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AI in Fashion: Applications with Use Cases & Role in the Industry

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AI in Fashion Industry

Wearing clothes is not only a necessity of humans, instead, they get the chance to show off their style, beauty, personality, and lavish lifestyle. The fashion industry is one of the biggest in the world with market size of US$ 3 trillion as of 2018.

Artificial Intelligence (AI) in fashion is changing this industry by playing a crucial role in the various key divisions. From design to manufacturing, logistic supply chain and marketing, AI in fashion is playing a big role in transforming this industry.   

Actually, in the age of digitalization, AI and machine learning based technologies in the fashion industry are providing an automated solution to manufacturers helping them to leverage the intelligence of AI into fashion and exhaust the best possibilities into their field.

So, right here we brought a great discussion, how artificial intelligence is changing fashion with use cases, role and impact of AI on this industry.

How AI is Changing Fashion: Role & Impact on the Industry

How AI is Changing Fashion

AI in Fashion Design – A Clothing Designer

The design and patterns with the right color combination are the key points to design a costume to make it attractive for the customers. AI can detect the new trends with demand in projecting the new trend reducing the forecasting error.

Trends in the fashion industry change very fast with new designs or patterns come every day in the market. Designers need to keep pacing with new styles. And AI algorithms can analyze designs through images to copying popular styles.

AI in Fashion Design

And after analyzing the data set of designer clothes and AI uses such data to see what does and does not sell well in the markets and create or recommend designers to build and launch a completely new design in the market.

Retailers giants like Amazon and Walmart now have their own clothing brands and are using the machine learning systems that can identify the spot and, in the not-too-distant future, design fashion trends that customers also prefer to buy.

AI in Fashion Manufacturing, Supply Chain & Fashion Store

In fashion, apparel manufacturing is the labor-intensive industry. From sewing to sorting or dresses, there are mundane tasks now AI can perform with a better level of accuracy at faster speed reducing the extra cost spent on workers.  

AI in Fashion Manufacturing

AI robots and computer-enabled machines can easily stitch the fabrics with perfection while at the same time it can also detect faults in fabric and offer quality assurance to ensure that the actual design shades will suit the new colors.

Video: AI Robots in Fashion & Sewing

Also Read: How AI Is Creating New Job Opportunities For Low-Skilled Workers?

AI in inventory and supply chain management is facilitating to speed-up by improving routes, cutting the logistic supply and shipping cost.

ai in supply chain

Using the AI companies automate logistics and supply chain processes for faster delivery or find alternate routes for vehicles derailed by unforeseen circumstances such as bad weather or road construction.

While, AI in fashion store also uses algorithms with humans-in-the-loop as virtual personal stylists. This AI-based system recommends the best items for them a human stylist picks the final suggested products as per the body types.  

AI in Fashion Retail

AI and machine learning in retail are also providing an automated solution to monitor the customer’s activities while shopping and visualize their sentiments to know what kind of products they prefer to buy and what they ignore.

AI in Fashion Retail

AI can also track footfalls in retail shops or record the shopping experience of the customers with option to get feedback on how was their experience while shopping at the retail shops with an opportunity to improve their services.    

And visual perception based AI models also helps store owners to keep the records of the inventory in their store and also categorize items in-store helping store owners to manage their inventory with AI-backed automated solution.

AI Fashion Stylist – Styling the Fashion Accessories  

Moreover, the use of AI in fashion is also allowing each one of us to find those elusive perfect outfits that suit our body type and fashion preferences.

Such AI-enabled clothes and outfits are not only tailored for different occasions and weather, but also to the user’s style, body type, colours, and the latest fashion trends. 

AI Fashion Stylist

iLUK is an AI-based personal stylist, using the Computer Vision-based and 3D Reconstruction technology at its heart to make personal styling based on technology possible. It is designed as a pod that will be placed at a retail outlet.

In AI fashion stylist computer vision and 3D reconstruction based technology are used to develop a 3D avatar of the customers.

The measurement data are then fed into the AI software to analyze your body-shape, with an identical body mass, shape, size and colour, while suing your face to show the outfits.

Also Read: How To Pick Dress For Your Body Shape: Tips for Women Body Types

Van Heusen created a retail environment complete with a “Virtual Trial” mirror which lets users see how outfits would look on them by simply scanning the item’s barcode and standing in front of the mirror as virtual garments are projected onto their reflection.

Video: Van Heusen Style Studio for AI Fashion Stylist

The fitting rooms house interactive mirrors as well, letting shoppers try and compare different outfits by snapping pictures of them in each outfit for them to then compare them side by side to help them make a decision quickly without wearing it.

AI in Fast Fashion with Smart Mirror

Similarly, AI powered smart mirror is used by the retailer store simplifying the shopping experience of the customers with virtual visualization of clothes how they look on you even without putting the clothes actually on your body.

AI in Fast Fashion

The AI smart mirror is installed in the changing room of retail stores with touch screen glasses that relay information on whether or not a person is inside, they will also help to get information about the item the customer has brought into the store. 

For such smart mirrors, clothing racks are RFID enabled and use gyro-sensors and Bluetooth low-energy chips allowing the articles selected by shoppers automatically show up in the Smart Mirror.

See how windows virtual mirror works, showing a shopper “trying on” outfits, superimposing it on the image of her body in the mirror, following her movements, and even changing the color of items at her voice command

Video: How Windows Virtual Mirror Works?

And using this mirror customer can look at different sizes and colour options and also receive personalized mix-and-match options to complete the look.

Rebecca Minkoff’s flagship New York City connected store features a large, interactive mirror that shows off the latest Rebecca Minkoff brand content. The shopper can use the mirror to browse various looks and add them to their fitting room to try on.

Video: AI Interactive Smart Mirrors

The interactive mirrors in each fitting room give the user the option to contact a stylist, change the lighting, and attach the items she has tried on during the visit to a personal profile so they can be accessed during future visits to the store.

And be able to receive intelligent recommendations based on the clothing saved. Items can be sent directly from the fitting room to checkout to finish the shopping.

AI in Online Fashion with Recommendation in Ecommerce

Similarly, just like retail fashion stores, AI is playing a game-changer role in online shopping and Ecommerce business. While browsing or searching the fashion items on e-commerce sites, AI recommends the other similar items, as per your color preference, budget and other attributes.

AI in Online Fashion

Actually, machine learning technology is used here to analyze your filtering behavior and what kind of products you are looking for. Analyzing your search history data it recommends the other suitable items probably you should check.   

AI in Visual Search – To Find the Products Using Camera

Nowadays E-commerce stores AI-based visual search technology used to understand the content and context of these images and return a list of related results. You can use your camera to capture an object and search the same online.

AI in Visual Search

AI-based visual search technology allows retailers to suggest thematically or stylistically related items to shoppers in a way they would struggle to do using a text query alone.

Actually, the AI visual search model is trained with huge amount of data sets that contain the annotated images making the clothing items recognizable to machines. 

AI in computer vision visual search

And with the help of ML algorithms machines can recognize separate objects within a picture. This enables additional shop-the-look use cases that create cross-sell opportunities for the sellers.

While on the other hand, for media companies visual search is an opportunity to transform fashion sites into a personalize shopping experience. 

Role of Artificial Intelligence and its Impact on the Fashion Industry

Nowadays, AI is playing a crucial in the fashion industry with huge potential to make this AI integrated into various other subfields. It is powering the manufacturers to redefine how fashion businesses engage and interact with their customers.

AI role and impact on fashion

AI-enabled applications and system are enhancing the customer’s experience that goes beyond personalized ads, notification alerts on price drops, or chatbot assistance.

With this kind of technology, fashion brands strive to put customization at the forefront for customers during their buying journey. 

Moreover, AI will not only help designers to predict the upcoming trends, visualized by the current fast-changing-environment, but also examine and minimize the impacts on the environment while producing the fashion garments and accessories.

Further, 3D printing technology is also going to be used with the help of AI. 3D printing will enable professionals to transcend any boundaries of design, as it gives them a chance to turn the most improbable projects into reality.

Artificial Intelligence Impact on Fashion

Customers now becoming aware to use the AI-enabled features while searching or buying clothing or fashion accessories online. Customers can take a photo and match the accessories and clothes over brands to get the same design. 

Artificial Intelligence Impact on Fashion

Apart from that, it is also reducing the errors and making the product delivery process fast through automated warehousing management.

Companies or brands can now ask for feedback and suggestions through AI featured applications.

Artificial intelligence impact on fashion will make this industry more smart and intelligent in understanding the sentiments and fashion taste of customers.

In the long-term, it will have a constructive impact on the entire industry empowering the manufacturers to introduce the most demanding clothing lines timely with more precision.

Whereas, at the customer end, it will facilitate them to explore more products as per their choice, personality and affordability with the personalized shopping experience.

Customers will easily find the right costumes and fashion accessories with the option to visualize wearing the same accessories virtually to get the best one at one click.        

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Artificial Intelligence

What is the Difference Between AI, Machine Learning & Deep Learning?

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ai vs machine learning vs deep learning

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the most widely used interchangeable words creating confusion among many people globally.

Although, these three terminologies are typically used interchangeably, but they all are different from each other especially in terms of their applications, capabilities, and results.                 

Understanding the difference between AI, ML, and deep learning is important to utilize the precise applications of these jargons and take the right decision while dealing with AI, ML, or DL related projects.

Before we start, I would like to show you few images (see below) that will give an overview, how AI, ML, and DL are different from each other or how these three terminologies are related to each other.

The easiest way to understand their relationship is to visualize them as concentric circles with AI — which is a broader area, then ML — which is the branch or subset of AI, and finally deep learning — which is a part of the subset of ML, fitting inside both or you can say – DL is driving today’s AI explosion due to more complex inputs and outputs.

I think these highly illustrative images cleared some doubts and misconceptions about these jargons. But you need to go through more definitions with a few sets of useful examples and use cases that will help you understand these concepts better.

What is Artificial Intelligence?

As the name denotes, AI is a broader concept used to create an intelligent system that can act like human intelligence.  The terms – “Artificial” and “intelligence” means “a human-made thinking power”.

Basically, AI is the field of computer science used to incorporate human intelligence into machines, so that such machines or systems can think (not exactly) and take sensible decisions like humans. 

Also Read: Where Is Artificial Intelligence Used: Areas Where AI Can Be Used

And such AI-enabled machines can perform specific tasks very well and sometimes even better than humans — though they are limited in scope. And to develop such machines AI training data sets are processed through machine learning algorithms. 

To be more precise, AI-enabled systems don’t need to be pre-programmed, instead such algorithms are used, that can work with their own intelligence. And machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks are used to create such systems.

Example of AI in Daily Life

Smart Home Devices, automated mail filters in our Gmail, Self-driving cars, Chatbots, AI Robots, Drones and AI Security Cameras are the popular examples where AI in integrated. Though, there are many more other applications, devices, systems and machines works on AI principles helping humans in various areas across the globe.

Also Read: How Can Artificial Intelligence Benefit Humans

What is Machine Learning?

As the name suggests, machine learning empowers the computer system to learn from past experiences earned through training data. As of now, you got to know machine learning is the subset of artificial intelligence, in fact, it is the technique used to develop AI-enabled models.

What is Machine Learning

Machine Learning is used to create various types of AI models that learn by themselves. And as much as it gets more data, it gets better at learning and gives more accurate results.

Let’s take an example of how machine learning and algorithms work while making predictions. ML is actually a process of training the algorithms to learn and make the decisions as per the learning.

While training an ML-based model, we need certain machine learning training data sets to feed into the algorithm allowing it to learn more about the processed information.        

Today, machine learning is being utilized in a wide range of sectors including healthcare, agriculture, retail, automotive, finance and so many more.

Machine Learning Examples in Real Life

Recommendation on your Mobile or Desktop based on your web search history, Virtual Assistance, Face & Speech Recognition, Tag or Face Suggestion on Social Media Platforms, Fraud Detection, Spam Email Filtering, are the major examples of machine learning in our daily life. Most of the AI devices are developed through machine learning training.    

What is Deep Learning?

It is the subset of machine learning that allows computers to solve more complex problems to get more accurate results by far out of any type of machine learning. 

Deep learning uses the Neural Network to learn, understand, interpret and solve crucial problems with a higher level of accuracy.

What is Deep Learning

DL algorithm-based neural networks are roughly inspired by the information processing patterns that are mainly found in the human brain. 

While learning, understanding, and predicting just like we use our brains to recognize and understand certain patterns to classify various types of information, deep learning algorithms are mainly used to train machines for performing such crucial tasks easily.

Whenever we try to perceive new information, the brain tries to compare it with the items known to the brain before making sense of it. In deep learning – neural network algorithms employ to perceive new information and give results accordingly.

Actually, the brain usually tries to decode the information it receives and archives this through classification and assigning the items into various categories.

Let’s take an example – As we know DL uses a neural network which is a type of algorithms aiming to emulate the way human brains make decisions.

The notable difference between machine learning and deep learning is that the later can help you to understand the subtle differences. Because DL can automatically determine the features to be used for classification, while ML needs to make understandable these features manually. 

Finally, the point is compared to ML, DL requires high-end machines and a substantially huge amount of deep learning training data to give more accurate results.

Deep Learning Examples in Real Life

Automated Translation, Customers Shopping Experience, Language Recognition, Autonomous Vehicles, Sentiment Analysis, Automatic Image Caption Generation & Medical Imaging Analysis are the leading examples of deep learning in our daily life.           

Summing-up 

Machine learning is already being used in various areas, sectors, and systems but deep learning is more indispensable for the healthcare sector where the accuracy of results can save the lives of humans. Though, countless opportunities lie for machine learning and deep learning to make the machines more intelligent and contribute to developing a feasible AI model.

In the healthcare and medical field, AI can diagnosis disease using the medical imaging data that are fed into deep learning algorithms to learn the tumors or other life-threatening diseases. Now deep learning is giving excellent results, even performing better than radiologists

Finally, in all types of AI, ML or DL models working on computer vision-based technology needs a huge amount of training data for object detection. These datasets help them to learn the patterns and utilize similar information for predicting the results when used in real-life.

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Artificial Intelligence in Robotics: How AI is Used in Robotics?

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AI in Robotics

Robots were the first-known automated type machines people got to know. There was a time when robots were developed for performing specific tasks, yes such machines were earlier developed without any artificial intelligence (AI) to perform only repetitive tasks.

But now the scenarios are different, AI in getting integrated into robots to develop the advanced level of robotics that can perform multiple tasks, and also learn new things with a better perception of the environment. AI in robotics helps robots perform the crucial tasks with a human-like vision to detect or recognize the various objects.            

Nowadays, robots are developed through machine learning training. A huge amount of datasets is used to train the computer vision model, so that robotics can recognize the various objects and carry out the actions accordingly with right results.       

And, further, day-by-day, with more quality and precise machine learning processes, robotics performance is getting improved. So, right here we are discussing the machine learning in robotics and types of datasets used to train the AI model developed for robots.

How AI is Used in Robotics?

The AI in robotics not only helps to learn the model to perform certain tasks but also makes machines more intelligent to act in different scenarios. There are various functions integrated into robots like computer vision, motion control, grasping the objects, and training data to understand physical and logistical data patterns and act accordingly.    

And to understand the scenarios or recognize the various objects, labeled training data is used to train the AI model through machine learning algorithms. Here, image annotation plays a key role in creating a huge amount of datasets helping the robotics to recognize and grasp different types of objects or perform the desired action in the right manner making AI successful in the robotics.     

Application of Sensors in Robotics

The sensor helps the robots to sense the surroundings or perceive the visuals of the environment. Just like five key sensors of human beings, combinations of various sensing technologies are used in the robotics. From motion sensors to computer vision for object detection, there are multiple sensors providing a sensing technology into changing and uncontrolled environments making the AI possible in the robotics. 

Uses of Types of Sensors in Robotics:

  • Time-of-flight (ToF) Optical Sensors
  • Temperature and Humidity Sensors
  • Ultrasonic Sensors
  • Vibration Sensors
  • Millimeter-wave Sensors

Nowadays a wide range of increasingly more sophisticated and accurate similar sensors, combined with systems that can fuse all of this sensor data together is empowering robots to have increasingly good perception and awareness for the right actions in real-life.  

Application of Machine Learning in Robotics

Basically, machine learning is the process of training an AI model to make it intelligent enough to perform specific tasks or some varied actions. And to feed the ML algorithms, a set of data is used at a large scale to make sure AI models like robotics can perform precisely. As much as training data will be used to train the model, the accuracy would be at the best level. 

In robotics, it is trained to recognize the objects, with the capability to grasp or hold the same object and ability to move from one location to another location. Machine learning mainly helps to recognize the wide-ranging objects visible in different shapes, sizes and various scenarios.

Also Read: Where Is Artificial Intelligence Used: Areas Where AI Can Be Used

And the machine learning process keeping running if robots detect new objects, it can make the new category to detect such objects if visible again in the near future. However, there are different disciplines of teaching a robot through machine learning. And deep learning is also used to train such models with high-quality training data for a more precise machine learning process.  

APPLICATION OF AI IN ROBOTICS

AI in robotics makes such machines more efficient with self-learning ability to recognize the new objects. However, currently, robotics are used at the industrial purpose and in various other fields to perform the various actions with the desired accuracy at higher efficiency, and better than humans.

Video: Most Advance AI Robots

From handling the carton boxes at warehouses, robotics is performing the unbelievable actions making certain tasks easier. Right here we will discuss the application of AI robotics in various fields with types of training data used to train such AI models.    

Robotics in Healthcare

Robotics in healthcare are now playing a big role in providing an automated solution to medicine and other divisions in the industry. AI companies are now using big data and other useful data from the healthcare industry to train robots for different purposes.

AI Robotics in Healthcare

Also Read: How AI Robotics is Used in Healthcare: Types of Medical Robotics

From medical supplies, to sanitization, disinfection and performing the remote surgeries, AI in robotics making such machines become more intelligent learned from the data and performs various crucial tasks without the help of humans.

Robotics in Agriculture

AI Robotics in Agriculture

In the agriculture sector, automation is helping farmers to improve crop yield and boost productivity. And robotics is playing a big role in the cultivation and harvesting the crops with precise detection of plants, vegetables, fruits, and other unwanted floras. In agriculture AI robots can perform the fruits or vegetable plucking, spraying the pesticides, and monitor the health conditions of plants.

Also Read: How AI Can Help In Agriculture: Five Applications and Use Cases

Robotics in Automotive

AI in Robotics in Automotive

The automobile industry moved to the automation that leads to fully-automated assembly lines to assemble the vehicles. Except for a few important tasks, there are many processes performed by robotics to develop cars reducing the cost of manufacturing. Usually, robotics is specially trained to perform certain actions with better accuracy and efficiency.

Robotics at Warehouses

AI Robotics at Warehouses

Warehouse needs manpower to manage the huge amount of inventory kept by mainly eCommerce companies to deliver the products to their customers or move from location to another location. Robotics is trained to handle such inventories with the capability to carefully carry from one place to another place reducing the human workforce in performing such repetitive tasks.

Robotics at Supply Chain

AI Robotics at Supply Chain

Just like inventory handling at warehouses, Robotics at logistics and supply chain plays a crucial role in moving the items transported by the logistic companies. AI model for robotics gets trained through computer vision technology to detect various objects. Such robotics can pick the boxes and kept at the desired place or load and unload the same from the vehicle at faster speed with accuracy.

Training Data for Robotics    

As you already know a huge amount of training data is required to develop such robots. And such data contains the images of annotated objects that help machine learning algorithms learn and recognize the similar objects when visible in the real-life.

Also Read: Top 5 Applications of Image Annotation in Machine Learning & AI

And to generate a huge amount of such training data, image annotation techniques are used to annotate the different objects to make them recognizable to machines. And Anolytics provides the one-stop data annotation solution to AI companies to render high-quality training data sets for machine learning-based model development.      

Also Read: What Is The Use And Purpose Of Video Annotation In Deep Learning

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Artificial Intelligence in High-Quality Embryo Selection for IVF

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artificial intelligence embryo selection IVF

IVF treatment is becoming a common practice in today’s reality, where 12% of the world population struggle to conceive naturally. But thanks to artificial intelligence in IVF, the whole process is going to help the embryologists to select the best quality embryos for in-vitro fertilization improving the success of conception through artificial insemination.

As per the latest study published in eLife, a deep learning system was able to choose the most high-quality embryos for IVF with 90% accuracy. Compared to trained embryologists, the deep learning model performed with an accuracy of approximately 75% while the embryologists performed with an average accuracy of 67%.

As per the research stated, the average success rate of IVF is 30 percent. The treatment is also expensive, costing patients over $10,000 for each IVF cycle with many patients requiring multiple cycles in order to achieve successful pregnancy.

Risk Factors in IVF Treatment

While multiple factors determine the success of IVF cycles, the challenge of non-invasive selection of the highest available quality embryos from a patient remains one of the most important factors in achieving successful IVF outcomes.

artificial intelligence in ivf

Currently, tools available to embryologists are limited and expensive, leaving most embryologists to rely on their observational skills and expertise. As selection of quality embryo increases the pregnancy rates, that is now possible with AI.

Also Read: How Artificial Intelligence Can Predict Health Risk of Pregnancy

Researchers from Brigham and Women’s Hospital and Massachusetts General Hospital (MGH) set out to develop an assistive tool that can evaluate images captured using microscopes traditionally available at fertility centers.

artificial intelligence embryo selection

There is so much at stake for our patients with each IVF cycle. Embryologists make dozens of critical decisions that impact the success of a patient cycle. With assistance from our AI system, embryologists will be able to select the embryo that will result in a successful pregnancy better than ever before,” said co-lead author Charles Bormann, PhD, MGH IVF Laboratory director.

AI in Embryo Selection through Machine Learning

The team trained the deep learning system (sub branch of machine learning) using images of embryos captured at 113 hours post-insemination. Among 742 embryos, the AI system was 90% accurate in choosing the most high-quality embryos.

ivf machine learning
AIVF’s deep learning and computer vision algorithms applied to time-lapse videos and stills of embryo development with proprietary markers and identifiers. Image Credit

The investigators further assessed the system’s ability to distinguish among high-quality embryos with the normal number of human chromosomes and compared the system’s performance to that of trained embryologists help in healthy baby growth in the womb.

Also Read:  What Causes A Baby To Stop Growing In The Womb During Pregnancy

The results showed that the system was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the US.

However, the deep learning system is meant to act only as an assistive tool for embryologists to make judgments during embryo selection but going to benefit clinical embryologists and patients. Actually, a major challenge in the field is deciding on the embryos that need to be transferred during IVF and such AI models can make right decisions. 

Machine Learning Training Data for AI Model

The research stated that deep learning model has potential to outperform human clinicians, if algorithms are trained with more qualitative healthcare training datasets. Advances in AI have promoted numerous applications that have the potential to improve standard-of-care in the different fields of medicine.

Though, few other groups use to evaluate different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a deep learning system trained on a large dataset to make predictions based on static images.

Such findings could help the couples become parents through IVF with higher chances of conceptions with right embryos selections. And further with more improvement in training development of AI systems will be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality embryos.

Watch Video:  Future of AI in Embryo Selection for IVF

Source: Health Analytics

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