Scientists to Merge Human Brain Cells with AI for Unprecedented Progress!

Scientists are embarking on a groundbreaking endeavor to merge human brain cells with artificial intelligence (AI). Funded by a $600,000 grant from Australia's Office of National Intelligence, a team of researchers is collaborating with Cortical Labs, a Melbourne-based startup, to explore the potential of this innovative technology.

Their initial success is already evident, as they have demonstrated that a cluster of approximately 800,000 brain cells in a Petri dish can play a game of "Pong." The core idea behind this project is to unite biology with AI, paving the way for revolutionary advancements in machine learning. The government's investment aims to unlock new possibilities for self-driving cars, autonomous drones, and delivery robots through this integration.

The researchers confidently claim that this new technology could eventually surpass the performance of existing purely silicon-based hardware. Adeel Razi, the team lead and associate professor at Monarch University, envisions profound implications in various fields such as planning, robotics, advanced automation, brain-machine interfaces, and drug discovery. Such advancements could provide Australia with a significant strategic advantage.

The unique aspect of this approach lies in enabling machine intelligence to "learn throughout its lifetime" akin to human brain cells. This capability allows AI to acquire new skills without sacrificing old ones and apply existing knowledge to novel tasks. The team aims to cultivate brain cells in a lab dish using the DishBrain system to explore the concept of "continual lifelong learning."

Although an ambitious project, its completion will undoubtedly take considerable time. The grant will be utilized to develop AI machines that replicate the learning capacity of biological neural networks. By doing so, the researchers seek to scale up the hardware and methods required, making them a viable alternative to traditional in-silico computing.

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1. AI to Learn Like Humans: Researchers at The Ohio State University have explored the potential of future AI algorithms to learn like humans by analyzing the impact of "continual learning" on their performance. Continual learning involves training a computer to continuously learn a sequence of tasks, utilizing knowledge from old tasks to improve learning new ones. The study addresses the challenge of "catastrophic forgetting" in AI agents, where they tend to lose information from previous tasks as they learn new ones. The research highlights that AI neural networks can recall information better when faced with diverse tasks in succession rather than similar ones. The findings suggest that optimizing algorithm memory involves teaching dissimilar tasks early in the learning process. Achieving such dynamic, lifelong learning in autonomous systems could lead to AI algorithms mimicking human learning capabilities. The implications of this work could enhance machine learning algorithms' scalability, adaptability, and performance in handling evolving environments and unexpected situations.

2. Teaching AI: Researchers from MIT, New York University, and UC Berkeley have developed a framework that allows humans to efficiently teach robots new tasks with minimal effort. When a robot fails to perform a task, the system generates counterfactual explanations, which describe the changes needed for success, and asks for user feedback. Utilizing this information, the system generates synthetic data to fine-tune the robot's machine-learning model quickly. Testing the technique in simulations showed that robots trained using this framework outperformed other methods and required less time from human users. The approach could enable general-purpose robots to efficiently learn and perform tasks in various environments, benefiting the elderly and individuals with disabilities.

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