Google Unleashes PaLM 2: The Next Step in Large Language Models
Google just announced their PaLM 2 LLM, a direct response to OpenAI's GPT-4, in the previous 48 hours, and here's everything we know about it.
Introduction
Google has turned up the heat in the AI race with its latest offering, PaLM 2, its next-generation large language model (LLM) that aims to compete with OpenAI's GPT-4. Announced at Google's I/O event, PaLM 2 is the latest in a series of cutting-edge machine learning developments from the tech giant, which is already powering 25 of Google's products, including the Bard conversational AI assistant.
What is PaLM 2?
PaLM stands for Pathways Language Model, with "Pathways" representing a machine-learning technique developed at Google. A successor to the original PaLM launched in April 2022, PaLM 2 is a family of LLMs trained on an enormous amount of data to perform next-word prediction tasks. This allows the model to output the most likely text after a prompt input by humans.
Google asserts that PaLM 2 is capable of supporting over 100 languages, offering capabilities in "reasoning," code generation, and multi-lingual translation. PaLM 2 comes in four sizes: Gecko, Otter, Bison, Unicorn, with Gecko being the smallest and capable of running on a mobile device.
Capabilities of PaLM 2
PaLM 2 distinguishes itself with its advanced reasoning tasks, including code and math, classification and question answering, translation, and natural language generation. This new model excels at understanding riddles and idioms, which require an understanding of ambiguous and figurative meanings of words, rather than the literal meaning.
Furthermore, PaLM 2 was pre-trained on parallel multilingual text and a large corpus of different languages, which helps it excel at multilingual tasks. The model was also trained on a large volume of webpage, source code, and other datasets. This versatility enables it to write popular programming languages like Python and JavaScript, but also to generate specialized code in languages like Prolog, Fortran, and Verilog.
Building and Evaluating PaLM 2
The development of PaLM 2 involved significant advancements over its predecessor. Google utilized a technique called compute-optimal scaling, enabling the model to be smaller but more efficient with better performance. This innovation results in faster inference, fewer parameters to serve, and a lower serving cost.
PaLM 2 also benefits from an improved dataset mixture, including hundreds of human and programming languages, mathematical equations, scientific papers, and web pages. This creates a more multilingual and diverse pre-training mixture than that of its predecessor.
Furthermore, Google updated the model architecture and training objectives, training PaLM 2 on a variety of different tasks. This approach helps the model learn different aspects of language more effectively.
In terms of evaluation, PaLM 2 achieved state-of-the-art results on reasoning benchmark tasks such as WinoGrande and BigBench-Hard. It also performed impressively on multilingual benchmarks such as XSum, WikiLingua, and XLSum, showing significant improvements over the original PaLM.
Responsible AI with PaLM 2
As a part of its commitment to responsible AI development, Google took significant steps in ensuring the safety and ethical use of PaLM 2. They removed sensitive personally identifiable information from the pre-training data and filtered duplicate documents to reduce memorization.
Furthermore, Google implemented improved multilingual toxicity classification capabilities in PaLM 2 and built-in control over toxic generation. They also carried out evaluations to assess potential harms and bias across a range of potential downstream uses for PaLM 2.
PaLM 2's Impact on Google's Generative AI Features
PaLM 2 is not just a research project. It's already powering several generative AI features and tools within Google's ecosystem. Here are some of the notable applications:
Bard: Google's Bard conversational AI assistant is one of the primary products benefiting from PaLM 2. Bard aims to supercharge users' creativity and productivity by providing a collaborative AI that can bring their ideas to life.
PaLM API: With the PaLM API, developers can now build generative AI applications using Google’s next-generation LLM, PaLM 2. This offers a new avenue for creating AI-driven solutions. This API will also most likely be integrated into more future Google services and products.
MakerSuite: This tool provides a fast, easy way for developers to prototype generative AI ideas and access the PaLM API. It simplifies the process of integrating PaLM 2's capabilities into new projects.
PaLM API in Vertex AI: Vertex AI is Google Cloud's managed machine learning service, and it now includes access to the PaLM API. This addition means that developers can build generative AI applications with PaLM 2’s capabilities in a cloud-based environment.
Generative AI in Workspace: PaLM 2 is already powering generative AI features in Google's Workspace products. For example, email summarization in Gmail and brainstorming and rewriting tools in Google Docs are among the features benefiting from this advanced language model.
Conclusion
In conclusion, Google's PaLM 2 represents a significant advancement in the world of large language models. Despite some criticisms and concerns, Google's efforts in pushing the boundaries of AI technology, while maintaining a commitment to ethical use, are paving the way for future developments in this field. As AI continues to evolve and become more integrated into our daily lives, models like PaLM 2 will become increasingly significant. The race for AI dominance is far from over, but with PaLM 2, Google has certainly made a powerful statement.