Beyond the Hype: AI's Promise, Shortcomings, and the Quest for AGI

AI’s Next Leap:

Real-Time Situation-Specific Learning on Device

New situations arise constantly, demanding AI systems that can learn specific, situation-critical information on-device in real-time without relying on datasets or re-training. By mastering these specific scenarios over time, such systems evolve toward general intelligence, enabling them to adapt instantly to real-world challenges as they unfold.
“The gap between current AI and truly adaptive intelligence isn’t one we can bridge by simply adding more layers or data. We need to rebuild AI’s foundations from the ground up.” OpenAI cofounder Ilya Sutskever says the way AI is built is about to change

AI’s Big Problem:

Evolving Systems for a Dynamic World

While AI systems excel at learning patterns from large datasets, they falter when faced with new or evolving situations that fall outside their training. It’s akin to teaching someone the general rules of driving but not how to handle a sudden thunderstorm or a roadblock. In dynamic and unpredictable environments, static systems fail to adapt, often with serious consequences.
“Traditional AI retraining cycles are both time-intensive and costly, involving extensive data collection, detailed labeling, substantial computational resources, and redeployment efforts. Furthermore, these cycles lack assurance of effectiveness, as there is no guarantee that the specific failure for which the system was redesigned will recur, rendering the process potentially futile.” — MIT Technology Review – The way we train AI is fundamentally flawed

Conclusion:

The Need for an AI Architecture for Real-Time Adaptability

To truly unlock AI’s potential, we must discover new AI architectures that learn and evolve in real-time, adapting as situations unfold. Real-time adaptability paves the way to Artificial General Intelligence (AGI), where AI can think, respond, and grow alongside humanity, driving innovation and solving the complexities of tomorrow.
“Traditional AI retraining cycles are both time-intensive and costly, involving extensive data collection, detailed labeling, substantial computational resources, and redeployment efforts. Furthermore, these cycles lack assurance of effectiveness, as there is no guarantee that the specific failure for which the system was redesigned will recur, rendering the process potentially futile.”

Several companies, venture capital firms ( Andreessen Horowitz, Sequoia, Khosla Ventures), and academic leaders (Yann LeCun, Fei-Fei Li, Pedro Domingos, Hava Siegelmann, Jay McClelland, Yoshua Bengio ) are actively pursuing and advocating for the development of foundational models for Artificial General Intelligence (AGI) :

Companies

Mostly focused on Language Models

Leading AI labs like OpenAI, Anthropic, and DeepMind are advancing model architectures with innovations like OpenAI’s reasoning-driven o1, Anthropic’s value-aligned Claude, and DeepMind’s retrieval-enhanced RETRO to push boundaries with breakthroughs in AGI-specific datasets and near-human comprehension Open AI just achieved a breakthrough on the AGI-specific ARC dataset
Anthropic said that their “Opus” model “exhibits near-human levels of comprehension, leading the frontier of general intelligence
P.G. Keerthana Gopalakrishnan, Google DeepMind Google Deepmind’s Six Levels of AGI | Liquid AI achieves worm-level AGI, kids are making their LLMs

“While these virtual world achievements are impressive, true AGI must be embodied in the physical world to transform how we live and work. Embodiment is Indispensable for AGI”

Skylark Labs – Embodied AGI for the Physical World

Skylark Labs has developed the world’s first AGI for physical security that learns and adapts instantly in the real world. While others focus on training large language models in risk-free virtual environments, our AI ensures real-world protection by adapting on-device from single experiences, just like humans—preventing any security threat from succeeding twice.

“Skylark Labs’ world-first Embodied AGI technology, powered by a brain-inspired architecture, redefines physical security by adapting instantly on edge devices in real-time to counter new threats without relying on historical data or re-training, ensuring vulnerabilities are never exploited twice. This groundbreaking dataset-free adaptation has revolutionized physical security for clients across defense, public safety, and transportation.” Amarjot Singh, PhD; Founder of Skylark Labs

This fundamental shift in AGI development means security systems no longer need to wait for retraining cycles or depend on massive datasets to address new threats. Our edge-based technology learns from each experience as it happens, creating truly adaptive physical security solutions that continuously strengthen their defenses in the real world—marking the next evolution in AGI for physical security applications.