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

😎 News From The Web

• HuggingChat. A ChatGPT-like UI for the OpenAssistant model, hosted by Hugging Face.

• G7 should adopt 'risk-based' AI regulation, ministers say. G7 agrees on "risk-based" AI regulation, but will maintain an open environment. EU & Japan have introduced AI policies.

• Partnering with Harvey: Putting LLMs to Work. Harvey uses GPT-4 to empower legal teams with custom models, data segregation, security, and compliance. Sequoia led its Series A to expand its AI app.

• The Andy Warhol Copyright Case That Could Transform Generative AI. The US Copyright Office determined recently that art created solely by AI isn’t eligible for copyright protection. Artists can attempt to register works made with assistance from AI, but they must show significant “human authorship”.

• Artificial Intelligence Radio. A web radio with AI-generated music.

• There’s a new AI unicorn that will make coders faster. AI-coding-platform Replit raised $97.4M in a funding round led by Andreessen Horowitz and Khosla Ventures on the back of growing popularity and 22.5M users.

• Palantir Demos AI to Fight Wars But Says It Will Be Totally Ethical. Peter Thiel's Palantir has launched the Palantir AI Platform to operate language models like GPT-4 on private networks. They insist on legal and ethical use.

• OpenAI closes $300M share sale at $27B-29B valuation. VCs have put in just over $300 million at a valuation of $27 billion – $29 billion. This is separate to a big investment from Microsoft announced earlier this year.

• Pinecone drops $100M investment on $750M valuation, as vector database demand grows. With the rise of LLMs in the public consciousness, companies are beginning to see the value of vector databases more and more.

• Amazon is developing an improved LLM to power Alexa. Amazon executives believe that the addition of an improved LLM will help Amazon work toward its goal of building “the world’s best personal assistant,” but acknowledged that it will be difficult to do so across many domains.

📚 Guides From The Web

• Introducing Hidet: A Deep Learning Compiler for Efficient Model Serving. Hidet simplifies implementing high-performing deep learning ops on modern accelerators. Integrated with PyTorch, improve model inference.

• Navigating the High Cost of AI Compute. AI infrastructure is costly due to high computational expenses for GPT models. Using specialized chips and transformer-based architectures can cut costs.

• The future of generative AI is niche, not generalized. Organizations aim to train tools on large language models for domain-specific data.

• A guide to prompting AI. Effective prompts can enhance AI output by adding context and constraints. However, overemphasizing prompts can lead to mistakes.

• Auto-GPT News & Updates. Auto-GPT aims to provide the best autonomous AI assistant for every person, ensuring that everyone can accomplish more every day. It has +100k stars on GitHub now.

• The Complete Beginners Guide To Autonomous Agents. AI-powered task agents automate digital tasks that were previously done by humans.

🔬 Interesting Papers and Repositories

• AIGC-Audio/AudioGPT: AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head. A resource for audio apps and tasks like speech recognition, synthesis, and sound detection.

• Stability AI releases DeepFloyd IF. A powerful text-to-image model that can smartly integrate text into images.

• Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond. A comprehensive and practical guide for practitioners and end-users working with Large Language Models in their NLP tasks.

• Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning. Even though the agents were optimized for scoring, in experiments they walked 156% faster, took 63% less time to get up, and kicked 24% faster than a scripted baseline.

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