agi

The article critiques Google's position in the ASI and AGI race, arguing that while Google leads in user integration, it is falling behind in the more lucrative AI development race.

Miles Brundage cautions that politicians' awareness of AGI is often overestimated and suggests dividing perceived attention by five for accuracy.

The article explores the competitive race towards achieving Artificial General Intelligence (AGI) and its implications for AI development.

An article discussing OpenAI's claim of solving 100 long-standing math problems within a month and its implications for the accelerated timeline of artificial general intelligence (AGI).

The author reflects on personal motivations for pursuing AGI and the singularity, sharing hopes for curing diseases and improving society, and invites others to share their dreams.

DeepMind's new head Koray announces that Gemini 4.0 is about to launch with Google's most powerful pre-training, aiming for AGI and ASI, and claiming early signs of recursive self-improvement.

A discussion questioning the timeline for achieving physical AGI, contrasting it with digital AGI expectations, and mentioning Elon Musk's Optimus robot in the context of future healthcare affordability.

The article proposes a definition of AGI focused on advanced intellectual abilities for scientific discovery and asks for predictions on when it will be achieved.

Google DeepMind has launched a new institute to advance discussions on AGI, featuring essays on AI transparency, evaluation frameworks, and safety principles.

A tweet argues that startups in the post-AGI era should not rely on pre-AGI business patterns, suggesting a need for innovative strategies.

The essay explores economic policies for AGI, examining historical technological disruptions and their effects on employment and the economy, with a focus on avoiding past harms while harnessing benefits.

The article questions the meaning of AGI as claimed by AI labs, noting how the term has evolved and including expert opinions on its definition and implications.

This article contrasts fictional AI scenarios from Terminator with real-world AI development, concluding that while AI poses significant risks, complete human extinction is unlikely, and international cooperation is essential to mitigate dangers.

The article proposes a method for continual learning in AI where the model autonomously decides what to learn and updates its weights in real-time, potentially enabling continuous self-evolution towards AGI.

The co-inventor of ChatGPT announces the release of a new AI model named Jev, trained with RLCD, claiming it is 20-200x faster, 40-400x cheaper, and optimized for composable intelligence as a path to AGI.

Researchers have decoded continual learning mechanisms in the fruit fly brain, potentially offering key insights for achieving true artificial general intelligence.

Greg Brockman discusses how OpenAI used 10,000 AI agents to solve the Navier-Stokes problem, highlighting AI's potential to drive scientific discoveries and advance towards AGI.

Greg Brockman describes how OpenAI used the Astra model to identify and fix critical vulnerabilities in their systems, highlighting a continuous AI-driven security loop and declaring the AGI era.

The article recommends a YouTube episode featuring Charlie O'Neill from Baseten, discussing how Kimi and GLM models are superior to Opus 5 and addressing skepticism about AI progress and AGI.

The author argues that AI is far from AGI and that Silicon Valley CEOs are overhyping AI risks to raise funds, while actual progress is incremental and not revolutionary.