A path towards AGI: What is the timeline for achieving AGI? and other insights from Dario Amodei

Dario Amodei , CEO of Anthropic , says: "I don't fully believe the straight line extrapolation, but if you believe the straight line extrapolation, we'll get there in 2026 or 2027."

He also says that considering AI's rapid growth can make us think that we'll really achieve AGI point in 2026 or 2027. But on this way to AGI, there could be unpredictable delays, such as:

- We could run out of data.

- A lack of sufficient computational resources, such as GPUs.

- As models become larger and more complex, unforeseen technical barriers might arise.

So extrapolating current trends might be overly optimistic.

Here are more Dario Amodei 's interesting insights on other AI aspects:

Human limits vs AI potential

There’s no ceiling below the level of humans. If we continue to scale up the models, we’ll at least get to the level that we’ve gotten to with humans.

AI development philosophy

Anthropic AI's "race to the top" is about trying to push the other AI players to do the right thing by setting an example. It’s about shaping the incentives to point upward instead of downward.

Safety testing:

? Models are tested both internally and externally for their safety, particularly for catastrophic and autonomy risks.

? Anthropic evaluates every new model for CBRN risks: chemical, biological, radiological, and nuclear."

? They want safety testing to be as fast as it can without compromising rigor.

The future of model scaling:

? Scaling is continuing. There will definitely be more powerful models coming from Anthropic than the models that exist today.

? If these models can autonomously perform tasks like research, they'll reach a threshold of true autonomy.

These insights about researchers' qualities are my favorite ones:

1) The number one quality, especially on the research side, is open-mindedness. It sounds easy, but it’s very hard to look at something with truly fresh eyes.

2) Often experience is a disadvantage because it prevents you from seeing with new eyes and being willing to experiment in simple, bold ways.

3) A basic scientific mindset—being willing to change one variable and see what happens—is transformative, even if it’s not a brilliant insight.

Aspiring AI researchers: Experiential knowledge is key

"My number one piece of advice is to just start playing with the models."

Insight about RLHF:

RLHF doesn’t make the model smarter. It doesn’t just make it appear smarter either. It bridges the gap between humans and the model. It’s like unhobbling the model.

Development of programming:

Programming will be one of the areas that changes fastest because it’s so close to the people building AI and it closes the loop between writing code, running it, and interpreting the results.

What if AI can handle more coder's tasks?

In typical real world coder's tasks, models have gone from 3% to 50% over 10 months.

As AI takes over 80% of a coder's tasks, the remaining parts—like high-level system design—will expand for humans to ensure productivity increases.

Full video: https://www.youtube.com/watch?v=ugvHCXCOmm4