How Companies Collect Meaningful Data from the Users

Introduction

In the world of today’s business, which is based more on exploitation of big data, data stands out as a valuable business asset that assists in making certain decisions and in enhancing user experiences. Businesses have different ways of collecting such information understanding how their websites and apps are accessed or even user activity on social networks. The objective is not only to acquire data but also to acquire data that is relevant in understanding and meeting product needs and customer interactions: user behavior and preferences.

Surveys and Direct User Input

Your audience data can be collected using direct user input method which is user input during feedback or surveys and even during registration on the website of the company. These types of users allow entities to get important information, such as customer tastes and preferences, as well as the level of customer satisfaction information, which makes it easy for companies to know what the customers require and where to improve.

Conclusion

Methods of data collection involving cookies, beacons and even social media integration allow for a more customized user experience in a company but at the same time user experience and data privacy needs to be maintained. As technology improves, the ability to foster user confidence and adhere to standards of ethical information usage will be essential.

Reference

How Companies Gather Data & What They Do With It – i creatives

The Evolution of Artificial Neural Networks: From 19's to the Modern AI

Introduction

Artificial neural networks, humans tried to develop computer systems that could learn and make decisions these systems are able to do tasks such as; recognizing patterns were developed as long as 1958. With years passing along with the appropriate advancements in technology, such networks have today transformed into complex problem solvers. In this blog, we will analyze the roots of ANNs, finally ending at an artificial intelligence breakthrough: deep learning.

Evolutionary Timeline of Neural Networks

In the 1950s perceptrons and the like made the foundations of neural networks. They were also very simple and nonspecialized because the technology of the time was not advanced enough but still these basic models created the embryo for a giant AI that was to come.

The Story of the Emergence of Deep Learning

The increase in capabilities of the neural network architectures was achieved from considerable advances made between 2011 to 2020 where GPUs and software tools like TensorFlow enabled vast amounts of data to be batched, increasing the speed of processing images, language and reasoning in AI.

Conclusion

It is correct to say that without the early networks AI would have never existed, however it is deep learning that has certainly taken the world by storm and with it the possibilities to apply AI have increased immensely, thus speeding up the process of any further developments in machine learning.

Reference

https://www.nvidia.com/en-us/deep-learning-ai/

Introduction to Object Detection

 

Introduction

Object detection is in fact an advanced sub-field of computer vision in which the computer is able to recognize and find objects in images or videos. Such a technology has grown roots and is now used in many applications and has revolutionized the way we interact with both the digital and the physical worlds in a way that they are even smarter than before.

This is in contrast to image recognition, which tends on a basic level to only detect a single main object in an image, where object detection seeks to locate and identify multiple objects on the image, each of which is enclosed in a rectangular boundary box. Every object is recognized and given a score for detection reliability. An object detection network is able to filter recognizable objects out of noise by analyzing the arrangement, texture, or outline of the focal pixels inside numerous other pixels. Because of its flexibility, it was also employed in self-driving cars, medicine, shops, safety, and production, and where it still encourages new developments.

Conclusion

In the future, as AI and machine learning become better and more capable, Object detection will evolve to the next level where it can analyze moving objects real-time. This evolution promises to bring new insights and applications and wider object detection application across sectors enhancing contextual understanding of machine objects.

Reference

Towards Data Science - Object Detection Applications

Basics of Image Recognition

Introduction: 

Automated image annotation is a process where text labels are placed automatically onto key components of an image, generally through the use of a computer system. For instance, such common labels include images of a ‘cat,’ a ‘car’ or a ‘building.’ Technology has made it possible to manage pictures automatically according to the people in them or the place. In most cases, image categorization is also important in radiology to identify diseases in x ray films as well as in self driving vehicles to assist in identifying and avoiding objects.

To determine the classification of a remote image, a class is as per the description for the image that needs to be shaped (for example say a cat or dog) and an appropriate label corresponding to the class Y is then assigned for various classes, for example Y=0 for a cat and Y=1 for a dog. Computers are known to process pictures as matrices containing numerous pixel intensity values, which are then employed to determine appropriate classes. Algorithms treat the raw images so that these labourious tasks are made into actual classifications although the computer cannot see.

Conclusion

Despite being apparent, image analysis practice is quite complicated. A few factors affecting the models include changes in illumination, change in the angle of imaging and also compactness of the background. These concerns must be resolved as part of the process of developing competent dependable models of computer vision.

Reference

GeeksforGeeks - Deep Learning for Image Classification