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
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
OpenTalks.AI
February 14, 2019
Science
0
740
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
490
OpenTalks.AI - Алексей Чернявский, Нейросетевые алгоритмы для повышения качества медицинских изображений
opentalks
0
440
OpenTalks.AI - Александр Громов, Устойчивость нейросетевых моделей при анализе КТ/НДКТ-исследований
opentalks
0
380
OpenTalks.AI - Денис Тимонин, Megatron-LM: Обучение мультимиллиардных LMs при помощи техники Model Parallelism
opentalks
0
520
OpenTalks.AI - Егор Филимонов, Возможности платформы Huawei Atlas и эффективный гетерогенный инференс.
opentalks
0
150
OpenTalks.AI - Александр Прозоров, Референсная архитектура робота сервисного центра в отраслях с изменчивыми бизнес-процессами
opentalks
0
390
OpenTalks.AI - Наталья Лукашевич, Анализ тональности по отношению к компании — с чем не справился BERT
opentalks
0
340
OpenTalks.AI - Константин Воронцов, Фейковые новости и другие типы потенциально опасного дискурса: типология, подходы, датасеты, соревнования
opentalks
0
450
OpenTalks.AI - Дмитрий Ветров, Фрактальность функции потерь, эффект двойного спуска и степенные законы в глубинном обучении - фрагменты одной мозаики
opentalks
0
480
Other Decks in Science
See All in Science
次代のデータサイエンティストへ~スキルチェックリスト、タスクリスト更新~
datascientistsociety
PRO
2
28k
良書紹介04_生命科学の実験デザイン
bunnchinn3
0
120
AIに仕事を奪われる 最初の医師たちへ
ikora128
0
1k
Algorithmic Aspects of Quiver Representations
tasusu
0
190
Agent開発フレームワークのOverviewとW&B Weaveとのインテグレーション
siyoo
0
420
機械学習 - SVM
trycycle
PRO
1
980
Lean4による汎化誤差評価の形式化
milano0017
1
430
AIによる科学の加速: 各領域での革新と共創の未来
masayamoriofficial
0
420
Rashomon at the Sound: Reconstructing all possible paleoearthquake histories in the Puget Lowland through topological search
cossatot
0
530
生成検索エンジン最適化に関する研究の紹介
ynakano
2
2k
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
530
あなたに水耕栽培を愛していないとは言わせない
mutsumix
1
250
Featured
See All Featured
世界の人気アプリ100個を分析して見えたペイウォール設計の心得
akihiro_kokubo
PRO
66
37k
Principles of Awesome APIs and How to Build Them.
keavy
128
17k
The SEO Collaboration Effect
kristinabergwall1
0
360
Heart Work Chapter 1 - Part 1
lfama
PRO
5
35k
The AI Search Optimization Roadmap by Aleyda Solis
aleyda
1
5.2k
The agentic SEO stack - context over prompts
schlessera
0
650
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
4.2k
Why You Should Never Use an ORM
jnunemaker
PRO
61
9.7k
The Curious Case for Waylosing
cassininazir
0
240
SEOcharity - Dark patterns in SEO and UX: How to avoid them and build a more ethical web
sarafernandez
0
120
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
12
1.4k
B2B Lead Gen: Tactics, Traps & Triumph
marketingsoph
0
57
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]