddit users…

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RAG for LLMs: An Overview 19:34

3d ago19:34

19:34

This paper examines the rapidly developing field of Retrieval-Augmented Generation (RAG), which aims to improve the capabilities of Large Language Models (LLMs) by incorporating external knowledge. The paper reviews the evolution of RAG paradigms, from the early "Naive RAG" to the more sophisticated "Advanced RAG" and "Modular RAG" approaches. It e…

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Certainty and OOD Detection in Medical Images and Multiple Sclerosis 7:14

4d ago7:14

7:14

This research paper investigates the challenges of detecting Out-of-Distribution (OOD) inputs in medical image segmentation tasks, particularly in the context of Multiple Sclerosis (MS) lesion segmentation. The authors propose a novel evaluation framework that uses 14 different sources of OOD, including synthetic artifacts and real-world variations…

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LLM DIFF Transformer with SoftMax Subtraction 12:48

4d ago12:48

12:48

This paper presents a new architecture for large language models called DIFF Transformer. The paper argues that conventional Transformers over-allocate attention to irrelevant parts of the input, drowning out the signal needed for accurate output. DIFF Transformer tackles this issue by using a differential attention mechanism that subtracts two sof…

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Data Pruning to Improve AI Performance 17:00

4d ago17:00

17:00

The source is a blog post that describes the author's journey in exploring the potential of data pruning to improve the performance of AI models. They start by discussing the Minipile method, a technique for creating high-quality datasets by clustering and manually discarding low-quality content. The author then explores the concept of "foundationa…

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OpenAI's o1 and Journey Learning 7:28

4d ago7:28

7:28

This paper details the authors' research journey to replicate OpenAI's "O1" language model, which is designed to solve complex reasoning tasks. The researchers document their process with detailed insights, hypotheses, and challenges encountered. They present a novel paradigm called "Journey Learning" that enables models to learn the complete explo…

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Forward and Backpropagation 6:25

4d ago6:25

6:25

Let's get into the core processes of forward propagation and backpropagation in neural networks, which form the foundation of training these models. Forward propagation involves calculating the outputs of a neural network, starting with the input layer and moving towards the output layer. Backpropagation then calculates the gradients of the network…

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MLE-bench for Engineering Tasks 10:23

4d ago10:23

10:23

This research introduces MLE-bench, a benchmark for evaluating how well AI agents perform machine learning engineering tasks. The benchmark is comprised of 75 Kaggle competitions, chosen for their difficulty and representativeness of real-world ML engineering skills. Researchers evaluated several state-of-the-art language models on MLE-bench, findi…

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AI in Dentistry: CNNs for Segmenting and Classifying Dental Images 8:33

5d ago8:33

8:33

This systematic literature review investigates the use of convolutional neural networks (CNNs) for segmenting and classifying dental images. The review analyzes 45 studies that employed CNNs for various tasks, including tooth detection, periapical lesion detection, caries identification, and age and sex determination. The authors explore the differ…

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AI in Dentistry: Diagnosing TMD with MRIs 7:14

5d ago7:14

7:14

This research paper proposes an AI-driven diagnostic system for Temporomandibular Joint Disorders (TMD) using MRI images. The system employs a segmentation method to identify key anatomical structures like the temporal bone, temporomandibular joint (TMJ) disc, and condyle. Using these identified structures, the system utilizes a decision tree based…

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AI in Dentistry: ChatGPT for Dental Diagnosis and Treatment 9:25

5d ago9:25

9:25

This research explores the potential for integrating ChatGPT and large language models (LLMs) into dental diagnostics and treatment. The authors investigate the use of these AI tools in various areas of dentistry, including diagnosis, treatment planning, patient education, and dental research. The study examines the benefits and limitations of LLMs…

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AI in Dentistry: OMG, Machine Learning that links TMD and OSA 12:28

5d ago12:28

12:28

This research paper explores the link between temporomandibular disorder (TMD) and obstructive sleep apnea (OSA). The authors created a machine learning algorithm to predict the presence of OSA in TMD patients using multimodal data, including clinical characteristics, portable polysomnography, X-ray, and MRI. Their model achieved high accuracy, wit…

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AI in Dentistry: Reading Intraoral Radiographs 5:13

5d ago5:13

5:13

This article describes a clinical validation study that investigates the effectiveness of a deep learning algorithm for detecting dental anomalies in intraoral radiographs. The algorithm is trained to detect six common anomaly types and is compared to the performance of dentists who evaluate the images without algorithmic assistance. The study util…

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AI in Dentistry: 3D X-Rays? Cone Beam Computed Tomography 8:16

5d ago8:16

8:16

Read more: https://arxiv.org/pdf/2306.03025Av Brian Carter

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AI in Dentistry: Design and create new teeth... VF Net for Dental Point Clouds 7:40

5d ago7:40

7:40

This paper introduces a new variational autoencoder called VF-Net, specifically designed for dental point clouds. The paper highlights the limitations of existing point cloud models and how VF-Net overcomes them through a novel approach, ensuring a one-to-one correspondence between points in the input and output clouds. The paper also introduces a …

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AI in Dentistry: Detecting occlusal contacts in dental images with AI 10:42

5d ago10:42

10:42

This research paper focuses on the development of a deep learning model, Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer), designed to automatically detect occlusal contacts in dental images. The model utilizes a combination of Vision Transformer and Fully Convolutional Network architectures and incorporates a Hierarchical Loss Fun…

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AI in Dentistry: Mental Foramen Detection and Segmentation 8:45

5d ago8:45

8:45

This research paper explores the use of deep learning to improve the accuracy of detecting and segmenting the mental foramen in dental orthopantomogram images. The authors compared the performance of various deep learning models, including U-Net, U-Net++, ResUNet, and LinkNet, using a dataset of 1000 panoramic radiographs. The study found that the …

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An Intro and History of Knowledge Graphs 8:21

5d ago8:21

8:21

This article from AI Magazine explores the rise of knowledge graphs (KGs) as a powerful tool for organizing and integrating information. It delves into the history of KGs, highlighting their evolution from early semantic networks to the large-scale, complex systems we see today. The article contrasts key approaches to building and using KGs, includ…

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LLMs, Knowledge Graphs, and Hallucination 8:55

6d ago8:55

8:55

This research paper examines the relationship between the size of language models (LMs) and their propensity to hallucinate, which occurs when an LM generates information that is not present in its training data. The authors specifically focus on factual hallucinations, where a correct answer appears verbatim in the training set. To control for the…

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AIs Improving Themselves? Automated Design of Agentic Systems 7:37

7d ago7:37

7:37

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 b…

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The Secret School for the World’s Best Startup Founders 11:45

7d ago11:45

11:45

This article from The Generalist examines Avra Capital, a new kind of venture fund founded by Anu Hariharan, a former Y Combinator executive. Avra’s unique approach combines a selective program for growth-stage entrepreneurs with a venture fund. The program provides founders with tactical masterclasses, taught by experienced CEOs, covering crucial …

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AI Art, DiTs and the new Dynamic Diffusion Transformer (DyDiT) 14:21

7d ago14:21

14:21

The provided sources describe a novel approach, Dynamic Diffusion Transformer (DyDiT), designed to improve the computational efficiency of Diffusion Transformer (DiT) models for image generation. DyDiT dynamically adapts its computational resources based on the varying complexities associated with different timesteps and spatial regions during imag…

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Bye James Cameron! Meta AI's NEW Movie Gen Model 11:29

7d ago11:29

11:29

This research paper from Meta AI describes "Movie Gen," a series of foundational models capable of generating high-quality videos and synchronized audio. The paper discusses the models' capabilities, including text-to-video synthesis, video personalization, video editing, and audio generation. It outlines the architecture, training process, and eva…

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Aha! Helping StartUp Developers Give a @#$ About Your Product 12:52

7d ago12:52

12:52

This article, written by the Head of Developer Community at SignalFire, a venture capital firm, provides a guide for startup founders on how to develop a successful developer relations strategy. The author emphasizes the importance of focusing on the "aha" moment, or the point at which developers experience the core value of a product. The article …

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Part II: Cheryl's Birthday and LLM Theory of Mind 6:19

7d ago6:19

6:19

The text explores the ability of Large Language Models (LLMs) to understand and reason about the knowledge states of different individuals. It does this by testing nine LLMs on the "Cheryl's Birthday Problem," a logic puzzle that requires the solver to deduce the correct birthday based on statements made by two people with varying levels of knowled…

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Cheryl's Birthday and LLM Theory of Mind Part I 8:18

7d ago8:18

8:18

This briefing document analyzes the logic puzzle "Cheryl's Birthday," its sequel, and a related variant. The document explores the origins of the puzzle, presents the puzzle statement and solution, examines a common incorrect solution, and discusses subsequent iterations of the puzzle. Origins "Cheryl's Birthday" is a knowledge puzzle that gained w…

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Beyond Keywords: Neural Retrieval with Context 6:39

7d ago6:39

6:39

This research paper proposes two methods for improving the performance of neural retrieval models by incorporating contextual information. The first method involves a training procedure that clusters documents into batches based on similarity, creating more challenging training examples. The second method introduces a new architecture that augments…

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Will o1 Ever Escape ChatGPT's Old Training? 7:41

7d ago7:41

7:41

This study investigates whether the reasoning abilities of large language models (LLMs) are still influenced by their origins in next-word prediction. The authors examine the performance of a new LLM from OpenAI called o1, which is specifically optimized for reasoning, on tasks that highlight the limitations of LLMs based on their autoregressive na…

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Multilayer Perceptrons (MLPs) in Deep Neural Network Architecture 10:55

8d ago10:55

10:55

Let's explore multilayer perceptrons (MLPs), a type of deep neural network architecture. The text first discusses the limitations of linear models and how they struggle to capture complex non-linear relationships in data. It then introduces hidden layers as a solution, explaining how they allow MLPs to represent non-linear functions. The excerpt ex…

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Where's Waldo? The Power of CNNs 8:50

8d ago8:50

8:50

This excerpt from Dive into Deep Learning explores the evolution of convolutional neural networks (CNNs) from basic multi-layered perceptrons (MLPs). It begins by showing the limitations of MLPs in processing high-dimensional data like images, particularly the large number of parameters required. The excerpt then introduces the concepts of translat…

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Softmax Regression in Linear Neural Networks 9:30

9d ago9:30

9:30

Let's get into the process of softmax regression, a method used in machine learning for classification problems where the goal is to predict which category a data point belongs to. It introduces the softmax function, which transforms outputs from a neural network into probabilities for each category, ensuring that they sum to 1. The cross-entropy l…

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Opportunities and Challenges with Knowledge Graphs 12:13

10d ago12:13

12:13

This article from the Artificial Intelligence Review examines the opportunities and challenges of knowledge graphs, a type of graph data that accumulates and conveys knowledge of the real world. The authors discuss how knowledge graphs are used in various AI systems, such as recommender systems, question-answering systems, and information retrieval…

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LoRA: The Original Paper on LoRA Fine-Tuning 9:47

10d ago9:47

9:47

This is a discussion of the original LoRA paper, which proposed a novel approach called Low-Rank Adaptation (LoRA) to make large language models (LLMs) more efficient for downstream tasks. LoRA avoids the computational and storage burden of traditional fine-tuning by freezing the pre-trained model weights and instead injects trainable low-rank matr…

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Transferring Knowledge from Large Models to Small Models 10:38

10d ago10:38

10:38

We discuss a research paper that proposes a new method called Adaptive Feature Transfer (AFT) for transferring knowledge from large foundation models to smaller, task-specific downstream models. AFT prioritizes transferring only the most relevant information from the pre-trained model to the downstream model, leading to improved performance and red…

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Weight Decay in Linear Regression 10:59

10d ago10:59

10:59

Let's talk about weight decay as a method of regularization to combat overfitting in machine learning models. Weight decay involves adding a penalty term to the loss function, which encourages the model to use smaller weights, thereby reducing the model's complexity and improving its ability to generalize to new data. The text introduces the mathem…

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RIG & RAG: Grounding AI in Reality with Super-Trustworthy Data 7:33

10d ago7:33

7:33

Google Research has developed a new set of open models, known as DataGemma, that aim to ground large language models (LLMs) in real-world data using Google's Data Commons knowledge graph. DataGemma's primary goal is to improve the factuality and trustworthiness of LLMs by mitigating the risk of hallucinations, which occur when LLMs generate incorre…

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Generalization in Machine Learning 13:26

10d ago13:26

13:26

In this episode, we explore the concept of generalization in machine learning, emphasizing the challenge of training models that can accurately predict outcomes on unseen data. The text explains how overfitting occurs when models become too specialized to the training data, leading to poor performance on new data. It introduces regularization techn…

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ARES RAG: An Automated Evaluation Framework for Retrieval-Augmented (New Research) 11:42

10d ago11:42

11:42

This research paper ("ARES: An Automated Evaluation Framework for Retrieval-Augmented") introduces ARES, an Automated RAG Evaluation System, designed to assess the performance of Retrieval-Augmented Generation (RAG) systems. RAG systems are designed to use retrieved information to generate responses to user queries. ARES evaluates these systems bas…

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Linear Regression 17:10

10d ago17:10

17:10

This episode is about linear regression, a fundamental statistical method used to predict a numerical value based on a set of features (input variables). It describes the key components of linear regression, including the model (a linear function that relates features to the target), the loss function (which quantifies the error between predictions…

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051: Secret Agentforce: Declassified! And more from Dreamforce 2024 18:11

13d ago18:11

18:11

Send us a text Our own Brian Thompson, VP of Product Marketing, joins CEO Nikola to discuss Salesforce's recent AgentForce announcement. This new initiative is presented as a big shift toward autonomous AI agents within large enterprises and while at least one the dyad sees it as a rebrand of Einstein Copilot, others may view it as a step towards m…

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Discipleship Targets Polarization 27:25

18d ago27:25

27:25

Transformational discipleship is redundant. Discipleship is intrinsically transformation into increasing Christlikeness. This is a case study of Ephesians that speaks with biblical authority to the current polarization within the church.Av Michael Gray

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050: Dialogue design. So hot right now. 22:45

19d ago22:45

22:45

Send us a text 50th episode! Damien welocmes newcomer to the pod Oliver Shoulson, PolyAI's senior dialogue designer, about what dialogue design entails and why it's crucial in the era of advanced language models (LLMs). Oliver explains that dialogue design goes beyond just scripting responses; it's about making AI interactions feel natural to human…

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