Artificial Intelligence (AI) market is posing to become billions of dollar industry in next few years, as global spending by nations on AI is likely to touch around $35.8 billion in 2029 which reports a growth of 44% over the amount spent in year 2018.
Such, impressive growth shows, AI holds huge potential to attract big organizations as well small enterprises attracting them to implement AI-enabled services for better growth in the business. However, working with AI you need immense amount of meticulous data to train the model so that it can give the precise results.
Actually, to train an AI or ML model a high-quality training data is required, which is a challenging task for AI developers or machine learning engineers. As, to get the human like complex decisions from machines you need enormous volumes of accurately labeled and annotated training data through images or videos.
With the growing AI demand, data science team are under pressure to complete the projects but acquiring the training data at a large scale is the real challenge they are facing right now.
Why Do Enterprises Face Data Issue for AI Strategy?
As per the research by Dimensional Research and Aiegion survey, enterprise machine learning is just beginning, machine learning engineers or data scientist team size is smaller and the expertise of growing data science is not yet compatible to matured ML projects expertize.
And acquiring the training data is the biggest challenge for the success of an AI project. As per the survey, 96% of the AI projects fail or not started due to lack of training data technology that leads to the inability to train the ML algorithms resulting failure of the project.
Half of the AI Projects Never Get Deployed
Nowadays, big organizations or enterprises having more than 100,000 employees are more keen to implement AI strategy into their business model – but only 50% of such enterprises currently have one. The survey reinforces that AI is at nascent in the enterprise, as 70% of them firstly invested in AI/ML projects in the last 24 months.
While on the other hand, over half of the enterprises report they have undertaken fewer than four AI and ML projects. And only half of the enterprises have released AI/ML projects into the development to build a fully-functional model.
And as per the survey research only, less than two-thirds of them indicated that their ML project reached the completion point that is being trained on labeled training data sets which are relatively at the initial stage in the ML project life cycle. And more revealing immaturity of ML in the enterprise, is that why half of the projects never deployed.
Survey Statistic Why AI/ML Projects Fail:
- 78% of AI/ML Projects Shut ate some stage Before Deployment
- 81% Admit the process of training AI with data is more difficult than they expected
- 76% struggle by attempting to label or annotate the training data on their own.
- 63% try to build their own labeling and annotation automation technology.
And as per the research, around 40% of failed projects reportedly stalled during training data-intensive phases like training data preparation, algorithms training model validation, scoring and post-deployment enhancement.
Top Reasons for AI Projects Failure:
- Lack of Expertise (55%)
- Unexpected Complications (55%)
- Training Data Problems (36%)
- Lack of Model Certainty (29%)
- Deficient Budget (26%), and
- Lack of Efficient Staff (23%)
As already bespeak, around two-thirds report that ML projects not able to progressed beyond proof of concept and algorithms development to the phase of training data. Mostly this phase is not favorable for such developments, as 80% report that training the algorithms is more challenging than the AI engineers have expected.
Reasons Why Training Algorithms Data is Challenging:
- Notenough data
- Datanot in a usable form
- Biasor errors in the data
- Don’thave the tools to label the data
- Don’thave the people to label data
Nevertheless, less than 4% have reported that training data has presented without any problems. Almost three-quarters of the AI engineers indicated that they try to label and annotate training data on their own. While around 40% suggested they rely wholly or partially on off-the-shelf, pre-labeled data to train their AI model.
Such issues, lead to 70% companies utilizing external services for their AI or ML projects with most of them focusing on data collection, labeling and annotations. As AI and ML engineers are rare to find and also expensive, the enterprise should find out external solution service providers for critical activities like data labeling and model scoring. This evidence is enough to outsource data annotation for more improved outcomes.
Enterprises designate a strategic value to their machine learning initiatives and expect AI and ML shall improve their businesses aspects and would be also disruptive in their sectors.
However, AI and ML projects are still at an early stage of development at enterprises. And data science and AI engineer teams are relatively small and experienced which affects the efficiency and outcome of these projects.
AI in Fashion: Applications with Use Cases & Role in the 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
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.
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 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
AI in inventory and supply chain management is facilitating to speed-up by improving routes, cutting the logistic supply and shipping cost.
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 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.
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.
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.
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.
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.
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-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.
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 Impacton 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-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.
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.
How AI Can Detect Low Sugar Level in Humans Without Blood Sample?
AI in healthcare is strengthening its presence with new capabilities to diagnosis the health conditions of people with an acceptable level of accuracy. Recently we have shared post explains how does Google AI detect breast cancer better than radiologists.
And now, a new study came out in which researchers developed a new AI-based technique that can detect the low sugar levels from raw ECG signals using the wearable sensors without any fingerpicking test or taking the blood samples.
Currently, apart from giving the blood samples at diagnosis centers, diabetic people use to measure glucose using the needles at their home with repeated fingerpicks over the day. This is a painful process deterring patient compliance.
But now a new technique developed by researchers at the University of Warwick works with an 82% accuracy, could be the best option for invasive finger-prick testing with a needle, especially for kids who are afraid of needles fingerpicking.
How AI Detect Low Sugar Levels or Hypoglycemia?
The University of Warwick researchers examined how ECG readings changed during a hypoglycaemic event, when blood sugar levels fall below four millimoles per litre. And they then used the AI system to recognize low levels compared to normal readings.
As per Dr Leandro Pecchia, from Warwick’s School of Engineering, the AI model has been trained to detect such hypoglycaemia via few ECG beats. This is possible as ECG can be detected in any circumstance, even while sleeping.
How AI is Trained to Detect Sugar Level in Humans?
The study is also explained how the AI was trained on the specific patterns of individual patients rather than on cohort data. This is because the ECG signals that correspond to hypoglycaemic events are different for each patient.
The figure shows the output of the algorithms over time – the green line represents normal glucose levels, while the red line represents the low glucose levels.
However, this makes difficult, or almost impossible to develop a single AI algorithm that could be rolled out for all patients. However, this may restrict the technology to some degree but allow more tailored treatment of individual patients based on their personal ECG data.
This result is possible because the Warwick AI model is trained with each subject’s own data. Intersubjective differences are so significant, that training the system using cohort data would not give the same results. Likewise, personalized therapy based on our system could be more effective than current approaches.
However, more precise and magnitude of training data will help the AI model learn to detect with more variations while diagnosing such diseases. Getting healthcare training data is crucial for AI engineers to develop such models that can detect the various types of diseases among humans.
To avoid diabetes and keep your body fit, make sure to do some workouts and regular exercise and control your sugar intake. A high or low sugar levels both are dangerous for health and if not cured with precautions, it can become life-threatening diseases.
Why it is important for diabetes patients to check their glucose levels regularly?
Diabetes is a serious life-long condition that occurs when the amount of sugar in the blood is too high because the body can’t use it properly. Patients have to regularly monitor their glucose levels to prevent them from developing any potentially fatal complications.
Type 1 diabetes patients are often recommended to test their blood sugar at least four times a day. For type 2 patients, doctors advise testing twice a day. Blood glucose levels should be between the ranges of 3.5–5.5 mmol/L before meals and less than 8 mmol/L, two hours after meals.
What is Hypoglycemia and How it is Dangerous for you?
Hypoglycemia (when blood sugar drops below 4 mmol/L) can occasionally lead to patients falling into comas in severe cases. However, it most often can be treated through eating or drinking 15-20g of fast-acting carbohydrate, such 200 ml of Lucozade Energy Original.
Sufferers can tell they are experiencing a hypo when they suddenly feel tired, have difficulty concentrating or feel dizzy. Type 1 diabetes patients are more likely to experience a hypo, because of the medications they take, including insulin.
Hyperglycemia (when blood sugar is above 11.0 mmol/L two hours after a meal) can also have life-threatening complications. It happens when the body either has too little insulin, seen in type 1,or it can’t use its supply properly, most often in type 2.
In the short-term, it can lead to conditions including ketoacidosis – which causes ketones to be released into the body. If left untreated, hyperglycemia can lead to long-term complications, such as impotence and amputations of limbs.
Regular exercise can help to lower blood sugar levels over time, and following a healthy diet and proper meal planning can also avoid dangerous spikes.
How Does Google AI Detect Breast Cancer Better Than Radiologists?
AI in healthcare is becoming more crucial with early detection of various diseases with better accuracy. Cancer is one the widespread deadly disease can be now detected through machine learning and AI-enabled automated machines.
Breast cancer is most common among women worldwide. However, more than 90% of women diagnosed with breast cancer at the earliest stage survive their disease for at least 5 years compared to around 15% for women diagnosed with the most advanced stage of the disease, which is now possible with AI.
Though, AI is already diagnosing breast cancer but owing to accuracy, the reliability among the doctors was less. But now Google has developed an AI system that can detect the early signs of breast cancer better than radiologists.
Research of Google Breast Cancer AI
In the research, the Google’s AI model called DeepMind AI was trained and tuned on a representative data set comprised of de-identified mammograms from more than 76,000 women in the U.K. and more than 15,000 women in the U.S.
The AI model is trained to see if it could learn to spot signs of breast cancer in the scans. The model was then evaluated on a separate de-identified data set of more than 25,000 women in the U.K. and over 3,000 women in the United States.
In this study, researchers conducted a test where Google’s AI system was pitted against six radiologists where the AI managed to outperform all six of them at accurately detecting breast cancer among these women at better accuracy.
Earlier, when a similar study is conducted in which the team first trained AI to scan X-ray images, then looked for signs of breast cancer by identifying changes in the breasts of the 28,000 women. They then checked the computer’s guesses against the women’s’ actual medical outcomes, reducing the reliability of such applications.
But now, the accuracy level has improved and reduced false negatives by 9.4% and cut down false positives by 5.7% for women in the US. In the UK, where two radiologists typically double-check the results, the model cut down false negatives by 2.7% and reduced false positives by 1.2% make AI more reliable.
How Does AI Detect Breast Cancer?
Breast cancer diagnosis is done by oncologist’s human knowledge and intuition of what major risk factors might be, such as age, family history of breast and ovarian cancer, hormonal and reproductive factors, and breast density.
While AI in breast cancer diagnosis, rather than manually identifying the patterns in a mammogram that drives future cancer, the MIT/MGH team trained a deep-learning model to deduce the patterns directly from the medical imaging data.
Video: How AI Model Improves Breast Cancer Detection on Mammograms
And using the information from more than 90,000 mammograms, the model detected patterns too subtle for the human eye to detect the cancer cells. The Google breast cancer AI algorithms are used to learn such patterns and predict.
You can check below an image showing the visualization of tumor growth and metastatic spread in breast cancer with screening to detect breast cancer early, before symptoms develop.
Actually, the demographics of the population studied by the authors are not well defined in the previous AI-based detection. As the performance of AI algorithms can be highly dependent on the population used in the training data sets.
And these training data sets are created by annotating the medical images of breast infected with cancer available in various formats like X-rays, CT Scan and MRI. A huge amount of such labeled data is used to train the AI algorithms.
There are many companies providing healthcare training data with annotated medical imaging to train the AI and ML models with accuracy.
And with the availability of such data, detection of various types of other common cancer through AI will become possible benefiting the humans saving their life from such maladies.
Also Read: How Can Artificial Intelligence Benefit Humans?
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