Is Artificial General Intelligence possible?
Session
Computer Science and Communication Engineering
Description
Artificial General Intelligence (AGI) aims to create a human like machine that can amalgamate, common sense reasoning, problem-solving, and the adaptability to combine knowledge over different domains. The possibility of AGI now is something plausible coming from the recent computational, empirical and theoretical progress. The underlying theories of Solomonoff’s inductive inference, Legg and Hutter’s universal intelligence and Hutter’s maximally intelligent agent put forward a precise mathematical initiation for comprehension of general learning and decision-making. Meantime, more practical progress in deep learning, reinforcement learning and Transfomers brought the emergent capabilities, which were only thought to be human like skills. The introduction of big models like GPT-3 and systems like AlphaGo, revealed that these systems have the capability to learn very complex reasoning and planning in a general domain rather than one task. The merging of scalable architecture, emergent behaviors and theoretical formalism advocate that the idea of AGI is an attainable pathway and not an abstract idea. The two crucial theories, Sutton’s “Bitter Lesson” and Silver et al.’s “Reward is Enough” showed that computation can scale and is able to learn for itself which then followed to better results when competing with human skills. Additionally, the studies done in different human abilities like creativity, curiosity-based learning or even in intrinsic motivation led on the idea that the universal principles of optimization can lead to the self-development and open-ended intelligence. As the rules of scaling continue to improve in one side and with the faster computing and big data, with the bigger models that can work in wider range of tasks in other side the line that separate narrow AI and general AI will start to disappear. The sum of all these great achievements advocate that AGI is not just a hypothetical idea but it is actually another imminent phase in AI revolution.
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-41-2
Location
UBT Kampus, Lipjan
Start Date
25-10-2025 9:00 AM
End Date
26-10-2025 6:00 PM
DOI
10.33107/ubt-ic.2025.80
Recommended Citation
Mehani, Ermira, "Is Artificial General Intelligence possible?" (2025). UBT International Conference. 12.
https://knowledgecenter.ubt-uni.net/conference/2025UBTIC/CS/12
Is Artificial General Intelligence possible?
UBT Kampus, Lipjan
Artificial General Intelligence (AGI) aims to create a human like machine that can amalgamate, common sense reasoning, problem-solving, and the adaptability to combine knowledge over different domains. The possibility of AGI now is something plausible coming from the recent computational, empirical and theoretical progress. The underlying theories of Solomonoff’s inductive inference, Legg and Hutter’s universal intelligence and Hutter’s maximally intelligent agent put forward a precise mathematical initiation for comprehension of general learning and decision-making. Meantime, more practical progress in deep learning, reinforcement learning and Transfomers brought the emergent capabilities, which were only thought to be human like skills. The introduction of big models like GPT-3 and systems like AlphaGo, revealed that these systems have the capability to learn very complex reasoning and planning in a general domain rather than one task. The merging of scalable architecture, emergent behaviors and theoretical formalism advocate that the idea of AGI is an attainable pathway and not an abstract idea. The two crucial theories, Sutton’s “Bitter Lesson” and Silver et al.’s “Reward is Enough” showed that computation can scale and is able to learn for itself which then followed to better results when competing with human skills. Additionally, the studies done in different human abilities like creativity, curiosity-based learning or even in intrinsic motivation led on the idea that the universal principles of optimization can lead to the self-development and open-ended intelligence. As the rules of scaling continue to improve in one side and with the faster computing and big data, with the bigger models that can work in wider range of tasks in other side the line that separate narrow AI and general AI will start to disappear. The sum of all these great achievements advocate that AGI is not just a hypothetical idea but it is actually another imminent phase in AI revolution.