Introduction

Artificial Intelligence (AI) has witnessed remarkable advancements in recent years, with various tech giants investing heavily in developing large language models (LLMs) to enhance natural language understanding and generation. This article delves into the technical details of Google’s Gemini, Meta’s Llama2, Amazon’s Olympus, Microsoft’s Orca 2, and Apple’s Ajax.

Google Gemini

Google’s Gemini, introduced by Demis Hassabis, CEO and Co-Founder of Google DeepMind, represents a significant leap in AI capabilities. Gemini is a multimodal AI model designed to seamlessly understand and operate across different types of information, including text, code, audio, image, and video.

Gemini is optimized for three different sizes:

• Gemini Ultra: The largest and most capable model for highly complex tasks.
• Gemini Pro: The best model for scaling across a wide range of tasks.
• Gemini Nano: The most efficient model for on-device tasks.

Gemini Ultra outperforms state-of-the-art results on various benchmarks, including massive multitask language understanding (MMLU) and multimodal benchmarks. With its native multimodality, Gemini excels in complex reasoning tasks, image understanding, and advanced coding across multiple programming languages.

The model is trained using Google’s AI-optimized infrastructure, including Tensor Processing Units (TPUs) v4 and v5e. The announcement also introduces Cloud TPU v5p, the most powerful TPU system to date, designed to accelerate the development of large-scale generative AI models.

Gemini reflects Google’s commitment to responsibility and safety, incorporating comprehensive safety evaluations, including bias and toxicity assessments. The model’s availability spans various Google products and platforms, with plans for further integration and expansion.

Meta Llama2

Meta’s Llama2 is an open-source large language model (LLM) designed as a response to models like GPT from OpenAI and Google’s AI models. Noteworthy for its open availability for research and commercial purposes, Llama2 is poised to make a significant impact in the AI space.

Functioning similarly to other LLMs like GPT-3 and PaLM 2, Llama2 uses a transformer architecture and employs techniques such as pretraining and fine-tuning. It is available in different sizes, with variations like Llama 2 7B Chat, Llama 2 13B Chat, and Llama 2 70B Chat, each optimized for specific use cases.

Llama2 was trained on 2 trillion tokens from publicly available sources, including Common Crawl, Wikipedia, and Project Gutenberg. The model undergoes training strategies, including reinforcement learning with human feedback (RLHF), to optimize safety and appropriateness of responses.

What sets Llama2 apart is its open nature, allowing users to access the research paper detailing its creation, download the model, and run it on various platforms. By providing transparency and openness, Meta aims to empower other companies to develop AI applications with more control.

Amazon Olympus

Amazon, in its pursuit of AI excellence, is working on an ambitious large language model (LLM) codenamed “Olympus.” With a staggering 2 trillion parameters, Olympus aims to rival leading models from OpenAI and Alphabet. Led by Rohit Prasad, former head of Alexa, the team behind Olympus brings together expertise from Alexa AI and Amazon’s science team.

Amazon’s strategy involves training homegrown models to make its offerings more appealing on Amazon Web Services (AWS), catering to enterprise clients seeking top-performing models. While Amazon has trained smaller models like Titan and collaborated with AI startups such as Anthropic and AI21 Labs, there’s no specific timeline for the release of Olympus.

Large language models (LLMs) are crucial for AI tools that learn from extensive datasets to generate human-like responses. Despite the increased costs associated with training larger models, Amazon is committed to investing in LLMs and generative AI.

Apple Ajax

Apple’s investment in artificial intelligence is evident through its Foundational Models unit, focusing on conversational AI. Headed by John Giannandrea, Apple’s head of AI, this unit is dedicated to improving Siri and developing AI models across multiple teams.

Apple is working on advanced LLMs, including Ajax GPT, trained on over 200 billion parameters, surpassing the capabilities of OpenAI’s GPT-3.5. The models have applications ranging from customer interaction in AppleCare to automating multistep tasks with Siri.

In addition to conversational AI, Apple has Visual Intelligence and Multimodal AI units developing image generation models and models capable of recognizing and producing images, video, and text simultaneously.

Apple’s commitment to AI innovation is reflected in its pursuit of powerful models and diverse AI applications, ensuring advancements in Siri and other AI-powered features.

Microsoft’s Orca 2

Microsoft’s Orca 2 employs a teacher-student training scheme, where a larger LLM acts as a teacher for a smaller one, aiming to improve the performance of the student model. The training involves teaching the student various reasoning techniques and selecting the most effective strategy for specific tasks.

Orca 2 outperformed baseline models, including Llama 2 and ChatGPT, on reasoning benchmarks. The model’s performance is evaluated on tasks such as language understanding, text completion, and summarization. Microsoft’s innovative training methodology involves “Cautious Reasoning,” where prompts eliciting specific problem-solving strategies are used during teacher training, and the prompts are erased during student training.

The comparison with other LLMs, including GPT-4 and Llama 2, demonstrates Orca 2’s competitive performance. Microsoft’s approach aims to address the challenges of hosting large LLMs and emphasizes the effectiveness of smaller models when fine-tuned.

Conclusion

The landscape of large language models continues to evolve, with major tech players pushing the boundaries of AI capabilities. From Google’s Gemini to Meta’s Llama2, Amazon’s Olympus, Apple’s Ajax, and Microsoft’s Orca 2, each model brings unique features and applications. The open nature of Llama2 and the innovative training schemes of Orca 2 showcase the diverse approaches in AI research. As these models shape the future of AI applications, transparency, responsibility, and safety remain central to their development and deployment.