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Machine Learning

Machine Learning applications in everyday life

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Undoubtedly Machine Learning, as well as artificial intelligence, have significantly changed our world in the last few decades. Today it seems to be a breakthrough on how ML and AI applications are utilized for positive development to the point that it is hardly recognizable whether it is real or only being sensationalized. As Machine Learning and artificial intelligence are mimicking our brains, reading our minds or driving our cars there is a very thin difference lies between reality and smart applications.

Let’s dive deep and discuss the top four Machine Learning applications in everyday life:

Here are a few of the interesting and amazing ways how Machine Learning is impacting our everyday lives.

  1. Making your mind on what to eat

Dine-in check it out? Chinese? What do you feel like eating today? Fortunately, Machine Learning can help you make this decision and find exactly what are you looking for. These simple forms of technology already exist in various restaurants review apps, maps, food delivery services that track what kind of food you might like to eat at what time of the week. They also make predictions about what you might need at your location, time of the day, history etc.

Eventually, this data is compiled and based on the reviews on various food sites. And text analysis is performed to find out that what kind of rice or noodles is popular in those regions. Hence, by combining all of this analysis with brands, regions and other ratings, smart applications of Machine Learning are advising you with the best variety of noodles you are looking for. Technology has improvised your life as well as your decisions just by analysing a few of the parameters like region, ratings or dining histories and more.

2. Moderating Social Media

You might be aware that one of the brand leaders like Facebook, has already turned to artificial intelligence for keeping its platform in check and also helping its people to the fullest. Artificial intelligence they have developed is playing a crucial role of human moderators today.

Facebook algorithm is already trained to recognize fact-check misinformation as well as its pictures. Also, a few of the prime features identify duplicate images that are often circulated by bots as Meme or infographics for trolling. This Machine Learning application has helped various social media platforms mitigate the risk of circulating false information and images that are often utilized for nefarious purposes. 

Hence, today artificial intelligence can detect any post which is similar or not the same as the banned content. In this way, Machine Learning applications today is making the work of the moderators easy to police the social media platforms.

3. To help fight Covid-19

Almost all industries starting from automotive manufacturers to retailers everyone has stepped up to help and contribute to get over the current crisis and the data science community is no exception here. Learning is completely instrumental in projecting and tracking the spread of the virus and, now that has helped in slowing down the spread as well.

Various stores and workplaces that are re-opening have integrated multiple Machine Learning algorithms that ensure employees and customers are safe in the environment. The existing security cameras are being equipped with artificial intelligence technologies that inform the managers when people are not properly maintaining social distancing or wearing masks in the office space.

Though few of the technologies already existed, AI and ML has advanced this technology by monitoring the safety gears for the safety measures if properly followed or not in various working environments which further helps in reducing the spread of the virus.

4. Helping in better translation

There must have been times when you have pulled out your phone for translating a phrase that you might not understand. You might have also searched on how to pronounce a word in the right way. Are you aware that translation and speech technology are nothing new and are available for years now?

Hence translating to related model languages is one thing and translating a 2500-year-old hand-carved language into modern English is the pain and that might be time-consuming as well as a task of an expert archaeologist. But with a touch of AI-powered computer vision can make this possible and automatic at the same time today as various ancient Persian cuneiform are translated by researchers with the help of which also ensure accuracy. Hence, this technology just does not work on ancient tablets but, this language could be translated even handwritten into cursive through a computer vision model.

Through the above-mentioned ways, ML has entered our daily lives and made it easy for us and some of us have might not notice it also. These are just a few examples of how Machine Learning got entwined in our daily lives and there will be even more in near future.

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Data Labeler offers excellent data labeling services for Machine Learning and AI projects. We offer quality-labeled, convenient and customized datasets which increases your competitive advantage and growth. Are you looking for data annotation services, contact us now – sales@datalabeler.com

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Artificial Intelligence

How ML and AI is changing the Finance Game?

Data labeler - ai and machine learning in fintech

Rise of Fintech:

Financial Services and Financial Technology companies are rampantly utilizing artificial intelligence solutions to meet their business goals increase their efficiency lower their budgets and improve their operations effectively. Machine Learning in banking is gaining wide popularity in the Fintech sector starting from public relations to investment decisions and minimum. But, how exactly finance companies today are incorporating Artificial Intelligence technology to drive desired results.

Present-day personalized Machine Learning development is utilized in multiple industrial sectors. In the world of finance, there is an aggressive rise of artificial intelligence applications which are predicted to reach 7305.6 million USD by end of 2022. Machine Learning algorithms are widely used in financial work for pattern identification fraud detection financial analytics platform insurance and expense management and more.

Here’s how Machine Learning and Finance today is going hand in hand:

  • Insurance and InsurTech

Now detect customers’ risks profile and provide the right plan to them and all this you can do is by leveraging Machine Learning algorithms and quoting optimal prices and helps in managing the claims easily. It also helps you improve your customer satisfaction and reduce cost-effectively.

  • Financial Analytics Platform

Financial analytics helps you with differing perspectives on multiple financial data of a given business. Provides relevant insights that facilitate strategic decisions as well as actions that could improve your overall performance of the business. It is a unified solution that combines technologies to meet business requirements across the end-to-end analytics lifecycle, from data storage, data preparation determiners, and other data analytics processes.

These analytics platforms help the data scientist in discovering various data sources and hardware across the organization. It also helps in improving and integration by deploying models which can be assessed by any environment via an API.

  • Regulatory Compliance

By utilizing natural language processing for the fast scan of legal and regulatory documents for various compliance issues Machine Learning algorithms and artificial intelligence technologies come in handy while doing so at a massive scale. These technologies manage thousands of paperwork without any human interactions seamlessly with the least errors.

  • Detection of Fraud

Machine Learning and artificial intelligence technologies seamlessly detect fraudulent and abnormal financial behavior in general regulatory compliance matters as well as and workflows. It improves the compliance matters as well as the workflows with its efficient ability to detect any fraud or errors. Hence, it decreases your operation cost and limits your exposure to fraudulent documents.

  • Artificial Intelligence Chatbot

With the rise of artificial intelligence chatbots and mobile application assistant applications, you can monitor your personal finances effectively. You can get your own finance assistant and set your savings goals and spending rates according to your wish. Moreover, your finance assistant will also handle your finances and provide you with the insights to reach your desired financial targets in no time.

Conclusion:

Machine Learning in Fintech can evaluate massive data sets of simultaneous transactions in real-time and their ability to learn from the results and update models minimizes human output.

Partner with Data Labeler to bring projects to market safely, quickly, and successfully.

Data Labelers save your time and money by combining deep subject matter expertise with AI and deliver you high-quality personalized datasets to train your Machine Learning models seamlessly.

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Artificial Intelligence

AI and ML has embarked its journey into the Healthcare Sector

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Deep Learning applications are looking for more data every day. And the more quality and high labeled data a developer feeds into an AI model the more accurate is the inferences. However, creating datasets is one of the biggest challenges developers and data scientists are facing today while building their machine learning models and Artificial Intelligence initiatives.

But today there are a lot of startups that are rising and have successfully created a web platform that can help the researchers as well as companies manage and analyze data labels in workflow and also utilize AI-enabled segmentation tools for improving the quality of their training data sets. Hence when the labels are accurate Artificial Intelligence models launch quickly and reach a high level of accuracy in no time.

A lot of Artificial Intelligence and Machine Learning startups are seamlessly building new initiatives for the healthcare sector. For example, how NVIDIA T4 GPU has its inference in Google Cloud contributed in Healthcare radiology by speeding up the customer labeling by 10x and reduced the labeling error rate by 15% or more.

Let’s dive deep into understanding how data annotation and machine learning are helping in the advancement of the modern Health Sector:

  • Trainingdata.io

Trainingdata.io chose to develop its platform on a cloud-based platform used to scale up and down usage seamlessly based on client demand. Companies are making the most of this tool which can choose whether to use the interface online or connected to the cloud in the backend or also use a containerized application for running on their own premises of the GPU system.

Artificial Intelligence themes in Healthcare sectors must make sure that the information of the patient is secure. Balzano, a Switzerland-based startup that is building deep learning models for a geologist with the help of training data dot link to an open promisors server of Nvidia v100 tensor GPU. For developing advanced data sets for musculoskeletal orthopedics-related tools. The company has labeled more than a hundred radiology images each month and has adopted training data.in saving the company a lot of engineering efforts for building a similar solution from scratch.

Trainingdata.io also allows the startup to annotate and segment the features of the knee and cartilage more effectively. As they are ramping up their annotation process they are certain that the platform will empower them to leverage AI capabilities to their best and ensure the segmented images are of high quality.

  • Viz.ai

Viz.ai is yet another robust Artificial Intelligence platform who are improving the Healthcare sector with their inclusion software system. One of the primary features of the software is the promise to reduce the time of the treatment, improve the access to care, and speed up the diffusion of medical innovation. They tend to accelerate the time-saving and increase the provider productivity and with more time the providers could treat additional patients or recharge effectively. Patients with multidisciplinary needs can coordinate and improve outcomes for patients. Viz.ai was founded by Dr. Chris Mansi, a neurosurgeon who was frustrated by the delays and processes in the medical industry to make the best use of Artificial Intelligence to transform the healthcare sector to work effectively.

Viz.ai specializes in using Artificial Intelligence to synchronize the stroke in the systems and reducing systematic delays which stand between the patient and the life-saving treatments. It is a remarkable way of making the best possible use of cutting-edge technology to transform stroke workflow and patient care. Please dot status products that detect an alert stroke to the teams to suspected large vessel occlusion stroke and complete CT perfusion studies in their network itself just within minutes. Later, the stroke team consults in real-time through a HIPAA-compliance mobile interface driving the treatment as fast as possible to save the patient’s life.

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Data Annotation and Labeling is a thriving need in the industry and if you have not thought about it till now, who knows you might be lagging.

Data Labeler unleashes a wide range of services for your respective business needs. Contact us now to know how we can help you grow your business with AI and ML – sales@datalabeler.com

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Data Labeling

The Crucial Role of Data Labeling in Boosting the Healthcare Sector

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The unexpected hit of the pandemic has thrown several challenges to the global medical sector. Health workers are working day and night to fight against the deadly disease. However, to boost the healthcare facilities AI and ML have been contributing in multiple healthcare areas such as disease prevention and control, medical research or diagnosis, patient treatment, and management. Artificial Intelligence and machine learning empower the system to improve its capacities and effectiveness by automating various health care activities. Presently AI and ML are helping robots and digital assistants with real-time analysis which is empowering doctors for providing effective and personalized treatments seamlessly.

The rapid growth of the data labeling industry as well as striving integration of machine learning and artificial intelligence has touched almost all sectors. This is because unlabeled raw data is everywhere and present in huge quantities. Most machine learning and artificial intelligence algorithms need data labeling and annotation to learn and train themselves.

What is a Data Labeling?

A data labeling platform has transformed the industrial sectors through advanced tools for real-time workflow management. Developers could define and begin a data labeling process that provides API for data transfer. These platforms enable users to audit the data quality.

Hence, data labeling is a procedure by which annotators tag several data types like images, videos, text, audios with the help of computers, and once it is finished the manually label datasets are fed into machine learning algorithms to train AI models. This is why data annotation is not only laborious work but it is also a time-consuming process. Most of the time companies buy labeling tools, opt for data labeling services or pick in-house teams.

Here’s how the Healthcare sector is benefited by Data Labeling and Annotation Services:

  • Medical Image Labeling

High-quality training data is crucial for creating machine learning models which aid in improving the medical imaging diagnosis. But there is a great challenge in the availability of high-quality training data. More precisely medical imaging annotations are performed by specialists who are both time-consuming coffee. Therefore, cleaning the data is one of the most important parts and also 80% of the work. Hence the lack of good quality data sets arises as a big challenge in the machine learning industry limits the availability of providing the specific answer to a specific question only if the right data is available.

Now retinal images are developed via automated Diagnostic systems for conditions like diabetic retinopathy or age-related macular in this way massive medical images are being labeled under various conditions. This identification of small structures usually takes a lot of time for experts with high accuracy. Thus medical image labeling helps a great deal.

A few of the common applications are artificial intelligence semantic segmentation which is used for diagnosis in the liver and brain. Polygon Annotations are used in multiple dentistry applications, bounding boxes are used in detecting kidney stones. Medical image annotations provide appropriate results with great accuracy in the early detection of the diseases. Medical imaging diagnosis is also regarded as one of the powerful methods of future applications in the healthcare sector.

How is labeling transforming today’s healthcare sector?

  • Data being the Key

As machine learning is the study of computer algorithms that enhance automatically that enhances your experiences automatically it is also a part of artificial intelligence. It empowers the algorithm’s ability to learn from the training data and also identify patterns as well as make decisions with very little human intervention.

Many organizations and enterprises make use of AI in their business practices where data plays a big role because while training your algorithm needs high-quality level data. Hence data is the key and there are few labeling tools in the medical industry such as Regional Segmentation, Key Points, and Medical OCR.

About Us:

Data Labeler is a human-powered data annotation service provider that caters to high-quality training for your multiple machine learning and artificial intelligence projects. We at Data Labeler provide results that are thoroughly assessed and analyzed by a robust human workforce as well as machines.

We affirm the maximum accuracy rate of data labeling and annotation. Personalized high-quality annotation services according to the customer requirements and demands apart from that we assure you of no data leak as the data is compressed and preprocessed.

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