Deepfakes are an AI technology based created realistic fake images or videos of targeted people by swapping their faces another person saying or doing things that are not actually done by them. And people start believing in such fake videos, as it is not recognizable with the normal human eye, it requires in-depth analysis.
How Do Deepfakes Work?
A Deepfake video seems like an original content having the person doing some kind of action or speaking on a topic. And while creating such fake videos, multiple images of the targeted person from different angles are used to superimpose on the original face.
Video: How Deepfake is Created
Images are compromised with faces and other body parts, to make it look original while creating videos, the voice is also cloned with a targeted person using the AI-enabled tools to simplify the process and match the lips moving according to words spoken.
Deepfake videos affect the life of popular personalities in our society. Politicians, actors or actresses, other celebrities and notable personalities from the corporate world. AI and machine learning-based tools are used to generate such content. But with the help of Deepfake detection services, such fake videos can be detected.
And Deepfake works like creating sensational news stories about popular personalities that people love to watch and also share with others or include in their gossips. And Deepfake porn videos gets more attention and invites more hitting on adult sites increasing online visitors count on such portals.
Disadvantages of Deepfakes
Rather than benefiting anyone, this AI-based technology has disadvantages affecting different groups of our society. Apart from creating fake news and propaganda, deepfake is majorly used for revenge porn to defame notable celebrities.
As soon as fake videos go viral people believe initially, and keep sharing with others makes the targeted person become embarrassed watching such unusual acts.
Until and unless an official statement of the targeted personality not comes, many people start believing making their life difficult, especially when they are criticized by their fans via social media platforms like FaceBook, Twitter or Instagram.
Though, it is harmful to the society but it also has few advantages like creating extraordinary attention among the online audience making the web page popular on the search engine, as more number of people start searching on such erotic topics.
And few celebrities who are not known to everyone also become famous overnight, as people start searching reading about them, what they do, what is their profession and more about their personal background and current reputation in the market.
People become more cautious about such news
Another factual advantage of deepfake is, it makes us become aware about such fake things and we should not believe in everything we see around us. Once we find that it is fake we learn and next time when such contents come through similar sources, we take time to believe or do some research to authenticate the news.
Though, such benefits are only for the audience, they don’t have any significant advantage to the person who becomes a victim of such misuse of technology. And detecting such fake images or videos timely, only can help acknowledged people to not become a victim of deepfake and if happens should be detected timely to stop further circulation.
Anyway, whatever the impact on different sections of our society, but there are many companies providing deepfake detection services to recognize the fake images or videos and control further circulation for such fake contents.
What Is Computer Vision In Machine Learning And AI: How It Works?
Thanks to AI and machine learning, computer vision technology is getting upgraded with improved versions of visualizing making perception through machines reliable. Actually, this is completely related to computer-based visual processing of objects.
What is Computer Vision in Machine Learning and AI?
Computer vision is simply the process of perceiving the images and videos available in the digital formats. In Machine Learning (ML) and AI – Computer vision is used to train the model to recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use.
The main purpose of using computer vision technology in ML and AI is to create a model that can work itself without human intervention. The whole process involves methods of acquiring the data, processing, analyzing and understanding the digital images to utilize the same in the real-world scenario.
How Does Computer Vision Work?
Computer vision in machine learning is used for deep learning to analyze the data sets through annotated images showing an object of interest in an image. It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.
This process depends subject to the use of various software techniques and algorithms, that are allowing the computers to recognize the patterns in all the elements that relate to those labels and make the predictions accurately in the future. Computer vision can be only utilized only with image processing through machine learning.
How Computer Vision is Different from Image Processing?
Both are part of the AI technology used while processing the data and creating a model. The difference between computer vision and image processing in computer vision helps to gain high-level understanding from images or videos.
For instance, object recognition, which is the process of identifying the type of objects in an image, is a computer vision problem. In computer vision, you receive an image as input and you can produce an image as output or some other type of information.
Whereas, image processing doesn’t need such a high level of understanding of image. In fact, it is the sub-field of signal processing but also applied to images. For example, if you have noisy or blurred images, then under image processing the deblurring or denoising is done to make the object in the image clearly visible to machines.
The image process task involves filtering, noise removal, edge detection, and color processing. In entire processing, you receive an image as input and produce another image as an output that can be used to train the machine through computer vision.
The main difference between computer vision and image processing are the goals (not the methods used). For example, if the goal is to enhance the image quality for later use, which is called image processing. If the goal is to visualize like humans, like object recognition, defect detection or automatic driving, then it is called computer vision.
Application and Role of Computer Vision in AI and ML
The applied science of computer vision is expanding into multiple fields. From AI development to machine learning, it is playing a significant role in helping the machines identify the different types of objects in their natural environment.
From simple home tasks to recognizing human faces, detecting the objects in autonomous vehicles, or combating with enemies in war, computer vision the only technology giving an edge to AI-enabled devices to work efficiently.
The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flying like drones and manufacturing, etc.
Actually, to create the computer vision-based model the labeled data is required for supervised machine learning. And image annotation is the data labeling technique used for creating such labeled images for computer vision.
Many companies providing the data annotation service for computer vision providing the image annotation solution for AI and machine learning.
Rendering the high-quality training data using the best tools and techniques allowing computer vision to help algorithms train the model to perform accurately in real-life use.
How Chatbots Can Transform Your Business: AI Chatbot Six Benefits
AI-enabled chatbot is becoming popular among the companies, curious about their customers to help them all-the-time as per their ease. Actually, such AI automated chat application system comes with multiple benefits, that a business owner can utilize and improve their customer service while growing their business.
As per the Gartner recent reports – by the end of 2020, around 85% of business relationships will be managed by the customer without interacting with humans, which will encourage more demand and adoption of self-help service.
And chatbots are the best self-help applications improving its communication process with more critically implemented natural language processing to learn about human interactions in their native-speaking style to communicate easily.
In upcoming days, most of the brands including newly emerging business enterprises will use this AI-based technology into their business operations and improve the overall customer relationship experience while generating more revenue.
Hence, here we will learn how a chatbot can help to improve your customer service and transform your business to achieve new goals and reputation in the markets transformed through chatbot benefits.
Six Chatbot Benefits for Business and Customer Service
#1 Round-the-clock Customer Support
The best part of AI-based chatbot is, available all-the-time for the customers to solve their queries as per their needs. As we know there is no need for humans to operate or manage this application, it works itself and can answer the questions asked by the customers related to products or services offered by your company.
Companies serving overseas customers have different time-zones, and customers in working hours contacting their service provider in another country running with sleeping time can be only handled by such automated chatbots.
#2 Multiple Customer Engagements at a Time
Another interesting point of adopting chatbot into your business is – it can handle multiple customers at a time, which is not possible for a person to interact or chat with different people at a time without any response time delay or waiting time.
The multiple customer handling capabilities will not only serve different customers at a time but will also minimize your customer service expenses while improving the customer engagement process in terms of solving the multiple issues quickly.
#3 Reduce Human Errors and Emotional Support
Humans can do mistakes but the machine doesn’t do that until a major bug affects the system which is rare, as such applications are integrated only after full testing. Similarly, chatbots don’t do any mistakes while answering the customer’s questions.
Identically, unlike humans, chatbots don’t have feelings, so they cannot be influenced by the people asking any kind of emotionally attached questions. The human customer service agent can change their minds or can do favor against the company policy, while chatbots cannot be influenced by such psychological factors.
#4 Cross-platform Accessibility to Customers
In the smartphones and tablets era, people are accessing various services through such gadgets. And chatbots can be accessible through multiple types of platforms and devices making available to customers anywhere as per their ease.
These chatbots can be designed and developed as per the different devices and operating systems that can be integrated with customizing options for instant chat. Chatbots are accessible through desktop PC, laptops and smartphones.
#5 Better Understanding with Data Gathering
When humans in customer service interact with end-users and end their conversation, they don’t look at the chat data, except the concerning factors to solve their queries. While AI-enabled bots can gather chatbots training data to analyze the sentiments of people, learn human speech through NLP and understand them better.
And this kind of crucial data is not only important to understand them and serve with better service, but also help chatbot developers to train their next model with more accurate data sets for better results through automated chatbot system.
#6 Save the Time and Cost of the Company
Last but not the least, if you integrate the chatbot system in your business to provide the automated online help or support system to your customers you will save the cost and time of the company in managing and operating such services.
Human employees can be retained only by paying monthly payouts, but chatbots need a one-time cost in development and execution. Human’s salary needs to be hiked while chatbot needs only updates at a few extra costs which are only if you have subscribed to any third party chatbot service provider to use on your website for support.
AI benefiting humans in different ways, and AI-enabled Chatbots will not only provide you an edge to your business but would also help to serve your customer better. Using such bots if you can get to know what exactly your customers seeking and understand their feelings through sentiment analysis to serve them best.
How AI Is Creating New Job Opportunities For Low-Skilled Workers?
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 the jobs easily with 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.
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.
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