As per the World Economic Forum’s job report, algorithms and intelligent machines are expected to create 133 million new roles globally while displacing around 75 million by 2022 – which is a total net gain of 58 million jobs, not killing them.
While, Gartner estimates AI will create 2.3 million new jobs in 2020 while eliminating 1.8 million positions. And according to Dun & Bradstreet 2019 report, 40% of organizations are adding more jobs, thanks to adopting AI, whereas only 8% are cutting jobs because of implementing such new technology into their operations and management.
However, according to Oxford academic duo Carl Benedikt Frey and Michael Osborne estimates published in the document “The Future of Employment: How Susceptible Are Jobs to Computerization?“, by the end of 2030 47% of American jobs are at high risk.
Artificial Intelligence (AI) based developments are on rapid speed – From self-driving cars, to robots removing weeds from farm fields or managing the inventory in assembly lines, warehouse or virtual assistant apps assisting people in solving their queries and computers able to detect cancer accurately is because of AI applied science.
Artificial Intelligence and machine learning oriented automation system will create, eliminate or change job roles, and how much is the question of discussion among the economic advisories and job data experts.
AI Creating New Jobs Rapidly for Highly-Skilled Professionals
Yes, its true AI is creating new job opportunities for highly-skilled professionals like software engineers, AI or machine learning engineer, data analyst, data scientists, digital communicators and online specialists. As these professionals are playing a key role in AI developments using their cognitive analytical and coding skills.
And as per the tops job sites and recruitment consultants, AI job postings as a percentage of overall job postings at on such websites rapidly increased and reached nearly doubled in the last two years. While searching the jobs in AI fields increased just 15 percent showing a huge gap between demand and availability of such professionals.
Reuters Graphic Showing Gap Between AI Job Openings and Job Seekers
This demand and supply gap between job openings and job seekers, is also pushing the pay scale bar of these professionals. As per the industry experts, the average salaries for AI-related jobs advertised by the companies on career sites rose 11% between October 2017 and September 2018 to $123,069 annually.
Will AI create jobs for low-skilled workers or not?
Highly-Skilled engineers and other professionals can easily find the jobs in AI fields, but the question arises here is that – automation and artificial intelligence will make low-skilled jobs disappear compare to these specialized and highly knowledgeable workers.
As per the job data in various countries, it is apparently visible that low-skilled workers are losing their job and becoming unemployed with lesser opportunists in job market due to rise of AI-oriented developments and implementation around the world.
Though, AI and automation have great scope of replacing repetitive and predictable cognitive and physical tasks. But there is a hidden side of AI and machine learning and that is rarely discussed by the experts, even most of the people not aware how AI is creating jobs not killing them even for low-skilled workers with great opportunities.
AI Creating New Jobs for Low-Skilled Workers
Data is the fuel requires to automated the AI and most of the machine learning algorithms need to be trained with huge volume of data sets. And training the machines with labeled datasets comes under the process of supervised learning.
In fact, a computer can take decisions or inferences, but only when you show enough examples with the respective solutions to individual problems. You can teach a neural network to recognize pictures of a car by feeding the network with thousands of images of a car while specifying every time to the algorithm.
The more pictures of cars you give, it will learn better and becomes more helpful in recognizing the images at a faster speed. And these AI and machine learning training data need to be annotated by someone – off course by humans.
Labeled Data Required for AI and Machine Learning
Humans-are-in-the-loop everywhere, in AI and ML development huge quantity of dataset, is required. And to generate the labeled data, thousands of human working hours required to annotate each image manually with precision.
AI and ML are escalating into vital fields, like healthcare and medical care. Automated pattern recognizing software is used in radiology, pathology, cardiology, oncology and even psychiatry helping doctors to detect different kind of diseases timely.
Medical imagining files like X-rays, CT Scans, MRI, ECGs and Ultrasound are manually annotated with the help of image annotation services highlighting pathological signals to doctors. And similarly, various other NLP based data for speech recognition are also highly in demand among the AI and ML developers for various industries.
A New Assembly Line for Low-skilled Workers
Though, there are various companies has developed software tools that are utilized by humans to annotate the different types of images. While many organizations have internal employees and many of them outsource the manual data labeling to others.
And such outsourcing is most probably done to underdeveloped or developing nations like India, China or African nations where the cost of labor is comparatively low. And these annotators are working in a big team, even some of the companies are working with more than 50,000 people, drawing from a pool of more than a million of annotators working worldwide in the day and night shifts producing the huge quantity annotated images.
Low-skilled workers basically work in manufacturing companies. But now companies become smarter implementing an autonomous or robotic system to perform the repetitive tasks with better speed and efficiency which were earlier performed by humans.
But right now in the age of rapidly growing AI development, data annotation is new assembly line providing the new opportunity to such workers. And these new types of jobs would not exist without machine learning algorithms which are at the revolutionary stage.
Data labeling is much more different from working in manufacturing assembly lines where workers perform physically exhausting demanding tasks. While in labeling data they need to more engaged in more cognitive tasks that are performed just sitting at one place on a chair in front of computers, it’s also repetitive but safe from machineries.
Data Annotation Doesn’t Require High-end Computer Skills
However, data annotation is not an easy job, as it requires training and meticulous attention to perform each task. You have to draw polygon or bounding box annotation around the various types of objects in an image or need to pinpoint the landmarks using the mouse and keyboards.
And while doing this you need to ensure the accuracy, because the quality of the data set is very important for the success of machine learning algorithm. In various industries like self-driving or autonomous driving, fallacious training data can be a cause of death due to crash or accidents becomes the prime reasons for fail of AI projects.
Though, annotating the data is a time-consuming task, but it is very essential to teach the machines how to perceive various situations while running on the road and take precise decisions accordingly to avoid such disasters and providing the safe driving.
Job Opportunities for Both – Highly-skilled and Low-skilled
Envisaging the high volume characteristics of the tasks data annotation for AI creating jobs with great opportunity for low-skilled workers, especially for people living in developing or undeveloped countries where the job market is very low for unskilled group.
Many companies in such countries like Cogito are hiring the undergraduates, or fresh graduates and unskilled people training them creating jobs at large scale helping and improving the socio-economic situation of the entire country.
Thanks to AI, not only highly skilled professionals, but unskilled people are now also finding jobs easily with a satisfying pay scale fulfilling their basic needs. Further with more developments in unexploited fields AI and ML will create more jobs for low-skilled workers which accounts for a major population of many countries around the world. People might also consider getting a certification exam to establish a career in tech. They could look for something like the rhcsa exam, data analytics exam, etc.
Is Data Annotation Job Good for them?
The other side of this story is that, instead of learning more skill-based knowledge, such employees stuck in low-skilled jobs, which is economically not good for their long-term growth and developments.
But companies and organizations, in an attempt to operate in the market with hyper-competitive pricing with higher margins, are keeping annotators salary tremendously low as much as below $1 per hour which is below minimum wage.
Actually, in the digital era, such organizations are adopting this line of business which also sponsoring a new kind of slavery in the digital era. And the hunger of data annotation is so big, that in short-term this kind approach will be monetarily more rewarding for the companies compare to other lines of business.
While from the long-term perspective, it will affect the whole economy, as it will determine high employee churn rate, bad quality in the output, and negative impact on various communities.
Such companies, not only upsetting social norms by exploiting workers and running the business unethically, but this kind of unfair business practice is also harmful to the whole industry across the world will also impact the AI sector.
Though, AI is already a controversial technology, due to lots of societal, ethical and moral concerns associated with its developments. And this kind of dirty games by corporates can cost them when it will affect the entire sector with cascading effects.
In the nutshell, the human race of automation with AI is eliminating and creating millions of jobs worldwide in various sectors. And it is also not necessarily true that all the jobs created by AI are only for high-skilled and knowledgeable professionals but low-skilled workers are also getting new opportunities with multiple job options.
Although, the rate at which such jobs are created might not match the rate at which other low-skilled positions are disappearing globally. AI is at the growing stage, and the availability of annotated data and the need to access such data sets will grow exponentially over the next years, means a steep rise in demand for data annotators.
What is the Difference Between AI, Machine Learning & Deep Learning?
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the most widely used interchangeable words creating confusion among many people globally.
Although, these three terminologies are typically used interchangeably, but they all are different from each other especially in terms of their applications, capabilities, and results.
Understanding the difference between AI, ML, and deep learning is important to utilize the precise applications of these jargons and take the right decision while dealing with AI, ML, or DL related projects.
Before we start, I would like to show you few images (see below) that will give an overview, how AI, ML, and DL are different from each other or how these three terminologies are related to each other.
The easiest way to understand their relationship is to visualize them as concentric circles with AI – which is a broader area, then ML – which is the branch or subset of AI, and finally deep learning – which is a part of the subset of ML, fitting inside both or you can say – DL is driving today’s AI explosion due to more complex inputs and outputs.
I think these highly illustrative images cleared some doubts and misconceptions about these jargons. But you need to go through more definitions with a few sets of useful examples and use cases that will help you understand these concepts better.
What is Artificial Intelligence?
As the name denotes, AI is a broader concept used to create an intelligent system that can act like human intelligence. The terms – “Artificial” and “intelligence” means “a human-made thinking power”.
Basically, AI is the field of computer science used to incorporate human intelligence into machines, so that such machines or systems can think (not exactly) and take sensible decisions like humans.
And such AI-enabled machines can perform specific tasks very well and sometimes even better than humans – though they are limited in scope. And to develop such machines AI training data sets are processed through machine learning algorithms.
To be more precise, AI-enabled systems don’t need to be pre-programmed, instead such algorithms are used, that can work with their own intelligence. And machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks are used to create such systems.
Example of AI in Daily Life
Smart Home Devices, automated mail filters in our Gmail, Self-driving cars, Chatbots, AI Robots, Drones and AI Security Cameras are the popular examples where AI in integrated. Though, there are many more other applications, devices, systems and machines works on AI principles helping humans in various areas across the globe.
What is Machine Learning?
As the name suggests, machine learning empowers the computer system to learn from past experiences earned through training data. As of now, you got to know machine learning is the subset of artificial intelligence, in fact, it is the technique used to develop AI-enabled models.
Machine Learning is used to create various types of AI models that learn by themselves. And as much as it gets more data, it gets better at learning and gives more accurate results.
Let’s take an example of how machine learning and algorithms work while making predictions. ML is actually a process of training the algorithms to learn and make the decisions as per the learning.
While training an ML-based model, we need certain machine learning training data sets to feed into the algorithm allowing it to learn more about the processed information.
Machine Learning Examples in Real Life
Recommendation on your Mobile or Desktop based on your web search history, Virtual Assistance, Face & Speech Recognition, Tag or Face Suggestion on Social Media Platforms, Fraud Detection, Spam Email Filtering, are the major examples of machine learning in our daily life. Most of the AI devices are developed through machine learning training.
Machine learning is most prevalent in the development of fraud detection software. It has made the job of fraud analysts more efficient because it allows them to devote their time to more strategic tasks. If you want to learn more about the companies that offer Fraud detection software in your country, do a quick Google search for ‘Internal Fraud Prevention in New York‘ or whatever geographic region you are in.
What is Deep Learning?
It is the subset of machine learning that allows computers to solve more complex problems to get more accurate results by far out of any type of machine learning.
Deep learning uses the Neural Network to learn, understand, interpret and solve crucial problems with a higher level of accuracy.
DL algorithm-based neural networks are roughly inspired by the information processing patterns that are mainly found in the human brain.
While learning, understanding, and predicting just like we use our brains to recognize and understand certain patterns to classify various types of information, deep learning algorithms are mainly used to train machines for performing such crucial tasks easily.
Whenever we try to perceive new information, the brain tries to compare it with the items known to the brain before making sense of it. In deep learning – neural network algorithms employ to perceive new information and give results accordingly.
Actually, the brain usually tries to decode the information it receives and archives this through classification and assigning the items into various categories.
Let’s take an example – As we know DL uses a neural network which is a type of algorithms aiming to emulate the way human brains make decisions.
The notable difference between machine learning and deep learning is that the later can help you to understand the subtle differences. Because DL can automatically determine the features to be used for classification, while ML needs to make understandable these features manually.
Finally, the point is compared to ML, DL requires high-end machines and a substantially huge amount of deep learning training data to give more accurate results.
Deep Learning Examples in Real Life
Automated Translation, Customers Shopping Experience, Language Recognition, Autonomous Vehicles, Sentiment Analysis, Automatic Image Caption Generation & Medical Imaging Analysis are the leading examples of deep learning in our daily life.
Machine learning is already being used in various areas, sectors, and systems but deep learning is more indispensable for the healthcare sector where the accuracy of results can save the lives of humans. Though, countless opportunities lie for machine learning and deep learning to make the machines more intelligent and contribute to developing a feasible AI model.
In the healthcare and medical field, AI can diagnosis disease using the medical imaging data that are fed into deep learning algorithms to learn the tumors or other life-threatening diseases. Now deep learning is giving excellent results, even performing better than radiologists.
Finally, in all types of AI, ML or DL models working on computer vision-based technology needs a huge amount of training data for object detection. These datasets help them to learn the patterns and utilize similar information for predicting the results when used in real-life.
Artificial Intelligence in Robotics: How AI is Used in Robotics?
Robots were the first-known automated type machines people got to know. There was a time when robots were developed for performing specific tasks, yes such machines were earlier developed without any artificial intelligence (AI) to perform only repetitive tasks.
But now the scenarios are different, AI in getting integrated into robots to develop the advanced level of robotics that can perform multiple tasks, and also learn new things with a better perception of the environment. AI in robotics helps robots perform the crucial tasks with a human-like vision to detect or recognize the various objects.
Nowadays, robots are developed through machine learning training. A huge amount of datasets is used to train the computer vision model, so that robotics can recognize the various objects and carry out the actions accordingly with right results.
And, further, day-by-day, with more quality and precise machine learning processes, robotics performance is getting improved. So, right here we are discussing the machine learning in robotics and types of datasets used to train the AI model developed for robots.
How AI is Used in Robotics?
The AI in robotics not only helps to learn the model to perform certain tasks but also makes machines more intelligent to act in different scenarios. There are various functions integrated into robots like computer vision, motion control, grasping the objects, and training data to understand physical and logistical data patterns and act accordingly.
And to understand the scenarios or recognize the various objects, labeled training data is used to train the AI model through machine learning algorithms. Here, image annotation plays a key role in creating a huge amount of datasets helping the robotics to recognize and grasp different types of objects or perform the desired action in the right manner making AI successful in the robotics.
Application of Sensors in Robotics
The sensor helps the robots to sense the surroundings or perceive the visuals of the environment. Just like five key sensors of human beings, combinations of various sensing technologies are used in the robotics. From motion sensors to computer vision for object detection, there are multiple sensors providing a sensing technology into changing and uncontrolled environments making the AI possible in the robotics.
Uses of Types of Sensors in Robotics:
- Time-of-flight (ToF) Optical Sensors
- Temperature and Humidity Sensors
- Ultrasonic Sensors
- Vibration Sensors
- Millimeter-wave Sensors
Nowadays a wide range of increasingly more sophisticated and accurate similar sensors, combined with systems that can fuse all of this sensor data together is empowering robots to have increasingly good perception and awareness for the right actions in real-life.
Application of Machine Learning in Robotics
Basically, machine learning is the process of training an AI model to make it intelligent enough to perform specific tasks or some varied actions. And to feed the ML algorithms, a set of data is used at a large scale to make sure AI models like robotics can perform precisely. As much as training data will be used to train the model, the accuracy would be at the best level.
In robotics, it is trained to recognize the objects, with the capability to grasp or hold the same object and ability to move from one location to another location. Machine learning mainly helps to recognize the wide-ranging objects visible in different shapes, sizes and various scenarios.
And the machine learning process keeping running if robots detect new objects, it can make the new category to detect such objects if visible again in the near future. However, there are different disciplines of teaching a robot through machine learning. And deep learning is also used to train such models with high-quality training data for a more precise machine learning process.
APPLICATION OF AI IN ROBOTICS
AI in robotics makes such machines more efficient with self-learning ability to recognize the new objects. However, currently, robotics are used at the industrial purpose and in various other fields to perform the various actions with the desired accuracy at higher efficiency, and better than humans.
Video: Most Advance AI Robots
From handling the carton boxes at warehouses, robotics is performing the unbelievable actions making certain tasks easier. Right here we will discuss the application of AI robotics in various fields with types of training data used to train such AI models.
Robotics in Healthcare
Robotics in healthcare are now playing a big role in providing an automated solution to medicine and other divisions in the industry. AI companies are now using big data and other useful data from the healthcare industry to train robots for different purposes.
From medical supplies, to sanitization, disinfection and performing the remote surgeries, AI in robotics making such machines become more intelligent learned from the data and performs various crucial tasks without the help of humans.
Robotics in Agriculture
In the agriculture sector, automation is helping farmers to improve crop yield and boost productivity. And robotics is playing a big role in the cultivation and harvesting the crops with precise detection of plants, vegetables, fruits, and other unwanted floras. In agriculture AI robots can perform the fruits or vegetable plucking, spraying the pesticides, and monitor the health conditions of plants.
Robotics in Automotive
The automobile industry moved to the automation that leads to fully-automated assembly lines to assemble the vehicles. Except for a few important tasks, there are many processes performed by robotics to develop cars reducing the cost of manufacturing. Usually, robotics is specially trained to perform certain actions with better accuracy and efficiency.
Robotics at Warehouses
Warehouse needs manpower to manage the huge amount of inventory kept by mainly eCommerce companies to deliver the products to their customers or move from location to another location. Robotics is trained to handle such inventories with the capability to carefully carry from one place to another place reducing the human workforce in performing such repetitive tasks.
Robotics at Supply Chain
Just like inventory handling at warehouses, Robotics at logistics and supply chain plays a crucial role in moving the items transported by the logistic companies. AI model for robotics gets trained through computer vision technology to detect various objects. Such robotics can pick the boxes and kept at the desired place or load and unload the same from the vehicle at faster speed with accuracy.
Training Data for Robotics
As you already know a huge amount of training data is required to develop such robots. And such data contains the images of annotated objects that help machine learning algorithms learn and recognize the similar objects when visible in the real-life.
And to generate a huge amount of such training data, image annotation techniques are used to annotate the different objects to make them recognizable to machines. And Anolytics provides the one-stop data annotation solution to AI companies to render high-quality training data sets for machine learning-based model development.
Artificial Intelligence in High-Quality Embryo Selection for IVF
IVF treatment is becoming a common practice in today’s reality, where 12% of the world population struggle to conceive naturally. But thanks to artificial intelligence in IVF, the whole process is going to help the embryologists to select the best quality embryos for in-vitro fertilization improving the success of conception through artificial insemination.
As per the latest study published in eLife, a deep learning system was able to choose the most high-quality embryos for IVF with 90% accuracy. Compared to trained embryologists, the deep learning model performed with an accuracy of approximately 75% while the embryologists performed with an average accuracy of 67%.
As per the research stated, the average success rate of IVF is 30 percent. The treatment is also expensive, costing patients over $10,000 for each IVF cycle with many patients requiring multiple cycles in order to achieve successful pregnancy.
Risk Factors in IVF Treatment
While multiple factors determine the success of IVF cycles, the challenge of non-invasive selection of the highest available quality embryos from a patient remains one of the most important factors in achieving successful IVF outcomes.
Currently, tools available to embryologists are limited and expensive, leaving most embryologists to rely on their observational skills and expertise. As selection of quality embryo increases the pregnancy rates, that is now possible with AI.
Researchers from Brigham and Women’s Hospital and Massachusetts General Hospital (MGH) set out to develop an assistive tool that can evaluate images captured using microscopes traditionally available at fertility centers.
There is so much at stake for our patients with each IVF cycle. Embryologists make dozens of critical decisions that impact the success of a patient cycle. With assistance from our AI system, embryologists will be able to select the embryo that will result in a successful pregnancy better than ever before,” said co-lead author Charles Bormann, PhD, MGH IVF Laboratory director.
AI in Embryo Selection through Machine Learning
The team trained the deep learning system (sub branch of machine learning) using images of embryos captured at 113 hours post-insemination. Among 742 embryos, the AI system was 90% accurate in choosing the most high-quality embryos.
The investigators further assessed the system’s ability to distinguish among high-quality embryos with the normal number of human chromosomes and compared the system’s performance to that of trained embryologists help in healthy baby growth in the womb.
The results showed that the system was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the US.
However, the deep learning system is meant to act only as an assistive tool for embryologists to make judgments during embryo selection but going to benefit clinical embryologists and patients. Actually, a major challenge in the field is deciding on the embryos that need to be transferred during IVF and such AI models can make right decisions.
Machine Learning Training Data for AI Model
The research stated that deep learning model has potential to outperform human clinicians, if algorithms are trained with more qualitative healthcare training datasets. Advances in AI have promoted numerous applications that have the potential to improve standard-of-care in the different fields of medicine.
Though, few other groups use to evaluate different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a deep learning system trained on a large dataset to make predictions based on static images.
Such findings could help the couples become parents through IVF with higher chances of conceptions with right embryos selections. And further with more improvement in training development of AI systems will be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality embryos.
Watch Video: Future of AI in Embryo Selection for IVF
Source: Health Analytics
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