Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using #DeepReinforcementLearning
Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers'
Link
🔭 @DeepGravity
Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers'
Link
🔭 @DeepGravity
arXiv.org
Which Channel to Ask My Question? Personalized Customer Service...
Customer services are critical to all companies, as they may directly connect
to the brand reputation. Due to a great number of customers, e-commerce
companies often employ multiple communication...
to the brand reputation. Due to a great number of customers, e-commerce
companies often employ multiple communication...
Introducing #Google Research Football: A Novel #ReinforcementLearning Environment
The goal of reinforcement learning (RL) is to train smart agents that can interact with their environment and solve complex tasks, with real-world applications towards robotics, self-driving cars, and more. The rapid progress in this field has been fueled by making agents play games such as the iconic Atari console games, the ancient game of Go, or professionally played video games like Dota 2 or Starcraft 2, all of which provide challenging environments where new algorithms and ideas can be quickly tested in a safe and reproducible manner. The game of football is particularly challenging for RL, as it requires a natural balance between short-term control, learned concepts, such as passing, and high level strategy.
Link
🔭 @DeepGravity
The goal of reinforcement learning (RL) is to train smart agents that can interact with their environment and solve complex tasks, with real-world applications towards robotics, self-driving cars, and more. The rapid progress in this field has been fueled by making agents play games such as the iconic Atari console games, the ancient game of Go, or professionally played video games like Dota 2 or Starcraft 2, all of which provide challenging environments where new algorithms and ideas can be quickly tested in a safe and reproducible manner. The game of football is particularly challenging for RL, as it requires a natural balance between short-term control, learned concepts, such as passing, and high level strategy.
Link
🔭 @DeepGravity
research.google
Introducing Google Research Football: A Novel Reinforcement Learning Environment
Posted by Karol Kurach, Research Lead and Olivier Bachem, Research Scientist, Google Research, Zürich The goal of reinforcement learning (RL) is ...
Deep Gravity
#DeepFovea: #Neural Reconstruction for Foveated Rendering and Video Compression using Learned #Statistics of Natural Videos Link to the paper #FacebookAI 🔭 @DeepGravity
#DeepFovea: Neural Reconstruction for Foveated Rendering and Video Compression using Learned Statistics of Natural Videos
In order to provide an immersive visual experience, modern displays require head mounting, high image resolution, low latency, as well as high refresh rate. This poses a challenging computational problem. On the other hand, the human visual system can consume only a tiny fraction of this video stream due to the drastic acuity loss in the peripheral vision. Foveated rendering and compression can save computations by reducing the image quality in the peripheral vision. However, this can cause noticeable artifacts in the periphery, or, if done conservatively, would provide only modest savings. In this work, we explore a novel foveated reconstruction method that employs the recent advances in generative adversarial neural networks. We reconstruct a plausible peripheral video from a small fraction of pixels provided every frame. The reconstruction is done by finding the closest matching video to this sparse input stream of pixels on the learned manifold of natural videos. Our method is more efficient than the state-of-the-art foveated rendering, while providing the visual experience with no noticeable quality degradation. We conducted a user study to validate our reconstruction method and compare it against existing foveated rendering and video compression techniques. Our method is fast enough to drive gaze-contingent head-mounted displays in real time on modern hardware. We plan to publish the trained network to establish a new quality bar for foveated rendering and compression as well as encourage follow-up research.
Link
#Facebook
🔭 @DeepGravity
In order to provide an immersive visual experience, modern displays require head mounting, high image resolution, low latency, as well as high refresh rate. This poses a challenging computational problem. On the other hand, the human visual system can consume only a tiny fraction of this video stream due to the drastic acuity loss in the peripheral vision. Foveated rendering and compression can save computations by reducing the image quality in the peripheral vision. However, this can cause noticeable artifacts in the periphery, or, if done conservatively, would provide only modest savings. In this work, we explore a novel foveated reconstruction method that employs the recent advances in generative adversarial neural networks. We reconstruct a plausible peripheral video from a small fraction of pixels provided every frame. The reconstruction is done by finding the closest matching video to this sparse input stream of pixels on the learned manifold of natural videos. Our method is more efficient than the state-of-the-art foveated rendering, while providing the visual experience with no noticeable quality degradation. We conducted a user study to validate our reconstruction method and compare it against existing foveated rendering and video compression techniques. Our method is fast enough to drive gaze-contingent head-mounted displays in real time on modern hardware. We plan to publish the trained network to establish a new quality bar for foveated rendering and compression as well as encourage follow-up research.
Link
🔭 @DeepGravity
Facebook Research
DeepFovea: Neural Reconstruction for Foveated Rendering and Video Compression using Learned Statistics of Natural Videos
Foveated rendering and compression can save computations by reducing the image quality in the peripheral vision. However, this can cause noticeable artifacts in the periphery, or, if done conservatively, would provide only modest savings. In this work, we…
Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with #Deep Feature Maps
3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging #data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches object portions using an extended search. Experimental results confirm that the proposed method achieves 2.96 consistent tracking accuracy and 35.48 than the state-of-art methods.
Link
🔭 @DeepGravity
3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging #data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches object portions using an extended search. Experimental results confirm that the proposed method achieves 2.96 consistent tracking accuracy and 35.48 than the state-of-art methods.
Link
🔭 @DeepGravity
The Mind at Work: Guido van #Rossum on how #Python makes thinking in code easier
A conversation with the creator of the world’s most popular programming language on removing brain friction for better work
“You primarily write your code to communicate with other coders, and, to a lesser extent, to impose your will on the computer.”
—Guido van Rossum
Link
🔭 @DeepGravity
A conversation with the creator of the world’s most popular programming language on removing brain friction for better work
“You primarily write your code to communicate with other coders, and, to a lesser extent, to impose your will on the computer.”
—Guido van Rossum
Link
🔭 @DeepGravity
Dropbox
The Mind at Work: Guido van Rossum on how Python makes thinking in code easier
A conversation with the creator of the world’s most popular programming language on removing brain friction for better work.
A massive new #blackhole discovered in #MilkyWay !
Astronomers in China made a surprising discovery: a massive black hole in our galaxy. Called LB-1, this one is about 70 times the mass of the sun. It's the first time a black hole of this size has been detected in the Milky Way.
Link CNN
YouTube
🔭 @DeepGravity
Astronomers in China made a surprising discovery: a massive black hole in our galaxy. Called LB-1, this one is about 70 times the mass of the sun. It's the first time a black hole of this size has been detected in the Milky Way.
Link CNN
YouTube
🔭 @DeepGravity
CNN
Scientists find ‘monster’ black hole so big it shouldn’t exist | CNN
Scientists have discovered the black hole LB-1, so massive that it shouldn’t exist.
A #DeepLearning framework for #neuroscience
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, #cognitive and motor tasks. Conversely, #ArtificialIntelligence attempts to design computational systems based on the tasks they will have to solve. In artificial #NeuralNetworks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of #deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
Link
🔭 @DeepGravity
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, #cognitive and motor tasks. Conversely, #ArtificialIntelligence attempts to design computational systems based on the tasks they will have to solve. In artificial #NeuralNetworks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of #deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
Link
🔭 @DeepGravity
Nature Neuroscience
A deep learning framework for neuroscience
A deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation. Richards et al. argue that this inspires fruitful approaches to systems neuroscience.
The Top 10 #Books on #AI recommended by #ElonMusk and #BillGates
1 Superintelligence: Paths, Dangers, Strategies
2 The Singularity Is Near: When Humans Transcend Biology
3 Life 3.0: Being Human in the Age of Artificial Intelligence
4 Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World
5 The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
6 Machine, Platform, Crowd: Harnessing Our Digital Future
7 AI Superpowers: China, Silicon Valley, and the New World Order
8 The Sentient Machine: The Coming Age of Artificial Intelligence
9 Our Final Invention: Artificial Intelligence and the End of the Human Era
10 Army of None: Autonomous Weapons and the Future of War
Link
🔭 @DeepGravity
1 Superintelligence: Paths, Dangers, Strategies
2 The Singularity Is Near: When Humans Transcend Biology
3 Life 3.0: Being Human in the Age of Artificial Intelligence
4 Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World
5 The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
6 Machine, Platform, Crowd: Harnessing Our Digital Future
7 AI Superpowers: China, Silicon Valley, and the New World Order
8 The Sentient Machine: The Coming Age of Artificial Intelligence
9 Our Final Invention: Artificial Intelligence and the End of the Human Era
10 Army of None: Autonomous Weapons and the Future of War
Link
🔭 @DeepGravity
Medium
The Top 10 Books on AI recommended by Elon Musk and Bill Gates
The best books on AI for leaders in the new Age, recommended by the likes of Elon Musk, Bill Gates, Eric Schmidt, Reid Hoffman, Mark…
3 Main Approaches to #MachineLearning Models
Machine learning encompasses a vast set of conceptual approaches. We classify the three main #algorithmic methods based on #mathematical foundations to guide your exploration for developing models.
Link
🔭 @DeepGravity
Machine learning encompasses a vast set of conceptual approaches. We classify the three main #algorithmic methods based on #mathematical foundations to guide your exploration for developing models.
Link
🔭 @DeepGravity
KDnuggets
3 Main Approaches to Machine Learning Models - KDnuggets
Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.
François #Chollet is the creator of #Keras, which is an open source #DeepLearning library that is designed to enable fast, user-friendly experimentation with #deepNeuralNetworks. It serves as an interface to several deep learning libraries, most popular of which is #TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class #AI researcher and software engineer at #Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of #ArtificialIntelligence. This conversation is part of the Artificial Intelligence podcast.
Link
🔭 @DeepGravity
Link
🔭 @DeepGravity
YouTube
François Chollet: Keras, Deep Learning, and the Progress of AI | Lex Fridman Podcast #38
Google’s new ‘#ExplainableAI” (#xAI) service
#Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction.
Link
🔭 @DeepGravity
#Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction.
Link
🔭 @DeepGravity
Medium
Google’s new ‘Explainable AI” (xAI) service
Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. We take a look at the intent.
Gilbert Strang: #DeepLearning and #NeuralNetworks
Part of Lex Fridman conversation with Gilbert Strang
Gilbert Strang is a professor of mathematics at #MIT and perhaps one of the most famous and impactful teachers of #math in the world. His MIT OpenCourseWare lectures on linear algebra have been viewed millions of times.
🔭 @DeepGravity
Part of Lex Fridman conversation with Gilbert Strang
Gilbert Strang is a professor of mathematics at #MIT and perhaps one of the most famous and impactful teachers of #math in the world. His MIT OpenCourseWare lectures on linear algebra have been viewed millions of times.
🔭 @DeepGravity
YouTube
Gilbert Strang: Deep Learning and Neural Networks
Full episode with Gilbert Strang (Nov 2019): https://www.youtube.com/watch?v=lEZPfmGCEk0
Subscribe to this channel if you like clips and to the main channel if you like full length episodes: https://www.youtube.com/lexfridman
(more links below)
Podcast…
Subscribe to this channel if you like clips and to the main channel if you like full length episodes: https://www.youtube.com/lexfridman
(more links below)
Podcast…
#Meta #TransferLearning for factorizing representations and knowledge for #AI - Yoshua #Bengio
Abstract:
Whereas #MachineLearning theory has focused on generalization to examples from the same distribution as the training data, better understanding of the transfer scenarios where the observed distribution changes often in the lifetime of the learning agent is important, both for robust deployment and to achieve a more powerful form of generalization which humans seem able to enjoy and which seem necessary for learning agents. Whereas most machine learning algorithms and architectures can be traced back to assumptions about the training distributions, we also need to explore assumptions about how the observed distribution changes. We propose that sparsity of change in distribution, when knowledge is represented appropriately, is a good assumption for this purpose, and we claim that if that assumption is verified and knowledge represented appropriately, it leads to fast adaptation to changes in distribution, and thus that the speed of adaptation to changes in distribution can be used as a meta-objective which can drive the discovery of knowledge representation compatible with that assumption. We illustrate these ideas in causal discovery: is some variable a direct cause of another? and how to map raw data to a representation space where different dimensions correspond to causal variables for which a clear causal relationship exists? We propose a large research program in which this non-stationarity assumption and meta-transfer objective is combined with other closely related assumptions about the world embodied in a world model, such as the consciousness prior (the causal graph is captured by a sparse factor graph) and the assumption that the causal variables are often those agents can act upon (the independently controllable factors prior), both of which should be useful for agents which plan, imagine and try to find explanations for what they observe.
Lecture
🔭 @DeepGravity
Abstract:
Whereas #MachineLearning theory has focused on generalization to examples from the same distribution as the training data, better understanding of the transfer scenarios where the observed distribution changes often in the lifetime of the learning agent is important, both for robust deployment and to achieve a more powerful form of generalization which humans seem able to enjoy and which seem necessary for learning agents. Whereas most machine learning algorithms and architectures can be traced back to assumptions about the training distributions, we also need to explore assumptions about how the observed distribution changes. We propose that sparsity of change in distribution, when knowledge is represented appropriately, is a good assumption for this purpose, and we claim that if that assumption is verified and knowledge represented appropriately, it leads to fast adaptation to changes in distribution, and thus that the speed of adaptation to changes in distribution can be used as a meta-objective which can drive the discovery of knowledge representation compatible with that assumption. We illustrate these ideas in causal discovery: is some variable a direct cause of another? and how to map raw data to a representation space where different dimensions correspond to causal variables for which a clear causal relationship exists? We propose a large research program in which this non-stationarity assumption and meta-transfer objective is combined with other closely related assumptions about the world embodied in a world model, such as the consciousness prior (the causal graph is captured by a sparse factor graph) and the assumption that the causal variables are often those agents can act upon (the independently controllable factors prior), both of which should be useful for agents which plan, imagine and try to find explanations for what they observe.
Lecture
🔭 @DeepGravity
YouTube
Meta transfer learning for factorizing representations and knowledge for AI - Yoshua Bengio
Speaker: Yoshua Bengio Title: Meta transfer learning for factorizing representations and knowledge for AI Abstract: Whereas machine learning theory has focus...
#NeuralNetworks: Feedforward and #Backpropagation Explained & Optimization
What is neural networks? Developers should understand backpropagation, to figure out why their code sometimes does not work. Visual and down to earth explanation of the math of backpropagation.
Link
🔭 @DeepGravity
What is neural networks? Developers should understand backpropagation, to figure out why their code sometimes does not work. Visual and down to earth explanation of the math of backpropagation.
Link
🔭 @DeepGravity
Machine Learning From Scratch
Neural Networks: Feedforward and Backpropagation Explained
What is neural networks? Developers should understand backpropagation, to figure out why their code sometimes does not work. Visual and down to earth explanation of the math of backpropagation.
Semantic Segmentation of Thigh Muscle using 2.5D #DeepLearning Network Trained with Limited Datasets
Purpose: We propose a 2.5D #deep learning #NeuralNetwork (#DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets. Enables operator invariant quantitative assessment of the thigh muscle volume change with respect to the disease progression. Materials and methods: Retrospective datasets consist of 48 thigh volume (TV) cropped from CT DICOM images. Cropped volumes were aligned with femur axis and resample in 2 mm voxel-spacing. Proposed 2.5D DLNN consists of three 2D U-Net trained with axial, coronal and sagittal muscle slices respectively. A voting algorithm was used to combine the output of U-Nets to create final segmentation. 2.5D U-Net was trained on PC with 38 TV and the remaining 10 TV were used to evaluate segmentation accuracy of 10 classes within Thigh. The result segmentation of both left and right thigh were de-cropped to original CT volume space. Finally, segmentation accuracies were compared between proposed DLNN and 2D/3D U-Net. Results: Average segmentation DSC score accuracy of all classes with 2.5D U-Net as 91.18 mean DSC score for 2D U-Net was 3.3 DSC score of 3D U-Net was 5.7 same datasets. Conclusion: We achieved a faster computationally efficient and automatic segmentation of thigh muscle into 11 classes with reasonable accuracy. Enables quantitative evaluation of muscle atrophy with disease progression.
Link
🔭 @DeepGravity
Purpose: We propose a 2.5D #deep learning #NeuralNetwork (#DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets. Enables operator invariant quantitative assessment of the thigh muscle volume change with respect to the disease progression. Materials and methods: Retrospective datasets consist of 48 thigh volume (TV) cropped from CT DICOM images. Cropped volumes were aligned with femur axis and resample in 2 mm voxel-spacing. Proposed 2.5D DLNN consists of three 2D U-Net trained with axial, coronal and sagittal muscle slices respectively. A voting algorithm was used to combine the output of U-Nets to create final segmentation. 2.5D U-Net was trained on PC with 38 TV and the remaining 10 TV were used to evaluate segmentation accuracy of 10 classes within Thigh. The result segmentation of both left and right thigh were de-cropped to original CT volume space. Finally, segmentation accuracies were compared between proposed DLNN and 2D/3D U-Net. Results: Average segmentation DSC score accuracy of all classes with 2.5D U-Net as 91.18 mean DSC score for 2D U-Net was 3.3 DSC score of 3D U-Net was 5.7 same datasets. Conclusion: We achieved a faster computationally efficient and automatic segmentation of thigh muscle into 11 classes with reasonable accuracy. Enables quantitative evaluation of muscle atrophy with disease progression.
Link
🔭 @DeepGravity