Posts for Tag: Meta

The Promise of Seamless Cross-Language Communication

I am very interested in text-to-speech, speech-to-text, and speech-to-speech (one language to another), and I follow the Whisper project closely, the only open-source project out of OpenAI. When Dr. Yann LeCun recently shared a project called SeamlessExpressive on 𝕏 (formerly Twitter) about speech-to-speech, I wanted to try it out. Here is my video of testing it using the limited demo they had on their site:

I don't speak French, so I'm not sure how it came out from a translation and expression point of view, but it seems interesting. I tried Spanish as well, and it seemed to work the same way. This project, called Seamless, developed by Meta AI scientists, enables real-time translation across multiple languages while preserving the emotion and style of the speaker's voice. This technology could dramatically improve communication between people who speak different languages.  The key innovation behind Seamless is that it performs direct speech-to-speech translation rather than breaking the process into separate speech recognition, text translation, and text-to-speech synthesis steps. This unified model is the first of its kind to:

  • Translate directly from speech in one language into another.  
  • Preserve aspects of the speaker's vocal style, like tone, pausing, rhythm, and emotion.
  • Perform streaming translation with low latency, translating speech as it is being spoken rather than waiting for the speaker to finish.

Seamless was created by combining three main components the researchers developed: 

  • SeamlessM4T v2 - An improved foundational translation model covering 100 languages.  
  • SeamlessExpressive - Captures vocal style and prosody features like emotion, pausing, and rhythm.
  • SeamlessStreaming - Enables real-time translation by translating speech incrementally.  

Bringing these pieces together creates a system where a Spanish speaker could speak naturally, conveying emotion through their voice, and the system would immediately output in French or Mandarin while retaining that expressive style. This moves us closer to the kind of seamless, natural translation seen in science fiction.

Overcoming Key Challenges

Creating a system like Seamless required overcoming multiple complex challenges in speech translation:  

Data Scarcity: High-quality translated speech data is scarce, especially for preserving emotion/style. The team developed innovative techniques to create new datasets.  

Multilinguality: Most speech translation research focuses on bilingual systems. Seamless translates among 100+ languages directly without needing to bridge through English.

Unified Models: Prior work relied on cascading separate recognition, translation, and synthesis models. Seamless uses end-to-end speech-to-speech models.  

Evaluation: New metrics were created to evaluate the preservation of vocal style and streaming latency.

The impacts of having effective multilingual speech translation could be immense in a world where language continues to divide people. As one of the researchers explained:

"Giving those with language barriers the ability to communicate in real-time without erasing their individuality could make prosaic activities like ordering food, communicating with a shopkeeper, or scheduling a medical appointment—all of which abilities non-immigrants take for granted—more ordinary."

Celebrating a Powerhouse of AI: FAIR's First Decade

Today marks an important milestone for Meta's Fundamental AI Research (FAIR) team – 10 years of spearheading advancements in artificial intelligence. When FAIR first launched under the leadership of VP and Chief AI Scientist Yann LeCun in 2013, the field of AI was finding its way. He assembled a team of some of the keenest minds at the time to take on fundamental problems in the burgeoning domain of deep learning. Step by step, breakthrough upon breakthrough, FAIR's collective brilliance has expanded the horizons of what machines can perceive, reason, and generate.

The strides over a decade are simply striking. In object detection alone, we've gone from recognizing thousands of objects to real-time detection, instance segmentation, and even segmenting anything. FAIR's contributions in machine translation are similarly trailblazing – from pioneering unsupervised translation across 100 languages to the recent "No Language Left Behind" feat. 

And the momentum continues unabated. This year has been a standout for FAIR in research impact, with award-garnering innovations across subareas of AI. Groundbreaking new models like Llama are now publicly available—and FAIR's advancements already power products millions use globally.

While future progress will likely come from fusion rather than specialization, one thing is evident – FAIR remains peerless in its ability to solve AI's toughest challenges. With visionary researchers, a culture of openness, and the latitude to explore, they have their sights firmly fixed on the future.

So, to all those who contributed to this decade of ingenuity – congratulations. And here's to many more brilliant, accountable steps in unleashing AI's potential.


Large Language Models for Code: The Promise of Code Llama

Large language models (LLMs) such as ChatGPT, Google Bard, Claude, etc., have taken the world by storm. These LLMs can chat with humans, answer questions, and generate articles or code. Under the hood, these systems use advanced neural networks trained on massive amounts of text data. Meta AI researchers open-sourced a new LLM called Code Llama. Code Llama is explicitly designed for understanding and generating code. As software engineers, this technology has enormous implications for how we may build and interact with software in the future. How Code Llama works and what it might mean for our field.

The Building Blocks of Code Llama

Code Llama leverages an existing general-purpose LLM called Llama 2. Meta AI trained this model on a mixture of web pages, books, code repositories, and more - about 2 trillion words total! This gave Llama 2 a broad understanding of natural language.

The researchers then took Llama 2 and trained it further on code. Not just one language like Python or JavaScript but a diverse mix spanning many programming languages. They fed it another 500 billion tokens of code from publicly available sources like GitHub. 

This additional "code diet" helped the model gain a much deeper understanding of programming language syntax, structure, naming conventions, and more. The resulting system was dubbed Code Llama.

Specializing Code Llama for Real-world Uses

The base Code Llama model learned a lot about code, but the researchers went further to tailor it for real applications:

  • They trained some versions to focus specifically on Python code. This Python-specialized edition is called Code Llama - Python.
  • They enabled the 7B and 13B parameter versions to do "infilling." Given code with a missing section, Llama can predict what should go in the middle based on the surrounding context.
  • They fine-tuned the models to handle highly long code inputs - up to 100,000 tokens. This unlocks reasoning across entire code repositories.
  • They trained specialized Code Llama - Instruct models to follow natural language instructions better. This improves the model's helpfulness and safety.

These optimizations result in a family of models tailored for researching and deploying AI coding assistants.

Benefits over Other AI Code Models

Other AI systems are out there for generating and understanding code, like GitHub Copilot and DeepMind's AlphaCode. So what makes Code Llama unique?

  • First, it's one of the most significant open-source AI code models. The 34 billion parameter version is publicly available for anyone to use and build on top of.
  • Second, because it's trained on such diverse data spanning many programming languages, it has more vital general coding skills than models trained on just one language like Python.

Finally, optimizations like infilling and long context handling enable new applications like autocompleting code within a whole file or repository. Capabilities like this open the doors for integrating LLM coding assistants into IDEs and developer workflows.

Potential Impacts on How We Code

What could leveraging Code Llama look like for developers? The range of possibilities is vast:

  • Code autocompletion: Llama could suggest entire function bodies or classes as you type, speeding up development.
  • Documentation generation: Llama could write docstrings for functions based on their signatures and your surrounding code. 
  • Bug finding: Given a code snippet and failing test case, Llama could locate issues and explain them.
  • Code search: Llama could instantly find usages of functions across an entire codebase, improving code navigation.
  • Code translation: Llama could "translate" code between programming languages like JavaScript and Python.
  • Boilerplate generation: Pass a description of something you want to build, and Llama could generate starter code, tests, templated files, etc., to speed up kicking off new projects.

And that's just scratching the surface! As Llama-like models advance, they could profoundly alter how we develop software.

Of course, AI assistance also comes with risks if not thoughtfully implemented. Biases or errors in training data could lead to incorrect or harmful suggestions. Developer jobs may be impacted as AI takes on more coding tasks. And reliance on AI could atrophy human skills over time.  Responsible development and deployment of this technology will be critical. But if we forge prudently, Code Llama represents an exciting step towards more empowering and productive coding environments. The future looks bright for AI, enhancing human innovation!

Latest Paper on Code Llama

Recently, Meta's AI scientists have released a paper titled Code Llama: Open Foundation Models for Code, which can be found here. This paper presents an impressive technological achievement. However, we should maintain a balanced perspective and critically examine this work's merits and limitations.

On the positive side, open-sourcing, a large AI code model, pushes forward innovation and research in this space. The scale of Code Llama, with models up to 34 billion parameters, raises the bar for what's possible with LLMs for programming languages.

The multi-language training is also a boon. Rather than siloing the model to just one language like Python, the diversity of training data makes Code Llama more generalizable. The machine learning principles behind this transfer learning approach are solid.

Optimizations like infilling and extended context handling unlock new applications for code intelligence and auto-completion within real-world software projects, not just short code snippets. And the overall performance of Code Llama on benchmarks is impressive.

However, a critical eye finds some avenues for improvement. Code Llama still needs to catch up to closed-source models like DeepMind's AlphaCode in some coding tasks. 

There are also limitations around extrapolating to sequence lengths longer than seen during training. While multi-language training is promising, English remains the primary mode of interaction. Enhancing Code Llama's abilities for non-English programming languages could make it more inclusive globally.

And pursuing responsible and ethical development of such powerful technology is an ongoing process, not a box to check. We must continue learning about and mitigating bias, toxicity, and misuse risks as Code Llama advances.

10 FUN FACTS FROM THIS PAPER

Here are ten fun facts from the Code Llama paper:

  1. Llama 2 trained on 2 trillion words, 2x English Wikipedia.
  2. Code Llama used 500 billion code tokens, like 9M Harry Potters.
  3. Largest model has 34 billion parameters, 5x Earth's population.
  4. Trained on multiple languages, not just Python or Java.
  5. 7B and 13B versions can autocomplete missing code.
  6. Handles up to 100,000 tokens, or 650+ printed pages.
  7. Special versions are safer and more responsive.
  8. Used "self-instruct" for automatic training data.
  9. Sets new records on coding benchmarks.
  10. "Red teaming" ensures security and fairness.

In summary, Code Llama is an important step forward. Maintaining perspective on its current capabilities and limitations will lead to healthier progress. Evaluating this work critically helps push the field towards its full potential impact while avoiding hype and overpromising. If developers temper expectations but stay excited, the future looks bright for AI in code!

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Democratizing AI: A Nuanced Look at Yann LeCun and Meta's Llama 2

Recently, Meta's Chief AI scientist and Computer Science's Turing Award winner (considered as prestigious as the Nobel Prize), Dr. Yann LeCurn, shared a tweet about this article that was written about his stance on the Large Langauge Model (LLM) craze, the future of AI, the necessity of open source AI platform, and his anti-AI-doomer stance. Here is how we distilled this article for you.

The release of Meta's new large language model Llama 2, led by Yann LeCun, has reignited debates on AI access and safety. LeCun's advocacy for open-source AI clashes with many peers' warnings about existential threats. This complex issue requires nuance.

From Caution to Risk-Taking

LeCun initially criticized OpenAI's public ChatGPT demo as too risky for established firms like Meta. However, Meta's launch of Llama 2, freely available to all, aligns with LeCun's stance on open-source AI. He argues this is the only way to prevent control by an elite few. However, Llama 2 is not fully open source since its training data remains private.

The Double-Edged Sword of Open Source AI 

LeCun believes open access allows more rapid AI improvement through collective intelligence. However, critics point to increased misuse risks, like spreading misinformation. There are merits to both views. Wider adoption can accelerate refinement, yet caution is warranted given the technology's early stage. It's a delicate balancing act.

The Existential Threat Debate

Some top AI researchers have warned of extinction risks comparable to nuclear weapons. LeCun disputes this "doomer narrative." Current models still need more essential intelligence and are prone to incoherent outputs. Both positions have weight. Fears shouldn't be dismissed, but nor should progress be halted by alarmism. 

Progress Requires Risk-Taking

Today's AI, like early automobiles, may seem dangerous, but it can improve safety. Dismissing risk entirely would be reckless, but so would banning innovation. With thoughtful regulation and public engagement, AI can evolve to minimize harm.

A Nuanced Way Forward

Rather than absolutist takes on AI, nuance is needed. Access enables advancement, but controlled access allows responsible stewardship. AI is transformative yet still early-stage. With transparent development and inclusive debate, the benefits could outweigh the risks. LeCun's stance, while bold, moves the conversation forward.

The release of Llama 2 will accelerate AI capabilities for better or worse. Yann LeCun provides a vital counterpoint to AI doomsaying, but caution is still warranted. There are no easy answers, only tradeoffs to weigh carefully. If AI is the future, then it must be shaped inclusively. Multiple perspectives will give rise to the most balanced path ahead.

About Dr. Yann LeCun

Unlike most AI scientists, Dr. Yann LeCun is active on social media such as 𝕏 (formerly Twitter) and YouTube. If you want to get a hold of his social media activity,  check out CPROMPT's WHO IS WHO section, where his information is available in one place. CPROMPT's AI automatically summarizes his recent tweets daily so that you can keep up with his overall thinking and attitude on AI.

View Yann LeCun Profile on CPROMPT.AI

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