How Open Plan Offices Impact Collaboration

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One of the main selling points of open plan offices is that they are good for collaboration. Their ability to support ease of communication among employees is supposed to encourage teams to work effectively together. This is then supposed to offset their damage to individual work that requires high levels of concentration.

Except the evidence is mounting that they aren’t even that good for supporting collaboration. Earlier this year a study from Karlstad University, Sweden found that open-plan office spaces not only harm collaboration, but also reduce employee happiness.

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Are Managers Really as Horrible as We Think They Are?

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If you’re like me, every article you’ve ever read about managers vs. leaders bagged on managers while praising leaders. Not surprising, right? People hate being "managed" and nearly everyone fancies themselves as being (or becoming) a leader. 

There may come a time when managers are obsolete, but for now, these are both valuable roles – they’re just different.

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Real-Time Analysis of Popular Uber Locations Using Apache APIs

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According to Gartner, smart cities will be using about 1.39 billion connected cars, IoT sensors, and devices by 2020. The analysis of location and behavior patterns within cities will allow optimization of traffic, better planning decisions, and smarter advertising. For example, the analysis of GPS car data can allow cities to optimize traffic flows based on real-time traffic information. Telecom companies are using mobile phone location data to provide insights by identifying and predicting the location activity trends and patterns of a population in a large metropolitan area. The application of Machine Learning to geolocation data is being used in telecom, travel, marketing, and manufacturing to identify patterns and trends for services such as recommendations, anomaly detection, and fraud.

In this article, we discuss using Spark Structured Streaming in a data processing pipeline for cluster analysis on Uber event data to detect and visualize popular Uber locations.


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Deep Dive Into Computer Vision With Neural Networks: Part 1

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Machine vision, or computer vision, is a popular research topic in artificial intelligence (AI) that has been around for many years. However, machine vision still remains as one of the biggest challenges in AI. In this article, we will explore the use of deep neural networks to address some of the fundamental challenges of computer vision. In particular, we will be looking at applications such as network compression, fine-grained image classification, captioning, texture synthesis, image search, and object tracking.

Network Compression

Even though deep neural networks feature incredible performance, their demands for computing power and storage pose a significant challenge to their deployment in actual application. Research shows that the parameters used in a neural network can be hugely redundant. Therefore, a lot of work is put into increasing accuracy while also decreasing the complexity of the network.


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Machne Learning vs. Deep Learning

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Objective

In this article, we will study a comparison between Deep Learning and Machine Learning. We will also learn about them individually. We will also cover their differences on various points. Along with a Deep Learning and Machine Learning comparison, we will also study their future trends.

Introduction to Deep Learning vs. Machine Learning

a. What is Machine Learning?

Generally, to implement Artificial Intelligence, we use Machine Learning. We have several algorithms that are used for Machine Learning. For example:


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Understanding the Confusion Matrix

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The confusion matrix is one of the most popular and widely used performance measurement techniques for classification models. While it is super easy to understand, its terminology can be a bit confusing.

Therefore, keeping the above premise under consideration, this article aims to clear the "fog" around this amazing model evaluation system.


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Testing Features of ML Models

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In this post, you will learn about different types of test cases that you could come up for testing features of the Data Science/Machine Learning models. Testing features are one of the key sets of which needs to be performed for ensuring the high performance of Machine Learning models in a consistent and sustained manner.

Features make the most important part of a Machine Learning model. Features are nothing but the predictor variable, which is used to predict the outcome or response variable. Simply speaking, the following function represents y as the outcome variable and x1, x2, and x1x2 as predictor variables.


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19 AI Technologies That Are Currently Dominating

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1. Natural Language Generation

Natural Language Generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy.

It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop.


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When Machine Learning Meets the Business World

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Businesses have always been at the forefront as early adopters of new technologies. Advancements in computing like Machine Learning have already made a notable impact on the business world. With business operations and processes spread across varying levels, the inclusion of a Machine Learning framework can prove worthwhile in increasing efficiency, productivity, and speed.

Machine Learning has found widespread acceptance among enterprises. MIT Technology Review and Google Cloud recently published a report based on their studies in Machine Learning and its adoption. The reports state that about 60 percent of the respondents have already implemented Machine Learning into their business.


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Image Processing With Deep Learning

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Image titleComputers today can not only automatically classify photos, but they can also describe the various elements in pictures and write short sentences describing each segment with proper English grammar. This is done by the Deep Learning Network (CNN), which actually learns patterns that naturally occur in photos. Imagenet is one of the biggest databases of labeled images to train the Convolutional Neural Networks using GPU-accelerated Deep Learning frameworks such as Caffe2, Chainer, Microsoft Cognitive Toolkit, MXNet, PaddlePaddle, Pytorch, TensorFlow, and inference optimizers such as TensorRT.

Neural Networks were first used in 2009 for speech recognition and were only implemented by Google in 2012. Deep Learning, also called Neural Networks, is a subset of Machine Learning that uses a model of computing that’s very much inspired by the structure of the brain.


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