Towards general reasoning // core feature of intelligence

Reasoning involves problem understanding, planning, and solving

Reasoning tactics/methods:
- Recognize and follow the predefined steps (reasoning template): Chain of Thoughts, Tree of Thoughts, Graphs etc.
- Code generation and execution (problem is transformed into code), CoC
- Neuro-symbolic reasoning (the problem is translated into a symbolic representation, and a symbolic engine then infers the results)
- Agents (high level concept: anything above can be mixed)

All tactics require formalization (NLP/NLU), that can be done by LLMs or other tech.

General reasoning simulation is possible

Here is a summary of the key points from the interview:

- Demis Hassabis, CEO of DeepMind, discusses the progress and future of AI systems like language models and multimodal models.

- He sees AI progressing rapidly, with AGI-like systems potentially emerging within the next decade through continued scaling and algorithmic innovations.

- Challenges discussed include improving grounding and factuality, adding capabilities like planning and search, interpretability to understand what concepts the models are building, and alignment to ensure they behave as intended.

- On governance, Hassabis argues it must involve many stakeholders from civil society, academia, and government to decide how to responsibly deploy these powerful AI capabilities for benefiting humanity while mitigating risks.

- He provides insights into DeepMind’s latest model Gemini, which combines language with multimodal capabilities, the compute used, scaling tradeoffs, and future goals like combining with reinforcement learning systems.

- Overall, Hassabis expresses optimism about the transformative potential of AI for science, health and other domains if developed responsibly, while acknowledging the uncertainties that require a cautious, scientific approach.