Click to copy, then share by pasting into your messages, comments, social media posts and websites.
Click to copy, then add into your webpages so users can view and engage with this video from your site.
Report Content
We also accept reports via email. Please see the Guidelines Enforcement Process for instructions on how to make a request via email.
Thank you for submitting your report
We will investigate and take the appropriate action.
The Hardware Lottery (Paper Explained)
#ai #research #hardware
We like to think that ideas in research succeed because of their merit, but this story is likely incomplete. The term "hardware lottery" describes the fact that certain algorithmic ideas are successful because they happen to be suited well to the prevalent hardware, whereas other ideas, which would be equally viable, are left behind because no accelerators for them exists. This paper is part history, part opinion and gives lots of inputs to think about.
OUTLINE:
0:00 - Intro & Overview
1:15 - The Hardware Lottery
8:30 - Sections Overview
11:30 - Why ML researchers are disconnected from hardware
16:50 - Historic Examples of Hardware Lotteries
29:05 - Are we in a Hardware Lottery right now?
39:55 - GPT-3 as an Example
43:40 - Comparing Scaling Neural Networks to Human Brains
46:00 - The Way Forward
49:25 - Conclusion & Comments
Paper: https://arxiv.org/abs/2009.06489
Website: https://hardwarelottery.github.io/
Abstract:
Hardware, systems and algorithms research communities have historically had different incentive structures and fluctuating motivation to engage with each other explicitly. This historical treatment is odd given that hardware and software have frequently determined which research ideas succeed (and fail). This essay introduces the term hardware lottery to describe when a research idea wins because it is suited to the available software and hardware and not because the idea is superior to alternative research directions. Examples from early computer science history illustrate how hardware lotteries can delay research progress by casting successful ideas as failures. These lessons are particularly salient given the advent of domain specialized hardware which makes it increasingly costly to stray off of the beaten path of research ideas.
Authors: Sara Hooker
Links:
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/yannic-kilcher-488534136/
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
Category | Science & Technology |
Sensitivity | Normal - Content that is suitable for ages 16 and over |
Playing Next
Related Videos
[ML News] Chips, Robots, and Models
3 days, 22 hours ago
ORPO: Monolithic Preference Optimization without Reference Model (Paper Explained)
4 days, 11 hours ago
TransformerFAM: Feedback attention is working memory
6 days, 5 hours ago
[ML News] Llama 3 changes the game
1 week, 3 days ago
Warning - This video exceeds your sensitivity preference!
To dismiss this warning and continue to watch the video please click on the button below.
Note - Autoplay has been disabled for this video.