AI and the Future of Recruitment Jakub Zavrel About me Founder - PowerPoint PPT Presentation

AI and the Future of Recruitment Jakub Zavrel

About me Founder of Textkernel (2001), since 2015 part of CareerBuilder group. New venture: Zeta Alpha (2019) R&D background in AI, Machine Learning & NLP since 1990. Born 1970 in Prague, raised in Rotterdam, lives in Jakub Zavrel Amsterdam with wife and 2 teenage daughters. Eclectic music tastes. AI for People and Jobs / Semantic Recruitment / Labor @jakubzavrel Market Analysis / Natural Language Processing / Machine Learning / Search & Match / Enterprise Software zavrel@gmail.com

Labour Market: a Language Gap I like programming, but I’m interested do take on more project management responsibility Is there a job in our organisation that better fits my degree? We are looking to hire: I’d like to work on our mobile strategy. I’ve helped a friend An experienced tech team team lead develop a mobile app. The ideal candidate has: I’d like to do more with my organisational - min. 5yr of experience talent. - Certfied scrummaster - Exp. w/iOS, Android Completed academic studies Computer Science or related 30% travel for customer presentations

Why? In the next 5-10 years, AI will reach a level of near or super human performance in many domains, including understanding language and connecting supply and demand. AI will fundamentally change the way people and jobs connect in the global marketplace, and will remove market barriers caused by language meaning variation and lack of transparency. What will this mean to recruiters and job seekers?

What do recruiters do? Attracting Processing Selecting Chasing Line candidates candidates candidates Managers Employer Branding Data Entry Interviews WTF?! Advertising Screening Assessment Understanding Re-engagement Job offers the job Scheduling Contracts Scoring Referral Sourcing Basic information AI Revolution: lowers the cost of repetitive cognitive tasks...

AI: The miracle algorithm?

Right candidate, right time, right price!

So what if you have technology that actually understands : • the real requirements of the job • the key factors to be successful • the type of person most likely to succeed • what your offer should be

So what do we mean by AI?

The power of machine learning https://youtu.be/TmPfTpjtdgg “Human - level control through deep reinforcement learning” V Mnih, et al. Nature , 2015

Understanding concepts and relationships “Deep Visual - Semantic Alignments for Generating Image Descriptions” Andrej Karpathy, Li Fei-Fei, et al. CVPR , 2015

thispersondoesnotexist.com- Generating Deep Fakes “A Style - Based Generator Architecture for Generative Adversarial Networks” Karras, et al. arXiv , 2018

AI learning to use tools? https://bair.berkeley.edu/blog/2019/04/11/tools/ “Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight”, Xie, et al. ArXiv, 2019

See: https://openai.com/blog/better-language-models/ February 14 th , 2019. https://techcrunch.com/2019/02/17/openai-text-generator-dangerous/ Try it yourself: https://talktotransformer.com/

What does Machine Learning give us? Ability to solve problems with unknown rules

What does Machine Learning give us? Pattern discovery in volumes of data no human being is able to digest

What does Machine Learning give us? Optimize solutions to problems with any value that we can define

What does Machine Learning give us? Any human behavior for which we can collect sufficient data can be automated by AI / Machine Learning

AI is Mainstream

Why now? Computing power, data, algorithms Deep Learning

Deep Learning is the ability for AI algorithms to automatically learn meaningful patterns in data using layered brain-like structures called Deep Neural Networks

Deep Learning: Hierarchically abstract information Use lots of unlabeled data • Deep multi-layered networks • Automatically finds relevant features •

Understanding, connecting and analyzing people and jobs

Given a job, how do we find the relevant candidates for the job among thousands or millions of CV’s?

Match! Match! models construct the Search! Data Model out of extracted information. Vacancy Extraction CV Extraction job=Java Ontwikkelar Vacancy Match CV Match Match! city=Amsterdam job=Java Developer Normalizer Normalizer langskill=Duits city=Amsterdam job=23 experience=7 langskill=German branch=IT job=23 experience=7 langskill=DE branch=IT Search! experience=5..10 langskill=DE loc=Amsterdam+10 experience=7 loc=Amsterdam Data Model job branch langskill location experience The result is a QUERY that fits the Search! The XML fits the Search! Data Model and is Data Model. It is semantically enriched when semantically enriched when INDEXING . executed

item 1 item 2 item 3 Previous Machine Learning Approach: - Hidden Markov Models - Conditional Random Fields

Deep Learning for CVs and Jobs Recurrent Neural Networks (LSTM) Date Job - Company Word embeddings CEO CF CO CT O O O developer manager engineer Input: 2001 CEO at Textkernel

Extract! 4.O Disrupting the playing field A qualitative leap through Deep Learning Deep Learning ACCURACY Textkernel CV parsing (competition)

Intentions/Needs Intentions/Needs Vacancy parsing CV parsing Semantic Semantic Match! job=Java understanding understanding Ontwikkelar job=Java city=Amsterdam Developer job=23 langskill=Duits city=Utrecht job=23 Search! branch=IT experience=7 langskill=German branch=IT langskill=DE experience=7 langskill=DE Data Model experience=5..10 experience=7 loc=Amsterdam+30 loc=Utrecht job branch langskill location experience Behavior: Behavior: Clicks, applies Clicks, shortlist Learning To Rank, Mine knowledge

Example of Matching

Turning the job description into a rich query

Learning from Feedback: • Which candidate is the best? • Which criterion is more important? • How about combination of criteria? • How about domain biases? • What if we can learn this automatically from feedback? • Learning to Rank • Reorder a pool of candidates through machine learning • E.g. Based on recruiter preference or past performance

Next Chapter: Deep Learning Matching

CV to Job Match: document embeddings CV CV Match! Job-title, it skills, query experience DLMatch! years, keywords... Search! JOBs JOBs

Deep Learning Matcher Distance calculation Emb Deep NN CV Applications Emb Deep NN jobs Learn representation in shared space of CV and Vacancy texts such that Relevant CVs are close to the Vacancy and Irrelevant CVs are far.

REGISTERED SECURITY NURSE JOB OFFICER JOB SECURITY OFFICER JOB SECURITY OFFICER

REGISTERED SECURITY NURSE JOB OFFICER JOB Push far SECURITY OFFICER JOB SECURITY OFFICER Push close

SECURITY OFFICER

Query Summary Efficient and organized surveillance professional with 15 years in security and safety compliance. Accomplished management professional specializing in creating, launching and operating retail locations. Experience 10/2014-Present Security Officer US Security 04/2014- 01/2015 Security Officer Bristol Protective Service 08/2012- 09/2013 Metro Task Force Patrolled the facility and served as a general security presence and visible deterrent to crime and rule infractions. Reported all incidents, accidents and medical emergencies to law enforcement. Patrolled industrial and commercial premises to prevent and detect signs of intrusion and ensure security of doors, windows and gates. Continuously monitored security cameras and fire, building and alarm systems. 08/2008-05/2009 Kitchen Director, Garden Day Care Center * Collaborated extensively with interdisciplinary care team to meet the nutritional needs of each Senior. Established healthful and therapeutic meal plans and menus. Encouraged clients and caregivers to follow recommended food guidelines for well-balanced diets. 08/2006-04/2008 Cashier, Kmart * Maintained up-to-date knowledge of store policies regarding payments, returns and exchanges. Excelled in exceeding daily credit card application. Created new processes and systems for increasing customer service satisfaction. Replenished floor stock and processed shipments to ensure product availability for customers. 10/2005-01/2006 Sales Associate, Express Clothing Store * Computed sales prices, total purchases and processed payments. Operated a cash register to process cash, checks and credit card transactions. Recommended merchandise based on customer needs. Explained information about the quality, value and style of products to Influence customer buying decision. Education 01/2016- 05/2016 Essex County College High School Diploma