Future Large Neural Language Model (LLM)
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A Future Large Neural Language Model (LLM) is a large neural language model that is a future predictive model.
- Context:
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- Example(s):
- Counter-Example(s):
- See: Multi-Lingual Neural Network-based Language Model (NLM).
References
2023
- https://bounded-regret.ghost.io/what-will-gpt-2030-look-like/
- In this post, I’ll use this approach to forecast the properties of large pretrained ML systems in 2030. I’ll refer throughout to “GPT2030”, a hypothetical system that has the capabilities, computational resources, and inference speed that we’d project for large language models in 2030 (but which was likely trained on other modalities as well, such as images). To forecast GPT2030’s properties, I consulted a variety of sources, including empirical scaling laws, projections of future compute and data availability, velocity of improvement on specific benchmarks, empirical inference speed of current systems, and possible future improvements in parallelism.
GPT2030’s capabilities turn out to be surprising (to me at least). In particular, GPT2030 will enjoy a number of significant advantages over current systems[2], as well as (in at least some important respects) current human workers:
- GPT2030 will likely be superhuman at various specific tasks, including coding, hacking, and math, and potentially protein design (Section 1).
- GPT2030 can “work” and “think” quickly: I estimate it will be 5x as fast as humans as measured by words processed per minute [range: 0.5x-20x][3], and that this could be increased to 125x by paying 5x more per FLOP (Section 2).
- GPT2030 can be copied arbitrarily and run in parallel. The organization that trains GPT2030 would have enough compute to run many parallel copies: I estimate enough to perform 1.8 million years of work when adjusted to human working speeds [range: 0.4M-10M years] (Section 3). Given the 5x speed-up in the previous point, this work could be done in 2.4 months.
- GPT2030's copies can share knowledge due to having identical model weights, allowing for rapid parallel learning: I estimate 2,500 human-equivalent years of learning in 1 day (Section 4).
- GPT2030 will be trained on additional modalities beyond text and images, possibly including counterintuitive modalities such as molecular structures, network traffic, low-level machine code, astronomical images, and brain scans. It may therefore possess a strong intuitive grasp of domains where we have limited experience, including forming concepts that we do not have (Section 5).
- In this post, I’ll use this approach to forecast the properties of large pretrained ML systems in 2030. I’ll refer throughout to “GPT2030”, a hypothetical system that has the capabilities, computational resources, and inference speed that we’d project for large language models in 2030 (but which was likely trained on other modalities as well, such as images). To forecast GPT2030’s properties, I consulted a variety of sources, including empirical scaling laws, projections of future compute and data availability, velocity of improvement on specific benchmarks, empirical inference speed of current systems, and possible future improvements in parallelism.