algorithm

Ethical questions in software engineering

Ethical questions in software engineering

March 3, 2020

Software is changing the world, and software developers need to open their eyes to the link between ethics and software. Rotem Hermon outlines some examples of ethical questions involving software and algorithms. You'll explore technology, sense of self, politics, and truth, and you'll try to understand what you can do about it.
Autoscaling in reality: Lessons learned from adaptively scaling Kubernetes

Autoscaling in reality: Lessons learned from adaptively scaling Kubernetes

February 29, 2020

Andy Kwiatkowski takes a deep dive into how Shopify saved a million dollars a year in infrastructure costs by rolling its own autoscaler.
EasyAgents: Reinforcement Learning for people who want to solve real-world problems

EasyAgents: Reinforcement Learning for people who want to solve real-world problems

February 24, 2020

Reinforce Learning can be a game changer when you do not have training data, but are instead able to simulate an environment. Unfortunately, the theory of Reinforcement Learning is complex and the vast number of algorithms in that area adds to the burden for getting started. Easyagents takes some of the burden by making it a one-liner to run a Reinforcement Learning algorithm on your problem.
A Few Effective gcc/clang Optimizations for Embedded Systems

A Few Effective gcc/clang Optimizations for Embedded Systems

February 23, 2020

Compilers toolchains are at the core of the building the Embedded Linux Systems, it affects the full system from bootloaders, kernels to applications and platform software, compilers are software too, …
HARP: An efficient and elastic GPU-sharing system

HARP: An efficient and elastic GPU-sharing system

February 23, 2020

Pengfei Fan and Lingling Jin offer an overview of an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow.
Machine learning over real-time streaming data with TensorFlow

Machine learning over real-time streaming data with TensorFlow

February 23, 2020

In many applications where data is generated continuously, combining machine learning with streaming data is imperative to discover useful information in real time. Yong Tang explores TensorFlow I/O, which can be used to easily build a data pipeline with TensorFlow and stream frameworks such as Apache Kafka, AWS Kinesis, or Google Cloud PubSub.
Modular convolution considered beneficial

Modular convolution considered beneficial

February 23, 2020

Jack Chung, Chao Liu, and Daniel Lowell explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as accelerated linear algebra (XLA).
Unleashing SR Traffic Engineering capabilities with SR Flexible Algorithms

Unleashing SR Traffic Engineering capabilities with SR Flexible Algorithms

February 23, 2020

Have you ever wished you could tailor IGP computation to fit your own traffic engineering needs? Such as by computing: Paths considering a subset of the routers in …
About Space Invaders and automated scaling

About Space Invaders and automated scaling

February 22, 2020

Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.
Anomaly detection using deep learning to measure the quality of large datasets

Anomaly detection using deep learning to measure the quality of large datasets

February 22, 2020

Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data.
AI for financial time series forecasting and dynamic assets portfolio optimization

AI for financial time series forecasting and dynamic assets portfolio optimization

February 21, 2020

Real business usage of most advanced methods for financial time series forecasting (based on winning methods from M4 competition) and assets portfolio optimization (based on Monte Carlo Tree Search with neural networks - Alpha Zero approach). Complete investments platform with the AI workflow and real time integration with the brokers. Real usage demo.
Audience projection of target consumers over multiple domains: A NER and Bayesian approach

Audience projection of target consumers over multiple domains: A NER and Bayesian approach

February 21, 2020

AI-powered market research is performed by indirect approaches based on sparse and implicit consumer feedback (e.g., social network interactions, web browsing, or online purchases). These approaches are more scalable, authentic, and suitable for real-time consumer insights. Gianmario Spacagna proposes a novel algorithm of audience projection able to provide consumer insights over multiple domains.
Deep learning with Horovod and Spark using GPUs and Docker containers

Deep learning with Horovod and Spark using GPUs and Docker containers

February 20, 2020

Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan demonstrates the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.
Executive Briefing: A look at the future of online pricing and algorithm-led collusion

Executive Briefing: A look at the future of online pricing and algorithm-led collusion

February 20, 2020

In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like.
Executive Briefing: Fusing data and design

Executive Briefing: Fusing data and design

February 19, 2020

Data scientists feel naturally comfortable with the language of mathematics, while designers think in the language of human empathy. Creating a bridge between the two was essential to the success of a recent project at an energy company. Tim Daines and Philip Pilgerstorfer detail what they learned while creating these bridges, showcasing techniques through a series of aha moments.
Large-scale machine learning at Facebook: Implications of platform design on developer productivity

Large-scale machine learning at Facebook: Implications of platform design on developer productivity

February 19, 2020

AI plays a key role in achieving Facebook's mission of connecting people and building communities. Nearly every visible product is powered by machine learning algorithms at its core, from delivering relevant content to making the platform safe. Kim Hazelwood and Mohamed Fawzy explain how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook.
Predicting the quality of life from satellite imagery

Predicting the quality of life from satellite imagery

February 18, 2020

In many countries, policy decisions are disconnected from data, and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data.
Real-time AI for entity resolution

Real-time AI for entity resolution

February 18, 2020

Entity resolutiondetermining who is who and who is related to whomis essential to almost every industry, including banking, insurance, healthcare, marketing, telecommunications, social services, and more. Jeff Jonas details how you can use a purpose-built real-time AI, created for general-purpose entity resolution, to gain new insights and make better decisions faster.
Scalable AI and reinforcement learning with Ray

Scalable AI and reinforcement learning with Ray

February 18, 2020

Edward Oakes, Peter Schafhalter, and Kristian Hartikainen take a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at RISELab, and explore Rays API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms.
Democratization of data science: Using machine learning to build credit risk models

Democratization of data science: Using machine learning to build credit risk models

February 14, 2020

Tokyo Century was ready for a change. Credit risk decisions were taking too long and the home office was taking notice. The company needed a full stack data solution to increase the speed of loan authorizations, and it needed it quickly. Moto Tohda explains how Tokyo Century put data at the center of its credit risk decision making and removed institutional knowledge from the process.
Learning with limited labeled data

Learning with limited labeled data

February 12, 2020

Supervised machine learning requires large labeled datasetsa prohibitive limitation in many real world applications. But this could be avoided if machines could earn with a few labeled examples. Shioulin Sam explores and demonstrates an algorithmic solution that relies on collaboration between human and machine to label smartly, and she outlines product possibilities.
Online machine learning in streaming applications

Online machine learning in streaming applications

February 11, 2020

Stavros Kontopoulos and Debasish Ghosh explore online machine learning algorithm choices for streaming applications, especially those with resource-constrained use cases like IoT and personalization. They dive into Hoeffding Adaptive Trees, classic sketch data structures, and drift detection algorithms from implementation to production deployment, describing the pros and cons of each of them.
Scalable anomaly detection with Spark and SOS

Scalable anomaly detection with Spark and SOS

February 10, 2020

Jeroen Janssens dives into stochastic outlier section (SOS), an unsupervised algorithm for detecting anomalies in large, high-dimensional data. SOS has been implemented in Python, R, and, most recently, Spark. He illustrates the idea and intuition behind SOS, demonstrates the implementation of SOS on top of Spark, and applies SOS to a real-world use case.
The future of stablecoin

The future of stablecoin

February 9, 2020

With the emergence of cryptoeconomy, there is a real demand for an alternative form of money. Major cryptocurrencies such as Bitcoin and Ethereum have thus far failed to achieve mass adoption. Catherine Gu examines the paradigm of algorithmic design of stablecoins, focusing on incentive structure and decentralized governance, to evaluate the role of stablecoin as a future medium of exchange.
Wear Estimation for Devices with eMMC Flash Memory

Wear Estimation for Devices with eMMC Flash Memory

February 8, 2020

One of the most common persistent memory technology in today's embedded Linux devices is eMMC flash memory.The raw NAND flash used inside the eMMC chips has a limited number of write cycles. To get a …
AI for ophthalmology: Doing what doctors cant (sponsored by Dell Technologies)

AI for ophthalmology: Doing what doctors cant (sponsored by Dell Technologies)

February 7, 2020

The emphasis in AI is on replicating human performance. Examples abound: ImageNet, self-driving cars, etc. Its the same in medicine. Daniel Russakoff explains how Voxeleron LLC is working on whats nextAI algorithms that do things that humans cant, such as the prediction of age-related macular degeneration (AMD) progression, critical to successful treatment of this leading cause of vision loss.
Data science + design thinking: A perfect blend to achieve the best user experience

Data science + design thinking: A perfect blend to achieve the best user experience

February 6, 2020

Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products.
Mozart in the box: Interacting with AI tools for music creation

Mozart in the box: Interacting with AI tools for music creation

February 4, 2020

Alessandro Palladini explores the role of experts and creatives in a world dominated by intelligent machines by bridging the gap between the research on complex systems and tools for creativity, examining what he believes to be the key design principles and perspectives on making intelligent tools for creativity and for experts in the loop.