NEWS IN BRIEF: AI/ML FRESH UPDATES

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Teen Innovator Creates Robot Guide Dogs with NVIDIA Jetson

High school student Selin Alara Ornek uses NVIDIA Jetson for edge AI to create robot guide dogs for visually impaired, aiming to prevent bullying and aid health monitoring with real-time notification capabilities. Ornek, a self-taught robotics developer from Istanbul, is recognized globally for her innovative projects and plans to deploy IC4U in smart cities using next-gen platforms like Jetson...

The Fatal Flaw in AI: Tom Cruise Problem

Linguist Emily Bender and computer scientist Timnit Gebru critique language models as 'stochastic parrots' lacking true understanding. Auto-regressive models like GPT-4 struggle with basic generalization, displaying a 'Reversal Curse' in answering simple questions.

Mitigating Model Collapse in AI with Synthetic Data

Synthetic data raises concerns of model collapse in AI development, but study may not reflect real-world practices and advancements. Omission of standard mitigation techniques and quality control in study limits applicability to industry scenarios.

Streamline Audio Program Management with Amazon Bedrock

Information retrieval systems are evolving with AI solutions like Amazon Transcribe and Amazon Bedrock to efficiently search through audio files at scale. These services simplify the process of transcribing audio, cataloging content, and creating embeddings for easy querying.

AI vs Tradition: The Future of Sound Clashes

Fake AI vocals, including Donald Trump's, disrupt the Montego Bay clash scene, sparking debates on the future of the culture. The use of AI vocalists challenges authenticity and originality in the historic Sumfest Global Sound Clash tradition.

Scaling AI Training with RoCE Networks

AI networks interconnect GPUs for large-scale distributed training at Meta, using RDMA over Ethernet for high-performance communication. Specialized RoCEv2 networks support various AI workloads, including GenAI, ranking, and natural language processing, with a dedicated backend network for training clusters.

Recreating NanoGPT with JAX: A Step-by-Step Guide

Summary: Learn how to build a 124M GPT2 model with Jax for efficient training speed, compare it with Pytorch, and explore the key features of Jax like JIT Compilation and Autograd. Reproduce NanoGPT with Jax and compare multiGPU training token/sec between Pytorch and Jax.