The Artificial Intelligence (AI) field is changing rapidly. Advanced models like Google’s Gemini and the rumored OpenAI Q* project are leading this change. These developments are making researchers rethink their priorities and the direction of generative AI.

Timothy R. McIntosh and his team have conducted a detailed study. They looked at how generative AI is evolving. Their focus was on the impact of Mixture of Experts (MoE), learning from multiple modes, and the steps towards Artificial General Intelligence (AGI). The study stresses the need for AI development to be ethical and centered around humans. This ensures it aligns with society’s values and benefits.

I believe this paper is such a great summary on the current state of the cutting edge in generative AI research, I’ve decided to summarise it into the following key points for easier reading.

Google Gemini and OpenAI Q* are changing the way we approach generative AI research. They focus on MoE architectures, learning from multiple modes, and moving towards AGI.

Google Gemini has made big strides in AI that can handle multiple modes. It can process text, images, audio, and video all in one. Its design allows it to understand complex scenes and conversations better. This sets new standards in AI.

OpenAI Q, though still a speculation, aims to combine large language models with advanced algorithms. This could lead to AI that learns on its own and understands the world like humans do. The potential of Q to mix structured learning with creativity is very promising.

These advancements challenge old methods and encourage new research directions. The focus on MoE, learning from multiple modes, and AGI is reshaping the field.

MoE models are a big step forward. They can handle huge models more efficiently. But, they face challenges like complex routing, imbalance among experts, and dilution of probabilities.

MoE models are groundbreaking for generative AI. They use multiple expert modules to manage huge amounts of data efficiently. This allows for training huge models without using as much computing power.

These models are great for tasks like personalized medicine and financial risk assessment. They can specialize in different areas, improving performance.

However, MoE models have their challenges. These include complex routing of data, keeping a balance among experts, and avoiding dilution of probabilities. Researchers are working on solutions for these problems.

Despite these challenges, MoE models are a major advancement. They offer scalability and specialization. We can expect more innovations in this area.

Multimodal AI is changing how machines understand and interact with the world. Google’s Gemini is leading the way by integrating different types of data.

Multimodal AI allows machines to understand a wider range of human inputs. By combining text, images, audio, and video, these systems get a more complete understanding of the world.

Google’s Gemini is a standout in multimodal AI. It goes beyond traditional models by integrating different types of data seamlessly. This allows for a deeper understanding of complex scenes.

This advancement has big implications for many industries. In healthcare, it can improve diagnostics. In autonomous vehicles, it can enhance safety.

However, developing multimodal AI systems comes with challenges. Creating diverse datasets, managing scalability, and ensuring user trust are key issues. Researchers are working on these challenges.

The rumored OpenAI Q* could be a big step towards AGI. It aims to combine large language models with advanced learning algorithms.

OpenAI Q* could bring us closer to AGI. It combines large language models with algorithms like Q-learning and A*. This could enable AI to learn on its own and understand the world like humans do.

Q* is expected to excel in self-learning and understanding complex information. It could interpret human communication better and make AI more personal and engaging.

The development of Q* could change how AI interacts with the world. It could make AI systems more intuitive and human-like.

The increase in AI-related preprints is challenging the peer-review process. McIntosh et al. emphasize the need for governance and ethical guidelines for AI.

The rise in AI-themed preprints is putting pressure on the peer-review system. This leads to concerns about the quality and reliability of research.

The study suggests a hybrid model for peer review. This would combine traditional methods with community-based reviews. This could help manage the influx of AI research.

The study also calls for ethical guidelines for AI development. As AI becomes more advanced, it’s important to ensure it aligns with human values.

My own thoughts…

The study by McIntosh et al. is a compelling look at the future of AI.

The advancements in models like Google Gemini and the potential of OpenAI Q* are exciting.

However, the ethical and societal implications are significant. As AI evolves, ensuring it aligns with human values is crucial.

The challenges in scholarly communication and the need for a hybrid peer-review model are also important issues. As an AI researcher, I’m excited about the possibilities of MoE models and multimodal learning. But I also recognize the need to address the ethical and practical challenges that come with these advancements.

References

[1] T. R. McIntosh, T. Susnjak, T. Liu, P. Watters, and M. N. Halgamuge, “From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape,” arXiv:2312.10868v1 [cs.AI], Dec. 2023, [Online]. Available: https://arxiv.org/html/2312.10868v1