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Most Research in Deep Learning is a Total Waste of Time - Jeremy Howard | AI Podcast Clips
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This is a clip from a conversation with Jeremy Howard on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2NG4qwr If you enjoy these, consider subscribing, sharing, and commenting below. Full episode: http://bit.ly/2NG4qwr Full episodes playlist: http://bit.ly/2EcbaKf Clips playlist: http://bit.ly/2JYkbfZ Podcast website: https://lexfridman.com/ai Jeremy Howard is the founder of fast.ai, a research institute dedicated to make deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a former president of Kaggle as well a top-ranking competitor there, and in general, he's a successful entrepreneur, educator, research, and an inspiring personality in the AI community. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Segments Timeline
The Wastefulness of Deep Learning Research
Jeremy Howard critiques the current state of deep learning research, arguing that much of it is a total waste of time. He explains how the pressure to publish leads scientists to focus on familiar topics rather than practical applications, resulting in minor advances that lack significant impact. This segment highlights the disconnect between theoretical research and real-world problem-solving in AI.
"so much fast they had students and researchers and the things you teach are pragmatically minded I practically minded freaking figuring out ways how to solve real problems and fast right so from your ..."
The Importance of Transfer Learning
In this segment, Howard emphasizes the transformative potential of transfer learning in deep learning. He argues that improving transfer learning could enable more people to achieve world-class results with fewer resources. Despite its significance, he notes that few researchers are focusing on this area, illustrating a gap between academic interests and practical needs in AI development.
"all right that I was getting it yeah it's it's a problem in science in general scientists need to be published which means they need to work on things that their peers are extremely familiar with and ..."
Active Learning: A Neglected Approach
Jeremy Howard discusses active learning, a method that enhances human involvement in machine learning processes. He points out that while companies are innovating in this area out of necessity, it remains underexplored in academic research. This segment sheds light on the practical challenges faced by practitioners and the need for more focus on active learning methodologies.
"but almost nobody works on that or another example active learning which is the study of like how do we get more out of the human beings in the loop where's my favorite homage yeah so active learning ..."
The Breakthrough of Transfer Learning in NLP
Howard shares his personal experience with transfer learning in natural language processing (NLP), recounting how he developed a successful algorithm called GLM fit. He highlights the surprising effectiveness of his approach, which outperformed existing methods despite his initial lack of expertise in the field. This segment illustrates the potential for practical applications to drive significant advancements in AI research.
"care about practical results the funny thing is like I've only really ever written one paper I hate writing papers and I didn't even write it it was my colleague sebastian ruder who actually wrote it ..."