Grok-2 Actually Out, But What If It Were 10,000x the Size?

The Nugget

Grok-2's latest version is out, but its full potential remains to be tested and understood due to the lack of accessible documentation or APIs.

Make it stick

📏 10,000x: By 2030, models could be scaled 10,000 times larger than GPT-4.
🧩 Hidden concepts: Recent studies suggest that language models can learn latent or hidden concepts, hinting at the potential for developing internal world models.
🤖 Performance gaps: Grok-2 competes closely with Claude 3.5 Sonic but occasionally falls short on certain benchmarks.
📸 Fake realism: We are close to achieving real-time photo realism, complicating trust in online visuals.

The largest version of Grok-2 has been released without extensive documentation, model cards, or APIs, limiting comprehensive evaluation.
Official performance benchmarks show Grok-2 closely trailing behind Claude 3.5 Sonic in various tests.

Epoch AI's Paper: Predicts that by 2030, AI models could be scaled to 10,000 times the size of GPT-4, despite data scarcity, chip production capacity, and power constraints being major bottlenecks.
Larger models are likely to demonstrate significant performance improvements, potentially developing richer world models and appearing more intelligent.

Concerns around the increasing prevalence of fake images and videos, driven by tools like Google's Pixel and AI models like Flux.
The development of internal models or understanding in AI is still debatable, with some evidence suggesting that language models are learning more than surface-level statistical correlations.
Future breakthroughs may require not just more data but a revolution in data labeling to improve model understanding and accuracy.

Real-time photo realism might soon become indistinguishable from reality, impacting trust in digital interactions and online content.
Some experts propose zero-knowledge proofs as a solution for verifying online identities amidst the rise of powerful deep fakes.
Google's DeepMind is working on tracing AI outputs back to their data sources for fair attribution and compensation.

"Ultimately it goal it seems is to be maximally truthful."
"Forget World coin or fingerprints we might be able to use what’s called zero-knowledge proofs to provide personhood credentials."
"[By 2030]... about 10,000 times the scale of GPT-4."
"If they can infer hidden functions that X will cause Y, they can start to figure out the world more concretely."
"AI models might develop richer internal world models and just feel more intelligent."

This summary contains AI-generated information and may have important inaccuracies or omissions.