{"id":33255,"date":"2016-11-18T14:09:59","date_gmt":"2016-11-18T22:09:59","guid":{"rendered":"https:\/\/www.smartrecruiters.com\/blog\/?p=33255"},"modified":"2017-10-17T09:46:19","modified_gmt":"2017-10-17T16:46:19","slug":"meetup-report-rise-of-the-machines-machine-learning-and-ai-takeover-recruiting","status":"publish","type":"post","link":"https:\/\/www.smartrecruiters.com\/blog\/meetup-report-rise-of-the-machines-machine-learning-and-ai-takeover-recruiting\/","title":{"rendered":"Meetup Report: Rise of the Machines – Machine Learning and AI takeover recruiting"},"content":{"rendered":"
Talent Acquisition teams could be faced with a fantastic future: A recruiter posts a job, publishes it with one click to job boards around the web, gets a couple hundred candidates, an intelligent machine automatically sorts through this stack of candidates, and highlights the top 10 most qualified candidates tuned to the specifications for that job, the company and the team. <\/span><\/p>\n This dream of matching the right candidates to the job, which seemed so far off 10 years ago, is rapidly becoming reality. With machine learning and AI coming into its own in the last 2 years, AI is everywhere.<\/strong><\/span><\/p>\n Big players are also jumping into the ring. IBM announced in September that they are launching <\/span>Watson for recruiting<\/span><\/a>. The tool will add an intelligence layer on top of a recruiting ATS or CRM system to rank candidates based on a fit score. Google announced on Tuesday that they are launching a new <\/span>Cloud Jobs API in partnership with resume database giants such as Careerbuilder and Dice<\/span><\/a>. This new machine learning service will apply Google\u2019s algorithms to help job seekers to figure out their fit for a particular job. <\/span><\/p>\n At a recent <\/span>Hiring Success meetup <\/span><\/a>hosted at SmartRecruiters, a group of panelists included:<\/span><\/p>\n <\/p>\n They all shared insights on how their unique machine learning solutions are addressing the problem of matching jobs to candidates. Here are the 3 biggest things we learned from our panel: <\/span><\/p>\n Smarter. Better. Faster.<\/b><\/p>\n The difference between the success of machine learning technology today and the failures of machine learning technology 10 years ago is the convergence of data quality, server infrastructure, and the maturity of machine learning algorithms. <\/span><\/p>\n Our panelists highlighted how their technologies are approaching the problem from different angles. For example:<\/span><\/p>\n Entelo<\/b><\/a> allows companies to mine insights from the Social web to supplement candidate profiles with social insights. They are also building their machine learning algorithm on top of this data.<\/span><\/p>\n Brilent<\/b><\/a> is incorporating graph search technology to quantify the relationships between candidate\u2019s work experiences, skills and education.<\/span><\/p>\n AmazingHiring<\/b><\/a> applies a supervised learning model to use recruiters\u2019 knowledge for intelligent searching.<\/span><\/p>\n Restless Bandit<\/b><\/a> has structured their technology and machine learning algorithms to constantly search through companies\u2019 databases of past resume data. <\/span><\/p>\n As companies train their models with more data, these technologies will continue get better and better at distinguishing the attributes that make a candidate successful in their job.<\/span><\/p>\n Not Human vs. Machine, but Human + Machine<\/b><\/p>\n Recruiters should not fear a takeover by machines<\/b>. Machine learning models are only as good as the data and the feedback loop that they receive. These models always need a constant flow of training data in order to get better. For example, both Brilent and Restless Bandit allow recruiters to tune the importance of specific attributes on a job to job basis. The signals for successful machine intelligence, is a mixture of the perceived quality of candidate from the recruiter, and the ultimate hiring decision made by the recruiting team. <\/span><\/p>\n Panelists also shared their perspectives on the effectiveness of machine intelligence, as its impact depends on the different areas of recruiting it\u2019s applied to. Human beings are not tuned to perform boring repetitive tasks. Computers however are designed with an endless capacity for repetitive computations. In such cases of high-volume recruiting and for professions with clearly defined skill sets, machines can help recruiters to be more efficient and productive. An area such as \u201cExecutive search\u201d is a different story. The role of the recruiter here is to be the salesperson and advocate for their company. <\/span><\/p>\n Machines are still a ways off from replicating that human relationship. Recruiters should take advantage of the technology to optimize their processes. Machines and recruiters, working together, will identify the best talent. <\/span><\/p>\n What\u2019s Next<\/b><\/p>\n\n
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Meetup Report: Rise of the Machines – Machine Learning and AI takeover recruiting<\/a><\/blockquote>