Wednesday, AI 101: What is Long Short-Term Memory and the recent xLSTM?

Friday, deep dive into another AI Unicorns.

Hedge funds like Britain's Man Group and Singapore's FengHe Fund Management are betting on South Korea's chipmakers, driven by AI demand for high-end memory chips. Their investments in SK Hynix and Samsung Electronics have boosted South Korea's KOSPI index, anticipating growth fueled by government backing and rising chip prices.

BTW, an interesting announcement related to South Korea will be waiting for you when we are back from the break in August. Stay tuned.

The famous VC firm is amassing over 20,000 GPUs, including Nvidia H100s, to attract AI startups. Through its initiative, "oxygen," the firm plans to rent these chips to secure equity in companies needing high-end computing power. If AI is the new electricity, GPU, apparently, is the new oxygen 🤔.

OpenAI has defined five levels to measure progress towards artificial general intelligence (AGI). They are:

• Level 1: Current AI that can interact conversationally with people.

• Level 2 (Reasoners): AI systems that can solve basic problems like a human with a doctorate-level education.

• Level 3 (Agents): AI systems capable of taking actions on a user’s behalf over several days.

• Level 4: AI that can innovate and come up with new ideas.

• Level 5 (Organizations): AI that can perform the work of entire organizations.

OpenAI OpenAI develops project Strawberry for advanced reasoning. This initiative involves developing models capable of planning and navigating the internet autonomously, aiming to improve AI's reasoning skills and move closer to human-like intelligence.

Time Series Are Not That Different for LLMs by Henri Lai

How Much Energy Will It Take To Power AI? by Contrary Research

AI’s $100bn question: The scaling ceiling by Exponential View

It is the latest and a very important breakthrough in AI efficiency, providing a 2-4x speedup while maintaining accuracy. Developed by researchers from Colfax, Meta, NVIDIA, Georgia Tech, Princeton, and Together AI, it uses asynchronous Tensor Cores and FP8 low-precision support to improve GPU utilization. This enhancement allows for better handling of long contexts and optimized performance on modern GPUs, paving the way for new applications and more efficient transformer-based AI systems.

Internet of Agents: Proposes a collaborative framework integrating diverse autonomous agents to overcome limitations in multi-agent systems, enhancing intelligence and interaction →read the paper

AgentInstruct: Develops an agentic framework that autonomously generates synthetic data to teach language models new skills, significantly improving model performance →read the paper

GTA: Introduces a benchmark to evaluate language model agents in real-world scenarios, highlighting existing models' limitations in tool-use capabilities →read the paper

Mobility VLA: Combines Vision-Language Models and topological graphs for effective multimodal instruction navigation in complex environments →read the paper

MambaVision: Develops a hybrid architecture that integrates Transformer self-attention into the Mamba model, enhancing performance in various vision tasks →read the paper

PaliGemma: Combines a vision encoder and a language model to effectively transfer knowledge across diverse vision-language tasks →read the paper

Vision Language Models are Blind: Reveals significant perceptual limitations of Vision Language Models in basic visual tasks, highlighting a gap in their visual processing capabilities →read the paper

MJ-BENCH: Introduces a benchmark for evaluating multimodal judges in text-to-image generation, assessing their performance on various criteria including safety and bias →read the paper

Unified Database: Integrates vector and scalar indices to enhance query performance for large language models →read the paper

H2O-Danube3 Technical Report: Presents small LLMs optimized for mobile devices, highlighting efficient operation and accessibility →read the paper

SPREADSHEETLLM: Enhances LLMs' ability to handle complex spreadsheet data through advanced serialization and compression techniques →read the paper

Q-GaLore: Combines quantization and adaptive low-rank projections to reduce memory usage during LLM training →read the paper

Inference Performance Optimization for Large Language Models on CPUs: Optimizes LLM inference on CPUs using techniques like SlimAttention and an INT8 KV cache approach →read the paper

Toto: Introduces a foundation model for time-series forecasting optimized for observability metrics →read the paper

Gradient Boosting Reinforcement Learning: Extends gradient boosting techniques to reinforcement learning for improved performance on structured tasks →read the paper

Autoregressive Speech Synthesis without Vector Quantization: Proposes an autoregressive TTS model that enhances output diversity and robustness →read the paper

LETS-C: Utilizes language embeddings for time-series classification, demonstrating high performance with reduced computational costs →read the paper

Evaluating Language Model Context Windows: Benchmarks long context models and introduces techniques to improve accuracy in QA tasks →read the paper

Lynx: Develops an open-source model for detecting hallucinations in Retrieval-Augmented Generation systems →read the paper

Speculative RAG: Enhances Retrieval-Augmented Generation by verifying drafts generated by specialized models, improving performance and reducing latency →read the paper

Lookback Lens: Detects contextual hallucinations in LLMs using attention maps, providing a tool to reduce hallucinations →read the paper

Associative Recurrent Memory Transformer: Develops a new architecture for processing long sequences efficiently using associative memory →read the paper

Human-Like Episodic Memory for Infinite Context LLMs: Integrates features of human episodic memory into LLMs to manage infinite context lengths →read the paper

MUSCLE: Introduces a model update strategy that minimizes negative flips during LLM updates, ensuring consistent task performance →read the paper

Characterizing Prompt Compression Methods for Long Context Inference: Evaluates methods for prompt compression in LLMs, identifying best practices for accuracy and efficiency →read the paper

PAS: Develops a plug-and-play system for augmenting LLM prompts, improving performance with minimal human intervention →read the paper

InverseCoder: Enhances code LLMs by generating natural language instructions from code, improving model diversity and performance →read the paper

On scalable oversight with weak LLMs judging strong LLMs: Investigates scalable oversight methods like debate and consultancy for supervising advanced AI, recommending debate for effectiveness →read the paper

On Leakage of Code Generation Evaluation Datasets: Identifies contamination sources in code generation datasets and introduces a cleaner benchmark for evaluating LLMs →read the paper

An Accurate Detection is Not All You Need to Combat Label Noise in Web-Noisy Datasets: Proposes a hybrid approach to improve classification performance in noisy datasets by combining unsupervised learning with noise detection methods →read the paper

Self-Recognition in Language Models: Investigates whether LLMs can recognize their own outputs, revealing insights into model decision-making processes →read the paper

From Loops to Oops: Studies fallback behaviors of LLMs under uncertainty, detailing how advanced models handle errors and uncertainties →read the paper

Understanding Visual Feature Reliance through the Lens of Complexity: Analyzes how deep learning models prioritize features based on complexity, impacting model decisions →read the paper