From google gemini to openai q*(q-star): A survey of reshaping the generative artificial intelligence (ai) research landscape.

Key Points

There are three major directions expected ahead for the focus on GenAI research
- Architectures like Mixture of Experts, Multimodel Learning
- Emerging trends like AGI
- Aligment for ethics, bias mitigation, etc
- Solving problems like Reducing Hallucination, Misinformation etc
Mixture of Experts:
- Intial direction was laid by switch transformers, and one of the latest is Mistral 8x7B model
- A gating mechanism is used to leverage only the part of model, or subset of experts.
Multimodel learning:
- Google's Gemini has setup a good benchmark in this.
AGI:
- OpenAI's speculated Q-star algorithm is expected to leverage following:
  - Reinformancement learning (Q-learning)
  - Path defining approaches like A-star
  - Knowledge, creativity and versatility of LLM
Alignment: This would see a huge traction to
- Reduce Hallucination
- Curtail Misinformation or deep fake detection
- Bias Mitigation
- Privacy preservation
- Etthics and Societal norms
Some interesting points about what could go redundant and could go as emerging trends
- RNN, supervised leanring and Finetuning would be redundant for AGI
- AI Ethics and Human Value Alignemnt would be inherently unresolvable problem as there is a diverse spectrum for human ethics and values.

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