4 Charts That Show Why AI Progress Is Unlikely to Slow Down

Hey, everyone, how are you doing today?

The original material that inspired this post comes from TIME:

But, here's the 1-2min summarized version (with AILG spicy taste) for you busy folks:

1. AI has surpassed humans at a number of tasks and the rate at which humans are being surpassed at new tasks is increasing

In the last decade, AI has rapidly advanced, even surpassing human abilities in some areas. A recent U.S. Senate hearing on AI regulation underscored the pace of this progress, with some describing the advancements as "scary."

2. The amount of compute used to train AI systems has been increasing since 1950, the rate of increase increased in 2010

AI progress is often seen as unpredictable, but it's driven predictably by three factors: compute, data, and algorithms, with much of the last 70 years advancements coming from greater computational processing power and more data.

The increase in computation has allowed for more detailed modeling and has spurred the development of larger-scale models and partnerships between leading AI companies and tech giants.

3. The number of data points used to train AI models has increased dramatically over the last seventy years

AI systems analyze relationships in data to build models, with more data points enhancing accuracy. Language models like LlaMa use this to recognize word sequences, trained on one billion data points. This is a vast increase from earlier models like Perceptron Mark I, trained on just six.

4. Algorithmic progress means that less compute and data are required to achieve a given level of performance

Algorithms guide how AI systems use computational power to model data relationships, and developers constantly seek efficiency. Research from Epoch indicates that improved algorithms every nine months effectively double computation budgets.

Conclusion…

AI is getting better and faster, and there's plenty of room to grow.

The only real bottleneck we might run into is a slowdown that may occur as high-quality language data depletes.

But, with increases in compute and data efficiency, innovations are likely to overcome potential bottlenecks in high-quality language data.

This acceleration heralds exciting possibilities for advancements in various fields, embracing a future of increased capability and understanding.

This speed-up means we could see some really exciting improvements in different areas, opening up a future where we can do more and understand things better.