What exactly is machine learning in a recruiting context?

Here is the thing really; we do not have AI, IMO, not yet. We have ML (machine learning) which means code does things for us — code that we write.  Machines do not have free will; neither do I when it comes to dating now that I think of it, but they do what they are asked to do. It is simply, simple. Yet we have the buzz word commandos out there, who are thought leaders, that love to drop these two little letters to impress an audience that has no clue about tech. yet Pandora’s Box has been opened and off to buzzword bingo we play. Everyone have their markers?

The terminology has been a misused misnomer for a while now so I thought I would do a little research on the actual meaning.

In short, the best answer is that: Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart.”

Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.

Early Days

Artificial Intelligence has been around for a long time – the Greek myths contain stories of mechanical men designed to mimic our own behavior. Very early European computers were conceived as “logical machines” and by reproducing capabilities such as basic arithmetic and memory, engineers saw their job, fundamentally, as attempting to create mechanical brains.

As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.

Artificial Intelligences – devices designed to act intelligently – are often classified into one of two fundamental groups – applied or general. Applied AI is far more common – systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category.

Generalized AIs – systems or devices which can in theory handle any task – are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art.

The Rise of Machine Learning 

Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has grown to.

One of these was the realization that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.

The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.

Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.

Neural Networks

The development of neural networks has been very key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias.

A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.

Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.

Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.

These are all possibilities offered by systems based around ML and neural networks. Thanks in no small part to science fiction, the idea has also emerged that we should be able to communicate and interact with electronic devices and digital information, as naturally as we would with another human being. To this end, another field of AI – Natural Language Processing (NLP) – has become a source of hugely exciting innovation in recent years, and one which is heavily reliant on ML.NLP applications attempt to understand natural human communication, either written or spoken, and communicates in return with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language, and to learn to respond in a way that a particular audience is likely to comprehend.

A Case of Branding?

We LOVE to chat about branding and I am for one a fan of personal branding.  Artificial Intelligence, and in particular today ML, certainly has a lot to offer. With its promise of automating mundane tasks as well as offering creative insight, industries in every sector from banking to healthcare and recruiting are reaping the benefits. So, it’s important to bear in mind that AI and ML are something else they are products which are being sold consistently, and lucratively, I am looking at you Facebook, Amazon, and Google, to name a few easy targets.

Machine Learning has certainly been seized as an opportunity by marketers. After AI has been around for so long, it’s possible that it started to be seen as something that’s in some way “old hat” even before its potential has ever truly been achieved. There have been a few false starts along the road to the “AI revolution”, and the term Machine Learning certainly gives marketers something new, shiny and, importantly, firmly grounded in the here-and-now, to offer.

The fact that we will eventually develop human-like AI has often been treated as something of inevitability by technologists. Certainly, today we are closer than ever and we are moving towards that goal with increasing speed. Much of the exciting progress that we have seen in recent years is thanks to the fundamental changes in how we envision AI working, which have been brought about by ML.

In other words the computers are not making choices without you; they are not dating your boyfriend or girlfriend.  They are not driving cars or flying planes without having been programmed to do so. I am pretty sure we are OK for now, insert ominous laugh here…but using good ML tools will help us save time, energy, and money in making those placements. 

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Director of Staffing Solutions at Engage Talent

Derek Zeller draws from over 20 years in the recruiting industry, and he currently is the Director of Recruiting Solutions and Channels with Engage Talent. The last 16 years he has been involved with federal government recruiting specializing within the cleared IT space under OFCCP compliancy. He has experience with both third party agency and in-house recruiting for multiple disciplines. Using out-of-the-box tactics and strategies to identify and engage talent, he has had significant experience in building referral and social media programs, the implementation of Applicant Tracking Systems, technology evaluation, and the development of sourcing, employment branding, and military and college recruiting strategies. Derek currently lives in the Portland, Oregon area. Follow Derek on Twitter @Derdiver or connect with him on LinkedIn.




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Derek Zeller draws from over 20 years in the recruiting industry, and he currently is the Director of Recruiting Solutions and Channels with Engage Talent. The last 16 years he has been involved with federal government recruiting specializing within the cleared IT space under OFCCP compliancy. He has experience with both third party agency and in-house recruiting for multiple disciplines. Using out-of-the-box tactics and strategies to identify and engage talent, he has had significant experience in building referral and social media programs, the implementation of Applicant Tracking Systems, technology evaluation, and the development of sourcing, employment branding, and military and college recruiting strategies. Derek currently lives in the Portland, Oregon area. Follow Derek on Twitter @Derdiver or connect with him on LinkedIn.

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