x

75% of people globally want unbiased news (Pew Research) but are concerned they're not getting that.

Get balanced coverage with Symby. See how stories are covered across diverse outlets and spot when one side covers a story the other skips.

PODCAST

AI Post Transformers

AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

technology

All Episodes

00:12:31

ELASTIC: Linear Attention for Sequential Interest...

AI Post Transformers ·

2025/10/31

The February 12, 2025 KuaiShou Inc paper introduces **ELASTIC**, an Efficient Linear Attention for SequenTial Interest Compression framework designed to address the **scalability issues** of traditional transformer-based sequential recommender systems, which suffer from quadratic complexity with respect to sequence length. ELASTIC achieves this by proposing a **Linear Dispatcher Attention (LDA) layer** that compresses long user behavior sequences into a more compact representation, leading to

en

00:14:01

Anthropic: Introspective Awareness in LLMs

AI Post Transformers ·

2025/10/31

On October 29, 2025 Anthropic presented research investigating the existence of **functional introspective awareness** in large language models (LLMs), specifically focusing on Anthropic's Claude models. The core methodology involves using **concept injection**, where researchers manipulate a model's internal activations with representations of specific concepts to see if the model can accurately **report on these altered internal states**. Experiments demonstrate that models can, at times, notice

en

00:14:03

Small Versus Large Models for Requirements...

AI Post Transformers ·

2025/10/31

The October 24, 2025 collaboration between many universities have published a paper thst compares the performance of **Large Language Models (LLMs)** and **Small Language Models (SLMs)** on requirements classification tasks within software engineering. Researchers conducted a preliminary study using eight models across three datasets to address concerns about the **high computational cost and privacy risks** associated with using proprietary LLMs. The results indicate that while LLMs achieved an

en

00:17:25

Hyper-Scaling LLM Inference with KV Cache Compression

AI Post Transformers ·

2025/10/31

The June 5, 2025 collaboration between University of Edinburgh and Nvidia paper introduces the concept of **inference-time hyper-scaling** for large language models (LLMs), which aims to boost reasoning accuracy by allowing for longer or more parallel token sequences within the same computational budget. The core bottleneck is identified as the size of the key–value (KV) cache, which grows linearly and dominates inference cost. To address this, the authors propose **Dynamic Memory Sparsification

en

00:14:59

Architectural Scaling Laws for Efficient LLMs

AI Post Transformers ·

2025/10/31

The October 21, 2025 collaboration paper between UW-Madison and Amazon Web Services discuss the critical role of the **Multi-Layer Perceptron (MLP) intermediate size f_size as the primary architectural component for introducing non-linearity and complexity within Large Language Models (LLMs). The MLP layer achieves this by taking the hidden state d_model projecting it up to the expanded f_size, applying a **non-linear gating function** (like SwiGLU), and then projecting it back down. The balance

en

Small Versus Large Models for Requirements...

AI Post Transformers ·

2025/10/31

The October 24, 2025 collaboration between many universities have published a paper thst compares the performance of Large Language Models (LLMs) and Small Language Models (SLMs) on requirements classification tasks within software engineering. Researchers conducted a preliminary study using eight models across three datasets to address concerns about the high computational cost and privacy risks associated with using proprietary LLMs. The results indicate that while LLMs achieved an average F1

en

Quest: Query-Aware Sparsity for Efficient LLM Inference

AI Post Transformers ·

2025/10/31

The August 26, 2024 academic paper introduces Quest, a novel algorithm designed to improve the inference efficiency of Long-Context Large Language Models (LLMs) by addressing the costly self-attention process caused by a large Key-Value (KV) cache. Quest utilizes Query-Aware Sparsity to dynamically identify and select only the critical KV cache pages based on the current query token, which significantly reduces the required memory movement during decoding. Unlike previous Query-Agnostic methods that

en

Anthropic: Introspective Awareness in LLMs

AI Post Transformers ·

2025/10/31

On October 29, 2025 Anthropic presented research investigating the existence of functional introspective awareness in large language models (LLMs), specifically focusing on Anthropic's Claude models. The core methodology involves using concept injection, where researchers manipulate a model's internal activations with representations of specific concepts to see if the model can accurately report on these altered internal states. Experiments demonstrate that models can, at times, notice

en

Architectural Scaling Laws for Efficient LLMs

AI Post Transformers ·

2025/10/31

The October 21, 2025 collaboration paper between UW-Madison and Amazon Web Services discuss the critical role of the Multi-Layer Perceptron (MLP) intermediate size f_size as the primary architectural component for introducing non-linearity and complexity within Large Language Models (LLMs). The MLP layer achieves this by taking the hidden state d_model projecting it up to the expanded f_size, applying a non-linear gating function (like SwiGLU), and then projecting it back down. The balance between

en

Flash-LLM: Efficient LLM Inference with Unstructured...

AI Post Transformers ·

2025/10/31

The September 19, 2025 Alibaba paper introduces Flash-LLM, a novel software framework designed to enable cost-effective and highly-efficient inference for large generative models by supporting unstructured sparsity on high-performance tensor cores. The authors observe that the primary bottleneck in large language model (LLM) inference is the memory bandwidth limitation during "skinny" matrix multiplications, rather than the arithmetic processing of tensor cores. Flash-LLM addresses this

en

ELASTIC: Linear Attention for Sequential Interest...

AI Post Transformers ·

2025/10/31

The February 12, 2025 KuaiShou Inc paper introduces ELASTIC, an Efficient Linear Attention for SequenTial Interest Compression framework designed to address the scalability issues of traditional transformer-based sequential recommender systems, which suffer from quadratic complexity with respect to sequence length. ELASTIC achieves this by proposing a Linear Dispatcher Attention (LDA) layer that compresses long user behavior sequences into a more compact representation, leading to linear time

en

Hyper-Scaling LLM Inference with KV Cache Compression

AI Post Transformers ·

2025/10/31

The June 5, 2025 collaboration between University of Edinburgh and Nvidia paper introduces the concept of inference-time hyper-scaling for large language models (LLMs), which aims to boost reasoning accuracy by allowing for longer or more parallel token sequences within the same computational budget. The core bottleneck is identified as the size of the key–value (KV) cache, which grows linearly and dominates inference cost. To address this, the authors propose Dynamic Memory Sparsification (DMS), a

en

00:31:45

ATTENTION2D and lean attention: Distributed...

AI Post Transformers ·

2025/10/29

We cover two new innovations from Microsoft extending ideas from the original old **FlashAttention**. Flash Attention is an IO-aware attention algorithm for Transformers designed to address the quadratic time and memory complexity of standard self-attention on long sequences. By using **tiling and recomputation** to minimize slow **High Bandwidth Memory (HBM)** accesses in favor of fast **on-chip SRAM**, FlashAttention achieves significant wall-clock speedups for training models like BERT and GPT-2,

en

00:30:40

Sentence-BERT: Siamese Networks for Sentence Embeddings

AI Post Transformers ·

2025/10/29

The provided text introduces **Sentence-BERT (SBERT)**, a modification of the popular **BERT** and **RoBERTa** language models, designed to efficiently generate **semantically meaningful sentence embeddings**. The authors address the significant **computational overhead** of using standard BERT for tasks requiring sentence-pair comparisons, such as semantic similarity search and clustering, which can take hours for large datasets. SBERT utilizes **siamese and triplet network structures** to create

en

00:15:40

TxGNN: Foundation Model for Zero-Shot Drug Repurposing

AI Post Transformers ·

2025/10/29

The source provides excerpts from a scientific paper introducing **TxGNN**, a novel graph foundation model designed for **zero-shot drug repurposing**, which aims to identify therapeutic candidates even for diseases with no existing treatments or limited molecular data. Developed by researchers affiliated with institutions like Harvard Medical School and Stanford University, this model leverages a **medical knowledge graph (KG)** and a graph neural network (GNN) to predict drug indications and

en

TxGNN: Foundation Model for Zero-Shot Drug Repurposing

AI Post Transformers ·

2025/10/29

The source provides excerpts from a scientific paper introducing TxGNN, a novel graph foundation model designed for zero-shot drug repurposing, which aims to identify therapeutic candidates even for diseases with no existing treatments or limited molecular data. Developed by researchers affiliated with institutions like Harvard Medical School and Stanford University, this model leverages a medical knowledge graph (KG) and a graph neural network (GNN) to predict drug indications and contraindications

en

Sentence-BERT: Siamese Networks for Sentence Embeddings

AI Post Transformers ·

2025/10/29

The provided text introduces Sentence-BERT (SBERT), a modification of the popular BERT and RoBERTa language models, designed to efficiently generate semantically meaningful sentence embeddings. The authors address the significant computational overhead of using standard BERT for tasks requiring sentence-pair comparisons, such as semantic similarity search and clustering, which can take hours for large datasets. SBERT utilizes siamese and triplet network structures to create fixed-size sentence

en

ATTENTION2D and lean attention: Distributed...

AI Post Transformers ·

2025/10/29

We cover two new innovations from Microsoft extending ideas from the original old FlashAttention. Flash Attention is an IO-aware attention algorithm for Transformers designed to address the quadratic time and memory complexity of standard self-attention on long sequences. By using tiling and recomputation to minimize slow High Bandwidth Memory (HBM) accesses in favor of fast on-chip SRAM, FlashAttention achieves significant wall-clock speedups for training models like BERT and GPT-2, enabling them

en

00:17:33

STAR: Sub-Entry Sharing TLB for Multi-Instance GPU...

AI Post Transformers ·

2025/10/26

These April 29, 2024 paper provides an overview of the challenges associated with using **NVIDIA's Multi-Instance GPU (MIG)** technology, specifically focusing on the address translation mechanism in the **A100 GPU**. The papers reveal, primarily through **reverse-engineering efforts**, that the L2 and L3 Translation Lookaside Buffers (**TLBs**) utilize a compression design where each entry comprises **16 sub-entries** to enhance memory capacity management. A major problem arises because the **L3

en

00:16:21

Strata: Efficient Hierarchical Context Caching for...

AI Post Transformers ·

2025/10/26

The August 26, 2025 collaboration between Stanford, NVIDIA, Shanghai Jiao Tong University, University of Michigan, University of Colorado Boulder, Carnegie Mellon University introduces **Strata**, a hierarchical context caching framework designed to improve the performance of serving Large Language Models (LLMs) with long context windows. The core problem Strata addresses is that while caching key-value (KV) states is essential for efficiency, transferring large, fragmented cached contexts from

en

990 results

Similar Podcasts

Lead With AI

Tamara Nall

Linux in the Ham Shack

Black Sparrow Media

Data Engineering Podcast

Tobias Macey

Eye On A.I.

Craig S. Smith

Reflections

Livewire Labs