Artificial intelligence (AI) and machine learning (ML) are nowadays one of the hottest topics in the tech towns across the globe. China is heavily investing on R&D on AI and other developed nations like US, UK and Germany are also in the race to develop AI-base robots, machines, software, cars and business applications that can work without any human intervention.
Sometimes AI and ML are used interchangeably, making a kind of confusion among people who listen about the technological developments into this field. So, before we clarify the difference between AI and Machine Learning we need to simplify with the basic definition of both to make it more easily understandable while unfolding the dissimilarities between them.
What is Artificial Intelligence?
In a very simple and layman language, “Artificial intelligence is a theory and development of computer-based systems that can works behaving like human intelligence”. It is kind of study how to train computers so they can perform with their own thinking in different situations.
AI also sometimes called machine intelligence because it is demonstrated by machines. And nowadays automobiles, gadgets and equipment in healthcare or sector are using AI-enabled devices to reduce the repetitive tasks and minimize the human efforts.
What is Machine Learning?
Machine learning is a branch or you can say a subset of artificial intelligence in the field of computer science allowing machines to learn by its own without being explicitly programmed. Actually, with the help of an algorithm, it uses a machine learning training data to learn certain patterns and behaviours of the particular action to respond accordingly.
The prime aim of ML is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. And the process of teaching initiates with observations of data such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we feed into them.
Difference between Artificial Intelligence and Machine Learning with examples
Artificial Intelligence is a wider concept that involves the research and development of machine learning based applications. ML is the sub-field of the AI and its main aim is to increase the accuracy by learning with high-quality training data while AI works as a computer program aimed to increase the chance of success and not the accuracy.
AI is you can say a final model that performs in decision making while ML allows the system to learn new things from the data. Machine learning process basically encompasses creating self-learning algorithms, whereas AI leads to develop a system to reposed like human intelligence and behave accordingly into different circumstances.
Self-driving cars, virtual apps like Google Assistant and Crotona, Alexa and Siri are nice examples of artificial intelligence. All these models have been trained with a huge amount of high-quality data with deep learning algorithms to work flawlessly and make human work easier. Cogito is the one the well-known AI training data companies, providing high-quality datasets for machine learning in various industries like healthcare, e-commerce and retail etc.
AI-enabled robotics and machines are various other best examples you can see around you or on the internet. But remember, artificial intelligence is a broad term that represents the general concept of machines being able to carry out smart tasks, and machine learning is a specific subset of algorithms for AI that helps to learn from data and perform accordingly.
To know more about AI and ML differences watch this video:
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 is 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.
How AI Training Data Can Be A Security Threat To Your Company?
The race into Artificial Intelligence (AI) flying field is becoming more expeditious with new and more innovative developments across the world. However, such competitive approach also posing a new threat to companies working on such projects.
Every coin has two sides – AI is also brining such risk which needs to understand timely. Actually, I’m talking about the training data that is primarily used in model training and development. Though, there are many security issues with AI-based model development and applications developed using this technology.
But training data is the information you upload into the algorithm to create your AI model is highly vulnerable towards the hackers and fraudsters, if they manage to access that. If such crucial data landed into wrong hands, it can be mischievously manipulated to crash your computer networking system or breach the privacy and security of your company.
Your AI-based application is also equally susceptible to hacks or data leaks, hence you need to protect your data and we have discussed right here what are the major issues you need to consider while working on such AI-based projects.
Are you storing your data at a safe place?
If you are using cloud-based AI services where you upload your labeled data on such online data servers. You don’t know if this cloud is directly under control of your service provider, or do they own the entire stack. Moreover, always remember that maybe your cloud is secured, but not necessary it is used in a secured manner.
As per the global research and advisory firm, Gartner’s prediction up to 2022, at least 65% of cloud security failures will be occurred due to customer’s fault.
Hence, you need to make sure use the secured cloud-based AI services and also need to audit threats and risks that your employees usage may pose to the cloud.
Who possess your training data?
This is a big question – Who owns your training data? Actually, when you use a cloud-based machine learning algorithm to develop an AI-based model, you have to upload your training or testing data into the services algorithm.
Let’s take an example – Suppose you are using an NLP look through your call data which a proprietary data providing a competitive edge is a very sensitive data that you are legally bound to protect from unauthorized users.
Here, you need to make sure you do not permit any rights to your data and that you will own the final model created with that data. So, carefully check the “terms of service”, license agreements and other fine print before signing up for using any cloud-based AI technology.
And if you find anything unfavorable in their “terms of service” or ambiguous you can terminate the service and go with other reliable service providers.
Similarly, you also need to make sure you are not using unmoderated publicly available datasets for your AI or ML training data, as this can open you up to malevolent data source that can seriously poison your application or fail the model.
Do you clean up the data from such servers?
Once your AI model training is completed, it is necessary to delete all vestige from the servers used in training. Finally, you are going to use the AI application and whenever you kick-off your AI project, it’s easy to carry off with the possibilities it opens out.
Usually, managers emphasize speed and innovation, but take your time to understand what is going on behind the scenes. This will definitely help you in long-run in securing your application from mishandling while ensuring the privacy of the customer’s data.
Acquiring the machine learning training data is not difficult but keeping it secured and private during the model development is very important for every company enthusiastic in such developments.
Thus, consider such aspects while working on such projects, especially when you use a cloud-based algorithm to build an AI model and stay ahead in the competitive market without losing your privacy and data safety.
Reasons Why AI and ML Projects Fail Due to Training Data Issues
Artificial Intelligence (AI) market is posing to become billions of dollar industry in next few years, as global spending by nations on AI is likely to touch around $35.8 billion in 2029 which reports a growth of 44% over the amount spent in year 2018.
Such, impressive growth shows, AI holds huge potential to attract big organizations as well small enterprises attracting them to implement AI-enabled services for better growth in the business. However, working with AI you need immense amount of meticulous data to train the model so that it can give the precise results.
Actually, to train an AI or ML model a high-quality training data is required, which is a challenging task for AI developers or machine learning engineers. As, to get the human like complex decisions from machines you need enormous volumes of accurately labeled and annotated training data through images or videos.
With the growing AI demand, data science team are under pressure to complete the projects but acquiring the training data at a large scale is the real challenge they are facing right now.
Why Do Enterprises Face Data Issue for AI Strategy?
As per the research by Dimensional Research and Aiegion survey, enterprise machine learning is just beginning, machine learning engineers or data scientist team size is smaller and the expertise of growing data science is not yet compatible to matured ML projects expertize.
And acquiring the training data is the biggest challenge for the success of an AI project. As per the survey, 96% of the AI projects fail or not started due to lack of training data technology that leads to the inability to train the ML algorithms resulting failure of the project.
Half of the AI Projects Never Get Deployed
Nowadays, big organizations or enterprises having more than 100,000 employees are more keen to implement AI strategy into their business model – but only 50% of such enterprises currently have one. The survey reinforces that AI is at nascent in the enterprise, as 70% of them firstly invested in AI/ML projects in the last 24 months.
While on the other hand, over half of the enterprises report they have undertaken fewer than four AI and ML projects. And only half of the enterprises have released AI/ML projects into the development to build a fully-functional model.
And as per the survey research only, less than two-thirds of them indicated that their ML project reached the completion point that is being trained on labeled training data sets which are relatively at the initial stage in the ML project life cycle. And more revealing immaturity of ML in the enterprise, is that why half of the projects never deployed.
Survey Statistic Why AI/ML Projects Fail:
- 78% of AI/ML Projects Shut ate some stage Before Deployment
- 81% Admit the process of training AI with data is more difficult than they expected
- 76% struggle by attempting to label or annotate the training data on their own.
- 63% try to build their own labeling and annotation automation technology.
And as per the research, around 40% of failed projects reportedly stalled during training data-intensive phases like training data preparation, algorithms training model validation, scoring and post-deployment enhancement.
Top Reasons for AI Projects Failure:
- Lack of Expertise (55%)
- Unexpected Complications (55%)
- Training Data Problems (36%)
- Lack of Model Certainty (29%)
- Deficient Budget (26%), and
- Lack of Efficient Staff (23%)
As already bespeak, around two-thirds report that ML projects not able to progressed beyond proof of concept and algorithms development to the phase of training data. Mostly this phase is not favorable for such developments, as 80% report that training the algorithms is more challenging than the AI engineers have expected.
Reasons Why Training Algorithms Data is Challenging:
- Not enough data
- Data not in a usable form
- Bias or errors in the data
- Don’t have the tools to label the data
- Don’t have the people to label data
Nevertheless, less than 4% have reported that training data has presented without any problems. Almost three-quarters of the AI engineers indicated that they try to label and annotate training data on their own. While around 40% suggested they rely wholly or partially on off-the-shelf, pre-labeled data to train their AI model.
Such issues, lead to 70% companies utilizing external services for their AI or ML projects with most of them focusing on data collection, labeling and annotations. As AI and ML engineers are rare to find and also expensive, the enterprise should find out external solution service providers for critical activities like data labeling and model scoring. This evidence is enough to outsource data annotation for more improved outcomes.
Enterprises designate a strategic value to their machine learning initiatives and expect AI and ML shall improve their businesses aspects and would be also disruptive in their sectors.
However, AI and ML projects are still at an early stage of development at enterprises. And data science and AI engineer teams are relatively small and experienced which affects the efficiency and outcome of these projects.
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