Man Vs Machine – How AI is Making the Workplace More Human

By Zachary Hadlee

Posted 3 years agoGROWTH

Artificial intelligence is massively taking ground as some global development firms have effectively built a video recruiting platform utilizing AI to identify new talent. There are many companies that do so, usually as a matter of necessity.

As though it’s not enough that we have to worry about deadly viruses circumnavigating the globe, we also have to worry about our jobs being stolen by C-3PO…..or do we?

Becoming human

The reality is that artificial intelligence will actually make our workplaces more human, not less. The reality is that artificial intelligence will actually make our workplaces more human, not less. The reality is that artificial intelligence (AI) development companies can actually make our workplaces more human, not less. That doesn’t mean downloading our ‘human-ness’ into robots as some of our popular films suggest but working side by side with them (although not in a weird new best friend kind of way).  In a recent Tedx Talk, Pedro Uria-Recio highlighted the ways in which AI will actually reinforce the human touch within our offices, warehouses and factories. 

Although some machines are created to save businesses money, it’s important to remember that all of this new technology has also been created to make the lives of us fragile human beings easier.  Our technology is all about making things better, including the working lives of our staff and a better user experience for those all important customers.

When done right, this can provide a more personal and intimate service for customers and, a happier workday for employees – creating more free time for creativity and fun stuff.  Let’s take a look at how this is done: 

Using AI to Close the Technology Talent Gap

Given that, today, job openings outpace available workers by 17%, there is always an obvious gap. This is especially true for the technology sector, because of the immense amount of innovations being recorded daily. Technology is developing and widening, beyond the rate at which human beings can master it.

From only a handful of programming languages only two decades ago, we now have hundreds of them. Innovations such as Mixed Reality and Blockchain were not the norm years ago, but they are now. Humans are trying hard to keep up as the gap widens. 

In addition, lately, there have been serious concerns about the higher education sector. The biggest challenge is, what most tertiary institutions teach is not directly relevant in the present world, which is changing rapidly. What many university students learned twenty years ago is now most likely outdated. As such, alternative platforms have risen to fill this gap through short courses after which a student would earn a certificate.

However, the responsibility remains on employers to filter through this lack of employability and identify those who make the best fit for their available jobs. This brings up back to the subject of artificial intelligence, and TopDevz’s recruitment process.

Man vs machine – The TopDevz Example

In order to solve its recruitment challenges, the TopDevz Academy was born. It is a testing ground that all prospective employees must pass through to prove that they are qualified for the job.

  • Applicants are made to pass through a series of stages including a personality test and a soft skills interview. But scaling through those is only a preamble.
  • The most important and most difficult stage is the complex live coding stage. This is the point where the developers have to display their software development capabilities.
  • Afterward, an AI algorithm is deployed to grade each one’s work. This method guarantees that only the objectively best candidates get employed, and it has ensured that TopDevz’s culture of excellence is upheld over the years.

Using AI to scout for technology talents is a relatively new phenomenon and TopDevz is not the only pioneer in this regard. Catalyte is another artificial intelligence company that tackles the talent gap problem this way. They do so by getting random people to take tests. Afterward, people with the most potential in software development are trained for a few weeks before being employed as an apprentice. For both companies, this model has worked pretty well and enables them to meet up with the demands of clients in the most efficient way possible.

3 Benefits of AI in Scouting for Talents

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  1. Ease of Recruitment. The purpose of this manner of AI usage in recruitment is very unique and applicant-focused, in the sense that algorithms are deployed to find the best talents, not necessarily to make the recruiters’ work easier. Of course, this can make the work of recruiters’ easier, especially as regards having to otherwise manually sift through bulky files and interview numerous candidates. However, that is only a by-product and not the main purpose.
  2. Improved Quality of Eventual Employees. TopDevz only hires senior developers who are very experienced. That is a result of using AI: improved quality of employees. In the mountain of applications recruiters often receive, it is not surprising that some excellent ones get overlooked and do not even make it to the interview stage. However, AI offers everyone a level playing ground and makes it easier to select those who perform well the most.

The benefit of improved quality of candidates supersedes every other possible benefit, particularly because of the intense competition among tech companies. This is because available workers and available jobs are not evenly matched, the companies have to ‘fight’ to attract the top talents and even keep them. Without an efficient recruitment system, a tech company would regretfully watch the most qualified tech candidates go to their competitors. Therefore, AI, though not without its own challenges, offers itself as an excellent solution. Through machine learning, recruiters can match the skills of applicants to the job requirements to determine who would be the best fit for a role. Many times, the insights gained by using AI are often overlooked during traditional recruitment.

In conclusion, a tech company would benefit a lot by tackling the talent gap with AI. This process might cost more time and money, but the effort is absolutely worth it. At this rate, technological advancements are not expected to slow down. However, its attendant challenges can be tackled headlong.

Factory reset

We’re not going to lie, the use of AI in automation means that some tasks which are traditionally conducted by humans, will now be performed by machines.  This doesn’t, however, mean that human employees will be replaced by beeping droids with quirky personalities (we’re looking at you, George Lucas)! 

Within our factories and warehouses, machines will play a bigger role in repetitive tasks which require little or no skill – which is actually a really good thing.  Why? Because this will leave employees free to concentrate on quantitative reasoning tasks which will lead to more fulfilled staff as well as reducing the number of production line injuries including RSI (repetitive strain injury). 

It’s estimated that, when it comes to ‘thinking’ tasks, these will continue to be covered by human beings 80% of the time – so there’s no need to ditch your CV just yet.    

Customer service

From chatbots to internet banking gizmos, there’s no doubt that the world of customer service is changing – for the better. Let’s face it, customer service can be tedious, frustrating and promote dark thoughts about customers on a bad day. 

As technology replaces some of the irksome and mundane aspects of customer service, professional and well trained customer service employees are able to hone and refine their skills.

For customers and colleagues, this will mean a much more reactive service as staff are no longer devoting time to very simple queries and transactions.  For example, a customer service representative in a bank is no longer answering calls about the bank’s transfer process (over and over again) and is therefore able to spend more time on complex customer queries – basically flexing his or her most important muscle – the brain!

An example of this is that, once upon a time, a bank cashier’s job would be to perform cash withdrawals for customers – an action which, these days, is performed by an ATM.  The cashier is, therefore, free to perform more complicated tasks and feel that bit more useful. 

A lesson in AI

Whilst it’s true that the power of artificial intelligence and machine learning is being harnessed in schools and colleges, teachers and professors don’t have to worry about their jobs anytime soon.  AI is currently being used to create interactive timetabling and study aids across the globe which has a number of benefits to humankind.

Firstly, these innovations teach children to be more independent in their studies and, secondly, teachers are able to direct their time and focus where it’s needed most.  For example, the teacher who is not physically handing out assignments and explaining the guidelines is likely to have more time to spend on a student who is having difficulty grasping a concept within the assignment. Another good example at the moment is the apparent need to teach children how to wash their hands!

Nice to meet you

The hospitality industry is another area in which, according to the doom-mongers, is set to resemble a scene in i-Robot.  In recent years, hotels and restaurants have taken advantage of machine learning in order to streamline their booking and customer service systems to save customers and staff time and effort.  While it’s true that some restaurants are now using robot waiters, this is, for the most part, a novelty and doesn’t herald an end to the human variety. 

Again, in these cases, AI is being used to perform straightforward tasks rather than the more human-centric ones.  Most hotels, bars and restaurants recognise that the majority of their guests enjoy interaction with human employees and have no plans to replace them.  As with other industries, taking away the mundane aspects of an employee’s job will result in a more engaged and contented workforce. 

Robot thieves

Regarding the concern that robots will ‘steal’ jobs, this is far from the truth. Surprisingly, we will still very much need humans in our workplaces.  In fact, an increase in technology will create not just more jobs but more skilled and interesting jobs which will increase job satisfaction. As well as the need for more people to actually design and build the AI systems, other roles may include: 

  • Interaction designers to liaise between personnel and machines
  • Simplicity consultants to streamline processes
  • Wellbeing coaches to improve health and harmony in the workplace
  • Analytic HR teams to analyse and identify areas where improvement – and more human staff is needed. 

In 2020, the working world is changing.  Following on from the ‘work smarter, not harder’ mantra, businesses are now recognising the benefits of healthy and happy staff – and the connection between this and production rates.  Job satisfaction is now a priority for most employers and this is where AI will shine.

By taking away tedious and time consuming tasks, employees are able to allow employees more creativity and input – making for a fully engaged team.  Not only does this greatly improve the day to day lives of employees but, the knock on effect is that customers are happier too as they receive a more involved – and more human – service. 

And in other news……….

In South Korea, the Hanwha Eagles Baseball team has found a novel way of making its stadium look full – by introducing robot fans.  The robots, which can cheer, chant and join in Mexican waves, are interactive and, fans at home can even transplant their own faces onto a robot fan!  

How Use Machine Learning (Big Data) To Boost Your Career Opportunities

You are probably thinking about your future. And you want to do what you love. Machine Learning. Are you fascinated as I am? Let’s talk about machine learning for a moment.

Machine learning is a branch of Computer Science that mainly deals with computer systems having the ability to learn and perform activities on its own without necessarily being given instructions. Arthur Samuel, a renowned scientist was the person who introduced the term machine learning back in the year 1959.

Machine learning includes several types of learning such as reinforcement learning, supervised learning and unsupervised learning. Many applications have been developed for machine learning in the computer science field. Some good examples include playing a game against an opponent or second player in gaming apps and automated systems in vehicles.

In the last couple of years, machine learning jobs have been evolving at a rapid rate with most organizations across the world creating openings in Machine Learning and Artificial Intelligence. You have a huge opportunity today to take the next step if you are interested in machine learning and you want to excel in this field. You can do it. It is always our attitude that hinders us from taking the next crucial step.

Start educating yourself


I used to think that Machine Learning is a difficult subject to understand and that only a few clever people could use it. But here is a fact: Machine learning is a branch of computer science. Therefore, most concepts originate computer science. You don’t necessarily need a degree but you need to master crucial aspects of computer science such as Algorithms, Mathematics, Data structures and Statistics.

Mathematics is the most important component to learn. And successful computer scientists encourage their students to love this subject. Remember, a poor attitude takes you nowhere. Most professionals were terrible in mathematics but they did not give up. They took the time to study and learn from their failures every single day. At the core of Artificial Intelligence is machine learning which needs your attention, interest and enthusiasm.


To develop a machine that learns, there are certain important subsets for you to be conversant with. They include Neutral networks which mainly focuses on how machines think and learn through classifying data similarly to human beings. Machines can predict and decide using a high accuracy level. Natural Language Processing which enables a machine to understand human language. As a machine evolves, it will respond in a way that another human being can easily understand. This subset is expected to evolve much more in the future.

Deep learning mainly deals with learning tools for machines and using them to solve problems and make better decisions. In this subset, information is processed using neural networks so that machines can get closer to thinking like human beings. It is described as the cutting edge in the intelligence field. Machines can use deep learning through voice commands, texts and images to come up with solutions or conclusions just like a human being.

But you might be asking yourself, what is Artificial Intelligence? Artificial Intelligence simply deals with making machines intelligent. It is a software that learns similarly to human beings and imitates them so that it can help in performing jobs faster and more efficiently than human beings.

Artificial Intelligence is evolving at a rapid rate today as never before. And so it offers you and me a huge potential. You will be happy to note that as technology evolves rapidly on many levels, so does learning. Artificial Intelligence and Machine Learning always go together.

It is important for you to know that Machine Learning is rapidly evolving every day. Therefore, more technology experts will be needed in the years to come mainly in this field. Machine learning involves learning several subjects such as mathematics, knowledge of business, statistics and technology. You also need to develop your logical skills to be successful in this field. Data analysis is very important in Machine Learning because the machine you develop will require an independent form of data for it to do its own things. As a data analyst, you can easily transform your career and succeed in the Machine Learning field. Python is a common programming language in this field. And it is included in many Computer Science Programs in the majority of the universities globally.

Your Career Path

You are needed in the Machine Learning field in the United States and the whole world. Machine Learning has proved to reduce a lot of work done by human beings. This means that the machines perform work effortlessly and much more efficiently than human beings. Most organizations across the world have started implementing automation and they are impressed with the results. In fact, Machine Learning will be implemented in the majority of these organizations to improve performance, production and efficiency while reducing expenses and errors and you and I can be part of that.

Your career path will start as a Machine Learning Engineer where you will be developing apps that perform a few common tasks normally done by human beings which will be used repeatedly to perform these tasks. Your apps should be error-free and produce great results.

Your Engineering role will then be followed by Architect position where your main task will be designing and developing prototypes for applications which need development.

Software engineers such as a Python developer in the Machine Learning field who have a lot of experience can switch their careers easily.

If you do not have any experience related to software engineering, you can easily start your career in Machine Learning if you have basic knowledge in important subjects in computer science such as Mathematics and Statistics.

Be conversant with job positions

In the field of machine learning, you can pursue several different roles. They include Machine Learning Engineer, Lead Machine Learning Engineer, Machine Learning Software Engineer, Machine Learning Engineer for Front and Back Office, Data Scientist, Principal Engineer, Senior Data Scientist in IT, Data Scientist in IT, Machine Learning Software Engineer and Senior Machine Learning Engineer.

As a beginner, you will start off as a Machine Learning Engineer who is actually knowledgeable in computer science subjects and strong in Statistics and Mathematics.

Salary Expectations

You can be rich! Can you believe it? In the United States, the average pay of a Machine Learning Engineer is $100950 per annum according to Payscale which provides salary information about different organizations. Further, this position does not require candidates to have more than ten years’ experience. mentions that the approximate salary of a Machine Learning engineer is $120,931 per annum.

Another famous site known as mentions that the average salary for a Machine Learning Engineer is $135,240 per annum.

Other positions have their respective average salaries. For example, a data scientist has a salary ranging from sixty-nine thousand to one hundred and thirty thousand dollars per annum. A Senior Data Scientist has a salary ranging from ninety-eight thousand dollars to one hundred and sixty thousand dollars per year. A Data Scientist specialized in IT has a salary ranging from ninety-two thousand to one hundred and sixty thousand dollars per year.

An outlook of your career

Machine Learning has several and different career paths with different salaries which are also big figures if you have noticed. This shows that anyone who wishes to enter this field like you and me, has a bright and exciting future ahead of him or her. This is a huge opportunity presenting itself to you today. Remember, the majority of people are going to use Machine Learning to help them perform different tasks effectively. Most of these tasks are usually boring and mind-numbing. Is it not a privilege for you to be a part of the solution in the future? You are surely going to reap the rewards if you start working on developing your career today.

After you enter into the Machine Learning field, several different paths open up for you such as Data Science, Artificial Intelligence and Data Analytics.

A professional in the IT field with excellent communication and technical skills and good mathematical and statistical background can ascend to the top in his or her career rapidly to a position such as Senior Architect in the Artificial Intelligence field or Machine Learning field.

In the United States, the job requirements of the Machine Learning Engineer position are increasing on a daily basis. Why? You may ask. Because the day to day activities or tasks of large organizations need to be very accurate and with no errors to maintain a large customer base. To accomplish this goal, only an experienced Machine Learning Engineer can develop such an application now and in the future to meet increasing demands.

Machine Learning Apps are urgently needed in many businesses across the world to secure and maintain customer details. A Machine Learning Engineer is among the people who can develop such an important app. Not only does he or she help businesses perform better but he or she also enables the rapid advancement of technology today.

To conclude

Computer science and mainly machine learning will evolve rapidly in the future. This is your chance to start developing your career and creating machines that will ease the work for human beings. Currently, there is a shortage of machine learning professionals due to the huge demand from organizations across the world. As organizations get bigger, the higher the demand for sophisticated applications. I have a friend who started learning computer science and machine learning at home. After a year, I could not believe the number of intelligent machines he had created.

The most interesting machine was the dog robot which obeyed all instructions and acted like a live dog. You can do it too. Be a part of the global solution today and not only will you help many people but you will also be richer than your friends and neighbors.

About the author Zachary Hadlee

Technology Journalist from London, currently based in Malaga. For 2 years now, I've been writing stories about how our internet works - and how it is changing. From artificial intelligence to UX things are happening today at a pace that can seem bewildering. I am the associate.

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