Weekly AI and NLP News — March 13th 2023

Here are your weekly articles, guides, and news about NLP and AI chosen for you by NLPlanet!

😎 News From The Web

• Microsoft mentioned the imminent release of GPT-4. Microsoft Germany recently presented their Large Language Models (LLM) like the GPT series as a disruptive force for companies and their Azure-OpenAI offering.

• Stability AI looks to raise funds at $4B valuation as artificial intelligence captivates investors.

• Google's Universal Speech Model (USM): State-of-the-art speech AI for 100+ languages. USM is a family of state-of-the-art speech models with 2B parameters, trained on 12 million hours of speech and 28 billion sentences of text, spanning 300+ languages.

• MuAViC: The first audio-video speech translation benchmark. Using visual information to improve performance for English speech recognition tasks.

• Man beats machine at Go. Amateur exploited weakness in systems that have otherwise dominated grandmasters.

• They thought loved ones were calling for help. It was an AI scam.

📚 Guides From The Web

• The State of Competitive Machine Learning. An analysis of the winning models of 200+ ML competitions in 2022.

• Emergent Abilities of Large Language Models. Emergence can be defined as the sudden appearance of novel behavior. Large Language Models apparently display emergence by suddenly gaining new abilities as they grow.

• The 100 most cited AI papers in 2022.

• Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU.

🔬 Interesting Papers and Repositories

• PaLM-E: An Embodied Multimodal Language Model. Inputs to the embodied language model are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings.

• Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting.

• Large Language Models Encode Clinical Knowledge. Introducing MultiMedQA, a benchmark combining six existing open-question answering datasets spanning professional medical exams, research, and consumer queries. Flan-PaLM is SOTA.

• Prismer: A Vision-Language Model with An Ensemble of Experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from readily-available, pre-trained domain experts, and kept frozen during training.

• Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners.

• Larger language models do in-context learning differently.

• MathPrompter: Mathematical Reasoning using Large Language Models. It uses the Zero-shot chain-of-thought prompting technique to generate multiple Algebraic expressions or Python functions to solve the same math problem in different ways and thereby raise the confidence level in the output results.

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