Artificial Intelligence

Trends Shaping Machine Learning in 2017

Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world of the tech world is Artificial Intelligence (AI). The two technological concepts, Machine Learning and Artificial Intelligence, are often used as interchangeable terms. However, it is important to understand that both technologies supplement each other and are essentially different in terms of their core functions.

It is often predicted by technology enthusiasts and social scientists that human beings in the workforce will soon be replaced by self-learning robots. It is yet to be seen whether there lies any truth in these predictions or not but for 2017, the following trends have been prominent in the development of Machine Learning.

Giant companies will develop Machine Learning based Artificial Intelligence systems

In 2016, we saw many prominent developments in the domain of Machine Learning, and numerous artificial intelligence applications found a way to our phone screens and caught our attention. In the previous year, companies just touched the tip of the iceberg and in 2017, we will continue to see more developments in the field of machine learning. Big names such as Amazon, Google, Facebook and IBM are already fighting a development war. Google and Amazon launched successful apps, which include Amazon Echo and Google Home, at the beginning of the year, and we have yet to see what these tech giants have in stores for their customers.

Algorithm Economy will be on the Rise

Businesses greatly value data to take the appropriate actions, whether it is to understand the consumer demand or comprehend a company’s financial standing. However, it is not the data alone they should value because without an appropriate algorithm, that data is worth nothing. Peter Sondergaard, Senior Vice President of Gartner Research, says that, “Data is inherently dumb and the real value lies in the algorithms which deduce meaningful results from a cluster of meaningless data”.

Algorithm Economy has taken center stage for the past couple of years, and the trend is expected to follow as we expect to see further developments in machine learning tools. The use of algorithm economy will distinguish small players from the market dominators in 2017. Small businesses that have just entered the transitional phase of embedding machine learning processes in their business models will be using canned algorithms in tools such as BI, CRM and predictive analysis. On the contrary, large enterprises will use proprietary ML algorithms.

Expect more Interaction between Machine and Humans

Google Home and Amazon Echo received an exceedingly positive response from the audience which made it evident that consumers perceive human-machine interaction positively. Innovative technologies embedded with machine learning processes prove to be helpful under various circumstances; for example, helping people with eyesight issues to navigate. But will they completely replace human-human interaction? Maybe 25 years down the road, but we do not see that happening anytime soon. Machine learning has made it increasingly possible for machines to learn new skills, such as to sort, analyze and comprehend. But nevertheless, there are certain limitations to it. Automated cars have frequently been tested, and even with modified algorithms and advanced technologies, the chance of an error is still present. This example alone is enough to convince that machines will not completely replace humans, at least not anytime soon.

Conclusion

Machine Learning and Artificial Intelligence is a promising field with much potential for growth. We have seen some recent developments in the sector which, not long ago, people believed were not possible. Therefore, we cannot give a definite verdict regarding the industry’s potential for growth. However for now, intelligent machines are only capable of handling the repetitive tasks and can follow a predetermined pattern. It lacks the skill to figure out things which are out of the ordinary, and we still require human intervention for keeping the chaos at bay in such situations.

Ronald van Loon is Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

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Source: Trends Shaping Machine Learning in 2017 | Ronald van Loon | Pulse | LinkedIn

Future of Automation in Recruitment, Forget Robotics for Now!

Future of Automation in Recruitment, Forget Robotics for Now!

Robotics | The HR Tech Weekly®

There are views that automation in recruitment is great as thеѕе systems wіll hеlр companies kеер track of activity and shortlist quicker durіng this exponential increase іn resumes аnd cover letters received these days, especially in volume roles. Tо ѕоmе within HR, recruiting with technology nееdѕ а lot оf work tо gеt tо whеrе it’s expected tо be. Thіѕ саn оnlу bе achievable wіth thе introduction оf robotics аnd automation іn thе hiring process аѕ technological advances ѕееm tо bе improving аll aspects оf оur lives, аnd business іѕ аt thе forefront оf thеѕе changes.

Onе оf thе biggest challenges wе face today, іn Human Resource Management, іѕ adapting thе HR Recruitment process tо meet thе Demands аnd Nееdѕ оf а Nеw Global Economy. Thе mission іѕ tо bring thе latest breakthroughs іn Automation, wіth а focus оn Artificial Intelligence, tо aid HR Recruitment wіth recruitment automation, іn order tо meet thіѕ nеw challenge. Thіѕ mission wіll bе achieved bу realising thе opportunities аnd addressing thе challenges presented bу Globalisation, wіth rеgаrdѕ tо HR Recruitment.

Thіѕ Breakthrough Idea іѕ аbоut creating а HR Automation tо streamline thе HR Recruitment process bу Freeing HR Managers, Recruiters & Employers frоm recruitment tasks geared mоrе fоr High-scale Computerised Logic, іn order fоr thеm tо kеер focusing оn thе Recruitment Tasks mоrе suited fоr Human HR Management Logic. In turn, thе potential tо bеѕt Hеlр Billions оf Jobseekers аnd Companies achieve thеіr employment goals, іn thе mоѕt efficient wау possible.

Tаkе а sneak peek аt whаt thе future holds fоr Recruitment automation wіth HR automation:

Thе current model аvаіlаblе fоr HR Recruitment offers mоѕtlу ad hoc Recruitment Standards, whісh wеrе developed аnd applied bу а handful оf HR Managers аnd Recruiters. Thаt model hаѕ proven іtѕеlf tо bе vеrу effective іn mаnу corporation durіng thе pre-Globalization era аnd hаѕ led tо prospering economies іn mаnу parts оf thе world. However, nоw dawns а nеw era оf Globalization wіth а nеw set оf opportunities аnd challenges.

Tо adapt оur current model wіth HR Automation tо deal wіth thеѕе nеw set оf changes, wе muѕt aggregate аnd utilise thе recruitment knowledge оf global resources efficiently. Thіѕ wіll involve а massive online coordinated effort bу millions оf hr managers, employers аnd recruiters teaching аnd learning frоm еасh оthеr а vast array оf recruitment standards. Especially because logic or algorithms built based on one or two or a handful of individuals “perceptions of the best” could be very different to the global collective perception or requirements especially in the changing world. Maybe that’s why we see a lot of new technologies emerging and algorithms being applied with not all actually benefiting the end users especially talent.

Tо put thе benefits оf collecting ѕuсh massive amounts оf data frоm HR Experts іn perspective, lеt uѕ briefly tаkе а lооk аt ѕоmе оf thе major benefits оn а global level. Wе wіll hаvе іn оur hands а globally standardised mechanism, wіth whісh wе саn advance global employment efficiency tо а level mоrе аррrорrіаtе tо thе era wе сurrеntlу live іn – Globalisation. In turn, thе benefits thіѕ project produces, іѕ nоt оnlу localised but аlѕо global. Thіnk оf іt аѕ creating thе bеѕt wау tо achieve thе mоѕt efficient Global GDP growth. This, Global GDP Growth, іѕ thе wау thаt wе bеlіеvе wіll lead tо economic prosperity tо levels previously thought impossible tо аll kinds оf people аll оvеr thе world аnd оn dіffеrеnt steps оf thе economic ladder.

Thе Recruitment Standards wе аrе talking аbоut hеrе аrе mаdе uр оf pairs оf Job Rules аnd questions. Thе job rules wіll define а group оf requirements thаt muѕt bе met bу а jobseeker, tо qualify fоr thе job fоr whісh thоѕе job rules apply. Thе job questions wіll facilitate thе preliminary аnd automated interview process оf а jobseeker thrоugh HR Automation, tо automatically pre-qualify оr dis-qualify а jobseeker’s ability tо meet thе job rules fоr whісh thоѕе job questions apply. Thеrе wіll bе multiple variations оf job questions wіth varying degrees оf difficulty depending оn thе seniority оf thе job thаt thе jobseeker іѕ applying for. Eасh job rule аnd question muѕt bе translated thrоugh automated means, іn аѕ mаnу popular languages аѕ possible, wіth thе required translation іn thе languages required іn thе relevant job role(s).

Algorithm Blog | The HR Tech Weekly®

Now, having said all of this as per my brief note above automation does not always mean a good thing. Let’s take an example of video interviewing: live face to face video interviewing great but the systems where a bunch of questions are asked by a robot and a candidate has to record themselves, not too effective and here’s why. Most candidates, let me rephrase, most people are not comfortable looking at themselves talking so this in itself can make them uncomfortable, and irrelevant. If a hiring organisation uses portals to shortlist based on “algorithms” rightly or wrongly, and then does not have time to interview a candidate more naturally in further stages – I may suggest you can stop recruiting. Because this way, you will only be able to recruiter better “performance artists” and “extroverts” and loose out on a lot of talent that can genuinely help you shape the future of your organisation.

A key lesson for many here is to learn to balance the use of automation, whilst also assessing what credible sources do those automation and algorithms come from. If it is a brain child of one or a handful of individuals not backed by science, psychology and/or a collective study of hundreds of thousands of professionals, you may want to think again before using them to hire your future talent. For insights on assessments, management and hiring of independent contractors you can contact me directly.

To read more on similar topics explore our blogs; to speak with us about employer’s hubs and how we can help transform your contractor talent management by bringing efficiencies through our simple cloud platform, get in touch. We are a free platform for interims with thousands of jobs refreshed daily, join us today.

About the Author:

Bhumika Zhaveri’s expertise lies in business strategy, change, human resources and talent management. Her experience is built over years in varied sectors where she has worked within Recruitment, Resourcing and HR. Now as Founder & CEO of InteriMarket a platform for Contract/Interim Talent Management. She is a firm believer of success through people, change and culture!


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Securing Competitive Advantage with Machine Learning | The HR Tech Weekly®

Securing Competitive Advantage with Machine Learning

How to Secure Your Competitive Advantage with Machine Learning | The HR Tech Weekly®

Business dynamics are evolving with every passing second. There is no doubt that the competition in today’s business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages.

Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Every idea that has completely transformed our lives was initially met with criticism. Acceptance is always followed by skepticism, and only when the idea becomes reality does the mainstream truly accept it. At first, data integration, data visualization and data analytics were no different.

Incorporating data structures into business processes to reach a valuable conclusion is not a new practice. The methods, however, have continuously improved. Initially, such data was only available to the government, where they used it to make defense strategies. Ever heard of Enigma?

In the modern day, continuous development and improvement in data structures, along with the introduction of open source cloud-based platforms, has made it possible for everyone to access data. The commercialization of data has minimized public criticism and skepticism.

Companies now realize that data is knowledge and knowledge is power. Data is probably the most important asset a company owns. Businesses go to great lengths to obtain more information, improve the processes of data analytics and protect that data from potential theft. This is because nearly anything about a business can be revealed by crunching the right data.

It is impossible to reap the maximum benefit from data integration without incorporating the right kind of data structure. The foundation of a data-driven organization is laid on four pillars. It becomes increasingly difficult for any organization to thrive if it lacks any of the following features.

Here are the four key elements of a comprehensive data management system:

  • Hybrid data management
  • Unified governance
  • Data science and machine learning
  • Data analytics and visualization

Hybrid data management refers to the accessibility and repeated usage of the data. The primary step for incorporating a data-driven structure in your organization is to ensure that the data is available. Then you proceed by bringing all the departments within the business on board. The primary data structure unifies all the individual departments in a company and streamlines the flow of information between those departments.

If there is a communication gap between the departments, it will hinder the flow of information. Mismanagement of communication will result in chaos and havoc instead of increasing the efficiency of business operations.

Initially, strict rules and regulations governed data and restricted people from accessing it. The new form of data governance makes data accessible, but it also ensures security and protection. You can learn more about the new European Union General Data Protection Regulation (GDPR) law and unified data governance over here in Rob Thomas’ GDPR session.

The other two aspects of data management are concerned with data engineering. A spreadsheet full of numbers is of no use if it cannot be tailored to deduce some useful insights about business operations. This requires analytical skills to filter out irrelevant information. There are various visualization technologies that make it possible and easier for people to handle and comprehend data.

Want to learn more about the topic? Watch replay of the live session with Hilary Mason, Dez Blanchfield, Rob Thomas, Kate Silverton, Seth Dobrin and Marc Altshuller.

Follow me on Twitter and LinkedIn for more interesting updates about machine learning and data integration.


Source: Securing Competitive Advantage with Machine Learning | Ronald van Loon | Pulse | LinkedIn

Don’t Trust Your Gut: 3 Guidelines for Evidence-Based Recruiting

Chess Algorithms

Experience is generally good. Employers love job candidates with impressive track records. But when we on the hiring decision-making side start gaining experience in recruiting, there is a dark side that we need to be aware of if we still want to be effective.

The problem with growing experience in recruiting positions is that you start to gain confidence in your judgement. And that gut feeling about job candidates clouds the decision-making of even the best of us.

The essence of evidence-based recruiting is that you build your recruiting practice on the best available scientific evidence. What is scientific evidence? It is not expert opinions, TED Talks or blog posts. Why not? Because they are opinions without rigorous methodology backing them up. Sure, there is often wisdom in the words of HR influencers, but in order to be effective, basic evidence-based guidelines should be in place.

In the core of evidence-based recruiting should be a hiring algorithm. Algorithm is simply a formula that calculates the score of each of your job candidates. Algorithmic decision-making is simple – you hire the candidate with the highest score. But an algorithm won’t work without variables. It is the recruiter’s responsibility to build the formula – decide what kind of data to gather from the candidates and which factors matter the most. But where to start?

Screening methods – the fairest of them all

I/O psychologists have been studying selection methods with meta-analytic methods for around a 100 years, and there is a clear consensus that General Cognitive Ability (GCA) – also known as General Mental Ability (GMA) or Intelligence Quotient (IQ) – is the most versatile and powerful of the methods commonly in use. Considering how simple-to-use and cheap methods there are available, it is a mystery why these tests are not more widely adopted in practice.

Especially as a screening method, GCA measure is powerful for a couple of reasons. First, for most jobs, the job requirements aren’t set in stone. Especially in startups or companies working in dynamic markets, the contents of employees’ jobs tends to change a lot. GCA is a measure that indicates how well the candidate would be able to learn new things. Second, and related, when the job requirements are complex or new, higher information processing capacity, which is what GCA essentially measures, helps candidates perform better.

Research suggests, that the best predictive validity is achieved when GCA is coupled with other methods that preferably are “MECE” – mutually exclusive and collectively exhaustive. This means that the other methods used should be strong as well, but they should measure different constructs that GCA tests measure. Famous companies such as Google measure GCA together with other variables – namely, “Googleyness” – that they have internally found predictive for future performance. Some evidence-based factors found in I/O psychology are conscientiousness and integrity, and most companies would actually get better results with these methods than with using classic unstructured job interviews as a go-to method. But I bet that…

You are going to interview anyway, so here is how to do it right

One common mistake that many recruiters make is not structuring their job interviews.

How do you expect to compare the candidates if you ask each of them different questions? And how do you expect to hire actual talent if you let human error come in between? If you use the so called “free talk” method (the losing method) to interview candidates, you are bound to simply get along better with some candidates than with others. If the recruiter was changed, the result would most likely be different too, and this is not a good indicator of the reliableness of the interview.

Structuring interviews takes some work, but it’s principles are fairly simple. Essentially, structured interview is an employment interview where

  1. the same questions are asked of each candidate in the same order
  2. free talk is minimised
  3. the evaluation criteria for each question are determined beforehand

The two best types of questions are behavioral and situational. Behavioral questions ask about candidates’ past performance in order to predict how the candidate is likely to perform in the future. Situational questions present hypothetical situations and ask how the candidate would proceed in a given situation.

The outcome of designing the structured interview should be an “interview booklet”. This guide provides a set of predetermined questions (based on variables you have deemed to be necessary for success in the job), room for note-taking and a guide for evaluation. It should be written in a way that anyone even without recruiting experience would be able to run the interview.

If you want to be really professional, have interviewers write down the answers of each candidate, and let someone else evaluate the answers. This obviously takes time, and you need to make the call whether the added value is worth it.

Decision time? Enter Excel

So. You have built your hiring algorithm (hopefully based on GCA and other reliable variables) and collected data to measure those variables using tests and structured interviews. Now it is time to be humble, and let your new best friend Excel make the decision for you.

When you let an algorithm decide for you, you are going to get an improvement of about 50% in predicting work performance. And the interesting fact is that even the most experienced recruiters with years of experience fail more often than algorithms.

Let’s go one step further than that. Even when there is a group of experts, and when they have more data available than your excel table (the algorithmic decision-maker), their decisions are worse. Why is this and what can you do to improve?

A likely reason, as mentioned, is that these bad choices arise from various psychological biases. We as humans are overly influenced by first impressions, personalities and our own values, among other things. Because hiring decisions are essentially prediction problems – ”which candidate would perform the best in the job?” – we should use statistical algorithms which are tools originally built for prediction problems.

This does not mean that experts are unimportant. They are a great source of insight in building the algorithm in the first place. But it does mean that HR professionals need to be humble and understand their limitations. Hiring managers need to be aware and continuously measure the success factors for each job in their company, but they need to restrain themselves when the decision-time comes.

Evidence-based decision-making is the first step towards next-generation recruiting. Most of the algorithmic methods discussed here are going to be adopted to various HR tech applications in the future, but by knowing the basics, you can already start making better decisions while waiting for Big Data and AI to become mainstream in the industry.

Further reading:

Danieli, O., Hillis, A., & Luca, M. (2016). How to Hire with Algorithms. Harvard Business Review, https://hbr.org/2016/10/how-to-hire-with-algorithms

Kuncel, N. R., Klieger, D. M., Connelly, B. S., & Ones, D. S. (2013). Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis. Journal of Applied Psychology, 98(6), 1060.

Levashina, J., Hartwell, C. J., Morgeson, F. P., & Campion, M. A. (2014). The structured employment interview: Narrative and quantitative review of the research literature. Personnel Psychology, 67(1), 241-293.

Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological bulletin, 124(2), 262.

Schmidt, F. L. (2002). The role of general cognitive ability and job performance: Why there cannot be a debate. Human performance, 15(1-2), 187-210.

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