Twitter’s New AI Tool Can Automatically Highlight the Best Part of Your Photo

Artificial Intelligence (AI) is slowly seeping into every aspect of our lives. And while the changes may not be prominent enough for all to see, you will notice small improvements that affect the overall experience. With the help of Machine Learning, researchers at Twitter have also made one some small, yet important tweaks to its photo-cropping AI Tool. Twitter will now be able to focus on the most important part of the photos in their newsfeed, and not just a random part of the image.

Like on all other social media channels, photographs and images have been an integral part of tweets as well. However, regular twitteratis can vouch for the fact that twitter hasn’t been the most friendly when it comes to highlighting images. We’re forced to overlook the slightly off-focus, oddly cropped thumbnails that appear on our newsfeed, and instead click to view the full image only if it’s an interesting read.

New Image-Cropping Method with Improved AI Features

Twitter has had the photo sharing feature since 2011, but the previous methodology used for image-cropping wasn’t viewer friendly. The reason for this was that Twitter’s previous AI was trained only for face detection, and if there weren’t any faces, it focused on the center of the image. This meant that any image with buildings or animals resulted in an off-focus thumbnail.

Recently, Twitter undertook some in depth research to overcome the limitations in its photo-cropping tool that led to awkwardly cropped preview images. Twitter researchers found two important AI tricks to detect the important parts of an image:

Identifying the Saliency of an Image

‘Saliency of an image’ is measured based on the part of an image that the human eye is most attracted to or that a person is most likely to focus on. The part of an image on which a person focuses is called a ‘highly salient region’. Academics have measured ‘saliency’ by using eye trackers that record those pixels that people fixated on while focusing on or looking at an image. People generally tend to focus on faces, text, animals and other objects and regions of high contrast. Using this data, neural networks and other algorithms can be trained to predict what people might like looking at.

Twitter will be using this feature to crop images so that it focuses on a region that the viewer will find most interesting. And twitter will do it at such speed that users will not experience any lag time while uploading images, everything will happen in real time.

Training Algorithms through Knowledge Distillation

The neural networks that deal with identifying the saliency of an image are usually too slow. As a result, these cannot be used for cropping the millions of images that are uploaded on twitter by the millisecond. So twitter found an alternative and made some optimizations to help its system perform 10 times faster that its standard method. This resulted in a highly advanced AI that can do intelligent cropping of photographs as and when they are being uploaded.

The development team at twitter used a technique called ‘knowledge distillation’. This enables to train their existing algorithms or data to quickly identify the most ‘salient’ parts of the photos or the area that catches the viewer’s eye the most.

Pruning of Images for Identification

The engineers at twitter also used a technique called ‘pruning’ to let the algorithm skip over features of the image that are not relevant for the identification. So, now twitter displays images that their previous algorithm could not detect properly, and these too in real time with better crops. This new AI tool also shows images of objects that were cropped previously as they could not ‘sit’ in the middle of the image but now appropriately cropped using the new tool. It also shows that the new AI feature recognizes text and can also adjust the crop to include a sign.

A Dramatic Feel to twitter Feeds

It’s a small yet important update that will dramatically improve the look and feel, and overall user experience of the twitter feed. This new update is being rolled out to all twitter users, and hopefully, it will put an end to all those awkwardly cropped thumbnail images.

This latest offering from twitter is now being available for twitteratis to use on desktop, iOS, and Android apps as reported by the company. So, next time when you use twitter, do not forget to thank the neutral networks for the enhanced images.

Key Insights into the Application of Robotics Process Automation ( RPA) in Supply Chain Management

Supply Chain Management is an integral and vital function that dictates the overall success of an organization. Global associations depend on seamless operations that allow the smooth flow of goods and services. A well-planned supply chain takes care of many activities that involve procuring raw materials from providers, screening existing inventory, moving finished goods to consumers and tracking shipments to ensure on-time delivery. These various small yet inter-dependent activities can be made more efficient by incorporating Robotic Automation Processes or RPA.

Robotic Automation Processes can significantly improve the production network and bring in higher output standards in the coming years. Both product and service industries are beginning to understand the importance of utilizing RPA innovation. Incorporating RPA will create simple production processes and remove weighty, time-consuming and unproductive activities that eat up important man hours.

We provide you with a detailed brief on RPA utilization to update supply and coordination techniques and create much better work flow processes. Which parts of the inventory network are suitable for automation and how can RPA be used to streamline the whole supply chain, increase efficiencies and reduce cost.

How Does Robotics Process Automation Work?

Robotic Process Automation is an automation process where software and intelligent data algorithms are used to imitate the activities that humans perform. Latest technological advances allow employees to configure their computer software or ‘bots’ to collate, capture and decipher existing applications and respond by processing a transaction, triggering an action and communicating with other digital devices. And all these activities are done with minimal or zero assistance from humans. RPA helps in reducing operations costs, eliminating human error and increasing efficiency, which in turn will lead to better output volumes and business performance.

Currently, a majority of supply chain experts are implementing Robotic Process Automation for individual tasks. However, industry-level processes are multi-faceted and involve various inter-related activities. It becomes a laborious and time-consuming process for supervisors to review the performance of each of these individual activities.

Robotic Automation for End-To-End Tasks

Enterprise Process Robots can automate a whole business process in relation to the regular computerized devices that have a singular approach. These Enterprise Robots remove silo activities and permit the processing of an entire module of procedures that involve various mini-activities. For example, quality-check, packaging, stock management and transportation can be clubbed together as one automated process.

The engineer who oversees these activities can automate the process by instructing the product robot about how an occupation is finished, which is called inserted process know-how. The tasks are organized as various individual processes which are then brought together, enabling the interdependent areas to work in tandem. For instance, if the robotic autonomy arrangement could discover or know that a stock room is full, it immediately ends stock obtainment or moves stock to another room that is accessible.

RPA is gaining inroads into various large supply chain management companies to expedite tasks that were once done manually. High-end bots can automate any business process and can be configured to set up tasks assigned by the user, supporting the existing core system. They are code free, scalable and can even be taught to take up additional jobs. Transactions happen within seconds and fasten the entire operational process.

Here is a quick example of a logistics company that was struggling to deal with customer requests to reduce product price, and at the same time tackling carrier demands for more money. The company was also faced with various other operational issues like multiple EDI systems, scalability, seasonal requirements, customer feedback, over-worked employees, among others. The company created a cost-effective digital workforce of 400 RPAs to automate many of these operational jobs, including order placements, billing, scheduling, delivery and customer service. They were able to triple their output and cut down costs significantly.

The Beginning of Intelligent Automation In Supply Chain Management

There are many advantages of incorporating RPA in Supply Chain Management. Although we are not yet in a position to automate all tasks, especially the exercises that take place in the front office, RPA can take over a majority of the inventory networking activities. RPA can work using existing processes with minimal re-engineering. With RPA, management can identify inconsistencies, structure date, interact with multiple systems and deliver vital insights to the user. Product items can be moved from the producer to provider to the client in a proficient and systematized way. The entire production network will have its own inbuilt intelligent system to make learned decisions and consistently improve performance.

The Future of Robotics and Automation

Looking forward, there is great potential and a world of possibilities that Robotics and Automation have brought to the production and services industry. We can expect to see vast improvements in products, delivery, outputs and even pricing if Robotics Process Automation tools are used efficiently. Production network engineers can further update these intelligent procedures and frameworks to grasp new information and utilize the information at a much higher capacity.

What remains to be seen is how rapidly and to what degree robots and computers can collaborate to create significant process changes in other industries and functions. The Future of robotics companies will be dependent on how well they can adjust to the quick changes taking place in sourcing, generation and conveyance as it is happening today, and how adaptable are they to exploit these new innovations.

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