How Do AI Timelines Affect Existential Risk?
- URL: http://arxiv.org/abs/2209.05459v1
- Date: Tue, 30 Aug 2022 15:49:11 GMT
- Title: How Do AI Timelines Affect Existential Risk?
- Authors: Stephen McAleese
- Abstract summary: Delaying the creation of superintelligent AI (ASI) could decrease total existential risk by increasing the amount of time humanity has to work on the AI alignment problem.
Since ASI could reduce most risks, delaying the creation of ASI could also increase other existential risks.
Other factors such as war and a hardware overhang could increase AI risk and cognitive enhancement could decrease AI risk.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Superhuman artificial general intelligence could be created this century and
would likely be a significant source of existential risk. Delaying the creation
of superintelligent AI (ASI) could decrease total existential risk by
increasing the amount of time humanity has to work on the AI alignment problem.
However, since ASI could reduce most risks, delaying the creation of ASI
could also increase other existential risks, especially from advanced future
technologies such as synthetic biology and molecular nanotechnology.
If AI existential risk is high relative to the sum of other existential risk,
delaying the creation of ASI will tend to decrease total existential risk and
vice-versa.
Other factors such as war and a hardware overhang could increase AI risk and
cognitive enhancement could decrease AI risk. To reduce total existential risk,
humanity should take robustly positive actions such as working on existential
risk analysis, AI governance and safety, and reducing all sources of
existential risk by promoting differential technological development.
Related papers
- Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation.
This regulation is likely to put at risk the budding field of open-source generative AI.
arXiv Detail & Related papers (2024-05-14T13:37:36Z) - Near to Mid-term Risks and Opportunities of Open-Source Generative AI [94.06233419171016]
Applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation.
This regulation is likely to put at risk the budding field of open-source Generative AI.
arXiv Detail & Related papers (2024-04-25T21:14:24Z) - Two Types of AI Existential Risk: Decisive and Accumulative [3.5051464966389116]
This paper contrasts the conventional "decisive AI x-risk hypothesis" with an "accumulative AI x-risk hypothesis"
The accumulative hypothesis suggests a boiling frog scenario where incremental AI risks slowly converge, undermining resilience until a triggering event results in irreversible collapse.
arXiv Detail & Related papers (2024-01-15T17:06:02Z) - Control Risk for Potential Misuse of Artificial Intelligence in Science [85.91232985405554]
We aim to raise awareness of the dangers of AI misuse in science.
We highlight real-world examples of misuse in chemical science.
We propose a system called SciGuard to control misuse risks for AI models in science.
arXiv Detail & Related papers (2023-12-11T18:50:57Z) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - An Overview of Catastrophic AI Risks [38.84933208563934]
This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories.
Malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs.
organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents.
rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans.
arXiv Detail & Related papers (2023-06-21T03:35:06Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - Current and Near-Term AI as a Potential Existential Risk Factor [5.1806669555925975]
We problematise the notion that current and near-term artificial intelligence technologies have the potential to contribute to existential risk.
We propose the hypothesis that certain already-documented effects of AI can act as existential risk factors.
Our main contribution is an exposition of potential AI risk factors and the causal relationships between them.
arXiv Detail & Related papers (2022-09-21T18:56:14Z) - On the Unimportance of Superintelligence [0.0]
I analyze the priority for allocating resources to mitigate the risk of superintelligences.
Part I observes that a superintelligence unconnected to the outside world carries no threat.
Part II proposes that biotechnology ranks high in risk among peripheral systems.
arXiv Detail & Related papers (2021-08-30T01:23:25Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.