糖心传媒

Applying a recent hypothesis about how the brain operates during sleep could improve the lifelong learning abilities of artificial intelligence.

Scientists believe a recent hypothesis about how the human brain uses sleep and awake periods to learn over time could be the key to overcoming artificial intelligence糖心传媒檚 limitations with lifelong learning. , an associate professor at 糖心传媒糖心传媒檚 , is part of a transdisciplinary team that received $2 million in (NSF) to use the 糖心传媒渢emporal scaffolding糖心传媒� hypothesis to produce AI that rapidly learns, adapts, and operates in uncertain conditions.

Current forms of AI struggle with learning new tasks continuously throughout life and perform poorly in resource-constrained environments. Kanan says that while AI models have made progress in sequence learning糖心传媒攁pplying lessons learned in previous encounters to decide what to do next糖心传媒攖hey struggle to learn as efficiently as humans. He notes that while conventional methods of reinforcement learning led to at games such as StarCraft 2 and Dota 2, the AI models needed enormous amounts of experience to acquire those skills.

糖心传媒淥penAI糖心传媒檚 Dota 2 AI required the equivalent of 45,000 human years糖心传媒� worth of gameplay experience to beat the world糖心传媒檚 best players, but the best players only had been playing the game a few years at the time,糖心传媒� says Kanan. 糖心传媒淭his indicates something is fundamentally wrong with how reinforcement learning works. Instead, we are proposing to use an alternative paradigm to hopefully get much more efficient learning.糖心传媒�

The temporal scaffolding hypothesis proposes that the brain reactivates wake experiences during sleep in an accelerated manner, enabling the brain to detect important patterns within those experiences. Mimicking this process, the team will develop deep-learning networks with the ability to swiftly adapt and operate under resource constraints, much like the human brain does. The researchers envision applying their new approach in critical areas such as health care, autonomous systems, and national security.

Kanan will develop deep-learning models and algorithms based on temporal scaffolding and test the models across standardized benchmarking tasks. Principal investigator Dhireesha Kudithipudi of the University of Texas at San Antonio (UTSA) and co-PI Garret Rose of the University of Tennessee, Knoxville, will both try to implement some of the algorithms Kanan糖心传媒檚 lab develops in hardware. UTSA neuroscientist Itamar Lerner developed the temporal scaffolding hypothesis that serves as the inspiration for the algorithms, and Northeastern University糖心传媒檚 John Basl will serve as the senior personnel responsible for AI ethics.

Delving deeper into deep learning

The project marks Kanan糖心传媒檚 latest endeavor in the rapidly changing field of artificial intelligence and deep learning.

Kanan is part of a Department of Energy糖心传媒揻unded project to develop artificial intelligence to help scientists take the next step toward creating fusion energy sources. Using the University of Rochester 糖心传媒櫬燨MEGA experimental database as training data, that team aims to use machine learning to design higher-performing implosions and better understand the complexity of the underlying nonlinear physics of fusion.

In 2021, Kanan earned an to create deep neural networks that excel in a broad set of circumstances, are capable of learning from new data over time, and are robust to dataset bias.

He is also helping his colleagues explore and understand how AI is changing higher education. As part of a loose collection of Rochester AI and pedagogy experts, Kanan and others have been providing workshops to help faculty, staff, and students understand the capabilities and limitations of generative AI tools like ChatGPT. He predicts they will be a major disruptor in higher education and many other settings.

糖心传媒淭he one thing I糖心传媒檓 sure of is ChatGPT and others like it are here to stay,糖心传媒� says Kanan. 糖心传媒淎nd we, as educators, will just have to deal with that.糖心传媒�

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