Low Latency in Shared Environments:
Interactive applications feel more responsive and provide a better user experience. Achieving low latency requires complex computation and coordination among numerous subsystems. Data centers are strategically placed to minimize the impact of the speed of light on latency. Shared environments, where multiple services and tasks run on the same machine, offer high utilization but can introduce variability and unpredictable effects. Variability is exacerbated by network congestion, background activities, and sudden bursts of foreground activities. Large fan-out systems, where a request is sent to many machines for processing, can significantly increase variability.

Variability in Shared Environments:
Variability can lead to unpredictable performance and delays in interactive applications. Shared environments can cause 1% of requests with long response times to impact a significant portion of overall requests. Good practices to reduce variability include isolating service jobs, prioritizing interactive tasks, and using techniques like tail latency budgets and request prioritization.

Learning High-Level Representations via Deep Learning:
Neural networks can learn high-level representations from data, enabling powerful applications like image recognition and natural language processing. Deep learning has shown remarkable performance in various domains, including computer vision, speech recognition, and machine translation. Transfer learning allows knowledge gained in one domain to be applied to other related domains, improving performance and reducing training time. Unsupervised learning techniques can extract useful features from unlabeled data, expanding the applicability of deep learning to a wider range of problems.

Prioritize Interactive Requests:
Prioritize interactive requests over non-interactive requests at the network level within the data center. Reduce head of line blocking by breaking large requests into smaller pieces and allowing smaller requests to jump in the middle.

Manage Expensive Background Activities:
Defer bursty activities, such as log compaction in a distributed storage system, until load is low.

Tolerate Variability in the System:
Use extra resources to make a predictable whole out of unpredictable parts, similar to fault tolerance. The time scale for dealing with variability is much shorter than for dealing with faults.

Latency Toleration Techniques:
There are multiple latency toleration techniques, with one being the focus of this talk. A CACM article in February outlined a few other techniques.

Cross-Request vs. Within-Request Adaptation:
Cross-Request Adaptation: Examines recent system behavior (load, CPU utilization, disk requests, etc.) Takes actions to improve future request latency on a timescale of tens of seconds or minutes Aims to balance load across the distributed system Within-Request Adaptation: Copes with slow requests while the user waits to get results faster Responds quickly and determines how to handle the situation

Backup Request with Cross-Server Cancellation Technique:
Sends a request to one replica and a backup request to a second replica The identity of the request and the other server are included in each request When the second server starts processing the request, it sends a message to the first server The first server can drop the request, deprioritize it, or continue processing it based on its resources The second server processes the request and sends a reply to the client This technique reduces the likelihood of both servers performing the request if they start processing it at roughly the same time The closer the servers are, the less likely this overlap will occur

Considerations:
The risk of both servers performing the request is reduced with faster networks and closer server proximity Handling a building fire within the building is not advisable The technique’s effectiveness is influenced by network speed and server proximity

High-Level Summary:
Cross-server cancellation can significantly reduce tail latency in distributed systems by sending requests to multiple replicas and canceling them if a faster response is received.

Latency Reduction in Idle Clusters:
In an idle cluster, cross-server cancellation can reduce the 99th percentile latency of high-level monitoring operations by 43%. The latency reduction is achieved with minimal additional disk reads, making it a cost-effective optimization.

Latency Reduction in Active Clusters:
Cross-server cancellation continues to provide latency reduction even when the cluster is running an intensive sorting job. The tail latency reduction remains significant, despite the increased load on the cluster.

Effective Trade-Off:
The latency reduction achieved through cross-server cancellation is a worthwhile trade-off for the additional disk reads it incurs.

Bonus Benefit:
Cross-server cancellation can effectively simulate a large batch cluster on an interactive cluster, providing similar read latencies at no additional cost.

Handling Server Failures:
In case of server failures during cross-server cancellation, the system can rely on existing mechanisms like TCP connection drops and timeouts to handle the situation.

High-Level Abstractions:
High-level abstractions can improve productivity by hiding the complexities of underlying systems. MapReduce is an example of a high-level abstraction that handles machine failures seamlessly.

Machine Learning Applications:
Machine learning applications often involve complex problems and large datasets. Statistical models can be used to solve these problems effectively.

Distinction between High-Level Systems and Applications:
High-level systems provide abstractions for building other software on top of them. Applications are built using these high-level systems and APIs.

The Scope of Innovation with Abstraction:
High levels of abstraction, with well-defined interfaces, can unlock innovation on both sides of the abstraction boundary. Implementations can be replaced and improved without affecting the interface’s specification, allowing for rapid progress.

Motivation for Learning from Raw Data:
Minimize software engineering effort in machine learning by eliminating the need for hand-engineered features. Data availability is not a limiting factor; there’s an abundance of data in various domains (text, visual, audio, user activity, Knowledge Graph).

Visual Tasks:
Object detection and localization in static images, despite variations in viewpoint, color, scale, and background. Reading house addresses from Street View imagery, handling a wide variety of fonts and organizations. OCR (Optical Character Recognition) of the world, associating text with locations and businesses. Extracting information like business hours and names from visual data.

Learning Common Visual Representations:
Develop a common visual representation useful for various visual tasks, beyond raw pixels. Build a system capable of handling multiple visual tasks and unsupervised training for general visual understanding.

Audio Tasks:
Speech recognition, understanding what is being said in audio waveforms, essential for voice queries and dictation.

Cross-Domain Representations:
Aim to build common representations across different domains (visual, audio, text). Generate textual labels for images and generate images from textual labels.

Utilizing Neural Networks:
Neural networks are employed to learn complex functions from data. Neural networks map input data (e.g., image pixels) to output labels (e.g., image content).

What are Neural Networks?:
Neural networks are inspired by the structure of human brains, consisting of interconnected neurons. Each neuron receives inputs, multiplies them with weights, and applies a non-linear function (typically max of zero) to generate an output. Neural networks are formed by connecting multiple neurons, allowing data to flow through weighted edges.

Learning in Neural Networks:
Learning involves modifying the weights of connections between neurons to minimize the error between the network’s predictions and desired outputs. Gradient descent is commonly used to optimize the weights, aiming to find the minimum value of the error function. However, finding the global minimum in complex functions with numerous parameters is challenging, and often results in settling in local minima.

Capabilities of Neural Networks:
Even small neural networks with two hidden layers can perform complex tasks, such as sorting numbers, multiplying numbers, and computing analytic functions. Deeper neural networks with 10 layers can achieve remarkable feats within a tenth of a second, including recognizing objects, understanding speech, and interpreting emotions.

Limitations and Conjectures:
The computational effort required by humans to perform these tasks is immense, yet their brains only process information through at most 10 layers due to the firing times of neurons. It is believed that any task a human can complete in 0.1 seconds can also be accomplished by a properly designed 10-layer neural network, provided it is large enough. There are examples of small organisms like lizards that possess perception, suggesting that the size of the network is not necessarily a limiting factor.

Data Requirements for Neural Networks:
The number of training examples should be roughly equivalent to the number of trainable connections in the network.

Training Time Considerations:
People generally prefer training times of no more than a few days or a week. Larger networks can be trained in the same amount of time by using faster neural network implementations.

Human Perception and Neural Networks:
Human perception is fast and can’t process more than 10 layers in 1/10th of a second. Neural networks require a large amount of training data, comparable to the amount of information humans accumulate through their visual system.

Essential Components for Neural Networks:
Big training sets with labeled data. Much faster computers for efficient training.

Factors Enabling Deep Learning:
Availability of larger datasets with labels. Faster computational resources.

Parallelization for Training Deep Networks:
Partitioning the network across multiple machines to increase computational resources. Local connections in the network minimize data transmission across machine boundaries.

Training Process:
Mini-batch, stochastic gradient descent is used for training. Data parallelism is employed to train different examples simultaneously. A centralized parameter service manages parameter updates.

Asynchronous Training:
Asynchronous training involves applying gradients to moved versions of the parameters. This approach works well for up to 10 to 100 replicas of the model.

Applications of Neural Networks:
Unsupervised learning with large datasets of unlabeled data. Example: Training a model on 10 million YouTube frames to reconstruct the input from learned representations.

Training Deep Neural Networks on Unlabeled Data:
Researchers trained a deep neural network on 10 million unlabeled images. After training, they analyzed the network’s highest layer and identified neurons that were most predictive of whether an image contained a face or not. One neuron was found to be specifically receptive to faces, demonstrating that the network had developed an internal concept of a face without ever being explicitly told what a face is.

Discovering Concepts in Unlabeled Data:
The same approach revealed another neuron that had developed a concept of a cat, highlighting the network’s ability to discover meaningful concepts from unlabeled data.

Impact of Hidden Units and Layers on Training Time:
Increasing the number of hidden units and layers generally leads to longer training times, with a slightly superlinear relationship.

Challenges in Determining Model Structure:
Currently, the number of hidden units per layer and the overall model structure are determined manually through trial and error. There is a need for methods that can automatically learn the optimal model structure.

Collaboration with the Speech Team:
The research team collaborated with the speech team to develop a deep neural acoustic model for processing small snippets of audio.

Introduction of Neural Acoustic Model:
Neural acoustic model aims to predict the phoneme being uttered in a given 10-millisecond interval of speech. It consists of fully connected layers with a softmax layer at the top for phoneme prediction.

Significant Improvement in English Speech Recognition:
Replacing the previous Gaussian mixture-based model with the neural acoustic model resulted in a 30% reduction in word error rate for English.

Transfer Learning for Multiple Languages:
To address the lack of label data for languages other than English, transfer learning is employed. The model is initially trained on English data, then fine-tuned for other languages, sharing the lower layers. This approach improves the performance for the target languages and slightly enhances English performance.

Shared Representation across Multiple Languages:
The lower layers of the model capture features common to multiple languages, such as those dictated by the human vocal tract. This shared representation benefits the performance of all languages trained jointly.

Modular Design for Adding New Languages:
The modular design allows for easy addition of new languages to the system by starting with the shared representation.

Benefits of Multiple Layers at the Top:
Experiments showed that having two layers at the top slightly improves performance compared to one layer.

Convolutional Neural Networks:
Developed by Alex Kucheski, Ilya Sutskever, and Jeffrey Hinton at the University of Toronto. Utilizes supervised training with labeled images. Employs convolutional layers to keep the same weights within a layer and apply them at different locations. Reduces the number of parameters in the model while maintaining computational efficiency. Allows for the creation of large models with fewer weights.

Fully Connected Layers and Softmax:
Fully connected layers are used at the top of the network. Softmax function is employed to predict which of a thousand different object classes something belongs to.

Applications of Convolutional Neural Networks:
Image classification: Google Plus photo search: Enables searching for photos based on object labels, even if they haven’t been explicitly labeled. Macrame Yoda detection: Demonstrates the ability of the model to recognize specific objects, such as a macrame Yoda, within a broader category of objects. Specialized tasks: Text detection in street scenes: The model can highlight text in street scenes, even with varying fonts and colors. The model can find text at different scales, as long as the training data represents various text sizes.

Efficiency of Convolutional Neural Networks:
The time taken for finding text using the model can be parallelized, reducing the processing time. The model works equally well on scaled images because the training data includes text at different scales.

Introduction:
Neural networks excel at processing dense numeric representations, but transforming words into a suitable format is crucial.

Embedding Vectors:
Each word is represented by a high-dimensional vector in embedding space. Words with similar meanings are positioned close together in this space. Models are trained to predict nearby words, refining the embedding vectors.

Properties of Embedding Spaces:
Linear arithmetic on embedding vectors yields meaningful results. Analogies, country capitals, and antonyms can be solved using vector operations.

Visualization of Embedding Spaces:
Projecting high-dimensional vectors to two dimensions reveals structure and semantic relations. Countries and their capitals follow a consistent directional pattern. Semantic relatedness is also evident in the vertical arrangement of vectors.

Conclusion:
Deep networks trained on raw data can learn high-level representations for various tasks. Automatically constructing models from raw data eliminates the need for manual feature engineering. Neural networks learn the necessary abstractions for accurate recognition and discrimination.

Questions and Answers:

Q: What is the training data for high-dimensional embedding vectors?
A: Text from news articles.

Q: How does the skip-gram model work?
A: It uses a single word to predict nearby words, with nearer ones being more heavily weighted in the training sample.

Q: Does the model include parts of speech?
A: No, but the embedding space makes it easy to train a model that can identify the part of speech of a given word.

Q: Has the speech recognition example been tested for non-Latin, non-European languages?
A: No, the results in the paper were for 10 European languages.

Q: Are there plans to combine analog and digital data?
A: Not currently.

Q: Could a smaller model be used for a simpler classification task?
A: Yes.

Q: Have cascaded models been experimented with?
A: Yes, but they are finicky and often it is better to train a single model.

Q: Which systems use backpropagation?
A: All the neural nets described in the presentation.

Q: Why not use unsupervised learning?
A: There are many labeled data sets available, and it is often better to train directly on those if you have enough data.

Key Points:
Unsupervised Training: When labeled data is unavailable for intermediate layers, features are discovered independently. Lower layer features resemble edge detectors, while higher layers detect more complex features like ears and noses. Bigger Data Sets and Bigger Networks: Larger data sets and bigger networks lead to improved performance on various tasks. Increased data size allows for better generalization and robustness. Human Visual Perception and Model Efficiency: Humans efficiently process visual data by capturing salient high-level features, which is similar to how deep learning models operate. Models focus on the most significant aspects of an image rather than capturing every detail. Convolutional Models for Translation, Rotation, and Scaling: Convolutional models handle translation well due to the shifting of activations in response to object movement. Rotation tolerance is achieved through regularization and data augmentation techniques, but models struggle with complete rotations. Parallelization and Stochastic Gradient Descent: Deep learning algorithms are not fully parallelizable due to the sequential nature of stochastic gradient descent. Asynchronous model replicas can mitigate sequentialness to some extent. GPUs and CPUs can be utilized for parallel computation within a single model. Financial Data and Programming Language Choice: Deep learning is being applied to financial data in various companies. C++ is commonly used for deep learning due to its efficiency and compatibility with existing infrastructure. Impact of Example Order on Model Performance: Changing the order of training examples generally affects the local minimum reached by the model. The actual output performance may be similar, but the model’s internal parameters will differ. Combining Knowledge Graph with Image Models: Mapping object labels to knowledge graph nodes allows for the integration of knowledge graph information into image models. The Knowledge Graph incorporates Freebase entries, enabling the use of Freebase data in deep learning tasks.