Google’s New Language Model Can Write Like a Human

Description 

Google has launched an AI-based language model which supports 1000 spoken languages worldwide. In its early phases, the idea is ambitious, but Google believes it will flash positive effects on its whole product ecosystem.

Introduction 

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About Google’s New Language Model Can Write Like a Human

One single AI language model that handles the “1,000 most spoken languages” in the world is the ambitious new initiative that Google has announced. The company is introducing an AI model which has covered over 400 languages as a first step towards achieving this aim.This model is referred to as possessing “the largest language coverage currently present in a speech model.

Although, language and AI have always been at the core of Google’s products, recent developments in machine learning, particularly the creation of potent, multi-functional “large language models” or LLMs, have given both fields a fresh focus.

Google has already started integrating these language models into services like Google Search, while concerned about the system’s usability. Language models have several weaknesses, including a tendency to reproduce harmful societal prejudices like racism and xenophobia and an inability to assess language sensitively for humans. Google famously dismissed these researchers after they published papers outlining these problems.

However, these models are well-versed in wide range of tasks, including translation and language synthesis (see Meta’s No Language Left Behind effort and OpenAI’s GPT-3). Google’s 1,000 Languages Initiative aims to develop a single system with extensive knowledge of all known languages instead of focusing on any particular functionality.

According to Zoubin Ghahramani, the vice president of research at Google AI, who was speaking to The Verge. Google thinks that creating a model this size will make it easier to bring various AI functionalities for languages lacking in online spaces and AI training datasets (also known as “low-resource languages”).

Large language models and language research can do a variety of jobs, which is one of their most intriguing features, according to Ghahramani. The same language model can translate, answer math problems, and translate robot commands into code. The truly intriguing thing about language models is that they’re developing into knowledge repositories, and by exploring them in various ways, you might uncover various kinds of practical functionality.

Google revealed the 1,000 language model during a launch event for fresh AI tools. A prototype AI writing assistant named Wordcraft, new research on text-to-video models, and an upgrade to the AI Test Kitchen app, which offers users limited access to developing AI models like Imagen’s text-to-image model, were also made available by the business.

Key factors of Google’s New Language Model Can Write Like a Human

PaLM-E

We now provide PaLM-E, a novel generalist robotics model that addresses these problems by conveying to a robotics system knowledge from several visual and linguistic domains. We started with PaLM, a robust big language model, then added sensor data from the robotic agent to “embodied” it (thus the “E” in PaLM-E). 

The primary difference between PaLM-E and earlier attempts to apply big language models to robotics is that with PaLM-E, we train the language model to directly consume raw streams of robot sensor data. The resulting model is a state-of-the-art general-purpose visual-language model that not only facilitates very effective robot learning but also maintains superior performance.

A generalist in visual language as well as an embodied language model

PaLM-E is a versatile robotics model that solves various tasks on various robots and modalities, including images, robot states, and neural scene representations. It also excels in visual and language tasks. PaLM-E-562B, built on PaLM-540B, combines PaLM and ViT-22B, achieving new visual-language OK-VQA benchmark performance without task fine-tuning.

How does PaLM-E work?

PaLM-E involves transforming sensor data into a pre-trained language model, similar to how natural language words are processed. Language models use token embeddings to represent text mathematically, predicting the next word token and iteratively generating longer texts by feeding the predicted word back to the input.

PaLM-E uses multimodal sentence inputs, such as text and images, to generate auto-regressive text. It trains encoders to convert inputs into natural word token embeddings, mapping them into words with the same dimensionality as word and image embeddings. This allows for inputs like “What happened between and?” forthe revision of the language model.

PaLM-E is a new paradigm for training generalist models, combining robot tasks and vision-language tasks through a common representation. It achieves significant positive knowledge transfer from both domains, improving robot learning effectiveness.

In comparison to training individual models on individual tasks, the results demonstrate that PaLM-E can address a large number of robotics, vision, and language problems simultaneously without performance degradation. Furthermore, the performance of the robot tasks is greatly enhanced by the visual-language data. PaLM-E may learn robotics tasks more effectively because of this transfer in terms of the number of instances needed to complete a task.

FAQs (Frequently Asked Question)

Define PaLM 2 Google.

Palm 2 is Google’s most recent Large Language Model (LLM), and it excels at complex logical thinking, programming, and mathematics. More than 100 languages are supported in addition to being multilingual.

When did PaLM 2 debut?

Google presented PaLM 2 at its yearly Google I/O keynote in May 2023. PaLM 2 is a 340 billion parameter model trained on 3.6 trillion tokens.Google introduced AudioPaLM, a speech-to-speech translation system, in June 2023. It makes use of the PaLM-2 design and initialization.

Google PaLM API: Is it free?

Developers can use the PaLM API for free during the public preview for internal prototyping and experimentation, however, usage is rate-limited and production applications are not allowed.