How Blockchain Can Save The Climate – The Poseidon Foundation

How Blockchain Can Save The Climate – The Poseidon Foundation

This article is sponsored by The Poseidon Foundation.

Let’s assume it took you about two seconds to read the above headline. You’re hopefully interested in reading on, but otherwise, I’m sure it feels like little else has happened. Well in those two seconds alone an area of forest nearly as large as a soccer pitch has been cut down worldwide, which equates to 18.7 million acres of forests being lost annually.

Protecting forests is vital - because when it comes to stopping climate change, we’re running out of time. The 2015 Paris Agreement set the target of limiting global warming to less than 1.5°C by 2100, but by today’s emission levels, we will have already exhausted the carbon budget to reach this target by 2027. The need to drastically limit greenhouse gas emission is clear and preventing deforestation is the easiest natural solution known to man.

Forests act as carbon sinks, absorbing carbon dioxide from the atmosphere, so protecting trees works to clean the air we breathe. However, when these trees are cut down and burnt, all the carbon they have stored over hundreds of years is released back into the atmosphere – making the problem even worse. Preventing deforestation also acts to ...


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Artificial Intelligence (AI) Helps with Skin Cancer Screening

Artificial Intelligence (AI) Helps with Skin Cancer Screening

This advertorial is sponsored by Intel®.

"The long-term goal and true potential of AI is to replicate the complexity of human thinking at the macro level, and then surpass it to solve complex problems—problems both well-documented and currently unimaginable in nature."1

Challenge

Skin cancer has reached epidemic proportions in much of the world. A simple test is needed to perform initial screening on a wide scale to encourage individuals to seek treatment when necessary.

Solution

Doctor Hazel, a skin cancer screening service powered by artificial intelligence (AI) that operates in real time, relies on an extensive library of images to distinguish between skin cancer and benign lesions, making it easier for people to seek professional medical advice.

Background and History

Hackathons have proven to be a successful way to channel energy and technical expertise into solving very specific problems and generating bright, new ideas for applied technology. Such is the case for the genesis of Doctor Hazel, a noteworthy project at the TechCrunch Disrupt’s San Francisco 2017 hackathon, co-developed by Intel® Software Innovator, Peter Ma, and Mike Borozdin, VP of Engineering at Ethos Lending and cofounder of Doctor Hazel. (see Figure 1).

Peter noted, "My cofounder and I had a very close mutual friend who died of cancer ...


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AI-Driven Test System Detects Bacteria In Water

AI-Driven Test System Detects Bacteria In Water

This advertorial is sponsored by Intel®.

“Clean water and health care and school and food and tin roofs and cement floors, all of these things should constitute a set of basics that people must have as birthrights.�1

 

– Paul Farmer, American Doctor, Anthropologist, Co-Founder,
Partners In Health


Challenge

Obtaining clean water is a critical problem for much of the world’s population. Testing and confirming a clean water source typically requires expensive test equipment and manual analysis of the results. For regions in the world in which access to clean water is a continuing problem, simpler test methods could dramatically help prevent disease and save lives.

Solution

To apply artificial intelligence (AI) techniques to evaluating the purity of water sources, Peter Ma, an Intel® Software Innovator, developed an effective system for identifying bacteria using pattern recognition and machine learning. This offline analysis is accomplished with a digital microscope connected to a laptop computer running the Ubuntu* operating system and the Intel® Movidius™ Neural Compute Stick. After analysis, contamination sites are marked on a map in real time.

Background and History

Peter Ma, a prolific contributor in the Intel® AI Academy program, regularly participates in hackathons and has won awards in a number of them. “I think everything started as a ...


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Pedestrian Detection Using TensorFlow* on Intel® Architecture

Pedestrian Detection Using TensorFlow* on Intel® Architecture

This advertorial is sponsored by Intel®.

Abstract

This paper explains the process to train and infer the pedestrian detection problem using the TensorFlow* deep learning framework on Intel® architecture. A transfer learning approach was used by taking the frozen weights from a Single Shot MultiBox Detector model with Inception* v2 topology trained on the Microsoft Common Objects in Context* (COCO) dataset, and then using those weights on a Caltech pedestrian dataset to train and validate. The trained model was used for inference on traffic videos to detect pedestrians. The experiments were run on Intel® Xeon® Gold processor-powered systems. Improved model detection performance was observed by creating a new dataset from the Caltech images, and then selectively filtering based on the ratio of image size to object size and training the model on this new dataset.

Introduction

With the world becoming more vulnerable to pronounced security threats, intelligent video surveillance systems are becoming increasingly significant. Video monitoring in public areas is now common; prominent examples of its use include the provision of security in urban centers and the monitoring of transportation systems. These systems can monitor and detect many elements, such as pedestrians, in a given interval of time. Detecting a pedestrian is an essential and ...


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Face Detection with Intel® Distribution for Python*

Face Detection with Intel® Distribution for Python*

This advertorial is sponsored by Intel®.

Artificial Intelligence (AI) can be used to solve a wide range of problems, including those related to computer vision, such as image recognition, object detection, and medical imaging. In the present paper we show how to integrate OpenCV* (Open Source Computer Vision Library) with a neural network backend. In order to achieve this aim, we first explain how the video stream is manipulated using a Python* programming interface and we also provide guidelines on how to use it. Finally, we discuss a working example of an OpenCV application. OpenCV is one of the packages that ship with Intel® Distribution for Python* 2018.

Introduction

Today, the possibilities of artificial intelligence (AI) are accessible to almost everyone. There are a number of artificial intelligence applications and many of them require the use of computer vision techniques. One of the most currently used libraries to help detection and matching, motion estimation, and tracking is OpenCV1. OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage and is now maintained by Itseez. The library is cross-platform and free for use under the open-source BSD license.

Usually, the OpenCV ...


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Lower Numerical Precision Deep Learning Inference and Training

Lower Numerical Precision Deep Learning Inference and Training

This advertorial is sponsored by Intel®

Introduction

Most commercial deep learning applications today use 32-bits of floating point precision (ƒp32) for training and inference workloads. Various researchers have demonstrated that both deep learning training and inference can be performed with lower numerical precision, using 16-bit multipliers for training and 8-bit multipliers or fewer for inference with minimal to no loss in accuracy (higher precision – 16-bits vs. 8-bits – is usually needed during training to accurately represent the gradients during the backpropagation phase). Using these lower numerical precisions (training with 16-bit multipliers accumulated to 32-bits or more and inference with 8-bit multipliers accumulated to 32-bits) will likely become the standard over the next year, in particular for convolutional neural networks (CNNs).

There are two main benefits of lower precision. First, many operations are memory bandwidth bound, and reducing precision would allow for better usage of cache and reduction of bandwidth bottlenecks. Thus, data can be moved faster through the memory hierarchy to maximize compute resources. Second, the hardware may enable higher operations per second (OPS) at lower precision as these multipliers require less silicon area and power.

In this article, we review the history of lower numerical precision training and inference and describe how ...


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Boosting Deep Learning Training & Inference Performance on Intel® Xeon® and Intel® Xeon Phi™ Processors

Boosting Deep Learning Training & Inference Performance on Intel® Xeon® and Intel® Xeon Phi™ Processors

This advertorial is sponsored by Intel®

In this work we present how, without a single line of code change in the framework, we can further boost the performance for deep learning training by up to 2X and inference by up to 2.7X on top of the current software optimizations available from open source TensorFlow* and Caffe* on Intel® Xeon® and Intel® Xeon Phi™ processors. Our system-level optimizations result in a higher throughput and a reduction in time-to-train for a given batch size per worker compared to the current baseline for image recognition Convolution Neural Networks (CNN) workloads.

Overview

Intel® Xeon® and Intel® Xeon Phi™ processors are extensively used in deep learning and high-performance computing applications. Popular deep learning frameworks such as TensorFlow*, Caffe*, and MxNet* have been optimized by Intel software teams to deliver optimal performance on Intel platforms for both deep learning training and inference workflows. With Intel and Google’s continuing collaboration, the performance of TensorFlow has significantly improved with Intel® Math Kernel Library (Intel® MKL) and Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN). Similarly, the Intel® Distribution of Caffe* also delivers significant performance gains on Intel Xeon and Intel Xeon Phi processors.

Training deep Convolution Neural Networks (CNNs) such as ResNet-50, GoogLeNet-v1, Inception-3, ...


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How to Disrupt Digital Advertising Through Blockchain?

How to Disrupt Digital Advertising Through Blockchain?

This article is sponsored by Pingvalue

How one open platform plans to transform the digital advertising market with its own cryptocurrency

Meet blockchain – the fintech newcomer that has taken the world by storm, redefining how businesses, governments, organizations and individuals interact. This decentralized ledger technology eliminates expensive third parties by providing an airtight verification process. It can authenticate any type of transaction, establishing trust and simplifying the movement of money, products and information worldwide.

Several Initial Coin Offerings (ICO) have appeared in recent years and the number is growing exponentially. The urgent need for a direct and trustworthy mode of interaction elevated the blockchain conversation from crypto-fan chatrooms to the most influential boardrooms.

Blockchain’s collaboration-based network has already impacted several industries, generating benefits for all parties involved. The digital advertising industry is next in line, with various startups attempting to use this technology to transform processes currently dominated by middlemen, fraud and a lack of measurability.

Make advertising transparent, relevant & rewarding

Many initiatives emerging from the digital advertising industry aims to reinvent the current model by harnessing blockchain to deliver transparency, relevance and rewards. A great thing is to create a people-centric approach, an open platform improves the customer experience while allowing for ...


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