More and more people have been saying that we might have AGI (artificial general intelligence) before 2030. Is that really plausible?
This article by Benjamin Todd looks into the cases for and against, and summarises the key things you need to know to understand the debate. You can see all the images and many footnotes in the original article on the 80,000 Hours website.
In a nutshell:
- Four key factors are driving AI progress: larger base models, teaching models to reason, increasing models’ thinking time, and building agent scaffolding for multi-step tasks. These are underpinned by increasing computational power to run and train AI systems, as well as increasing human capital going into algorithmic research.
- All of these drivers are set to continue until 2028 and perhaps until 2032.
- This means we should expect major further gains in AI performance. We don’t know how large they’ll be, but extrapolating recent trends on benchmarks suggests we’ll reach systems with beyond-human performance in coding and scientific reasoning, and that can autonomously complete multi-week projects.
- Whether we call these systems ’AGI’ or not, they could be sufficient to enable AI research itself, robotics, the technology industry, and scientific research to accelerate — leading to transformative impacts.
- Alternatively, AI might fail to overcome issues with ill-defined, high-context work over long time horizons and remain a tool (even if much improved compared to today).
- Increasing AI performance requires exponential growth in investment and the research workforce. At current rates, we will likely start to reach bottlenecks around 2030. Simplifying a bit, that means we’ll likely either reach AGI by around 2030 or see progress slow significantly. Hybrid scenarios are also possible, but the next five years seem especially crucial.
Chapters:
- Introduction (00:00:00)
- The case for AGI by 2030 (00:00:33)
- The article in a nutshell (00:04:04)
- Section 1: What's driven recent AI progress? (00:05:46)
- How we got here: the deep learning era (00:05:52)
- Where are we now: the four key drivers (00:07:45)
- Driver 1: Scaling pretraining (00:08:57)
- Algorithmic efficiency (00:12:14)
- How much further can pretraining scale? (00:14:22)
- Driver 2: Training the models to reason (00:16:15)
- How far can scaling reasoning continue? (00:22:06)
- Driver 3: Increasing how long models think (00:25:01)
- Driver 4: Building better agents (00:28:00)
- How far can agent improvements continue? (00:33:40)
- Section 2: How good will AI become by 2030? (00:35:59)
- Trend extrapolation of AI capabilities (00:37:42)
- What jobs would these systems help with? (00:39:59)
- Software engineering (00:40:50)
- Scientific research (00:42:13)
- AI research (00:43:21)
- What's the case against this? (00:44:30)
- Additional resources on the sceptical view (00:49:18)
- When do the 'experts' expect AGI? (00:49:50)
- Section 3: Why the next 5 years are crucial (00:51:06)
- Bottlenecks around 2030 (00:52:10)
- Two potential futures for AI (00:56:02)
- Conclusion (00:58:05)
- Thanks for listening (00:59:27)
Audio engineering: Dominic Armstrong
Music: Ben Cordell