Dr Johannes Nagele, Senior Principal AI Researcher and Consultant at [at], hosts the podcast "Nagele mit Köpfchen" in which he discusses the latest trends in AI with AI experts. In this episode, he talks to Ridger Zhou, a PhD student in electrical and computer engineering at the University of California, Santa Cruz.
Ridger stops his work SpikeGPT before, a AI-based model that combines spiking neural networks (SNNs) with traditional artificial neural networks (ANNs)and discusses both its possibilities and its disadvantages. Regarding the latter, Ridger notes that SNNs still cannot match the precision and performance of CNNs due to their binary nature. Nevertheless, he emphasises his assumption that SNNs could eventually outperform traditional neural networks in machine learning, as they are able to more closely mimic the functions of the human brain. In addition, how and why SpikeGPT-like models are particularly energy efficient will be discussed, as well as their promising applicability for tasks that require continuous learning. The audience will be encouraged to try out SpikeGPT for themselves.
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