The startup scene in Canada has been growing in recent years, with a number of successful companies emerging in a variety of industries. Canada has a strong economy and a well-educated workforce, which has helped to create a favorable environment for startups. In addition, the government has implemented various initiatives to support the growth of startups, including funding programs and tax credits.
The technology sector is particularly strong in Canada, with many startups focusing on areas such as artificial intelligence, cloud computing, and e-commerce. However, there are also a number of startups in other industries, such as healthcare, finance, and retail.
Overall, the startup ecosystem in Canada is thriving and there are many opportunities for entrepreneurs to launch and grow successful companies.
Every year around 95,000 new businesses are started in Canada, a few make a fortune. Let’s take a look at some of the Top successful Canadian Startups in 2023.
Loopio is one of Canada’s Fastest Growing Companies with three-year revenue growth of 268%. It has been ranked in Canada’s Top Growing Companies consecutively for four years by Globe and Mail’s Report on Business. It has twice secured its spot on the Deloitte Technology Fast 50™ list and LinkedIn’s Top Startups.
Loopio is a startup that helps companies answer RFPs, Security Questionnaires, and more. It helps the team to be very responsive, improves response quality, and helps them win more business.
2. Opencare
Co-founder
Cameron Howieson
Founded
2012
Headquarters
Toronto, ON, Canada
Industry
Health, Wellness & Fitness
Company size
11-50 employees
Total Funding Amount
$24.6 Mn
Co-founder of Opencare | Cameron Howieson
Open care’s vision is to connect users with top local dentists based on their own choices. It is a modern change that has a command over the traditional industry. Through connecting with specialized dentists in the area, Open care is highly focused on optimal health and is growing by leaps and bounds.
Opencare has built a selected network of top-rated dentists across North America. They are committed to delivering outstanding oral wellness.
3. Connected
Co-founder
Mike Stern
Founded
2014
Headquarters
Toronto, Ontario, Canada
Industry
Software Development
Company size
51-200 employees
Total Funding Amount
–
Co-founder of Connected | Mike Stern
Not particularly a design agency or consultant, Connected (now part of Thoughtworks) is a platform that helps brands build software products used in the research and development phase. The company has an access to a share option plan, where employees own shares in their company. It ranked one of LinkedIn’s top 25 startups, in Canada in 2019.
Company culture is widely built. Each Connected employee experiences an equal amount of respect and as a result, Connected is considered the best workplace. And the greatness includes a house for each employee if needed, along with progressive competitions amongst the groups periodically.
4. Ritual
Co-founder
Ray Reddy
Founded
2014
Headquarters
Toronto, Ontario, Canada
Industry
Consumer Services
Company size
201-500 employees
Total Funding Amount
$134.8 Mn
Co-founder of Ritual | Ray Reddy
Ritual is perhaps one of Canada’s best-known startup apps that helps users to get their pre-ordered foods from their favorite restaurants and coffee houses. With Ritual, there is no need to fight the crowds or stand in long queues.
Unlike India’s Zomato and Swiggy, this pre-ordering app is also known for its suitability and reliability. It also has the advantage of piggybacking on orders, where one of your mates can pick up more than one order on behalf of multiple people.
5. League
Founder
Michael Serbinis
Founded
2014
Headquarters
Toronto, Ontario, Canada
Industry
Hospitals and Health Care
Company size
501-1,000 employees
Total Funding Amount
$171.1 Mn
Founder of League | Michael Serbinis
League is a one-to-one platform for employees to engage with their lifestyle, health, and benefit programs. According to Linked in’s annual rankings, the rapidly growing hub is counted in Canada’s top startups-three years running.
Since its 7 years founding, League’s members have reached over 40 million and the company has raised $220 million to date. It grabbed The Next HealthTech Unicorn Award in 2021.
6. Maple
Co-founder
Brett Belchetz
Founded
2015
Headquarters
Toronto, Ontario, Canada
Industry
Hospitals and Health Care
Company size
51-200 employees
Total Funding Amount
$71.7 Mn
Co-founder of Maple | Brett Belchetz
With Maple, you can connect with Canadian licensed doctors for medical help 24/7. Online consultation isn’t new, but the Maple app takes things to a whole new level, providing immediate help from doctors and specialists.
On the contrary, with the launch of Maple, it has become way too easier to deal with immediate medical support from the healthcare professionals of your choice. Notably, a click of a button away. The two most recent investors are Loblaw Companies Limited and RBC Ventures, and the company raised $73 million in a sequence of funding.
7. integrate.ai
Founder
Steve Irvine
Founded
2017
Headquarters
Toronto, Ontario, Canada
Industry
Software Development
Company size
11-50 employees
Total Funding Amount
$49.6 Mn
Founder of Integrate.ai | Steve Irvine
Integrate.ai is one of the most talked about cross-industry in today’s technological world, which implies machine learning and innovative intelligence to target customer necessities before it is felt. However, a lot of people have already started implementing this technology app to improve their quality and outputs.
As customer data becomes increasingly on trend, integrate.ai became the world’s first AI-powered cross-industry intelligence network to master Design Certification. The Former Facebook and Instagram executive Steve Irvine is the founder and CEO of the company.
8. Borrowell
Founders
Eva Wong, Andrew Graham
Founded
2014
Headquarters
Toronto, Ontario, Canada
Industry
Financial Services
Company size
51-200 employees
Total Funding Amount
$92 Mn
Founders of Borrowell | Eva Wong, Andrew Graham
Borrowell is a financial technology company that offers Canadians free access to their credit score, including recommendations for financial tips and tools to improve credit scores. Listed as the World’s top financial technology company for its transparency, Borrowell will be a trailblazer for many such factual ideas.
Borrowell has been named one of the Best Workplaces in Canada every year since 2019. It has been recognized as Globe and Mail’s Top Growing Companies (2021), LinkedIn’s Top 15 Startups in Canada (2021), and CB Insights’ Top 250 Fintechs (2021). It is trusted by over 2 million Canadians.
9. Mejuri
Co-founder
Noura Sakkijha
Founded
2013
Headquarters
Toronto, Ontario, Canada
Industry
Retail Luxury Goods and Jewelry
Company size
201-500 employees
Total Funding Amount
$28 Mn
Co-founder of Mejuri | Noura Sakkijha
Mejuri is Canada’s most popular marketing-targeted company that deals directly with the consumer. Mejuri sells fine jewelry at comparatively-low prices and has expanded as a far-reaching jewelry house. The brand operates directly to consumers (online), to sell through brick-and-mortar storefronts. Re-launched in 2015, the company has female employees in maximum.
“I founded Mejuri because I saw a jewelry industry that was built for men gifting women and not women celebrating themselves. To me, the truest expression of Mejuri is mutual uplift: all of us supporting each other, and you, our community, feeling empowered to invest in yourself and, in turn, the community around you.” – Noura Sakkijha, CEO
10. Ada
Co-founder
Mike Murchison
Founded
2016
Headquarters
Toronto, Ontario, Canada
Industry
Software Development
Company size
201-500 employees
Total Funding Amount
$190.6 Mn
Co-founder of Ada | Mike Murchison
Ada is an artificial intelligence where businesses are allowed to speak directly with prospective customers through a chat box. As mountains of businesses pile up, customers feel discomfort, and thereby any businesses in particular need to be properly explained and analyzed, to be able to make better business decisions.
Ada’s growth has been augmented to date. To focus on issues of greater impact, Ada allows live agents too, providing a support role for any queries that customers have. It is recognized as one of the best leading customer service automation companies.
Conclusion
Canadian startups are growing abruptly, we see many new startups lining up in the space. Hence there is a sense of innovation to maintain a competitive edge, probably for the recognition of a leading sector and for the brand familiarity to reach across various platforms shortly.
FAQs
Is Canada good for a startup?
Yes, Canada is a financially safe place to do business as it has a stable economy, and there is support from both the national and local levels for the startup.
Which country is best for startups?
The United States is considered the best country for startups.
Which country has the most entrepreneurs?
The United States has the most number of entrepreneurs.
How many startups fail in Canada?
Business failure statistics show that about 96% of small businesses survive for one full year, 85% survive for three years and 70% survive for five years.
When director Steven Spielberg created the movie A.I., a wave of being-human swept across the world. We fancied much about A.I., but we also craved that human-like feel to be real and be loved. And although we have already started seeing the advent of A.I. on the horizon, it has still not matched the level of human imagination. However, when we open up the internet and type AI, we get loads of websites that claim to be creating AI content. This, although seems helpful, sucks away the creativity and originality out of the internet’s fabric. As a business paying for original content, you’d want to ensure that it indeed is original. And to help you detect unoriginal content, there are plenty of tools available that you can seek help from.
Originality.ai, as the name suggests, is a tool that uses AI to detect AI. Seems a bit paradoxical, but it uses machine learning and works around the sentence structure to check the originality of the text. This tool also detects plagiarism and ensures that the written work is original. If you are planning to consider Originality.ai as your go-to tool, here are the pros and cons to look for beforehand.
Pros:
It quickly identifies plagiarism and thus saves time and effort involved.
It can help ensure that written work is original, which can be important in academic or professional settings.
Originality.ai uses natural language processing (NLP) algorithms to understand the text and identify similarities, even when the text has been rephrased or paraphrased. The tool can also check the text against a user-defined database of sources, such as a list of sources provided by a professor or a company’s internal document.
It can also help users learn about proper citation and referencing practices.
Cons:
Like any other software, this, too, is prone to false positives. There might be instances where it might detect your original content to have been written by an AI. Don’t groan about it; rather, feel proud that you have some superhuman-level skills.
It may not be able to detect all instances of plagiarism, especially if the text has been substantially rephrased or paraphrased.
It may rely too heavily on the tool and not encourage users to develop their skills in identifying plagiarism.
Pricing:
$0.01 per credit, 1 credit scans 100 words.
Writer
Website
Writer.com
Ranking
4.6 out of 5
Best For
All Businesses
Writer – Top AI Content Detector Tool
Similar to Originality, Writer.com’s AI Content Detector tool also uses artificial intelligence and machine learning to check the originality of the text. The tool works by comparing the text being checked against a database of other texts to identify similarities. When a user submits text to Writer.com’s AI Content Detector, the tool scans the text and compares it to a database of other texts, including academic papers, websites, and other sources. The tool looks for similarities in the text, such as identical or similar phrases, sentences, or paragraphs. Here are the pros and cons to look for:
Pros:
Using NLP, it cannot just detect AI content but even helps brands get unique content to elate their image
Free for up to 1500 characters (nearly 300 words)
Can quickly create different types of content like social media copy, articles, quora answers, emails, etc.
It can quickly and efficiently check large amounts of text for plagiarism
Users can also get numerous suggestions regarding citations and referencing
It has an extension that you can add to Chrome, Word, Edge, Docs, etc.
Cons:
Might fail to detect plagiarism every time
It is also prone to false positive, which mean that sometimes even human-generated content can be detected as AI-generated. Phew!
Pricing:
Plan
Pricing
Team
$18/user/month
Enterprise
Contact the vendor
Copyleaks
Website
Copyleaks.com
Ranking
4.7 out of 5
Best For
Websites that need basic AI detection
Copyleaks – Top AI Content Detector Tool
Whenever we talk about plagiarism, we talk about Copyleaks. This company mainly works on helping companies be free from plagiarism. Apart from it, it also has a free tool that allows free 300-word AI content detection. But, should it be relied upon?
Pros:
Free to use
Covers different language models
Provides chrome extension
Best for plagiarism
APIs for large-scale detections
Cons:
Does not provide a measurement of how much content is original
The AI detector is a beta version, which means in the future it might be chargeable
Pricing:
Copyleaks offers various pricing plans. Have a look at the pricing details of some packages.
Plan
Monthly Pricing
Trial (10 pages)
Free
100 pages
$10.99/month
250 pages
$24.99/month
1000 pages
$75.99/month
2500 pages
$184.99/month
5000 pages
$349.99/month
10000 pages
$679.99/month
120000 pages and more
Custom Price
Sapling.ai
Website
Sapling.ai
Ranking
4.8 out of 5
Best For
Companies that need chatting assistants
Sapling.ai – Top AI Content Detector Tool
Founded in 2018 by Ziang Xie, this tool has become an excellent AI detection tool in no time. It claims to be an excellent messaging assistant for companies that deal with customers. However, apart from being a messaging tool, it also helps companies detect AI-generated content. But how good is it at doing this?
Pros:
Offers excellent grammar and spell checking
Extension and add-ons for Google Chrome and Docs
Creates excellent content like emails, blogs, etc., using NLP
Offers quick and excellent messaging support
Integration with over 40 websites and applications
Cons:
No extension yet for the Mac browser
Integration lacks quality
Has no add-on for Google Slides
Pricing:
Have a look at the pricing details of packages offered by Sapling.ai:
Plan
Monthly Pricing
Free
$0/month
Pro
$25/month
Enterprise
Contact the vendor
GLTR
Website
Gltr.io
Ranking
–
Best For
Small businesses that need detection at smaller levels
GLTR – Top AI Content Detector Tool
GLTR, or Giant Language Model Test Room, works on detecting the GPT language model. It was developed by a team hailing from MIT-IBM and Harvard NLP. The interface is very basic and can help you detect artificially generated content through its natural language processing (NLP) algorithm. Here are the pros and cons to consider:
Pros:
Can work well on the GPT model
It is free for all users
Provides results with reasonable accuracy
Provides results with visualization
Cons:
The scientific language used could be vague for non-tech users
User interface is a bit complex
Detection is limited to the GPT database
Pricing:
GLTR is a free AI-content detector tool making it accessible to everyone.
HUMAN OR MACHINE? – Test If Writing Was Written with AI
Hugging Face (OpenAI Output Detector)
Website
Huggingface.co
Ranking
4.8 out of 5
Best For
All businesses
Hugging Face (Open AI output Detector) – Top AI Content Detector Tool
Founded in the year 2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf, this company originally was targeted at teenagers. Now, Hugging Face is best known for its cutting-edge NLP models and its open-source library of pre-trained models. It has a machine learning model that identifies and segregates different types of texts like emails, blogs, news articles, posts, etc.
Pros:
It has an easy-to-use interface
Is free and open source
Was initially meant to detect the GPT
Cons:
The use case is limited to GPT-generated content only
Could be inefficient to tackle the latest GPT model
Pricing:
Hugging Face (Open AI output Detector) is a free AI content detector tool to use.
Crossplag
Website
Crossplag.com
Ranking
4.3 out of 5
Best For
All businesses
Crossplag – Top AI Content Detector Tool
Like other AI detection tools, Crossplag is based on machine learning and NLP. The software shows results on a scale of red to green, where red means unoriginal content and green means original. Crossplag is a powerful tool, forged by the brightest minds in the field of technology, that uses advanced algorithms to scan your text and identify any instances of plagiarism, big or small, with reasonable accuracy. Here are the pros and cons of Crossplag:
Pros:
Supports over 100 languages
Simple UI that detects plagiarism with reasonable accuracy
Could be helpful in several work areas like SEO, academics, corporate content, etc.
LTI integrations
Uses the latest tech in NLP and ML
Cons:
Not well adept to the latest GPT model
Pricing could be an issue for some
Where its plagiarism is reasonably good, its AI detection may give out false positives
Pricing:
Have a look at the pricing details of packages offered by Crossplag:
Plan
Monthly Pricing
Free
$0 upto 1000 word
Pay-as-you-go
$9.99 upto 5000 words
Bundle
$149.99 upto 100000 words
Custom
Contact the vendor
Content at scale
Website
Contentatscale.ai
Ranking
–
Best For
Small scale businesses because of its ability to process limited words at a time
Content at Scale – Top AI Content Detector Tool
Founded by Justin McGill in the year 2008, ‘Content at Scale’ offers automated content creation using the advanced NLP model and machine learning. They claim to have the ability to produce undetectable AI content using their advanced technology. What more? They even have a free AI content detection tool.
Pros:
Multi-language support
Developed by experts in SEO
Processes several NLP models to generate or detect AI content
Free-to-use tools
Cons:
Can process only 2500 characters (nearly 350-400 words) at a time
Is prone to false positives, which means that it can sometimes detect human-generated content as AI generated
Does not tell which part is AI-generated
Pricing:
Pricing for Content at Scale is based on your needs, but it can cost anywhere from $0.01/word to $0.018/word.
Kazan
Website
Kazanseo.com
Ranking
–
Best For
Small to medium businesses that can keep working on average results
Kazan – Top AI Content Detector Tool
Considering companies’ current focus on SEO, Kazan came up with its SEO-basedcontent generation and optimization app. Apart from SEO, Kazan is capable of generating AI-based content too. It also uses the NLP model to generate and detect AI-based content. Here are possible pros and cons to consider before paying for its services:
Pros:
Can process GPT3 model
Simple interface
Far excels the other free tools out there
Can scan content in bulk
Free-to-use tool
Cons:
Needs a minimum of 200 and a maximum of 400 words to process the results
Has a bit distractive interface that could feel annoying at times
Pricing:
Kazan is a free AI content detector tool.
GPT-2 Output Detector
Website
Openai-openai-detector.hf.space
Ranking
4.0 out of 5
Best For
Small to medium-scale businesses that need basic reporting
GPT-2 Output Detector – Top AI Content Detector Tool
It is the easiest tool out in the market that takes your text as input and provides you with output regarding plagiarism and AI content generation. You simply have to paste the content into the interface and it’ll start doing its work.
Pros:
Easy to handle and use
Provides free AI content detection
No word limit
Cons:
The results do not give accurate data every time
There is not much written about the software
Authenticity of its results is questionable
Pricing:
GPT-2 Output Detector is a free AI content detector tool.
Conclusion
Considering the pace with which AI is entering our lives, preserving life and originality in the content becomes vital. As a business, what you put for the users defines you and your vision. If you put content that looks dull and lacks life, users might get affected adversely. To tackle it, investing in AI content detection tools become important. While the free AI content detector tools are good for basic blogs, as a big company has lots of content, you might want to make sure that no space for error is left. Invest in tools like Originality.ai, Writer.com, Crossplag, Sapling, etc. Consider what your requirements are and choose the software rightly. As a business, you might not want to depend on free software whose reliability is questionable. If you are a small website, you may depend on Copyleaks for its trustable record in terms of plagiarism.
FAQs
What is AI-generated content?
The content that AI-generated content creators create while using existing content to build new content is called AI-generated content. AI-generated content is used in a variety of applications, including news summarization, creative writing, content marketing, and more.
Can AI-generated content be detected?
Yes, AI-generated content can be detected from various tools available in the market like Originality.ai, Writer, Copyleaks, Sapling.ai, and others.
How to detect AI-generated content?
There are various ways to detect AI-generated text. One common method is to use software to analyze text features – for example, how fluently it reads, how frequently certain words appear, or whether there are patterns in punctuation or sentence length.
How do AI-generated content detection tools work?
AI-generated content detection tools compare the text to a vast database of existing content. These tools utilize advanced algorithms to identify similarities in the text that may indicate that it was generated by AI.
How accurate are these tools in detecting AI-generated content?
The accuracy of AI-generated content detection tools can vary depending on the tool, the complexity of the AI-generated content, and the type of text being analyzed.
The fashion industry is no more about just making and selling clothes. Anybody who thinks they’ll set up a good-looking website, put tons of choices, and hope that customers will follow is living in La La Land.
It doesn’t work like that anymore. The fashion industry is leveraging the latest advancements in technology to boost sales and clientele. Social media is flooded with DIYs, small fashion brands, and fashion giants so it’s safe to say that the market is booming with options for customers.
Projected Revenue of the Indian Fashion Industry from 2017 to 2027
Brands are shifting towards AI to make their voice heard and stand out from the clutter. AI will be an inseparable part of our life. Predictions show Revenue of the Indian Fashion Industry will grow 16.32% annually (CAGR 2022-2027) to reach $39.42 billion by 2027. From automated messages to attention-grabbing notifications, from suggesting sizes to customers to building preferences, fashion brands all over the world are using AI to their benefit.
Here’s how AI is being used in the Fashion Industry:
Demand and Supply Projection
Brands like H&M are sitting on huge amounts of unsold clothing that will lead to lost money. This happens because nowadays brands focus on producing bulk clothing as per the latest trends which go out of fashion swiftly, making room for new trends and thus, production of more new clothes. This costs brands a lot of money and also contributes to wastage.
Brands are now using AI to predict sales according to trends, product type, color, price, and range factors. This is helping brands minimize the extra product and generate increased revenue as there is less money wastage. Returns are also reduced due to smart prediction of demands.
Automated Clothes Sorting
AI Robots used for Sorting Clothes
Sorting and arranging clothes can be a difficult task as it requires labor and time. Most warehouses have people doing it for you and they cannot work 24/7. It is also costly and ineffective. Therefore, big brands like GAP are testing AI to sort clothing for you according to size, color, or preferences that can be just put into a box and shipped to your house via a drone. Through deep learning, the robot can be trained to handle fragile items like sunglasses more gently than jeans.
Malls and stores are being monitored by a camera that tracks the product that is out of stock. They can automatically inform the manufacturer about the shortage. To make it even more efficient, the functioning is such built that they scan data to confirm whether the same product is lying in some other store and being sold so that it can be brought to another store. This reduces wasteful manufacturing and saves money.
Designing Clothes
Use of AI for Designing Clothes
Yes, you read that right. Your clothes might be designed by a robot rather than a human. Popular online fashion retailers like Amazon and Myntra are using AI to generate designs for clothes.
One of the brands on Myntra, Moda Rapido, sells clothes designed by robots. They take inspiration from data about customer preferences and social media trends. Fascinating right? We thought we are far from robots and here they are designing clothes for us.
AI is making the lives of fashion retailers easy by providing them with sorted data as per the likes of the customers. Earlier, we would see the clothes and find out what works for us but now, we get recommendations of clothes that we might like. This is done by AI by carefully analyzing previous orders and finding out color, style, and size preferences.
Conclusion
AI is changing the future of the fashion industry for the better. It is proven to be more sustainable, cost-effective, and user-friendly. But they will also cost many employees their jobs, not only in the lower posts but also as designers. Let’s see if robots can be great designers. For now, it can be concluded that we are much closer to the age of robots than we’d like to think.
FAQs
How AI is used in the fashion industry?
AI is used in the fashion industry to help improve the efficiency of manufacturing processes. AI systems are also used to spot defects in the fabric.
Which industries use AI the most?
Healthcare, Education, and Marketing are the sectors that employ AI the most.
Can AI design clothes?
Fashion designers are increasingly using artificial intelligence to design clothes as a tool for innovation.
How can Machine Learning be used in fashion?
Machine Learning uses existing fashion trends and customer data to produce a model to fit the market need. It boosts the design process.
Of course, the companies also need to analyze their revenue, profits, capital appreciation, market capitalization, number of investors, total assets and liabilities, and much more.
It is estimated that by 2025 the global data volume will touch 180 Zetta bytes, 572 Zetta bytes by 2030, and it may even go up to 5,00,000 Zetta bytes by 2050.
As you can guess, all this data is extremely big. All companies need someone who can analyze this humongous data, find recurring patterns and provide insights that help the company grow.
This is where a data scientist comes in.
Data science in simple words is applying advanced analytics techniques and scientific principles to extract information from data.
The valuable information extracted from the data is further used to improve business efficiency, find new business opportunities, improve the product or service, increase sales and ultimately boost revenue.
According to Payscale, the average salary of a data scientist in India is 8,73,310 a year.
What Does a Data Scientist Do?
Improve the quality of the data using machine learning techniques and identify patterns and trends.
Validate the data to ensure accuracy, completeness, and uniformity.
Create algorithms and data models that can find valuable information from the data.
Integrate data tools such as Python, R, SAS, or SQL in data analysis.
Find solutions and hidden opportunities from the data.
Skills Required to Become a Data Scientist:
Expertise in programming languages like Python, R Programming, SQL, and Scala.
Strong command of statistics and mathematics (Linear algebra and matrix, statistics, geometry, calculus, probability, regression, dimensionality reduction, and vector models)
Deep understanding of machine learning and artificial intelligence.
Web Scraping.
Data analysis and visualization.
Database management.
2. Artificial Intelligence (AI)
When we hear the word AI we usually think about an army of robots ruling mankind.
We might even think about Siri, Alexa, facial detection, chatbots, self-driving cars, movie recommendations that we get on Netflix, and much more.
Although these applications of Al are very basic. To be very frank, we haven’t scratched the surface of Al.
This means that every other company would be looking for professionals who know how to effectively use the power of Al for the growth of their business.
You can work with big companies like Apple, Microsoft, Google, Facebook, Adobe, Intel, and many more.
In 2020, LinkedIn listed Al specialist as the top emerging job.
According to Simplilearn, the entry-level annual average AI specialist salary in India is around 8 lakhs.
If you have a higher level of expertise and experience it can go as high as 50 lakhs.
There is a crossover between Al and machine learning. You will get to know more about machine learning next!
What Does an Al Specialist Do?
An Al specialist is responsible for creating machines and software problems that can analyze the data and make human-like decisions.
Use machine learning (ML) and neuro-linguistic programming (NLP) to build creative solutions for business problems.
Create solutions that will streamline tedious or repetitive business tasks.
Convert the machine learning models into application program interfaces (APIs).
Skills Required to Become an Al Specialist:
Proficiency in programming languages like Python, C++, and JavaScript.
Linear Algebra, probability, and statistics.
Bayesian networking (including neural nets).
Cognitive science theory.
Engineering.
Robotics.
Physics.
The above graph shows the application areas of AI in an organization in percentage as per the source light-it.net
3. Machine Learning
You must have heard about machine learning a lot of times these days. It is one of the most innovative and rapidly evolving industries that provides lucrative salary packages.
Machine learning can be defined as the subfield of Al where historical data is used by the software applications to produce accurate results and outcomes.
Basically, they are doing the task and learning from their experiences without being explicitly programmed to do so.
Differentiating between illegitimate and legitimate transactions in applications like PayPal and GPay, face detection in images, speech recognition, and medical diagnosis are all popular examples of machine learning.
According to Payscale, the average machine learning salary in India is Rs 686,281 per year, inclusive of bonuses and profit-sharing.
What Does a Machine Learning Engineer Do?
Design self-running software for predictive model automation.
Perform statistical analysis and fine-tune models using test results.
Discover, design, and develop analytical methods to support novel approaches to data and information processing.
Identify differences in data distribution that affect model performance.
Skills Required to Become a Machine Learning Engineer:
Proficiency in programming languages such as Java, R, Python, and C++.
Advanced understanding of linear algebra, calculus, and bayesian statistics.
Software engineering skills.
Knowledge of computer architecture.
The above graph shows the leading areas of machine learning adoption by organizations in percentage from source 99firms.com
4. Cloud Computing
More and more companies are moving towards cloud solutions.
It is estimated that by 2025, the cloud will store over 100 zettabytes of data in it.
In simple words, cloud computing can be defined as the storing and accessing of data and computing services such as servers, data storage, networking, and databases over the internet (cloud).
According to Ambitionbox, the average annual salary of cloud engineers in India is Rs 5.4 Lakhs. This number is based on 7.7k salaries received from cloud engineers.
What Does a Cloud Engineer Do?
Build and maintain cloud infrastructure.
Monitor cloud infrastructure components like networking and security services.
Migrate databases of the companies to the cloud.
Design cloud solutions for clients.
Manage software and hardware associated with the use of cloud computing.
Skills Required to Become Cloud Engineer:
Knowledge of programming languages such as SQL, Java, Python, Ruby, .NET, Golang, and PHP.
Cloud database management skills.
Understanding of DevOps practices.
Knowledge of open standards, such as XML (Extensible Markup Language), SOAP (Simple Object Access Protocol), WSDL (Web Services Description Language), and UDDI (Universal Description, Discovery, and Integration)
By 2023, mobile apps are expected to generate a revenue of $935 billion.
49% of people open an app 11+ times each day.
All these stats show us that if a company wants to succeed they need to develop its own apps.
This means that companies would be actively looking for mobile app developers.
If you know how to develop an awesome app you can earn a lot of money.
What Does a Mobile App Developer Do?
Understand what are the objectives of the brand and how it aligns with the customer needs.
Develop application programming interfaces (APIs) to support mobile functionality.
Create, program, and test the app on all mobile platform devices like smartphones and tablets.
Coordinate with the UI/UX designers.
Ensure all the things are functioning properly both in the front end and back end.
Skills Required to Become a Mobile App Developer:
Proficiency in Java, Kotlin, React.js, and Objective-C.
Knowledge of Syntax.
Understanding of Ul/UX design.
Angular.
Cross-platform application development.
Expertise in using GIT.
6. Ul Design
User Design (UI) refers to the visually appealing elements on the website or app like buttons, toggles, icons, images, colours, typography, and animations.
Ul designers are in high demand since every other company wants an attractive and convenient interface for their website or app.
What Does a UI Designer Do?
Design all the screens through which a user will move. Add buttons, icons, images, and animations.
Decide which fonts and colours to use.
Understand human behaviour and make an intuitive interface that is easy on the eye and simple to use.
Skills Required to Become a UI Designer:
Ability to solve problems with innovative solutions.
Understanding the impact of colours, fonts, and design elements on human psychology.
Knowledge of design principles.
Expertise in using design tools like Adobe Photoshop, Adobe Illustrator, Figma, Sketch, and Proto.io.
8. UX Design
A User experience (UX) is concerned with enhancing the personal experience a user will get through while interacting with your app or website.
Think of the last time when you were using an app to book a movie ticket or while you were browsing through the website.
Did you find it easy to navigate?
Was the experience of using the app smooth?
The UX designer is concerned with all of these interactions.
The demand for UX designers is rapidly increasing.
In 2020, LinkedIn ranked UX design as one of the top 5 in-demand skills.
On the other hand, Glassdoor has ranked the UX designer job at 24th position in their list of the best 50 jobs to have in 2022.
What Does a UX Designer Do?
Identify the goals, behaviour, and pain points of the user.
Understand what problem your brand is trying to solve.
Create flow diagrams, prototypes, and wireframing to help the client understand what the final product will look like.
Conduct A/B tests, polls, surveys, and usability tests to improve the user experience.
Skills Required to Become a UX Designer:
Wireframing and prototyping.
User testing.
UX writing.
User empathy.
Understand how users will interact with your design.
Key Differences Between UI and UX:
A lot of people get confused between UI and UX. Although both of them are different things. Both UI and UX designers play a very important role in product development.
UI Designer
UX Designer
A UI designer focuses on the graphical portions of mobile apps and websites
The UX designer focuses on the experience a user has with a product
Let’s take an example of a hotel booking app. The beautiful fonts, buttons, images, and fonts that you see in the app are UI
The loading speed of the app and the total number of clicks that you take to search, analyze and book a hotel on a hotel booking app is UX
8. Graphic Designing
Every other company is looking for innovative graphic designers who can visually communicate their brand message, increase brand visibility, build credibility, turn leads into customers and ultimately increase the conversion rate.
What Does a Graphic Designer Do?
Develop the overall layout and production design for books, advertisements, brochures, magazines, etc.
Build brand identity by designing logos and selecting the right color palettes and typography.
Develop an attractive packaging of a product.
Understand the brand message and the psychology of the target audience.
Skills Required to Become a Graphic Designer:
Expertise in using Photoshop, Illustrator, and InDesign.
Audience targeting.
Understanding of how UI and UX design work.
Creativity and ideation.
Understanding of colour theory and typography.
The above graph shows an estimated hourly salary of a graphic designer based on their skill level in Dollars
9. Social Media Marketing
I don’t have to explain to you the importance and craze of social media in today’s world.
A single notification from Instagram will disturb our whole working schedule.
Previously, it was used to communicate with our family and friends.
But, now social media can influence our decisions and change our behavioural patterns.
Companies use social media to increase brand awareness, generate leads, and increase sales.
Companies regularly need social media marketers who can utilize the power of social media and help them increase their sales.
What Does a Social Media Marketer Do?
Understand the interests, goals, professions, and demographics of the target audience and create social media posts that resonate with them.
Many people don’t understand the potential of this field.
But, let me tell you that every business on this planet needs a good copywriter.
Why?
Because every business needs someone who can use words that piques curiosity, connect emotionally, and increase sales.
Difference Between Copywriting and Content Writing:
Now, a lot of people get confused between copywriting and content writing.
Although these two fields are completely different, both of them are very important for the functioning of the business.
Copywriting
Content Writing
The sole purpose of copywriting is to inspire people to take an action like signing up for an email, calling a number, or buying a product.
The purpose of content writing is to inform or entertain the readers.
Copywriting is used in advertising.
A content writer writes blogs, social media posts, scripts for videos, whitepapers, etc.
Copywriters help companies to generate leads and increase sales from the trust built by content writers. Hence, they earn a lot more than content writers.
A content writer builds trust for the Copywriters to work upon.
Learning any of these on-demand digital skills will ensure you a bright future and with a good experience, you will get a high salary as well.
Since all of these skills have a huge demand in the future; building a career in these fields will always be fruitful for you.
Now, it’s not necessary that you have to choose skills from this list only. There are tons of other digital skills that we have not mentioned in this article.
So, research more about them and find the skill which you find interesting.
Do courses online and find internships. You can even learn more than 1 skill as well.
FAQs
What digital marketing skills are in demand?
Digital marketing skills that are in demand are Copywriting, Social media marketing, Paid advertising, Search engine optimization (SEO), Search engine marketing (SEM), and Content marketing.
Which skill is best for the future?
The skills that will have huge demand in the future are Artificial intelligence (AI), Mobile app development, Cloud computing, Blockchain, Data Science, UI/UX design, etc.
Which skill makes the most money?
Some of the high-paying skills are project management, mobile application development, cloud computing, etc.
What are the 6 basic skills in developing digital skills?
Six basic skills in developing digital skills are information fluency, collaboration fluency, solution fluency, media fluency, creativity fluency, and digital ethics.
Have you ever thought that it would be possible when we decide to input any text and simultaneously it would convert or generate an image by deciphering or processing what we want to convey through the write-up? For example, you wrote about an armchair in the shape of an avocado. Then, the image you imagined while writing the above sentence would be generated in front of you after some time. Which seems pretty cool and exciting, right?
Now, you would be thinking about what made it possible to carry out this work and its mechanism. That is why here in this article, we will talk about everything related to DALL-E, the image-generating software developed by OpenAI and the theory behind its functioning.
A 12-billion parameter version of the GPT-3, Dall-E is an artificial intelligence model developed by OpenAI capable of generating images from texts. It is the first artificial model that can carry out this phenomenon.
If you are now thinking about whether Dall-E can provide only simple input text illustrations, then you are pretty wrong. Dall-E can give rise to multiple illustrations with several alternatives on a single write-up. Interestingly, it could represent something more bizarre than what you imagined.
How Does Dall-E, the Text-To-Image Generator, Work?
Dall-E is not subjected to only the generation of unique plausible images from various sentences. It can also explore other sides of a complex language structure input in its platform. So, let us look at some of them and see how they work towards it:
Controlling Multiple Objects
AI-Generated Images by DALL-E
For instance, if there is a phrase containing multiple objects and different relationships, like a baby penguin wearing a blue hat, red gloves, green shirt, and yellow pants.
Dall-E does not confuse all the apparel with each other but rather combines each piece of information without mixing them up. However, it’s seen that the proper workability of Dall-E depends on how captions have been arranged and on avoiding misrepresentations.
Conjuring up Both Internal and External Structure
Dall-E is found to quickly draw both the internal and external structures of an object in an exemplary and exquisite manner like never before. But, the details that Dall-E shows can only be visible if referred to or viewed up close.
Adding Contextual Details
While describing a task of translating text to an image, there may be instances where a single caption could give rise to thousands of plausible images, and determining a single image would be hard. Moreover, there could be places where a particular addition of something could make the image more attractive and pleasant to see, but the user may not specify that detail in the caption.
This is where Dall-E stands relatively superior to other 3-D rendering machines or platforms where you can mention every detail ambiguously. For instance, if your text indicates that an image must include a particular detail that is not clearly stated, then Dall-E fills that detail in that excluded space and renders your image picture-perfect.
Workability in the World of Fashion
Next, let us look at how Dall-E fairs in the world of fashion and how it fares in having an excellent fashion sense. Dall-E works efficiently in its capability to provide a range of possibilities whenever two different colour codes are input into text, for example, a yellow and black sweater. Here, it can generate many combinations for how those two colours can be used.
But when it comes to different colours that are less common like olive or navy are conveyed in the text, Dall-E often gets confused regarding it. Sometimes, it recommends shades of light blue or different shades of blue and, likewise in the case of olive, it recommends different shades of brown or some brighter shades of green.
Combining Different Concepts
The creative nature of our language allows us to combine different concepts which are entirely unrelated, like real or imaginary, into one sentence. Along with this fact, Dall-E is also quite capable of combining two imaginary items and generating an image. Although, Dall-E may not always be successful in creating images having unrealistic details. For example, if we want to create a visualization of a snail made of a harp then Dall-E may get confused regarding the forms of the objects or the way it must combine both subjects.
However, it was an animal which is real, so what about an armchair in the shape of an avocado? Dall-E, in this case, tries to devise a solution closely related to the design and practically functional. But there could be instances when the image would not be adequate to what you wanted.
Why Is Dall-E Considered a Breakthrough in Today’s World?
Dall-E is considered a game changer in today’s world because earlier artificial intelligence was able to generate images but needed to see them beforehand to give rise to them. The discovery of Dall-E by OpenAI is revolutionizing the way we use AI with images as a single input of text can now lead to an image being represented closely, resembling what we imagined of it seamlessly.
Global AI Software Market Revenue from 2018 to 2025
Does Dall-E Matter to Us?
After getting a brief understanding of the functioning of Dall-E, we may be faced with a common question: will this machine-learning technique be the end for the creative thinkers or designers in the field? If computers can now generate original images through text, what work is left for humans, albeit artists, graphic designers, or illustrators, doing the same work?
One thing we need to clear out of our minds is that a discovery like Dall-E will not oversee an end to human capabilities or turn out to be a replacement for them but rather be an enhancement to our already evolving workforce.
No technology, after its introduction into the mainstream world, would be able to take over the existing structure just like that. In addition, Dall-E needs a specific language input to render some complex images. Sometimes those images may not be enough for you or up to your standards, depending on their usability.
Even though Dall-E may not be suitable for some purposes, it most definitely is beneficial to sectors like:
Ecommerce sites: When generating impactful and customer-oriented product images through different eCommerce sites, Dall-E becomes quite influential. Dall-E is a cheaper and more affordable option where designers can include extended dynamic imagery and a somewhat simpler option before the usual technical design.
Real estate sites: Another sector where Dall-E is pretty useful is real estate sites. Here, customers or real estate developers could generate images of structures based on how they want to build the place or buyers looking for places depending upon their favourability and specifications.
Other Features That Dall-E Users Can Enjoy
Some other features that users who have chosen Dall-E can enjoy are:
Editing
There could be instances where the image generated by Dall-E is not meeting your requirements. Then, Dall-E offers some of the best editing access that allows you to edit and change the image as per your need.
Variations
Users can add different types of variations on the image which was generated by Dall-E or even uploaded by the user on its platform inspired by the original picture.
Here are some security features that Dall-E is said to improve and offer to its users:
Reducing Misuse
Because of the unique abilities of Dall-E subjected to creating images from text, it is highly possible to be misused to some significant extent by different people. That is why Dall-E rejects users from uploading realistic images to its platform and also restricts users from creating images that depict the faces of celebrities or politicians to avoid any controversy.
Eliminating Bias
Dall-E has implemented a new technique in its security software that prevents it from creating any image containing bias, like tags of a specific gender, caste, or honours. It tries to replicate the true nature of the diversity of the population worldwide.
Preventing the Creation of Harmful Images
The content filters of Dall-E have been made efficient and effective to prevent people from violating the content policy. It doesn’t allow people to generate harmful images towards any organization, public figure, or adult content but stays true to its word of enabling creative expression.
Monitoring
Dall-E servers are constantly automated and humanly monitored to prevent people from misusing the platform.
Conclusion
In the end, after looking at some of the broad aspects of Dall-E, we can say this was machine learning, the artificial language we most probably needed. If you have a common question regarding whether it will take away the human workforce and make more people unemployed. Then, it certainly will not do that because it is still relatively new and needs to expand itself more to perform better in not only generating images out of the text. However, we must agree that this OpenAI development will undoubtedly change the way of working these days.
That is why, hopefully, after reading the above, you are now aware of Dall-E, its workability, and some other aspects that could also help you as a company in many ways.
FAQs
What is DALL-E?
In simple terms, DALL-E is a machine-learning model designed by OpenAI. It is designed to generate digital images from simple text descriptions.
What does DALL-E stand for?
The software, DALL-E is a blend of two names– WALL-E, the animated robot Pixar Character and Salvador Dali, the Spanish surrealist painter.
How expensive is DALL-E?
Users can create with DALL-E with 50 free credits during their first month of use, and 15 free credits every month. Also, they can buy additional credits in 115-generation increments for $15 with each text prompt worth 1 credit.
The facial recognition market is growing rapidly and this technology is being used in various sectors across the globe.
Gone are the days when we used to see face recognition technology in Sci-fi movies. Today, even a budget smartphone has a face recognition feature.
According to Statista, the global facial recognition market will grow at a compound annual growth rate of around 15.4% to reach 12.67 billion USD by 2028 from 5 billion USD in 2021.
The facial recognition system is used in schools, hospitals, banks, and many other places. Let’s see some amazing uses/trends of the facial recognition system.
Nowadays, facial recognition systems are used in various places. These include banks, hospitals, traffic signals, airports and more. The following are some of the most popular uses of facial recognition systems:
Facial Recognition Market Size Worldwide in Selected Years from 2019 to 2028
Finding Missing Children
Face recognition CCTV systems can be used to find missing children by allowing police to add a reference photo provided by the missing child’s parents and match it with past appearances of that face captured on video.
To find the time and location where the child has been declared missing, the police will use face recognition to search video sequences (video analytics).
In this way, the police can find out where the child was last seen. A real-time alert can trigger an alarm whenever there is a match.
In 2018, Delhi police traced 3,000 missing children using the facial recognition system.
Track Criminals
Using the same face recognition CCTV systems, police can track past criminals suspected of committing a crime.
Police can use the image of past criminals from their database to detect matches in live video.
Help the Blind
Listerine has developed a face recognition app that helps blind people. The app recognizes when the people around the blind person are smiling and vibrating.
This can help blind people better understand social situations and react in a better way.
Track College Attendance
Facial recognition can be used to track college attendance. Currently, to track attendance in college, students need to sign an attendance sheet.
But, the loophole here is that any student can sign for another student who is bunking the class.
Facial recognition ensures that attendance is recorded properly by tracking every student’s face. In China, tablets are used to scan students’ faces and match their photos against the database.
We have already seen electric cars but, did you imagine unlocking your cars with simple face recognition? China’s Byton has revealed a concept where users will be able to unlock their cars using face recognition.
In the future, the door handles or the engine will remain locked until the registered user sits in the car.
Diagnose Diseases
DiGeorge Syndrome Diagnosis – Uses of Facial Recognition System
Diseases that cause detectable changes in appearance can be diagnosed by the facial recognition system. This helps the healthcare industry experts to detect certain diseases in the early stage.
The National Human Genome Institute Research Institute is already using the facial recognition system to detect DiGeorge syndrome. It has helped diagnose the disease in 96% of cases.
Advertising is a make-or-break thing for any brand. If the advertisements resonate with the consumer then the conversion rates will eventually rise.
Facial recognition can help brands suggest better products to consumers after knowing their age and gender.
Airport Boarding
At present many airlines are using onboard facial recognition technology. More than 15 airports have face-matching systems in the US to help speed up the boarding process. Trials have taken place at Los Angeles airports among many others. In fact, this technology is making a lot of buzz in Europe too.
Recently, on the occasion of the 75th Independence day, the facial recognition system for passenger verification was launched in Delhi and Bengaluru airports.
A beta version of the DigiYatra was launched for android users where the app will automate passenger entry and verification at all touchpoints – airport entry, security check and boarding gate.
Conclusion
In the near future, the facial recognition system will become a necessity in marketing, hospitals, banks, and everywhere else. Biometrics technology has a lot of power and can revolutionize many things. It can make our lives easy and help speed up all the time-consuming things. As you can see from the above examples, the possibilities to use this technology are endless. Things that we used to see in the movies can now become a reality.
FAQs
How does a facial recognition system work?
A facial recognition system tracks your facial features and compares them with a database of photographs and videos.
How is facial recognition being used today?
The facial recognition system is used to track criminals, unlock cars, find missing persons and in targeted ads.
What are the problems with facial recognition?
Privacy concerns, lack of transparency and data breaches are some of the concerns related to facial recognition.
The mimicry of human intelligence is called Artificial Intelligence. Or the development of intelligent machines, thinking, and working like humans is called Artificial Intelligence. With the help of machine learning, we can develop Artificial Intelligence.
Machine Learning is an application of Artificial Intelligence that study the computer algorithm, learns automatically, and improves the experience. The computer, robotic machines, and machines have to learn how to respond in actions, it uses computer data structures and algorithms to create the Propensity Model.
The Propensity Model is a statistical model that is used to predict the behavior of your customer. The best example of artificial intelligence that we are using in our daily life is speech recognition like Alexa and Siri, problem-solving, learning, and planning.
How Artificial Intelligence is Helping in Business Today
Artificial Intelligence tools are supporting tools in our businesses today. It can work in the replacement of humans but it is facing many difficulties to complete common sense tasks in the real world. Artificial Intelligence is a form of software that can process and analyze data processes more quickly than human brains.
It can also return some actions and present them to human users. Artificial Intelligence can make decisions as opposed to traditional software. It can make the decision even in such a situation that has not been foreseen by the programmers before. It is very helpful in maintaining the relationship between the customers and the company.
Artificial Intelligence is changing the customer relations management system(CRM). High human intervention companies like Zohoand Salesforce have to remain up-to-date and accurate. AI helps these platforms to transform the customer relation management system into auto-correcting and self-updating that makes the best interaction with customers for you.
Artificial Intelligence In Business
Artificial Intelligence in Finance
Financial services are very important and too much use of AI in this industry. According to the McKinsey report, some companies have seen a profit margin of over ten percent higher than the industry average.
Artificial intelligence in finance can prove to be way much beneficial for the users. The decisions required in the financial sector might be needed to have a thorough check. With the help of AI, the work of checking the accounts can rarely take some time.
Also, it is scientifically proven that AI can make better data-driven decisions. AI can also help in financial service by completing some of the repetitive tasks in less time than human beings and with much proficiency.
The above graph shows the revenue collected from the artificial intelligence market worldwide in millions of US dollars for the years 2016-2022
Financial Services Using Artificial Intelligence
Financial services have seen a great peak in the use of artificial intelligence in their working pattern. Some of the most common uses are stated below.
Fraud Prevention
AI is used to increase revenue and cut the cost of the company. It is also very helpful in fraud prevention. In 2016, $16 Billion was stolen by fraudsters or identified as theft. The AI detects the client’s behavior, activities, locations, and buying habits that seem suspicious and unusual.
Trading
Artificial Intelligence and machine learning are very helpful in trading. They can gather data quicker than humans which helps to improve each day. It helps to eliminate the emotional aspects of trading.
Personalized Banking
Many banks and fintech companies are using AI to make better customer relationships and better service.AI helps the customer to understand the risk factors of various policies. It can create your financial plan by tracking your spending pattern, income, and goals.
Artificial Intelligence in Marketing
The marketing industry gets a boom with the development of AI. It helps to gather more and more data to create the best marketing plan and able to enhance marketing practices as well.
Marketing Attributes
It is very difficult to quantify the impact of various marketing channels like offline marketing TV, and Radio Billboards. To overcome this problem we need to execute two popular marketing techniques:- The attribution model and the marketing mix model.
An attribution model is a set of rules or analytical science determining the tactics of marketing contributing to sales or conversion. A marketing mix model is a marketing technique that is used to promote the brand product and services in the market.
Customer Profiling
An example of how customer profiling is carried out by AI
Customer profiling is a way to create the profiles of customers to understand decision concerns for your business. By creating profiles the customers are divided into smaller groups and a representative is given to each group with a photo, name, and description.
Logistics is an important part of each industry. With the combination of Artificial Intelligence logistics becomes more efficient by creating AIapplications like automated warehouses and automated vehicles.
Automated Warehouse
The above images show the use of robots in the fully automated warehouse
In an automated warehouse, thousands of robots are moving from point A to point B to fulfill the demand. The automated warehouse also reduces the cost of transportation, less warehouse space needed, and reduces overall costs.
Automated Vehicles
With the development of AI, it is very important to develop autonomous vehicles. The development of autonomous vehicles is very helpful for humans, they need rest and sleep while driving which leads to late delivery, now no longer transportation is limited.
Tesla is the best example of autonomous vehicles. Rolls-Royce cars and Intel are also in the queue of developing autonomous vehicles but they are very less known. Together they built an Intelligence Awareness System to allow the autonomous chips.
Artificial Intelligence in Retail Market
The retail industry is one of the most competitive industries. They need new inventions and technology to stand out from the crowd. AI in the retail market is applied in various processes of the retailer’s products and service cycle.
Chatbots And Robot Assistants
Google also has its own AI-based chatbot named “Meena”
The chatbot is an application of artificial intelligence which is used as a conversational agent. They communicate with visitors by using two primary methods:- web-based applications and standalone applications. For example, Pepper is a social humanoid robot designed by Softbank that is used in physical stores to engage customers and provide personal assistance to the customers.
Artificial Intelligence in Telecommunication
The Telecom industry is one of the biggest industries in the world. The value of this industry is very large so every difference matters. Three main artificial intelligence applications are used in the telecom industry:-
Churn Prediction Modelling
The prediction of detecting customers who are likely to cancel the contract or subscription is called churn prediction, It is very important as it gives a better understanding of future revenue. Churn prediction helps us to identify the areas of lacking customer service in your business.
Network Optimization
The improvement of the network is called network optimization. There are various ways to improve and monitor network performance:- global load balancing, minimizing latency, packet loss monitoring, and bandwidth management. By using artificial intelligence in network optimization we can increase speed, scalability, and effective result.
Predictive Maintenance
Predictive Maintenance refers to the monitoring of the condition of in-service equipment to reduce the risk of failures. It is also known as condition-based maintenance. By using artificial intelligence it can maintain the historical data, sensors data, and weather data to ensure the proper condition of a machine or when the machine needed servicing.
No one can say the future of artificial intelligence but you can predict the future of artificial intelligence. For example, artificial intelligence is ready to do common-sense tasks and it is easily handled by computers. We can say that robots are going to be a part of our day-to-day lifestyle in the future. Tesla is also innovating driverless cars and many companies are trying to innovate with AI technology. In the future artificial intelligence is going to lead our life.
Artificial Intelligence stands for the power of machines to behave like humans and possess a similar tendency to take decisions. With increasing technology, there is a significant growth noticed in AI projects. In today’s world, many services are dependent on AI.
The basic examples of AI include help desk solutions provided by chatbots, banking software uses AI to make their work easy, etc. The above article details the changes brought by AI into the business world and how it is developing regularly.
FAQs
How AI is changing the future of business?
Artificial intelligence allows business owners to provide a more personalized experience to their customers. AI can analyze vast data way more efficiently. As for the future, AI can prove to be more than just a help for businesses and many work will be AI-dependent.
What is Artificial Intelligence in Business?
Artificial intelligence (AI) refers to the ability of machines to understand the world around them, learn and make decisions, similar to the human brain. Thanks to AI, machines are getting smarter every day.
What are the benefits of artificial intelligence?
Artificial intelligence has many advantages in store for its opponent. Some of them can be minimal errors in work, improved customer satisfaction, fast-paced work, and automation of work.
How AI can solve business problems?
AI can solve business problems based on multiple factors. Some of the most common problems that can be solved by AI are security, scheduling and setting up appointments and working schedules for employees, improving customer and employee connections, etc.
Company Profile is an initiative by StartupTalky to publish verified information on different startups and organizations. The content in this post has been approved by mfine.
The focus of healthcare has been shifting toward the patient for quite some time now. The expansion of the health-tech industry is being driven by the availability of inexpensive healthcare paired with sustainable mobility. The medical technology business is growing due to changing terrain, improved healthcare delivery and funding methods, and a changing patient profile.
Even with all its flaws, India’s healthcare system has a lot moving for it on several fronts. Artificial intelligence can make accurate conclusions possible when it comes to patients’ data, thereby helping the patients achieve the healthcare quality they dream of. The government-led campaigns along with the companies like MFine are making the reach of healthcare broader and refining the healthcare practices to a huge extent.
MFine is an AI-powered, on-demand healthcare startup that gives customers access to online appointments and hospital-based linked healthcare. Telemedicine and teleconsulting programs via the MFine app are allowing medical knowledge to reach people much more easily and conveniently.
MFine merged with LifeCell International’s diagnostic arm on July 11, 2022, after being an independent company for over 5 long years. The new, merged entity will emerge as LifeWell.
Here’s learning “What is MFine?”, MFine owner name, Founders of MFine, MFine Funding, MFine Competitors in India, MFine merger, and everything else about the MFine company. Follow below to get an insight into the company:
MFine is an AI-powered on-demand healthcare startup that gives customers access to online appointments and hospital-based linked healthcare. Users can contact paediatricians, gynaecologists, obstetricians, and physicians from leading hospitals of their choice through chat or video to acquire prescriptions and/or routine treatment using the company’s service.
The doctors have exposure to a proprietary Assistive Intelligence platform that analyses symptoms and provides an accurate prediction for specialists to evaluate patients. This implies that now the doctor is aware of the patient’s condition even before they visit them, allowing for a quicker and more accurate diagnosis.
MFine aspires to make obtaining reliable healthcare simple, quick, and preventative. MFine was created by keeping the customer experience in mind, using a combination of cutting-edge technologies and collaborations with the finest medical centres.
MFine enables rapid and ongoing communication with the greatest doctors in the best hospitals. It makes use of cutting-edge technology to keep track of your health indicators and to keep all of your health data under your control and accessible. Digital wearables, smartphone apps, and at-home services provide much-needed ease and speed in bringing you the treatment you need when you need it. Furthermore, MFine also provides discounted health check packages in partnership with the hospitals.
According to research, India’s health market is predicted to develop at 39% at CAGR (Compound Annual Growth Rate) from FY2020 to FY2023, reaching 50 billion USD by 2033.
The health-tech market is presently valued at over $2 billion, and it is divided into six segments: telemedicine, e-pharmacy, fitness, wellness, healthcare IT, analytics, home healthcare, and personal health management. This represents less than 1% of India’s total healthcare business.
“The pandemic and adoption of technology in healthcare have brought a quantum shift in the sector. In recent years, we have seen some of the most significant deals, and the Indian health-tech sector has received close to $1.6 billion in funding since 2017,” said Rajeev Shah, MD, and CEO of RBSA Advisors.
According to the estimates, the Indian health-tech industry would reach $5 billion in 2023 and $50 billion in 10 years. With $700 million in revenue in 2020, e-pharmacies were the most profitable segment of the Indian health-tech market, followed by B2B health-tech ($60.2 million), B2B medical supplies ($28.8 million), other health-tech services ($100 million), e-diagnostics ($70 million), and teleconsultation ($45 million).
Machine learning, robotics, artificial intelligence, wearables, on-body gadgets, and blockchain, among other technologies, will have a significant impact on the future of healthcare. The use of cloud infrastructure in healthcare record management, as well as a greater focus on the digitalization of patient medical records, is expected to accelerate.
MFine – Name, Logo, and Tagline
MFine, as the name implies, shows what the firm does for its consumers, i.e., the patients. The mfine company makes it easy or patients to check up with physicians online and receive health tests at home, all with touch or the click of a button, thereby ensuring that they are fine.
MFine Logo
MFine’s slogan says, “MFine – one app for all your health needs.”
MFine – Founders
MFine was founded by Ajit Narayanan, Arjun Choudhary, Ashutosh Lawania, Prasad Kompalli in 2017.
Founders of MFine – Ajit Narayanan, Arjun Choudhary, Ashutosh Lawania, and Prasad Kompalli
Ajit Narayanan
Ajit Narayanan
Ajit is an electronics engineer with over 2 decades of experience in product development and organization building in e-commerce, consumer internet, mobile, healthcare, analytics, and integration platforms.
As a Co-founder, CTO, and Product Office of MFine, Ajit is in charge of the company’s product and technology strategy. Ajit was the CTO of Myntra, India’s largest e-commerce shop for fashion and leisure items, in a prior life. Ajit began his career in mobile technology at SAP, where he developed solutions for offline and online mobile application development, as well as domain programming languages for User Interfaces and API administration.
Arjun Choudhary
Arjun Choudhary
Arjun Choudhary, an IIT Roorkee alumni with over 10 years of expertise in sales, revenue, and growth, is in charge of the company’s Strategic Business Expansion. Before joining MFine, he worked at Myntra as a Senior Director of Growth and Sales, where he helped the company grow by 50 times. He’s also worked as a global capacity planning analyst for Amazon. Arjun is currently serving as the Chief Business Officer at MFine, along with being a Founding Member of the company.
Ashutosh Lawania
Ashutosh Lawania
Ashutosh Lawania is a software application developer and digital marketer with over 15 years of expertise. Ashutosh is known as one of the co-founders of the healthcare app MFine. He has also been the Co-founder and Head of Sales and Marketing at Myntra, an Indian e-commerce website where men and women can buy branded footwear, clothes, and accessories.
Before joining Myntra, Lawania co-founded Bytedge Solutions, a product engineering firm that specializes in assisting product development firms and startups with their product engineering and go-to-market processes. Lawania graduated from the Indian Institute of Technology in Kanpur with a Bachelor’s degree.
Prasad Kompalli
Prasad Kompalli
Prasad Kompalli, the former CBO of Myntra and Senior VP of SAP Labs, believes in harnessing technology for innovation, and his area of expertise is business strategy. He was a member of SAP’s top 200 worldwide executives, holding positions in Strategy and General Management. He completed his Postgraduate studies in Business Management at the European School of Management and Technologies (Berlin), IMD Switzerland, and INSEAD France, and he possesses 7 patents in data and mobile technology. Kompalli is known as the CEO and Co-founder of MFine.
MFine was last registered with a total count of employees ranging between 501-1000, on its Linkedin profile.
Ashutosh Lawania and Prasad Kompalli, both former top executives of Myntra, chose to start from scratch at the end of 2016. They’d helped Myntra grow into a great e-commerce consumer brand, but they noticed something similar was missing in healthcare. That was how MFine’s idea came to their mind.
MFine was founded in February 2017 as a healthcare platform. While they were not strangers to founding a business, MFine was a new experience for them. “It wasn’t easy persuading folks to join us and work with us when MFine was simply a notion on paper,” explains Kompalli.
Conversations with hospitals revealed that the customer experience needed to be improved.
“They needed an easy interface with limited typing, so we came up with a health keypad which collects data from reports without the user having to type. We ran a beta program and the results were promising,” Kompalli say
It was difficult for anyone without a medical or healthcare background to break into the healthcare ecosystem. Catching the early adopters, according to the creators, was challenging. People, on the other hand, quickly adapt to accessibility and ease.
The business accepts 100-120 cases every day from a variety of specialities. The business has collaborated with 20 hospitals in Bengaluru and has 70 doctors across ten specialities, offering voice, video, and chat support. The company’s founders are currently focusing on chronic illness management and the use of IoT to link medical equipment.
MFine – Vision and Mission Statement
MFine’s mission statement is to make quality healthcare available to consumers at scale.
MFine’s vision has always been to make excellent health more accessible, dependable, and hassle-free for everyone, driven by a love for providing care, spurred by an uncompromising emphasis on quality, and guided by ground-breaking artificial intelligence.
MFine – Business Model and Revenue Model
Instead of collecting individual physicians on its platform, MFine uses a unique methodology of working with prominent and trustworthy institutions. MFine can provide high-quality treatment from trustworthy doctors via a digital channel because of its hospital affiliations.
Gynaecology, dermatology, paediatrics, cardiology, and general medicine are among the app’s most popular specialities. Commissions from client consultations, lead-generation fees from hospitals, and corporate tie-ups are the main sources of revenue for the firm. It also has partnerships with local hospitals to provide accessible and dependable medical treatment.
MFine earns money by acting as a digital extension of its healthcare partners. That is, it deducts a portion of consumer expenditure. The firm claims to deal with over 500 doctors from 100 “elite” institutions, with a strong emphasis on technology.
MFine – Funding, and Investors
MFine, to date, has obtained $94+ mn over the 7 funding rounds that it has received funding in. The last funding round of MFine was the Series C round that came in on August 31, 2021, which was led by BEENEXT and Moore that poured in $46.39 mn. The company has been valued between $450-500 mn after the completion of the last funding round.
MFine has raised 7 rounds of funding. Check them out below:
Date
Round
Amount
Lead Investors
Aug 31, 2021
Series C
$46.39M
BEENEXT, Moore Strategic Ventures
Jan 18, 2021
Venture Round
$16M
Heritas Capital
Aug 31, 2020
Series B
$5.09M
Caretech Pte Inc
Jul 2, 2019
Debt Financing
$4.5M
Alteria Capital
Apr 22, 2019
Series B
$17.2M
SBI Ven Capital
May 17, 2018
Series A
$4.2M
Prime Venture Partners
Sep 1, 2017
Venture Round
$1.5M
Stellaris Venture Partners
MFine – Growth
MFine’s on-demand healthcare platform allows users to obtain virtual consultations and linked care programs from a network of hospitals. It was founded in 2017 by former Myntra executives Ashutosh Lawania and Prasad Kompalli, who were subsequently joined by Ajit Narayanan and Arjun Choudhary.
Over 3 million customers have used MFine services since its debut, with over 300,000 monthly transactions including medical consultations, diagnostic testing, e-pharmacy, and in-patient treatments. Mfine introduced a new layer to its virtual doctor consultations in October 2018 by connecting with laboratory and diagnostic services, giving its customers access to more than 700 diagnostic centres in 400+ locations throughout India. The company further has claimed to have a network of over 500 hospitals with 3000+ doctors.
Every month, over 100,000 people utilize MFine to schedule diagnostic testing. More than 6000 physicians, including some of India’s best, spanning 35+ specialities, work in over 700 reputable institutions. They assist millions of people in over 1000 locations around the country. Instead of aggregating doctors, it works with hospitals, allowing customers to consult doctors from their favourite hospitals through chat or video and receive prescriptions and basic care.
Since the outbreak of the COVID-19 pandemic and a rise in the uptake of digital health among Indians, MFine has grown 15% month on month. MFine is substantially investing in technology to make the telemedicine experience much easier and more effective for providing high-quality treatment.
During the Covid-19 in 2020, the firm claims to have provided teleconsultation for over a million people. To meet demand, the firm extended beyond cities to 1,000 villages throughout India, offering AI-powered self-assessment, long-term care programs for chronic diseases, and membership perks to both individuals and businesses.
MFine Merger
Mfine merged with LifeCell
MFine has merged with the diagnostic business of LifeCell International, as per the July 11, 2022, reports. After looking for numerous merger and acquisition opportunities for the past months, MFine has ultimately successfully merged. This merger has brought forth a new entity that will be termed as LifeWell.
As per the reports associated with the new, merged entity, LifeWell, it will stand as a full-stack digital health platform in the diagnostic space in contrast to the pure-play marketplace that MFine was. Besides, the joint entity has also raised $80 mn in a new round from OrbiMed. The total userbase of LifeWell has been combined to be estimated at over 6 mn users, which is growing at a rate of 100% Y-o-Y, revealed the companies in a joint press release. LifeWell is looking to serve more than 50 million users over the next 4 years.
This merger is the third major consolidation that the Indian healthcare/healthtech ecosystem has seen during the past couple of years. It was already seen that the unicorn Indian startup, Pristyn Care acquired Lybrate in a distress sale in June, and prior to that, we saw the merger of DocsApp and MediBuddy.
MFine Heart Rate Monitoring Feature
MFine unveiled its latest innovation by presenting the all-new heart-rate monitoring feature integrated with its app. MFine launched its heart-rate monitoring tool on its app on March 3, 2022, which is designed to help people keep a track of their heart rate without any external devices. As far as the latest news goes, nearly 700 people are using this tool of MFine to monitor their heart rate every day.
MFine Pulse
MFine developed an app-based SPO2 solution in early 2021, allowing users to monitor their oxygen levels without the requirement of separate equipment. Since then, the program has been utilized by over 250,000 people, with thousands more using it regularly. MFine will be adding heart rate and blood pressure monitoring to the product in the coming months.
As of April 2021, MFine has released MFine Pulse, a mobile application that can check blood oxygen levels with just a finger and a flash.
The technology, named MFine ‘Pulse,’ is now in beta testing for Android users and will be for iOS in a few weeks. Even though clinical studies for the tool are still ongoing, the tool looks to be promising, with an accuracy rate of 80%, according to a news release.
Thousands of customers are using the tool in the company’s Android beta rollout, generating hundreds of assessments every day that are put into machine learning techniques, which CTO Ajit Narayanan believes will increase the tool’s precision in the months ahead.
“For now, the goal is to make our SpO2 monitoring tool as accurate, if not more accurate, than pulse oximeters available at the pharmacy,” Narayanan said.
MFine Financials
MFine had last recorded Rs 70 lakhs in operating revenue in FY19, which surged by 7.3X to become Rs 5.12 crore in FY20. The total earnings of the company have been recorded at Rs 12.23 crore during FY20.
However, for the surge in its revenue, MFine also had to sacrifice a considerable amount of money because the losses of the company ballooned by 2.9X to become Rs 99.5 crore in FY20 from Rs 34.4 crore in FY19. The outstanding losses of the company were estimated in the FY20 fiscal to be Rs 140.2 crore.
MFine expenditures surged rapidly from Rs 34.4 crore that it spent in FY19 to become Rs 99.5 crore in FY20. The Prasad Kompalli-led company spent Rs 21.87 to earn a single rupee during FY20.
The losses for MFine further increased to Rs 102.7 cr in FY21, while its operational earnings went on to become Rs 12.9 crore during the same fiscal. The expenses of MFine were registered at Rs 116 crore.
The platform includes a sophisticated web of hundreds of machine learning algorithms and techniques across vision, hearing, and language processing modalities, allowing the virtual doctor to interpret data from numerous sources and propose the best next actions to the doctor.
According to Kompalli, the current problem is scaling innovation now that the prior hurdle of convincing people to switch to digital healthcare has passed. “We’re now facing innovative hurdles, such as how quickly we can scale up the ideas we made in the mobile and AI sector.”
In addition, the corporation is putting a lot of money into the platform’s AI capabilities.
They are working on inference capabilities to diagnose based on symptoms, patient history, and other data provided during the consultation, as well as computer vision capabilities to scan and automatically digitize diagnostic papers and interpret symptoms. Skills are being considered, such as determining the type of infection based on the sound of a cough, among other things.
MFine Layoffs
The Indian companies are seeing a spike in layoffs. MFine too announced its share of employee layoffs, where the company has laid off around 50% of its workforce, as of May 21, 2022. The BEENEXT-backed company has done away with 50% of its total workforce, and as per reports from close sources, a considerable chunk of the employee belongs to the operations, product, and marketing verticals. The latest layoffs were done, as per the sources, to reduce burn and extend the runway for the company. Source also claimed that the layoffs can go up as high as 70%.
Protests began within 2 days of the announcement of the MFine layoffs. Over 100 of the company’s employees started gathering outside the MFine Bengaluru office, demanding their full salaries for the month of May 2022, and an early release of their full and final settlement. People also claim that MFine has laid off the employees due to its inability to pay their salaries. The company has allegedly run out of funds. According to the employees, the pay cycle of MFine is from 20th to 20th, and they have served a full month that way and should be paid in full too. They were expecting an appraisal, added the employees, and had no idea about the company’s financial condition. The company, which had earlier announced to pay the employees 20 days’ salary for the month of May and said that it would credit the rest amount including that of the notice and period, and have their full and final payment settled, has now been pressurised by the employees to make the whole procedure fast and to credit the full month’s salary of May. The employees are also apprehensive about their salaries and mentioned that out of 3 offices, MFine has already closed 2, where only 1 office is currently operational, which might also be closed without clearing their dues.
Previously, Vedantu, Meesho, Cars24, Unacademy, and more have already laid off a considerable amount of their workforce due to the unstable financial grounds that they are standing on, and the fear of an impending recession.
MFine – Future Plans
MFine intends to treble its investment in Machine learning and artificial intelligence, mobile engineering, and device integration with this cash. Aside from that, it plans to grow its hospital network across the country and scale up newly released services like prescription delivery, preventative health screenings, and diagnostic testing.
FAQs
When was MFine founded and who founded MFine?
MFine was founded by Ashutosh Lawania, Prasad Kompalli, Ajit Narayanan and Arjun Choudhary in February 2017.
How is the MFine funding?
Looking at the MFine funding, the company has raised a funding more than $94 mn to date, as of July 2022.
How does MFine make money?
Commissions from client consultations, lead-generation fees from hospitals, and corporate tie-ups are the main sources of revenue for the firm. It also has partnerships with local hospitals to provide accessible and dependable medical treatment.
What is the use of MFine?
Mfine is an AI-powered on-demand healthcare startup that gives customers access to online appointments and hospital-based linked healthcare.
Which companies do MFine compete with?
Mfine’s top competitors include SeamlessMD, Happytal, Helium Health, MediBuddy, Pristyn Care, Doctor Insta, and Ferrum Health.
Has MFine been merged?
Yes, MFine, which was looking for a merger, has finally merged with LifeCell’s diagnostic unit to bring about a joint entity LifeWell, as per the reports dated July 11, 2022.
What if, you are told that in near future surgeries will be performed by Machines. Yes, machine learning has advanced rapidly that in near future it will be possible to perform medical surgeries with minimum or no interventions by a Physician. Machine learning is widely used in healthcare industry in 2022.
When we hear AI or machine learning the first thing that comes in our mind is Robots but machine learning is much more complicated than that. Machine learning has advanced in every possible field and revolutionized many industries such as healthcare, retail and banking. In this article, we will talk about how machine learning is bringing a change in the healthcare industry. So let’s get right into the business.
Machine learning is an application of AI that provides the system with the ability to learn and improve from experiences without being programmed. The primary focus is to allow the computers to learn automatically without any human intervention or assistance. The process of learning begins with observations of data and finding patterns in data and making better decisions in the future. Machine learning has started making it place in India.
Machine Learning in Healthcare
The Healthcare industry has seen many advancements, but machine learning is one such advancement that has enhanced the performance of healthcare businesses. Machine learning has advanced a lot but the best machine learning tool in healthcare industry is a doctors brain. Many physicians worry that machine learning will dominate the healthcare industry.
Machine learning should be focused on how it can be used to augment patient care and employed as a tool by physicians to improve clinical use. Machine learning can never replace physicians because even if it reached supremacy, the patient always needs the human touch and caring of a physician.
How Machine Learning Is Used in Healthcare?
Machine Learning is taking turns and has entered into various industries and it looks like its not going to stop at any moment. It has also started showing its caliber in the Healthcare industry. Some of the ways it is used there are:
Identifying Disease and Diagnosis
Scientists are already working on machine learning models that predict disease or early diagnosis of disease and illness. A UK based technology startup feebris is working on artificial intelligence algorithms for precise detection of complex respiratory conditions. MIT’s Computer Science and Artificial Intelligence Lab has developed a new deep learning-based prediction model that can predict the development of breast cancer in advance up to five years.
Robotic Surgery
The application of robotics in healthcare is rapidly growing since it began in 1980. Robots performing surgery still might sound untrustworthy to many but in near future, it will be commonly used in surgeries. Robotics is also used in hospitals to monitor the patients and alert the nurses if there is human interaction required.
The robotic assistant can locate the blood vessel and draw the blood with less pain and anxiety for the patient. Robots also prepare and dispense medications and vaccines in pharmacological labs. In large facilities robotics are used as a cart to carry medical supplies. Speaking about robots replacing humans is not happening anywhere soon, robotics can only assist the doctors but can never replace them.
Medical Imaging Diagnosis
Medical imaging diagnosis is a process or technique in which visual representation of tissue or internal parts of organs are created to monitor health, diagnose and treat diseases. It also helps in creating database of anatomy and physiology. Surgical interventions can be avoided if medical imaging technology like ultrasound and MRI are used.
Uses of Machine Learning in Healthcare Industry
Machine learning algorithms can rapidly process massive amount of medical images and they can be precisely trained to identify the details in CT scans and MRI’s. A deep learning team from US, France and Germany have developed algorithm that can diagnose skin cancer more accurately than a dermatologist.
Pros of Machine Learning in Healthcare
More and more healthcare companies are choosing Machine Learningbecause pf its benefits. Some of the benefits are:
Patterns Are Easily Identified
A great ability of machine learning is that it can identify patterns and data precisely which might not be possible by a human. It can process massive amount of patterns and data rapidly with ease. With new innovation all of these are possible.
Smart Health Records
Maintaining health records is an exhaustive process so machine learning is used to ease the process and reduce the time and efforts required for maintaining health records. Machine learning in today’s world is working on cutting edge technologies for maintaining smart data records.
Minimum Human Intervention
Machine learning adapts overtime by learning from patterns and data. The primary benefit of machine learning is that it requires minimum intervention by humans and it can perform surgeries with ease.
Cons of Machine Learning in Healthcare
There is also a hesitation as well because with benefits Machine Learning has some cons as well. Some of them are:
Data Acquisition
Machine learning adapts through patterns and data sets and it requires a massive data sets and patterns to train its algorithms. The data should be precise and of good quality.
Take Time to Learn and Adapt
Machine learning requires enough time for its algorithms to learn and adapt to the patterns and data so that it can deliver accurate results. It requires additional computer power to function.
High Error-Susceptibility
Machine learning is highly susceptible to errors, it requires massive amount of data and if it is not provided with sufficient amount of data it may not function properly. Any inaccurate data fed to the machine may end up in undesirable result.
In-Med Prognostics Launches Neuroshield
InMed Prognostics Logo
One of the prime example of Machine Learning in the health industry is how In-Med Prognostics, a health tech company developing AI based brain health diagnostic and prognostic tools, launched NEUROShield, a cloud-based Clinical Decision Support Tool for neurological disorders. The NeuroShield technology uses pattern recognition and deep learning for the development of clinical biomarkers for early prognosis and differential diagnosis.
NEUROShield, powered by state-of-the-art AI, utilizes 3D based MRI images to their full potential by extracting data and providing volumetric analysis for the brain and its various structures. Use of cutting edge, AI technology enables them to perform analysis, on both Indian and Caucasian brains, thus unlocking a global scalability potential for their platform. AI is increasing innovation in the enterprises.
Future of Machine Learning in Healthcare
The development in machine learning will be able to automatically detect most of the diseases in its early stage. It will also increase the efficiency and accuracy in disease detection to reduce the burden on doctors. AI and Machine Learning will revolutionize the future healthcare industry.
Machine learning has advanced rapidly in every field such as navigation, business, retail, and banking but progressing in the healthcare industry is difficult because of the limited availability of data and lack of highly skilled scientists. Machine learning still requires improvements and several factors need to be improved.
Conclusion
Machine learning in the healthcare domain has become more popular and widely used in the healthcare industry. ML is helping patients and clinicians in many different ways by making their work easy. Some of the most common applications of machine learning are automating medical billing, clinical decision support, and the development of clinical care guidelines. There are a lot of applications of machine learning that are under research and development. In the future, we’ll be seeing a lot of applications of ML in the healthcare sector getting implemented and making the lives of humans easy.
FAQs
What is ML healthcare?
Scientists and researchers are using machine learning (ML) to churn out a number of smart solutions that can ultimately help in diagnosing and treating an illness.
How is machine learning used in medical diagnosis?
Studying physiological data, environmental influences, and genetic factors allow practitioners to diagnose diseases early and more effectively. Machine learning allows us to build models that associate a broad range of variables with a disease.
What is machine learning in medicine?
Machine learning (a subset of artificial intelligence) plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records, and the treatment of chronic diseases.
What are the benefits of AI in healthcare?
Integrating AI into the healthcare ecosystem allows for a multitude of benefits, including automating tasks and analyzing big patient data sets to deliver better healthcare faster, and at a lower cost. According to Insider Intelligence, 30% of healthcare costs are associated with administrative tasks.
How is Machine Learning used in hospitals?
Machine Learning analyzes data throughout a healthcare system to mine, automate and predict processes. It has been used to predict ICU transfers, improve clinical workflows and even pinpoint a patient’s risk of hospital-acquired infections.
The article is contributed by Biswajit Das, Founder – Brandintelle Services
Businesses & governments cannot do without AI/ML. Yet if we take it too seriously we may be misled, sometimes with very serious effects. But the reality is that in future, both governments & corporations may have to appoint AI/ML ombudsmen to check & control AI/ML algorithms.
We have all faced occasional inquiries from our respective Direct & Indirect Tax Departments pertaining to a past period – which suggest that we have evaded taxes. The notice further asks us to prove that we are not evaders by furnishing documents that we had already furnished years ago. These are the results of AI/ML algorithms which are run by Tax Departments on the vast amounts of data that they have aggregated. In most cases, they serve as irritants, forcing citizens to submit the same documents multiple times at their own time & cost.
Apart from the stress that this generates on semi-literate or otherwise challenged taxpayers, there is the odd case where the allegation by the algorithm can be of a more serious nature – without any justification. Repercussions of such AI/ML “activism” can be far more serious in-patients medical records as the following case shows.
In 2021, Wired magazine reported a case of a patient who was suffering from a chronic condition which caused her great pain. Although the situation was not critical, the medicines were clearly not working. And surgery was very risky. To alleviate her pain, her physician naturally regularly prescribed opiates as painkillers. for multiple years.
After many years, her physician recommended a hospital visit – for a second opinion as well as a possible alternate treatment. The patient visited & was asked to register as an in-patient for some examinations. She was accommodated & tests were conducted. Meanwhile, she ran out of her opiate painkillers & asked for a fresh prescription. This was a normal request as far as the patient was concerned – but it resulted in a deadlock situation. The hospital did not prescribe the medicine & nor did it give any reason. As soon as the tests were completed, they requested her to leave the hospital as they could not treat her any longer. Upset & perplexed, the patient went home & asked for an appointment with her regular physician. But she was in for a rude shock! Her physician refused to treat her. After much pleading, she was able to extract the reason – her identity was “blacklisted” as a substance abuser and on the basis of this, the medical profession was practically asked to boycott her. It took her many months to reverse the situation.
What had happened behind the scenes just before this period is interesting. The hospital had signed up to outsource its Hospital Management System along with Electronic Medical Records to a third party along with a group of regional hospitals & regional physicians. The third party managed to aggregate patient medical records from all the hospitals, & physicians into a data lake. And as is the custom today, built some “intelligence” into its Hospital Management System with AI/ML. The algorithm that was published had an inbuilt logic that apparently categorised all prescription opiate users as “substance abusers” if they used opiates for more than a certain (undisclosed) period! The Hospital Management software automatically “blacklisted” such patients & barred them from any medical treatment!
Current & future generations may rely on AI/ML as the ultimate decision-making tool. But in reality, AI/ML simply points to signs, trends & predictions all based on applied statistics (from the eighties & earlier!) which are applied to available historical data. This was not possible till the price-availability of computing & storage power crossed a threshold level – which happened in the last 5 years.
But here’s the thing: both data & algorithms are prepared by human beings. Therefore both are imperfect. And to make it worse, both data & algorithms are ‘opaque’ to end-users, making some victims & others perpetrators!
Yet we tend to treat the predictions & pointers as though they were the Gospel truth!
While these nudges & prediction techniques work in general, it can be fatally wrong at times. As the adoption of AI/ML proliferates, there will be a need to appoint regulators & ombudsmen who will have the right to examine & override decisions taken based on AI/ML algorithms.
