Meta's AI speaks 1,100+ languages
Plus: LIMA- Meta's powerful 65B LLM language model.
Hello, Engineering Leaders and AI Enthusiasts,
Welcome to the 25th edition of The AI Edge newsletter. In today’s edition, we bring you Meta’s flurry of AI advancements. Thank you everyone who is reading this. 😊
In today’s edition:
🎙️ Meta scaling Speech Technology to 1,100+ languages
💬 LIMA- Meta's powerful 65B LLM language model
📚 Knowledge Nugget: How to train your own Large Language Models
Let’s go!
Meta scaling Speech Technology to 1,100+ languages
Meta’s Massively Multilingual Speech (MMS) project aims to address the lack of speech recognition models for most of the world's languages, introduced Introducing speech-to-text, text-to-speech. Combining self-supervised learning techniques with a new dataset containing labeled data for over 1,100 languages and unlabeled data for nearly 4,000 languages.
The MMS models outperform existing ones and cover 10 times as many languages. The project's goal is to increase accessibility to information for people who rely on voice as their primary means of accessing information. The models and code are publicly available for further research and development. The project aims to contribute to the preservation of the world's diverse languages.
Why does this matter?
Meta's project is a classic example of how AI can be leveraged for previously impossible use cases. And we will only see more such innovations for Meta and other tech enterprises. Can’t be more excited for what an AI-enabled future holds for us.
LIMA- Meta's powerful 65B LLM language model
Meta’s AI researchers introduce LIMA - a new 65B parameter language model fine-tuned on 1,000 curated prompts and responses. It doesn't use reinforcement learning, yet generalizes well to unseen tasks. Compared with other models, LIMA's responses are either equivalent or preferred in 43% of cases compared to GPT-4 and even more so when compared to Bard and davinci003. This simple approach with limited instruction tuning achieves high-quality output. However, scaling up examples remains challenging despite LIMA's strong performance.
Why does this matter?
The LIMA language model has defied expectations by achieving remarkable performance with minimal training data. This breakthrough reduces reliance on extensive fine-tuning, making developing and deploying language models faster and more efficient.
Knowledge Nugget: How to train your own Large Language Models
LLMs have taken the world of artificial intelligence by storm. Yet, training these models remains a challenge, especially without relying on only a handful of large tech firms as technology providers.
Having invested heavily in training LLMs from scratch, Replit shares how to train your own LLMs in this article, from raw data to deployment in a user-facing production environment. The article also discusses the engineering challenges you may face and how to leverage vendors (like Hugging Face) that make up the modern LLM stack.
Why does this matter?
Like Replit, training your own models is essential primarily for customization, reduced dependency, and cost efficiency.
Customization allows tailoring your models to specific needs and platform-specific capabilities.
Reduced dependency avoids relying solely on a few AI providers and enables open-sourcing of models.
Lastly, cost efficiency helps make AI accessible to a global developer community by training smaller, more efficient models with reduced hosting costs.
What Else Is Happening
🚀 Text-to-video AI imagines a future on Mars with SpaceX (Link)
🖥️ Apple looks to advance AI efforts for iPhones, iPads, and other devices (Link)
🌍 XTREME-UP: A user-centric scarce-data benchmark for under-represented languages (Link)
🤝 TCS partners with Google Cloud to launch a new offering, TCS Generative AI (Link)
🏛️ OpenAI leaders propose IAEA-like international regulatory body for AI (Link)
💸 FlowX.ai raises $35M for its AI-based approach to application integration (Link)
🤖 Lifesaving Radio, the first AI-based healthcare radio station (Link)
Trending Tools
Dify: Open-source platform for LLMOps offering visual management of prompts, operations, and datasets. Create AI apps in minutes.
UX Writing Assistant: AI-powered tool to craft and refine copy in seconds. Get suggestions inspired by UX writing best practices.
LogicLoop AI SQL Copilot: AI SQL copilot. Ask data any questions using natural language. Auto-suggest, generate, fix, and optimize SQL queries.
BetterLegal Assistant: AI-powered extension to translate legal jargon into plain English. Save on attorney fees with AI analysis.
Kaiber: Join 2.5M artists creating content with advanced AI generation engine. Turn ideas into stunning visual stories.
Pixelied Image AI: Generate stunning visuals and high-quality assets with a few simple words. Bring your creative vision to life.
Sidekick: Accessible design made faster. Uses AI to scan design files for accessibility issues and suggest fixes.
Hatchways 2.0: Craft GitHub-based assessments within minutes. Simulate real-world tasks instead of LeetCode problems.
That's all for now!
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Thanks for reading, and see you tomorrow.