Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
OpenTalks.AI - Сергей Шумский, Символьное мышле...
Search
OpenTalks.AI
February 14, 2019
Science
0
720
OpenTalks.AI - Сергей Шумский, Символьное мышление роботов
OpenTalks.AI
February 14, 2019
Tweet
Share
More Decks by OpenTalks.AI
See All by OpenTalks.AI
OpenTalks.AI - Виктор Лемпицкий, Моделирование 3Д сцен: новые подходы в 2020 году
opentalks
0
480
OpenTalks.AI - Алексей Чернявский, Нейросетевые алгоритмы для повышения качества медицинских изображений
opentalks
0
420
OpenTalks.AI - Александр Громов, Устойчивость нейросетевых моделей при анализе КТ/НДКТ-исследований
opentalks
0
370
OpenTalks.AI - Денис Тимонин, Megatron-LM: Обучение мультимиллиардных LMs при помощи техники Model Parallelism
opentalks
0
500
OpenTalks.AI - Егор Филимонов, Возможности платформы Huawei Atlas и эффективный гетерогенный инференс.
opentalks
0
140
OpenTalks.AI - Александр Прозоров, Референсная архитектура робота сервисного центра в отраслях с изменчивыми бизнес-процессами
opentalks
0
370
OpenTalks.AI - Наталья Лукашевич, Анализ тональности по отношению к компании — с чем не справился BERT
opentalks
0
330
OpenTalks.AI - Константин Воронцов, Фейковые новости и другие типы потенциально опасного дискурса: типология, подходы, датасеты, соревнования
opentalks
0
430
OpenTalks.AI - Дмитрий Ветров, Фрактальность функции потерь, эффект двойного спуска и степенные законы в глубинном обучении - фрагменты одной мозаики
opentalks
0
460
Other Decks in Science
See All in Science
機械学習 - 授業概要
trycycle
PRO
0
230
My Favourite Book in 2024: Get Rid of Your Japanese Accent
lagenorhynque
1
110
データベース04: SQL (1/3) 単純質問 & 集約演算
trycycle
PRO
0
980
07_浮世満理子_アイディア高等学院学院長_一般社団法人全国心理業連合会代表理事_紹介資料.pdf
sip3ristex
0
590
Cross-Media Information Spaces and Architectures (CISA)
signer
PRO
3
31k
深層学習を用いた根菜類の個数カウントによる収量推定法の開発
kentaitakura
0
180
アナログ計算機『計算尺』を愛でる Midosuji Tech #4/Analog Computing Device Slide Rule now and then
quiver
1
250
KH Coderチュートリアル(スライド版)
koichih
1
45k
データから見る勝敗の法則 / The principle of victory discovered by science (open lecture in NSSU)
konakalab
1
130
コンピュータビジョンによるロボットの視覚と判断:宇宙空間での適応と課題
hf149
1
300
データベース03: 関係データモデル
trycycle
PRO
1
250
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1k
Featured
See All Featured
Code Reviewing Like a Champion
maltzj
525
40k
Reflections from 52 weeks, 52 projects
jeffersonlam
351
21k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
Practical Orchestrator
shlominoach
190
11k
Faster Mobile Websites
deanohume
309
31k
What’s in a name? Adding method to the madness
productmarketing
PRO
23
3.6k
Rails Girls Zürich Keynote
gr2m
95
14k
Dealing with People You Can't Stand - Big Design 2015
cassininazir
367
27k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
8
900
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
185
54k
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
31
2.2k
Fashionably flexible responsive web design (full day workshop)
malarkey
407
66k
Transcript
The Symbolic Mind of Robots Sergey Shumsky, 2019
Motivation: common architecture of robotic brains Reinforcement learning
Motivation: common architecture of robotic brains
Agenda I. Problem Deep Reinforcement Learning II. Solution Deep Symbolic
Learning III. Prospects Robot Operating System
Agenda I. Problem Deep Reinforcement Learning
AlphaZero: intuition + calculation ▪ Deep intuition: Yes ▪ Deep
planning: No ▪ Complexity of learning ∝ 2 , 3 TFLOPSyears – weights update 500 TFLOPSyears – search options
Google DeepMind research program "One way you can think about
our research program is: 'Can we build out from our perception, using deep-learning systems and learning from first principles? Can we build out all the way to high-level thinking and symbolic thinking?' " D. Hassabis (Google DeepMind)
Agenda I. Problem Deep Reinforcement Learning II. Solution Deep Symbolic
Learning
Deep symbolic learning ▪ Hierarchy of plans state sequences ▪
Planning and Learning in real time ▪ Complexity of learning ∝ ∝
Images and Symbols Coding Decoding Image ~ 106 bits Sensors
States Actions Effectors Image ~ 106 bits Symbols ~ 5 bits Planning
The trick: symbolic coding of images 2106 ~ , →
1 2 … > ( = 30, k = 7) symbols images = 210 <
Symbolic thinking ~ planning ▪ Coding of sequences ~ k
variants (words) ~ 30 Symbolic sequences can be remembered symbol +1 +2 ~ 30 ~ 30
Symbolic thinking ~ planning символ +1 +2 Image code Sequence
code (pattern)
Deep symbolic learning Layer L Layer L -1 1 2
…
Deep symbolic learning ▪ Learning useful patterns of interaction with
the world ▪ At all hierarchical levels ▪ In real time Encoder- Decoder +1 +1 Parser +1 Encoder- Decoder Parser symbols patterns symbols
Agenda I. Problem Deep Reinforcement Learning II. Solution Deep Symbolic
Learning III. Prospects Robot Operating System
Prospects Sensory intelligence Strategic intelligence Robotic intelligence Goal
setting and planning to achieve them Achieving the goal
Open AI gym Mountain car (Igor Pivovarov)
Interested? Join us!
[email protected]
[email protected]