Over the past few months, we have witnessed a remarkable surge in the development of Artificial Intelligence (AI) models. Many companies are investing significant resources to bring new and advanced capabilities to the market, capabilities that were previously thought to be unachievable. One of the primary driving forces behind this surge is the advancement of language models, such as GPT-4 and PaLM.
These models have been designed to understand human language, generate human-like text, and perform diverse array of tasks with exceptional precision and accuracy. This has opened up new opportunities for businesses, researchers, and developers to use language models for a variety of purposes, including natural language processing, chatbots, voice recognition, and text summarization, among others.
As a result, companies are now investing heavily in research and development to improve the capabilities of these models and stay ahead of the competition. This includes developing new algorithms, architectures, and training methods to create models that are more efficient, accurate, and adaptable to various tasks and environments.
The sudden surge in the development of AI models has created a highly competitive market, with companies vying to produce the best models that can provide the most value to their customers. As such, we can expect to see even more advanced language models and other AI models being developed in the near future, with new capabilities that were previously thought to be impossible.
In this deep-dive analysis, we will compare the architecture, features, and applications of GPT-4 and PaLM to understand how they stack up against each other. By exploring the technical details of each model, understanding of their capabilities and limitations.
Architecture
Before GPT-4 Copilot was released the predecessor was GPT-3 deep learning neural network model with 175 billion machine learning parameters. GPT-4 was the updated reformed and advanced version of GPT-3 which used transformer architecture with improved performance in understanding human language including the tone in the content, and meaning. GPT-4 was trained with whooping 1.5 trillion parameters to get impressively accurate output for the inputs from user. GPT-4 is equipped to accept both text and images as input and generate proper response from it.
It was equipped with self-learning capabilities that allowed it to learn from its own label without any manual intervention from humans.
Google introduced PaLM( Path-Augmented language model) in 2022, it is based on dense decoder-only transformer model architecture. Googles developed a new pathway system for distributed computation for accelerators. PaLM is trained with a 540 billion parameters. PaLM max efficiency was at 57.8% hardware FLOPs utilization and highest achieved by any LLMs at this scale. High utilisation allows improving the model training efficiency, which in turn can lead to better model performance and more accurate language generation.
Application and use cases of Microsoft Copilot
Microsoft AI powered GPT-4 offers natural language capabilities to all office 365 apps such as word, excel, PowerPoint, Outlook, Teams. This integration with O365 boosted the adoption and popularity of Copilot beyond expectations.
Insights from Unstructured business data
All AI models are modelled and trained with large amount of data, but it makes sense when we are able to leverage it in the context that you are working on. Microsoft Copilot has access to your business data from O356 and Microsoft graph, which in turn helps Copilot to generate more personalised answers based on the context that I am working on. Cool features like filtering unwanted noise in Teams meeting, generating macros in excel quickly and animating a slide are a few features to name.
Privacy and security
From an organisation perspective the important thing is privacy and security, Copilot has the capability to inherit organisation policies, compliances. Copilot boasts its capability to implement self-privilege across user groups.
Application and use cases of Google PaLM
Generative AI in Google workspace
The full set features of Google’s PaLM is not completely revealed yet, but from what I understood they have added PaLM Integration in Google Workspace. Similar to Copilot, PaLM can enable and help users with content generation using commands and keywords.
Code Generation Assistant with Bard
Google AI model helps developers build and remove typing in boilerplate code generative context specific code which increases developer productivity. The capabilities include code generation, UI component generation, data visualizations etc.
Some of the features the PaLM can be used is the federation search on documents in google cloud, insights and inference development from unstructured data from different sources. Currently the features of PaLM are limited, in coming days we may be able to see lot of advancement in it.
Conclusion
In recent months, there has been a significant increase in the number of AI-based applications and use cases, with many relying on popular models to power their functionality. It is expected that in the coming days, we will see a further proliferation of these models, with new capabilities and features being added to enhance their functionality.
Among these models, the GPT-4 Copilot is currently leading the AI race with its advanced features and capabilities. However, as the market continues to evolve, we can expect to see fierce competition between these models as they strive to innovate and outperform one another.
Overall, the continued development of AI-based models and applications represents a significant opportunity for businesses and organisations to improve their operations and deliver better outcomes for their customers. As such, it is important for companies to stay abreast of the latest developments in this space and invest in the tools and technologies that can help them stay ahead of the curve.
