Reward Is Enough (Machine Learning Research Paper Explained)

Описание к видео Reward Is Enough (Machine Learning Research Paper Explained)

#reinforcementlearning #deepmind #agi

What's the most promising path to creating Artificial General Intelligence (AGI)? This paper makes the bold claim that a learning agent maximizing its reward in a sufficiently complex environment will necessarily develop intelligence as a by-product, and that Reward Maximization is the best way to move the creation of AGI forward. The paper is a mix of philosophy, engineering, and futurism, and raises many points of discussion.

OUTLINE:
0:00 - Intro & Outline
4:10 - Reward Maximization
10:10 - The Reward-is-Enough Hypothesis
13:15 - Abilities associated with intelligence
16:40 - My Criticism
26:15 - Reward Maximization through Reinforcement Learning
31:30 - Discussion, Conclusion & My Comments

Paper: https://www.sciencedirect.com/science...

Abstract:
In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.

Authors: David Silver, Satinder Singh, Doina Precup, Richard S. Sutton

Links:
TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick
YouTube:    / yannickilcher  
Twitter:   / ykilcher  
Discord:   / discord  
BitChute: https://www.bitchute.com/channel/yann...
Minds: https://www.minds.com/ykilcher
Parler: https://parler.com/profile/YannicKilcher
LinkedIn:   / yannic-kilcher-488534136  
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/yannick...
Patreon:   / yannickilcher  
Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2
Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m
Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Комментарии

Информация по комментариям в разработке