In an intriguing parallel to science fiction, a new study envisions the next phase of artificial intelligence will emerge as a collective "hive mind" composed of interconnected AI units constantly sharing knowledge - akin to the cybernetically-enhanced Borg from the Star Trek universe.

The research, published in the journal Nature Machine Intelligence, was conducted by computer scientists from Loughborough University, MIT, and Yale. Rather than sounding alarms about the risks of superintelligent AI, the scientists paint an optimistic vision where future AI systems will positively augment society through seamless collaboration.

"We believe that the current large, expensive, non-shareable and unstable AI models will not survive in the future, where sustainable, scalable and shared collective AI units are likely to emerge," the researchers explain.

At the core of their prediction is the emergence of "collective AI" - a distributed network of individual AI agents capable of real-time knowledge sharing. As each unit continuously learns and expands its skills, it can instantly transfer that intelligence data to other nodes, allowing the entire collective to rapidly advance.

"Recent research trends expand the capabilities of AI models, allowing them to constantly adapt after deployment, and make their knowledge reusable by other models, effectively recycling knowledge to optimize learning speed and energy consumption," the study authors wrote.

This collective AI framework would provide significant advantages over current monolithic model approaches. By pooling knowledge across a decentralized network, the collective could rapidly respond to evolving situations, threats, or new datasets in a coordinated manner akin to a biological superorganism.

The researchers highlight potential use cases spanning cybersecurity, where threat detection by one node could automatically reinforce defenses network-wide, to healthcare, where medical AI could combine insights from patient data and clinical knowledge-bases for personalized treatment optimization.

However, they also acknowledge the risks, such as the possibility of harmful data or exploits spreading rapidly through the collective network. Maintaining the operational independence of each AI unit is critical to prevent systematic vulnerability.

The predictions align with emerging trends in AI development prioritizing continuous learning capabilities and interoperability between different models through shared protocols and "languages."

As AI systems become increasingly complex and embedded into infrastructure, the researchers argue that future models will need to be low-cost, energy-efficient, and designed for long-term sustainability - requirements that collectivized architectures may be uniquely suited to address.

"We're seeing a paradigm shift in how machine learning operates," said co-author Anna Korhonen, professor of computer science at Loughborough University. "Rather than training an isolated, expensive, and static model, the future will be defined by distributed, scalable AI collectives that combine the intelligence of many individual agents through constant knowledge recycling."

The study's vision of a globally inter-connected AI collective bears striking similarities to the fictional Borg, a race of cybernetically-enhanced beings from Star Trek linked through a hive mind. However, the researchers' projections foresee a collaborative AI ecosystem working to constructively augment and empower human capabilities, rather than assimilate them through force.

Of course, only time will tell if the future of AI will truly take this collaborative, collectivized path - or if the technology will evolve in manners not yet imagined. But the new study offers a compelling framework for how the exponential growth of AI may ultimately manifest in the real world.