Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning machine vision. In this study, using a commercially available UAV and a publicly available package for deep learning, we constructed a machine vision system for the automatic classification of trees. In our method, we segmented a UAV photography image of forest into individual tree crowns and carried out object-based deep learning. As a result, the system was able to classify 7 tree types at 89.0% accuracy. This performance is notable because we only used basic RGB images from a standard UAV. In contrast, most of previous studies used expensive hardware such as multispectral imagers to improve the performance. This result means that our method has the potential to classify individual trees in a cost-effective manner. This can be a usable tool for many forest researchers and managements.

On one hand, niches are deepening. Cutting edge applications of data science like deep learning are getting more advanced, and have already led to some clear specialization among data scientists. Even within deep learning, there are computer vision specialists whose roles are extremely well-defined.

There are many situations when running deep learning inferences on local devices is preferable for both individuals and companies: imagine traveling with no reliable internet connection available or dealing with privacy concerns and latency issues on transferring data to cloud-based services. Edge computing provides solutions to these problems by processing and analyzing data at the edge of network. Take the "Ok Google" feature as an example -- by training "Ok Google" with a user's voice, that user's mobile phone will be activated when capturing the keywords. This kind of small-footprint keyword-spotting (KWS) inference usually happens on-device so you don't have to worry that the service providers are listening to you all the time. The cloud-based services will only be initiated after you make the commands.

This post is the first in a two-part series, and is authored by Erika Menezes, Software Engineer at Microsoft. Visual content has always been a critical part of communication. Emojis are increasingly playing a crucial role in human dialogue conducted on leading social media and messaging platforms. Concise and fun to use, emojis can help improve communication between users and make dialogue systems more anthropomorphic and vivid. We also see an increasing investment in chatbots that allow users to complete task-oriented services such as purchasing auto insurance or movie tickets, or checking in for flights, etc., in a frictionless and personalized way from right within messaging apps.

A friend of mine who is about to start a career in artificial intelligence research recently asked what I wish I had known when I started two years ago. Below are some lessons I have learned so far. They range from general life lessons to relatively specific tricks of the AI trade. I hope others find them useful. I was initially very intimidated by my colleagues and hesitant to ask basic questions that might betray my lack of expertise. It was many months before I felt comfortable enough with a few colleagues to ask questions, and still my questions were carefully formulated.

You are free to share this article under the Attribution 4.0 International license. Computer scientists have created a deep-learning, software-coding application that can help human programmers navigate the growing multitude of often-undocumented application programming interfaces, or APIs. Designing applications that can program computers is a long-sought grail of the branch of computer science called artificial intelligence (AI). The new application, called Bayou, came out of an initiative aimed at extracting knowledge from online source code repositories like GitHub. Users can try it out at askbayou.com.

An open-source battle is being waged for the soul of artificial intelligence. It is being fought by industry titans, universities and communities of machine-learning researchers world-wide. This article chronicles one small skirmish in that fight: a standardized file format for neural networks. At stake is the open exchange of data among a multitude of tools instead of competing monolithic frameworks. The good news is that the battleground is Free and Open.

Google's Tensor Processing Unit (TPU), a custom-developed accelerator for deep learning, offers a fast and cost-efficient alternative to training deep learning models in the cloud: it is capable of training a ResNet-50 model on ImageNet in 12.5 hours -- for an equivalent of $81 of TPU compute time. At RiseML, we believe that machine learning engineers shouldn't have to worry about infrastructure. Recently, Google Kubernetes Engine (GKE), the managed Kubernetes offering by Google, started providing alpha level support for provisioning TPUs. Each TPU's lifetime is automatically bound to the lifetime of its job, so you only pay for your actual use. The combination of GKE and RiseML offers a hassle-free machine learning infrastructure that is easy-to use, highly scalable, and cost-efficient.

Most IT leaders have heard of deep learning, but few really understand how this new technology works. Deep learning burst onto the public consciousness in 2016 when Google's AlphaGo software, which was based on deep learning, beat the human world champion at the board game Go. Since then, deep learning has begun appearing in news reports and product literature with more frequency, but few organizations are actually using it today. The 2018 O'Reilly survey report How Companies Are Putting AI to Work Through Deep Learning found that only 28% of the more than 3,300 respondents were currently using deep learning. However, 92% believed that deep learning would play a role in their future projects, with 54% saying it would play a large or essential role in those initiatives.

AI is undergoing a period of massive expansion...