Table of Contents
1. Introduction
2. Gemini: A More Capable System than GPT-4
3. The Multi-Modality of Gemini
4. Training Gemini on YouTube Videos
5. Gemini's Potential in Robotic Manipulation
6. AGI Timelines and the Potential for Improvement
7. The Planning Capabilities of Gemini
8. DeepMind's Extreme Risks Paper and Long Horizon Planning
9. Asabis' Take on Accelerating AI Efforts and Managing Risks
10. Alphago's Approach and the Fusion with GPT-4
11. The Limitations of GPT-4 and the Need for Search and Planning
12. The Implications of a More Capable Model
13. The Urgency for Research on Evaluation and Controllability
14. Early Access to Foundation Models and the UK AI Task Force
15. The Need for a CERN-like Project for AI Alignment
16. The Race Against Time to Develop Safeguards
**Gemini: A More Capable System than GPT-4**
In a recent interview with Wired Magazine, Demis Hasabis, the head of Google DeepMind, made a bold statement about Gemini, a system that could surpass OpenAI's GPT-4 in terms of capabilities. Hasabis revealed that they are working on combining the strengths of AlphaGo-type systems with the language capabilities of large models. Before delving into the details of how this fusion might work, let's first understand the context of the Gemini announcement.
Sundar Pichai, CEO of Google, emphasized their focus on building more capable systems safely and responsibly, introducing Gemini as their next-generation Foundation model. Although still in training, Gemini is already showing impressive multimodal capabilities not seen in prior models. Pichai also hinted at new innovations that will be introduced, promising interesting developments. It's important not to underestimate DeepMind's track record, as they have been behind groundbreaking achievements like AlphaGo, AlphaZero, AlphaStar, and AlphaFold, which have had significant impacts in various domains.
Gemini's multi-modality is expected to be enhanced through training on YouTube videos, leveraging not only the text transcripts but also the audio, imagery, and comments. This approach aligns with OpenAI's utilization of YouTube data as well. It's intriguing to consider the potential future uses of YouTube by Google DeepMind beyond training AI models.
Recently, DeepMind released a paper on RoboCAD, a self-improving foundation agent for robotic manipulation. The paper demonstrates the ability to generalize to new tasks and robots, both through adaptation and zero-shot learning. Notably, the model itself can generate data for subsequent training iterations, forming a basic building block for autonomous improvement. This concept of using the model to generate data reminded me of a conversation I had with Ronan Eldan from Microsoft, where we discussed the potential of AGI and the importance of training models with more data and synthetic data.
Gemini's planning capabilities, inspired by DeepMind's earlier systems, aim to provide the system with new problem-solving abilities. However, DeepMind's Extreme Risks paper highlighted the potential dangers of long-horizon planning, emphasizing the need for careful evaluation and control. Asabis acknowledges the challenges of managing the risks associated with more capable AI systems while also accelerating their development.
The implications of Gemini's capabilities are vast, with potential benefits for scientific discovery, health, climate, and more. Asabis believes that AI, if developed correctly, will be the most beneficial technology for humanity ever. However, determining the risks and ensuring control over these advanced systems requires urgent research and evaluation tests. Asabis suggests giving academia early access to frontier models, fostering collaboration between academia, corporations, and governments.
The need for a CERN-like project, as proposed by Ian Hogarth and echoed by Satya Nadella, becomes apparent. Such an initiative would bring together various stakeholders to address the alignment problem and accelerate the development of safeguards. However, it remains crucial to understand the extent of DeepMind's workforce dedicated to these evaluations and preemptive measures.
In conclusion, Gemini represents a significant leap forward in AI capabilities, combining the strengths of AlphaGo-type systems with large language models. While the potential benefits are immense, the risks and challenges associated with such advanced AI systems must be addressed through rigorous research, evaluation, and collaboration among stakeholders.
Highlights
- Gemini, Google DeepMind's next-generation Foundation model, aims to surpass OpenAI's GPT-4 in capabilities.
- Training on YouTube videos enhances Gemini's multi-modality, leveraging audio, imagery, and comments.
- DeepMind's RoboCAD demonstrates the ability to generalize to new tasks and generate data for autonomous improvement.
- Planning capabilities in Gemini, inspired by DeepMind's earlier systems, offer new problem-solving abilities.
- The risks and control of advanced AI systems require urgent research and evaluation tests.
- Collaboration between academia, corporations, and governments is crucial to address the alignment problem and develop safeguards.
FAQ
**Q: How does Gemini compare to GPT-4?**
A: Gemini, Google DeepMind's upcoming model, is expected to be more capable than GPT-4, combining the strengths of AlphaGo-type systems with large language models.
**Q: How is Gemini trained?**
A: Gemini is trained on YouTube videos, utilizing not only the text transcripts but also the audio, imagery, and comments available on the platform.
**Q: What are the potential applications of Gemini's multi-modality?**
A: Gemini's multi-modality opens up possibilities for enhanced understanding and generation of content across various domains, including text
