Generative AI has dominated the technology space ever since the launch of ChatGPT model. As we enter 2024, we are here to witness more powerful large language models based on which Generative AI models work. We have given comparative analysis of top 5 generative AI tools to use in 2024 like ChatGPT, Bard (Gemini), Llama, Dall E-2. Each of these models has been built based on a set of parameters.
What are parameters?
Parameters are the numerical values that determine how a neural network processes input data and produce output data. They are learned from data during the training process, and they encode the knowledge and skills of the model. The more parameters a model has, the more complex and expressive it can be, and the more data it can handle.
Why are parameters important for developing advanced Generative AI?
Parameters are important because they determine models’ capabilities and performance. With a trillion parameters, GPT-4, Gemini type of large language models (LLMs) can handle multimodal data, perform complex tasks, generate coherent texts, and exhibit human-like intelligence. Here are some of the benefits of having more parameters:
Multimodal data: Unlike its predecessors, GPT-4 and Gemini can take both text and images as input, and generate text as output. This gives it a huge advantage over previous models, which could only handle text. By combining different types of data, GPT-4 and Gemini can perform more diverse and challenging tasks.
Top 5 Generative AI Tools to use in 2024.
- ChatGPT:
ChatGPT is by far the most popular Generative AI Tool launched in November 2022. It is a powerful large language model (LLM) that can generate human-like text based on input prompts. It became the fastest adopted consumer software application in history as within 2 months by January 2023 it crossed 100 million users. ChatGPT is trained on Transformer-based-deep-learning neural networks which enable context-based predictions. It has a wide range of applications, from content creation to language translation. It excels in generating coherent and contextually relevant responses in a conversation. ChatGPT is more suitable for human-like interactions and experience.
ChatGPT-4, the latest version of GPT-3, is trained on 1.7 trillion parameters, though OpenAI declined to disclose the exact number. ChatGPT-3 is one of the biggest and most powerful AI language-processing models out there, with over 175 billion parameters. GPT-3-5 allows users to feed a trained AI with a wide variety of worded prompts, such as questions, requests to write a piece of writing about a topic of your choice, or a myriad of other worded prompts. As mentioned above, GPT-3 is a language-processing artificial intelligence model. Basically, it’s a program that can read human language as it’s spoken and written, so it can understand the worded data it’s fed and what to give as output.
You can try ChatGPT with this link: ChatGPT (openai.com).
- Google Bard:
Google launched its own generative AI model Bard in March 2023 which was focused more on informative and comprehensive responses. The focus is more on factual accuracy. It’s best suited for research and retrieving factual information. The model was based on LaMDA family of large language models (LLMs) which eventually upgraded to PaLM with 540 billion parameters. Just like ChatGPT Google Bard is trained on Transformer-based-deep-learning neural networks which enables context-based predictions.
For Gmail users, Bard can even retrieve information from email like time of an event or a scheduled meeting in calendar. Bard can summarize key points from Google Docs and Google Drive so you can access your work on the go. Bard’s ability to scan real-time information about Maps, Flight and Hotels and its integration into different Google services can be useful in planning and scheduling. Unlike ChatGPT which is trained on existing databases up to a particular date, Google Bard fetches information real-time, making it more relevant.
- Google Gemini:
Google launched its most advanced Generative AI model Gemini, by building from group up for multimodality – reasoning seamlessly across text, images, audio, video, and code. Gemini 1.0 has three different versions:
- Gemini Ultra – Largest and Most Capable model for highly complex tasks.
- Gemini Pro – Best model for scaling across a wide range of tasks.
- Gemini Nano – Most efficient model for on-device tasks.
With a score of 90.0%, Gemini ultra is the first ever model to outperform human expert on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, law, medicine and ethics for testing both world knowledge and problem-solving abilities. In image processing, Gemini Ultra outperformed without assistance from optical character recognition (OCR) systems highlighting Gemini’s native multimodalities. Gemini 1.0 is trained to understand text, images, audio and more at the same time, giving unparalleled understanding of the context.
Gemini possesses the capability to comprehend, interpret, and produce code across most mainstream programming languages, such as Python, Java, C++, and Go. It demonstrates superior performance on a variety of coding benchmarks, notably HumanEval, which is a critical benchmark within the industry to assess coding task proficiency, as well as Natural2Code, an exclusive dataset we maintain in-house that relies on original content creation rather than sourcing from the internet.
Additionally, Gemini serves as a core component for more complex coding frameworks. As of December 13, 2023, both developers and corporate clients have been able to leverage Gemini Pro through the Gemini API available in Google AI Studio or through Google Cloud’s Vertex AI. Google AI Studio offers a complimentary, web-centric tool for developers, enabling rapid app prototyping and deployment with the convenience of an API key.
When a project demands a more robust, fully-managed AI service, Vertex AI steps in, allowing for Gemini customization with complete control over data. Users also benefit from the extensive security, safety, privacy, and compliance infrastructure that Google Cloud delivers. Vertex AI is designed to be a comprehensive hub for all generative AI needs, encompassing AI solutions, as well as capabilities for Search and Conversation. It also provides access to over 130 foundational models within a seamless, unified AI platform.
You can read more about Gemini on: https://blog.google/technology/ai/google-gemini-ai/#introducing-gemini
You can login to Gemini using this link: https://bard.google.com/chat
- Llama2:
Meta launched its own large language model (LLM) LLaMA, a collection of foundation language models with parameters ranging from 7 billions to 65 billions. In July 2023, Meta introduced its open source large language model (LLM0) Llama2 which is completely free for research and commercial use.
Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations.
Llama 2 is available in Microsoft Azure’s AI model catalog. This means that developers building with it can build with Microsoft Azure and use their cloud native tools to filter content and provide security features. Llama 2 is also optimized to run on Windows, this provides developers with a smooth workflow as they deliver generic AI experiences to clients across multiple platforms. Additionally, Llama 2 is natively available through AWS, Hugging Face and other providers.
More information on Llama – https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
More on LLaMA evolution: https://ai.meta.com/llama/
More information on Llama2: https://about.fb.com/news/2023/07/llama-2/
- Phi-2:
In addition to investing and supporting OpenAi’s ChatGPT model under LLM framework. Launched in December 2023 by Microsoft research, Phi-2 is a 2.7 billion-parameter Transformer-based language model. Phi-2 is trained on 1.4T tokens of synthetic data generated by GPT-3.5 and outperforms larger models on a variety of benchmarks among base language models with less than 13 billion parameters.
With a tailored data curation technique Phi-2 is able to achieve much better results even with smaller parameters as compared to large language models (LLMs).
With its compact size, Phi-2 is an ideal playground for researchers, including for exploration around mechanistic interpretability, safety improvements, or fine-tuning experimentation on a variety of tasks. We have made Phi-2 (opens in new tab)available in the Azure AI Studio model catalog to foster research and development on language models.
You can read more about Phi-2 here: https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
You can access Phi-2 here: Phi-2 (opens in new tab)
Gemini AI is Google’s latest Artificial Intelligence system rivalling ChatGPT