In a new paper in the journal Nature Machine Intelligence, leading computer scientists from around the world review recent machine learning advances converging towards creating a collective machine-learned intelligence. They propose that the convergence of such scientific and technological advances will lead to the emergence of new types of scalable, resilient and sustainable AI systems.
Loughborough University’s Dr. Andrea Soltoggio and colleagues recognize the striking similarities between Collective AI and many science fiction concepts.
One example they cite is The Borg, cybernetic organisms featured in the Star Trek universe, which operate and share knowledge through a linked hive-mind.
However, unlike many sci-fi narratives, the authors envision Collective AI will lead to major positive breakthroughs across various fields.
“Instant knowledge sharing across a collective network of AI units capable of continuously learning and adapting to new data will enable rapid responses to novel situations, challenges, or threats,” Dr. Soltoggio said.
“For example, in a cybersecurity setting if one AI unit identifies a threat, it can quickly share knowledge and prompt a collective response — much like how the human immune system protects the body from outside invaders.”
“It could also lead to the development of disaster response robots that can quickly adapt to the conditions they are dispatched in, or personalised medical agents that improve health outcomes by merging cutting-edge medical knowledge with patient-specific information. The potential applications are vast and exciting.”
The researchers acknowledge there are risks associated with Collective AI — such as the swift spread of potentially unethical or illicit knowledge — but highlight a crucial safety aspect of their vision: AI units maintain their own objectives and independence from the collective.
“This would result in a democracy of AI agents, significantly reducing the risks of an…
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