How far can we scale up? Deep Learning’s Diminishing Returns (Article Review)

#deeplearning #co2 #cost Deep Learning has achieved impressive results in the last years, not least due to the massive increases in computational power and data that has gone into these models. Scaling up currently promises to be a reliable way to create more performant systems, but how far can we go? This article explores the limits of exponential scaling in AI, and what people are doing to get around this problem OUTLINE: 0:00 - Intro & Overview 1:00 - Deep Learning at its limits 3:10 - The cost of overparameterization 5:40 - Extrapolating power usage and CO2 emissions 10:45 - We cannot just continue scaling up 13:25 - Current solution attempts 15:25 - Aside: ImageNet V2 17:50 - Are symbolic methods the way out? Paper: https://spectrum.ieee.org/deep-learning-computational-cost Image by Ralf Vetterle from Pixabay: https://pixabay.com/images/id-1752876/ Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n