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Meet PaLM 2, Google’s latest effort to get back to the AI race

Google I/O brings a fresh LLM to the AI race. Meet Bard AI’s new engine, PaLM 2. Can it compete with GPT4 and help Google in the AI race?

We asked this question directly to Google’s AI chatbot Bard, and here is its answer:

“Yes, PaLM 2 has the potential to compete with GPT4 and help Google in the AI race. Palm 2 is a newer model than GPT4, and it has been trained on a larger dataset of text and code. This means that Palm 2 has the potential to be more powerful and versatile than GPT4.

In addition, PaLM 2 is open source, which means that it is available to anyone to use and improve. This means that Palm 2 has the potential to be more widely adopted than GPT4, which is only available to a select few companies.

Finally, Google has a lot of resources and expertise in the field of AI. This means that Google is well-positioned to develop and improve PaLM 2, and to use it to create new and innovative products and services.

Overall, PaLM 2 has the potential to be a major player in the AI race. It is a powerful and versatile model that is available to everyone. Google has the resources and expertise to develop and improve PaLM 2, and to use it to create new and innovative products and services.”

-Bard AI

It’s an ambitious answer, but if Google doesn’t want to lose its position in the booming AI industry, this has to be right.

What is PaLM 2?

PaLM (Pathway Language Model) 2 is a new large language model (LLM) developed by Google and announced at its 2023 Google I/O conference. It is the second iteration of PaLM, which was launched in April 2022. It is designed to have improved capabilities in multilingualism, reasoning, and coding.

  • Multilingualism: PaLM2 is trained in over 100 languages, including idioms, poems, and riddles. It can also pass advanced language proficiency exams at the “expert” level.
  • Reasoning: PaLM2 can handle logic, common sense reasoning, and mathematics better than previous models. It was trained on a wide-ranging dataset that includes scientific papers and web pages that contain mathematical expressions.
  • Coding: PaLM2 entails a coding enhancement of substantial significance. This notable update encompasses comprehensive training across a repertoire of over 20 programming languages, encompassing both widely used and specialized ones such as Prolog and Fortran. Google highlights that its new LLM can even offer multilingual documentation elucidating its code generation process, rendering this development a potentially significant advancement for programmers seeking enhanced proficiency and comprehension.

PaLM 2 is expected to power over 25 Google products and features, such as Google Assistant, Google Translate, Google Photos, and Google Search. It is also expected to compete with OpenAI’s GPT-4, which is another LLM that has over one trillion parameters.

These improvements can be very helpful, and if you need a “light version” for mobile, Google has already thought about it. PaLM2 comes in four different sizes:

  • Gecko
  • Otter
  • Bison
  • Unicorn

Gecko is the smallest and fastest model that can work on mobile devices even when offline. Otter, Bison, and Unicorn are larger and more powerful models that can handle more complex tasks.

How does PaLM 2 work?

PaLM 2 is a neural network model that is trained on a massive dataset of text and code. The model is able to learn the relationships between words and phrases, and it can use this knowledge to perform a variety of tasks.

However, PaLM 2-powered Bard is still an experiment, according to Google. It can sometimes make mistakes, and it may not be able to understand all types of text or hallucinate. Google believes that as it continues to develop, it will become even more bugproof.

Google PaLM 2 parameters

In parallel with OpenAI’s approach, Google has opted to disclose limited technical specifics regarding the training methodology employed for this advanced model, including the exact parameter counts. Nevertheless, it is worth noting that PaLM 2 is a formidable model, boasting an impressive scale of 540 billion parameters.

Google’s provided information highlights the foundation of PaLM2 on their latest JAX framework and TPU v4 infrastructure, reflecting their commitment to leveraging cutting-edge technologies to facilitate the model’s development and performance.

What can you do with thanks to PaLM 2?

Palm 2, an advanced large language model (LLM) developed by Google, represents a cutting-edge breakthrough and is currently accessible to the general public now. Google’s latest LLM is trained on a massive dataset of text and code, and it is able to perform a wide range of tasks, including

  • Natural language understanding: PaLM2 can understand the meaning of text, even if it is complex or ambiguous.
  • Natural language generation: PaLM2 can generate text that is both coherent and grammatically correct.
  • Code generation: PaLM2 can generate code in a variety of programming languages.
  • Translation: PaLM2 can translate text from one language to another.
  • Question answering: PaLM2 can answer questions about text, code, and the real world.

With PaLM 2, Bard promises pretty much everything GPT4 has to offer in ChatGPT, with up-to-date information.

How to use PaLM 2?

The simplest way to use / access PaLM 2 is using Bard AI. To use Bard, simply click here.

Also, PaLM 2 will be available through the Google AI Platform. You can use it to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

To learn more about it, please visit Google and PaLM 2 technical report.

Comparison: PaLM 2 vs GPT4

In recent years, there has been a surge of interest in large language models (LLMs). These models are trained on massive datasets of text and code, and they can be used for a variety of tasks, including natural language processing, machine translation, and code generation.

Two of the most prominent LLMs today are PaLM 2 and GPT-4, developed by Google and OpenAI, respectively. In this blog post, we will compare these two models and see how they differ in terms of size, data, capabilities, and applications.

PaLM 2 vs GPT4: Size

One of the main factors that distinguish LLMs is their size, measured by the number of parameters they have. Parameters are the numerical values that determine how the model processes the input and generates the output. The more parameters a model has, the more complex and powerful it is, but also the more computationally expensive and difficult to train.

Verdict: PaLM 2 vs GPT4

It depends on your needs. If you need an LLM that’s strong at reasoning and logic, with a “Google it”  button, then PaLM 2 is the better choice. If you need an LLM that’s fast, good at generating text and has proved itself, then GPT-4 is the better choice.

Ultimately, the best way to choose an LLM is to try them both out and see which one works best for you. AI is a journey only limited to your imagination.