�ݺ�ߣshows by User: AIFrontiers

�ݺ�ߣshows by User: AIFrontiers / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: AIFrontiers / Fri, 30 Nov 2018 17:16:57 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: AIFrontiers Divya Jain at AI Frontiers : Video Summarization /slideshow/divya-jain-at-ai-frontiers-video-summarization-124507998/124507998 04divyajain1025am-181130171657
As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.]]>
As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.]]> Fri, 30 Nov 2018 17:16:57 GMT /slideshow/divya-jain-at-ai-frontiers-video-summarization-124507998/124507998 AIFrontiers@slideshare.net(AIFrontiers) Divya Jain at AI Frontiers : Video Summarization AIFrontiers As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/04divyajain1025am-181130171657-thumbnail.jpg?width=120&height=120&fit=bounds" /> As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.

Divya Jain at AI Frontiers : Video Summarization from AI Frontiers

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0 Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI /slideshow/training-at-ai-frontiers-2018-laioffer-data-session-how-spark-speedup-ai/123543124 rgu7pbsptxsebt7frx30-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415
Topic: How to use big data to enhance AI Outline: 1. Spark ETL Spark SQL Spark Streaming 2. Spark ML Spark ML pipeline Distributed model tuning Spark ML model and data lineage management 3. Spark XGboost XGboost introduction XGboost with Spark XGboost with GPU 4. Spark Deep Learning pipeline Transfer learning Build Spark ML pipeline with TensorFlow Model selection on distributed TF model]]>
Topic: How to use big data to enhance AI Outline: 1. Spark ETL Spark SQL Spark Streaming 2. Spark ML Spark ML pipeline Distributed model tuning Spark ML model and data lineage management 3. Spark XGboost XGboost introduction XGboost with Spark XGboost with GPU 4. Spark Deep Learning pipeline Transfer learning Build Spark ML pipeline with TensorFlow Model selection on distributed TF model]]> Tue, 20 Nov 2018 18:14:15 GMT /slideshow/training-at-ai-frontiers-2018-laioffer-data-session-how-spark-speedup-ai/123543124 AIFrontiers@slideshare.net(AIFrontiers) Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI AIFrontiers Topic: How to use big data to enhance AI Outline: 1. Spark ETL Spark SQL Spark Streaming 2. Spark ML Spark ML pipeline Distributed model tuning Spark ML model and data lineage management 3. Spark XGboost XGboost introduction XGboost with Spark XGboost with GPU 4. Spark Deep Learning pipeline Transfer learning Build Spark ML pipeline with TensorFlow Model selection on distributed TF model <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rgu7pbsptxsebt7frx30-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=120&height=120&fit=bounds" /> Topic: How to use big data to enhance AI Outline: 1. Spark ETL Spark SQL Spark Streaming 2. Spark ML Spark ML pipeline Distributed model tuning Spark ML model and data lineage management 3. Spark XGboost XGboost introduction XGboost with Spark XGboost with GPU 4. Spark Deep Learning pipeline Transfer learning Build Spark ML pipeline with TensorFlow Model selection on distributed TF model

Training at AI Frontiers 2018 - LaiOffer Data Session: How Spark Speedup AI from AI Frontiers

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0 Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 1: Heuristic Search /slideshow/training-at-ai-frontiers-2018-laioffer-selfdrivingcarlecture-1-heuristic-search/123543123 5fjo1ygvtxu4afb0yzzp-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415
Topic：Heuristic Search and how does it apply to self-driving cars? (for Beginners) Outline: 1. Technologies behind self-driving vehicles - Motion Planning - Decision Making 2. Graph Search Algorithms - Depth-First Search - Breadth-First Search - A* Search 3. Incremental Heuristic Search Algorithms - Repeated A* Search - Adaptive A* - Generalized Fringe-Retrieving A*]]>
Topic：Heuristic Search and how does it apply to self-driving cars? (for Beginners) Outline: 1. Technologies behind self-driving vehicles - Motion Planning - Decision Making 2. Graph Search Algorithms - Depth-First Search - Breadth-First Search - A* Search 3. Incremental Heuristic Search Algorithms - Repeated A* Search - Adaptive A* - Generalized Fringe-Retrieving A*]]> Tue, 20 Nov 2018 18:14:15 GMT /slideshow/training-at-ai-frontiers-2018-laioffer-selfdrivingcarlecture-1-heuristic-search/123543123 AIFrontiers@slideshare.net(AIFrontiers) Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 1: Heuristic Search AIFrontiers Topic：Heuristic Search and how does it apply to self-driving cars? (for Beginners) Outline: 1. Technologies behind self-driving vehicles - Motion Planning - Decision Making 2. Graph Search Algorithms - Depth-First Search - Breadth-First Search - A* Search 3. Incremental Heuristic Search Algorithms - Repeated A* Search - Adaptive A* - Generalized Fringe-Retrieving A* <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5fjo1ygvtxu4afb0yzzp-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=120&height=120&fit=bounds" /> Topic：Heuristic Search and how does it apply to self-driving cars? (for Beginners) Outline: 1. Technologies behind self-driving vehicles - Motion Planning - Decision Making 2. Graph Search Algorithms - Depth-First Search - Breadth-First Search - A* Search 3. Incremental Heuristic Search Algorithms - Repeated A* Search - Adaptive A* - Generalized Fringe-Retrieving A*

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 1: Heuristic Search from AI Frontiers

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0 Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Understanding /slideshow/training-at-ai-frontiers-2018-ni-lao-weakly-supervised-natural-language-understanding/123543122 ioedkhktrutax0gpceiw-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415
In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.]]>
In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.]]> Tue, 20 Nov 2018 18:14:15 GMT /slideshow/training-at-ai-frontiers-2018-ni-lao-weakly-supervised-natural-language-understanding/123543122 AIFrontiers@slideshare.net(AIFrontiers) Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Understanding AIFrontiers In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ioedkhktrutax0gpceiw-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.

Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Understanding from AI Frontiers

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0 Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-lecture 2: Incremental Search /slideshow/training-at-ai-frontiers-2018-laioffer-selfdrivingcarlecture-2-incremental-search/123543121 zew9xwy9sexfswc0q1oa-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415
LaiOffer Self-Driving-Car-lecture 2: Incremental Search]]>
LaiOffer Self-Driving-Car-lecture 2: Incremental Search]]> Tue, 20 Nov 2018 18:14:15 GMT /slideshow/training-at-ai-frontiers-2018-laioffer-selfdrivingcarlecture-2-incremental-search/123543121 AIFrontiers@slideshare.net(AIFrontiers) Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-lecture 2: Incremental Search AIFrontiers LaiOffer Self-Driving-Car-lecture 2: Incremental Search <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/zew9xwy9sexfswc0q1oa-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=120&height=120&fit=bounds" /> LaiOffer Self-Driving-Car-lecture 2: Incremental Search

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-lecture 2: Incremental Search from AI Frontiers

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0 Training at AI Frontiers 2018 - Udacity: Enhancing NLP with Deep Neural Networks /slideshow/training-at-ai-frontiers-2018-udacity-enhancing-nlp-with-deep-neural-networks/123543120 49dfwqp2qwskuq6g2w6s-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415
Instructor: Mat Leonard Outline 1. Text Processing Using Python + NLTK Cleaning Normalization Tokenization Part-of-speech Tagging Stemming and Lemmatization 2. Feature Extraction Bag of Words TF-IDF Word Embeddings Word2Vec GloVe 3. Topic Modeling Latent Variables Beta and Dirichlet Distributions Laten Dirichlet Allocation 4. NLP with Deep Learning Neural Networks Recurrent Neural Networks (RNNs) Word Embeddings Sentiment Analysis with RNNs]]>
Instructor: Mat Leonard Outline 1. Text Processing Using Python + NLTK Cleaning Normalization Tokenization Part-of-speech Tagging Stemming and Lemmatization 2. Feature Extraction Bag of Words TF-IDF Word Embeddings Word2Vec GloVe 3. Topic Modeling Latent Variables Beta and Dirichlet Distributions Laten Dirichlet Allocation 4. NLP with Deep Learning Neural Networks Recurrent Neural Networks (RNNs) Word Embeddings Sentiment Analysis with RNNs]]> Tue, 20 Nov 2018 18:14:15 GMT /slideshow/training-at-ai-frontiers-2018-udacity-enhancing-nlp-with-deep-neural-networks/123543120 AIFrontiers@slideshare.net(AIFrontiers) Training at AI Frontiers 2018 - Udacity: Enhancing NLP with Deep Neural Networks AIFrontiers Instructor: Mat Leonard Outline 1. Text Processing Using Python + NLTK Cleaning Normalization Tokenization Part-of-speech Tagging Stemming and Lemmatization 2. Feature Extraction Bag of Words TF-IDF Word Embeddings Word2Vec GloVe 3. Topic Modeling Latent Variables Beta and Dirichlet Distributions Laten Dirichlet Allocation 4. NLP with Deep Learning Neural Networks Recurrent Neural Networks (RNNs) Word Embeddings Sentiment Analysis with RNNs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/49dfwqp2qwskuq6g2w6s-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=120&height=120&fit=bounds" /> Instructor: Mat Leonard Outline 1. Text Processing Using Python + NLTK Cleaning Normalization Tokenization Part-of-speech Tagging Stemming and Lemmatization 2. Feature Extraction Bag of Words TF-IDF Word Embeddings Word2Vec GloVe 3. Topic Modeling Latent Variables Beta and Dirichlet Distributions Laten Dirichlet Allocation 4. NLP with Deep Learning Neural Networks Recurrent Neural Networks (RNNs) Word Embeddings Sentiment Analysis with RNNs

Training at AI Frontiers 2018 - Udacity: Enhancing NLP with Deep Neural Networks from AI Frontiers

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0 Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 3: Any-Angle Search /AIFrontiers/training-at-ai-frontiers-2018-laioffer-selfdrivingcarlecture-3-anyangle-search bwsi5kqdtie9gduwnwla-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415
LaiOffer Self-Driving-Car-Lecture 3: Any-Angle Search]]>
LaiOffer Self-Driving-Car-Lecture 3: Any-Angle Search]]> Tue, 20 Nov 2018 18:14:15 GMT /AIFrontiers/training-at-ai-frontiers-2018-laioffer-selfdrivingcarlecture-3-anyangle-search AIFrontiers@slideshare.net(AIFrontiers) Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 3: Any-Angle Search AIFrontiers LaiOffer Self-Driving-Car-Lecture 3: Any-Angle Search <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bwsi5kqdtie9gduwnwla-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=120&height=120&fit=bounds" /> LaiOffer Self-Driving-Car-Lecture 3: Any-Angle Search

Training at AI Frontiers 2018 - LaiOffer Self-Driving-Car-Lecture 3: Any-Angle Search from AI Frontiers

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0 Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning with Tensor2Tensor /slideshow/training-at-ai-frontiers-2018-lukasz-kaiser-sequence-to-sequence-learning-with-tensor2tensor/123543118 oorptajbsqwgurzwf2xe-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415
Sequence to sequence learning is a powerful way to train deep networks for machine translation, various NLP tasks, but also image generation and recently video and music generation. We will give a hands-on tutorial showing how to use the open-source Tensor2Tensor library to train state-of-the-art models for translation, image generation, and a task of your choice!]]>
Sequence to sequence learning is a powerful way to train deep networks for machine translation, various NLP tasks, but also image generation and recently video and music generation. We will give a hands-on tutorial showing how to use the open-source Tensor2Tensor library to train state-of-the-art models for translation, image generation, and a task of your choice!]]> Tue, 20 Nov 2018 18:14:15 GMT /slideshow/training-at-ai-frontiers-2018-lukasz-kaiser-sequence-to-sequence-learning-with-tensor2tensor/123543118 AIFrontiers@slideshare.net(AIFrontiers) Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning with Tensor2Tensor AIFrontiers Sequence to sequence learning is a powerful way to train deep networks for machine translation, various NLP tasks, but also image generation and recently video and music generation. We will give a hands-on tutorial showing how to use the open-source Tensor2Tensor library to train state-of-the-art models for translation, image generation, and a task of your choice! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oorptajbsqwgurzwf2xe-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=120&height=120&fit=bounds" /> Sequence to sequence learning is a powerful way to train deep networks for machine translation, various NLP tasks, but also image generation and recently video and music generation. We will give a hands-on tutorial showing how to use the open-source Tensor2Tensor library to train state-of-the-art models for translation, image generation, and a task of your choice!

Training at AI Frontiers 2018 - Lukasz Kaiser: Sequence to Sequence Learning with Tensor2Tensor from AI Frontiers

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0 Percy Liang at AI Frontiers : Pushing the Limits of Machine Learning /slideshow/percy-liang-at-ai-frontiers-pushing-the-limits-of-machine-learning-123121116/123121116 xula5u7esvqv7uddiiei-signature-2e11c9721bc6241cab50bbbb47f59bc34796f906f16714227a399f3c8aad7fd2-poli-181115182840
In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.]]>
In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.]]> Thu, 15 Nov 2018 18:28:40 GMT /slideshow/percy-liang-at-ai-frontiers-pushing-the-limits-of-machine-learning-123121116/123121116 AIFrontiers@slideshare.net(AIFrontiers) Percy Liang at AI Frontiers : Pushing the Limits of Machine Learning AIFrontiers In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/xula5u7esvqv7uddiiei-signature-2e11c9721bc6241cab50bbbb47f59bc34796f906f16714227a399f3c8aad7fd2-poli-181115182840-thumbnail.jpg?width=120&height=120&fit=bounds" /> In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.

Percy Liang at AI Frontiers : Pushing the Limits of Machine Learning from AI Frontiers

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0 Ilya Sutskever at AI Frontiers : Progress towards the OpenAI mission /slideshow/ilya-sutskever-at-ai-frontiers-progress-towards-the-openai-mission/123119210 02ilyasutskever900am-181115181026
I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.]]>
I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.]]> Thu, 15 Nov 2018 18:10:26 GMT /slideshow/ilya-sutskever-at-ai-frontiers-progress-towards-the-openai-mission/123119210 AIFrontiers@slideshare.net(AIFrontiers) Ilya Sutskever at AI Frontiers : Progress towards the OpenAI mission AIFrontiers I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/02ilyasutskever900am-181115181026-thumbnail.jpg?width=120&height=120&fit=bounds" /> I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.

Ilya Sutskever at AI Frontiers : Progress towards the OpenAI mission from AI Frontiers

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0 Mark Moore at AI Frontiers : Uber Elevate /slideshow/mark-moore-at-ai-frontiers-uber-elevate/122952381 05markmoore1100am-181114004654
Uber Elevate]]>
Uber Elevate]]> Wed, 14 Nov 2018 00:46:54 GMT /slideshow/mark-moore-at-ai-frontiers-uber-elevate/122952381 AIFrontiers@slideshare.net(AIFrontiers) Mark Moore at AI Frontiers : Uber Elevate AIFrontiers Uber Elevate <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/05markmoore1100am-181114004654-thumbnail.jpg?width=120&height=120&fit=bounds" /> Uber Elevate

Mark Moore at AI Frontiers : Uber Elevate from AI Frontiers

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0 Mario Munich at AI Frontiers : Consumer robotics: embedding affordable AI in everyday life /slideshow/mario-munich-at-ai-frontiers-consumer-robotics-embedding-affordable-ai-in-everyday-life/122951052 yhm46jaqqgq0skqgz1u8-signature-541f7bc80f791d2c7c934f156506dfe4a93736bc85ce263173ce286b82fbd57f-poli-181114000652
The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency.]]>
The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency.]]> Wed, 14 Nov 2018 00:06:52 GMT /slideshow/mario-munich-at-ai-frontiers-consumer-robotics-embedding-affordable-ai-in-everyday-life/122951052 AIFrontiers@slideshare.net(AIFrontiers) Mario Munich at AI Frontiers : Consumer robotics: embedding affordable AI in everyday life AIFrontiers The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/yhm46jaqqgq0skqgz1u8-signature-541f7bc80f791d2c7c934f156506dfe4a93736bc85ce263173ce286b82fbd57f-poli-181114000652-thumbnail.jpg?width=120&height=120&fit=bounds" /> The availability of affordable electronics components, powerful embedded microprocessors, and ubiquitous internet access and WiFi in the household has enabled a new generation of connected consumer robots. In 2015, iRobot launched the Roomba 980, introducing intelligent visual navigation to its successful line of vacuum cleaning robots. In 2018, iRobot launched the Roomba i7, equipped with the latest mapping and navigation technology that provides spatial information to the broader ecosystem of connected devices in the home. In this talk, I will describe the challenges and the potential of introducing consumer robots capable of developing spatial context by exploring the physical space of the home, and I will elaborate on the impact of AI in the future of robotics applications. Moreover, I will describe our vision of the Smart Home, an AI-powered home that maintains itself and magically just does the right thing in anticipation of occupant needs. This home will be built on an ecosystem of connected and coordinated robots, sensors, and devices that provides the occupants with a high quality of life by seamlessly responding to the needs of daily living – from comfort to convenience to security to efficiency.

Mario Munich at AI Frontiers : Consumer robotics: embedding affordable AI in everyday life from AI Frontiers

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0 Arnaud Thiercelin at AI Frontiers : AI in the Sky /slideshow/arnaud-thiercelin-at-ai-frontiers-ai-in-the-sky/122937782 04arnaudthiercelin1040am-181113193707
AI in the Sky]]>
AI in the Sky]]> Tue, 13 Nov 2018 19:37:07 GMT /slideshow/arnaud-thiercelin-at-ai-frontiers-ai-in-the-sky/122937782 AIFrontiers@slideshare.net(AIFrontiers) Arnaud Thiercelin at AI Frontiers : AI in the Sky AIFrontiers AI in the Sky <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/04arnaudthiercelin1040am-181113193707-thumbnail.jpg?width=120&height=120&fit=bounds" /> AI in the Sky

Arnaud Thiercelin at AI Frontiers : AI in the Sky from AI Frontiers

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0 Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimensional /slideshow/anima-anandkumar-at-ai-frontiers-largescale-machine-learning-deep-distributed-and-multidimensional/122930400 z1mhrcm8rq2mw6q5ceoj-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502
As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.]]>
As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.]]> Tue, 13 Nov 2018 18:25:02 GMT /slideshow/anima-anandkumar-at-ai-frontiers-largescale-machine-learning-deep-distributed-and-multidimensional/122930400 AIFrontiers@slideshare.net(AIFrontiers) Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimensional AIFrontiers As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/z1mhrcm8rq2mw6q5ceoj-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502-thumbnail.jpg?width=120&height=120&fit=bounds" /> As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.

Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimensional from AI Frontiers

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0 Wei Xu at AI Frontiers : Language Learning in an Interactive and Embodied Setting /slideshow/wei-xu-at-ai-frontiers-language-learning-in-an-interactive-and-embodied-setting/122930398 ydz8exc4tislwjoamwmo-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502
Language Learning in an Interactive and Embodied Setting]]>
Language Learning in an Interactive and Embodied Setting]]> Tue, 13 Nov 2018 18:25:02 GMT /slideshow/wei-xu-at-ai-frontiers-language-learning-in-an-interactive-and-embodied-setting/122930398 AIFrontiers@slideshare.net(AIFrontiers) Wei Xu at AI Frontiers : Language Learning in an Interactive and Embodied Setting AIFrontiers Language Learning in an Interactive and Embodied Setting <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ydz8exc4tislwjoamwmo-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502-thumbnail.jpg?width=120&height=120&fit=bounds" /> Language Learning in an Interactive and Embodied Setting

Wei Xu at AI Frontiers : Language Learning in an Interactive and Embodied Setting from AI Frontiers

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0 Sumit Gupta at AI Frontiers : AI for Enterprise /slideshow/sumit-gupta-at-ai-frontiers-ai-for-the-enterprise/122930397 rbyrbbmmtr2sotopagrd-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502
The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.]]>
The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.]]> Tue, 13 Nov 2018 18:25:02 GMT /slideshow/sumit-gupta-at-ai-frontiers-ai-for-the-enterprise/122930397 AIFrontiers@slideshare.net(AIFrontiers) Sumit Gupta at AI Frontiers : AI for Enterprise AIFrontiers The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rbyrbbmmtr2sotopagrd-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502-thumbnail.jpg?width=120&height=120&fit=bounds" /> The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.

Sumit Gupta at AI Frontiers : AI for Enterprise from AI Frontiers

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0 Yuandong Tian at AI Frontiers : Planning in Reinforcement Learning /slideshow/yuandong-tian-at-ai-frontiers-deep-reinforcement-learning-framework-for-games/122930396 cnqoteplrskohrh9ds3d-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502
Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.]]>
Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.]]> Tue, 13 Nov 2018 18:25:02 GMT /slideshow/yuandong-tian-at-ai-frontiers-deep-reinforcement-learning-framework-for-games/122930396 AIFrontiers@slideshare.net(AIFrontiers) Yuandong Tian at AI Frontiers : Planning in Reinforcement Learning AIFrontiers Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cnqoteplrskohrh9ds3d-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502-thumbnail.jpg?width=120&height=120&fit=bounds" /> Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.

Yuandong Tian at AI Frontiers : Planning in Reinforcement Learning from AI Frontiers

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0 Alex Ermolaev at AI Frontiers : Major Applications of AI in Healthcare /slideshow/alex-ermolaev-at-ai-frontiers-major-applications-of-ai-in-healthcare/122930395 j2wsqlhqrnkp1ucz2oja-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502
The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.]]>
The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.]]> Tue, 13 Nov 2018 18:25:02 GMT /slideshow/alex-ermolaev-at-ai-frontiers-major-applications-of-ai-in-healthcare/122930395 AIFrontiers@slideshare.net(AIFrontiers) Alex Ermolaev at AI Frontiers : Major Applications of AI in Healthcare AIFrontiers The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/j2wsqlhqrnkp1ucz2oja-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502-thumbnail.jpg?width=120&height=120&fit=bounds" /> The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient's medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.

Alex Ermolaev at AI Frontiers : Major Applications of AI in Healthcare from AI Frontiers

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0 Long Lin at AI Frontiers : AI in Gaming /slideshow/long-lin-at-ai-frontiers-ai-in-gaming/122930393 j1bgfaulrzcj04x7xctq-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502
Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.]]>
Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.]]> Tue, 13 Nov 2018 18:25:02 GMT /slideshow/long-lin-at-ai-frontiers-ai-in-gaming/122930393 AIFrontiers@slideshare.net(AIFrontiers) Long Lin at AI Frontiers : AI in Gaming AIFrontiers Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/j1bgfaulrzcj04x7xctq-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502-thumbnail.jpg?width=120&height=120&fit=bounds" /> Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.

Long Lin at AI Frontiers : AI in Gaming from AI Frontiers

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0 Melissa Goldman at AI Frontiers : AI & Finance /slideshow/melissa-goldman-at-ai-frontiers-morning-keynote/122930391 kv4nxbftdegasrs2ud1u-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502
AI in finance is having wide-ranging impact and solving some of the most critical societal problems. The talk gives overview of the opportunities of applying AI in finance with specific examples and highlights some of the unique challenges financial services firms face in deploying AI at scale.]]>
AI in finance is having wide-ranging impact and solving some of the most critical societal problems. The talk gives overview of the opportunities of applying AI in finance with specific examples and highlights some of the unique challenges financial services firms face in deploying AI at scale.]]> Tue, 13 Nov 2018 18:25:02 GMT /slideshow/melissa-goldman-at-ai-frontiers-morning-keynote/122930391 AIFrontiers@slideshare.net(AIFrontiers) Melissa Goldman at AI Frontiers : AI & Finance AIFrontiers AI in finance is having wide-ranging impact and solving some of the most critical societal problems. The talk gives overview of the opportunities of applying AI in finance with specific examples and highlights some of the unique challenges financial services firms face in deploying AI at scale. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kv4nxbftdegasrs2ud1u-signature-9648ab7f31af167b786b125c9fcc0d15ed1ebbdfe60a6a748ce8162c7370b676-poli-181113182502-thumbnail.jpg?width=120&height=120&fit=bounds" /> AI in finance is having wide-ranging impact and solving some of the most critical societal problems. The talk gives overview of the opportunities of applying AI in finance with specific examples and highlights some of the unique challenges financial services firms face in deploying AI at scale.

Melissa Goldman at AI Frontiers : AI & Finance from AI Frontiers

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0 https://cdn.slidesharecdn.com/profile-photo-AIFrontiers-48x48.jpg?cb=1542842965 AIFrontiers https://cdn.slidesharecdn.com/ss_thumbnails/04divyajain1025am-181130171657-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/divya-jain-at-ai-frontiers-video-summarization-124507998/124507998 Divya Jain at AI Front... https://cdn.slidesharecdn.com/ss_thumbnails/rgu7pbsptxsebt7frx30-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/training-at-ai-frontiers-2018-laioffer-data-session-how-spark-speedup-ai/123543124 Training at AI Frontie... https://cdn.slidesharecdn.com/ss_thumbnails/5fjo1ygvtxu4afb0yzzp-signature-2a1639f01782b3e7a09a16d6b2f7857e3275b3e3cd29747769f7c80c2b73c1f1-poli-181120181415-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/training-at-ai-frontiers-2018-laioffer-selfdrivingcarlecture-1-heuristic-search/123543123 Training at AI Frontie...