Sourcing and Matching: Artificial Intelligence vs. Human Cognition
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Transcript of Sourcing and Matching: Artificial Intelligence vs. Human Cognition
Sourcing and Matching Glen Cathey V.P. Recrui3ng, Kforce
What’s the big deal?
Hidden Talent Pools can be at least 30‐40% of each database/source
“When every business has free and ubiquitous data, the ability to understand it and extract value from it becomes the complimentary scarce factor. It leads to intelligence, and the intelligent business is the successful business, regardless of its size. Data is the sword of the 21st century, those who wield it well, the Samurai.”
‐Jonathan Rosenberg, SVP, Product Management @ Google
Finding people is easy…
Finding the right people IS NOT!
Stop wasting time trying to create difficult and complex Boolean search strings
Let “intelligent search and match applications” do the work for you
A single query will give you the results you need ‐ no more re‐querying, no more waste of time!
How do they really work?
Intuit experience by context = resume parsing
Parsing breaks down and extracts resume information
Well developed ontologies and taxonomies Hierarchical
Synonymous taxonomy Programmer, Software Engineer, Developer Tax Manager, Manager of Tax CSR, Customer Service Representative Ruby on Rails, RoR, Rails, Ruby Oracle Financials, Oracle Applications, e‐Business Suite, etc.
Some applications use complex statistical methods in an attempt to "understand" language and relationships between words
Keywords with the same or similar meanings in a natural language sense tend to be "close" in units of Google distance, while words with dissimilar meanings tend to be farther apart
Non‐interactive and unsupervised machine learning technique seeking to automatically analyze and define relationships between words and concepts
The design and development of algorithms that allow computers to evolve behaviors based on empirical data
A major focus is to automatically learn to recognize complex patterns and make intelligent decisions based on data
Aims to classify data (patterns) based either on a priori knowledge or on statistical information extracted from the patterns Ex. Computer‐aided‐diagnosis, spam/non‐spam
Finds approximate matches to a pattern in a string
Useful for word and phrase variations and misspellings
PROS and CONS
Can lessen or eliminate the need for recruiters to have deep and specialized knowledge within an industry or skill set
Reduce and even eliminate time spent on research
Reduce time to find relevant matches
Go beyond literal, identical lexical matching
Levels the playing field
Can make an inexperienced person look like a sourcing wizard
Good for simple/straightforward positions, where title matching works well and there is a low volume and variety of keywords
Good for a high volume of unchanging hiring needs
Good for teams with low search/sourcing capability
Removes thought from the talent identification process
Danger of eliminating the need for recruiters to understand what they’re searching for
Information technology, healthcare, and other sectors/verticals can create pose serious challenges
Apps find some people, miss or bury others
Level the playing field
Different users find and miss the same people
Belief that one search finds all of the best candidates is intrinsically flawed
Best candidates are not necessarily the ones with the “best” resumes
Can favor keyword rich resumes
Sr. Storage & Systems Administrator. The current environment is SAP & ORACLE on EMC storage and Unix Operating Systems. The current production database size is 9TB and total storage capacity in the SAN environment is more then 200TB spread across EMC frames like DMX3 & DMX2000, DMX800, 8830, 8730, 8430, CX3 & IBM shark F20 & 800.
All SAN storage is connected through the Mcdata 140 & 6064 director level switches and CISCO MDS 9000. Currently 8 switches are configured in the environment.
Primarily administering and managing SAN storage and secondary all high‐end servers. Configured all the EMC storage, creating Symdevices, converting Symdevices, and creating Metas, allocating/Unallocating to FA, Configuring BCV and SRDF using the Symconfigure manager & ECC 5.2. Creating zoning on MCDATA Switches using ECC as well as EFC connectrix manager software.
Storage Area Network (SAN) Engineer (Infrastructure Specialist) In this position I design, develop and test numerous storage solutions utilizing Network Appliance and EMC products for the largest enterprise active directory environment in the world (NMCI). I am responsible for meeting life cycle deliverables and the creation of engineering documentation for use during implementation. I also participate in the engineering of incentive "proof of concept" projects for potential corporate revenue for the next fiscal year. Some of these projects include Enterprise Quota management of file services and Distributed File Service (DFS) utilizing Windows 2003 R2 Server. This position requires interfacing with numerous vendors while maintaining and developing bleeding edge SAN technology for a fast paced environment servicing the Navy and Marine Corps customers ensuring we provide quality services to meet their proposed requirements.
Pre‐built taxonomies are limited in their completeness
Taxonomies are only as good as who created them
Cannot “think outside of the box”
Related words may not be relevant
Match primarily on titles and skill terms
Some applications rank results favoring recent employment duration
May not be legal/OFCCP compliant
Apps are not aware of who they didn’t find
Apps don’t “know” what you’re looking for
Applications are not truly intelligent
The capability of a machine to imitate intelligent human behavior
Artificial = humanly contrived
Source: Merriam‐Webster.com
The ability to learn or understand or to deal with new or trying situations: REASON; also: the skilled use of reason
The ability to apply knowledge to manipulate one’s environment or to think abstractly
Source: Merriam‐Webster.com
The power of comprehending, inferring, or thinking specifically in orderly rational ways
Infer = to derive as a conclusion from facts or premises
Source: Merriam‐Webster.com
Dr. Michio Kaku Theoretical physicist and futurist specializing in string field theory
Currently working on completing Einstein's dream of a unified field theory
What are his thoughts on AI?
“…pattern recognition and common sense are the two most difficult, unsolved problems in artificial intelligence theory. Pattern recognition means the ability to see, hear, and to understand what you are seeing and understand what you are hearing. Common sense means your ability to make sense out of the world, which even children can perform.”
‐ Dr. Michio Kaku
The job market of the future will be “dominated by jobs involving common sense (e.g. leadership, judgment, entertainment, art, analysis, creativity) and pattern recognition (e.g. vision and non‐repetitive jobs). Jobs like brokers, tellers, agents, low level accountants and jobs involving inventory and repetition will be eliminated.” ‐ Dr. Michio Kaku
Search and match applications do not possess any true cognitive power
Cognitive: involving conscious intellectual activity (as thinking, reasoning, or remembering)
Source: Merriam‐Webster.com
Matching apps do not have the dynamic ability to learn, understand and instantly relate new concepts and through direct experience and observation
They depend on taxonomies, statistical models, or semantic clustering to “understand” relationships and concepts
The human mind naturally organizes its knowledge of the world Carolinas HealthCare System, Charlo=e, NC Infec3on Preven3onist 1997‐present
Responsible for all aspects of infec3on preven3on and control for an 800+bed hospital. Uses science‐based research to perform infec3on preven3on. Conducts all aspects of surveillance, data analysis, and presents data to interdisciplinary teams, including the Infec&on Control Commi=ee. Uses computer‐based sta3s3cal analysis to present data. Serves as an educa3onal resource for staff, physicians, pa3ents, and the community for infec3ous and communicable diseases.
To process natural language, applications use complex statistical methods in an attempt to make sense of human language
To computers, sentences can be highly ambiguous, yielding hundreds or thousands of possible analyses
Humans automatically process and understand language, regardless of sentence length or complexity, ambiguity, incorrect grammar, etc.
We can udnretsnad any msseed up stnecene as lnog as the lsat and frsit lteetrs of wdros are in the crrcoet plaecs
Human sourcers can deduce potential experience, even with contradictory evidence or the absence of information
Applications are not “aware” of the issues associated with searching resumes
Human sourcers can specifically target Hidden Talent Pools
How do you target candidates your searches CAN’T find?
Well developed taxonomies, semantically generated query clouds and matching algorithms
Think + Perform Research For every title or keyword you are thinking of
using in your search, ask: 1. Would ALL candidates mention those titles and
keywords? 2. How many ways could the experience you’re
searching for be expressed?
Global Experience
What search terms might you use if you are looking for people with global experience?
Global, international, foreign, multinational, worldwide
Europe, European, EU, EMEA, Asia, Asia‐Pac, Pacific Rim, South America, Latin America, Americas, CALA (Caribbean and Latin America), Middle East
Canada, Japan, China, Russia, India, UK, United Kingdom, etc.
Countries, Offshore, Overseas
How do you target candidates your searches DON’T find?
Run Multiple Searches Start with maximum qualifications
Use the NOT operator to systematically filter through mutually exclusive result sets
End with minimum qualifications
Required: A,B,C
Explicitly desired: D,E
Implicitly desired: F
1. A and B and C and D and E and F
2. A and B and C and D and E and NOT F
3. A and B and C and D and NOT E and F
4. A and B and C and NOT D and E and F
5. A and B and C and NOT D and NOT E and F
6. A and B and C and D and NOT E and NOT F
7. A and B and C and NOT D and E and NOT F
8. A and B and C and NOT D and NOT E and NOT F
A mix of “man and machine,” integrating human knowledge and expertise into computer systems
Essentially ‐ the best of both worlds: Autopilot Manual override
AI/semantic matching engine
Taxonomies built by human SMEs that are continually modified and improved specifically for the organization
Resume/profile parsing
White box relevance weighting
Searchable tagging
Standard and extended Boolean in full text and field‐based search
AND, OR, NOT
Configurable proximity
Variable term weighting
Lucene and dtSearch are text search engines that support configurable proximity and term weighting
Resume Mirror
Burning Glass
Sovren
“Society has reached the point where one can push a button and immediately be deluged with…information. This is all very convenient, of course, but if one is not careful there is a danger of losing the ability to think.”
‐ Eiji Toyoda, Former President of Toyota Motor Corp
Information requires analysis
Matching apps move/retrieve information, but only PEOPLE can analyze and interpret for relevance
Sourcers and recruiters need technology that can enable their productivity
Intelligent search and match apps are not a replacement for creative, curious, analytical, investigative people
Do not seek to automate that which you do not understand and cannot accomplish manually!
“Computers move information, people do the work”
‐ Jeffrey Liker, Author of The Toyota Way
“The holy grail of search is to understand what the user wants. Then you’re not matching words; you’re matching meaning.”
– Amit Singhal
Can applications ever really know what we’re looking for?
The study of meaning, inherent at the levels of words, phrases, and sentences
5 Levels of semantic search
1. Skill words/title association, variants, and misspellings
Director of business development, business development director, etc.
JDE, JD Edwards, etc. 10Q = SEC reporting SAP = ERP JMPC, JP Morgan, JPMorganChase
2. Contextual Specifically where words, titles and skills are
mentioned in resumes Summary, education, recent work experience… Education vs. address (Harvard Ave.)
3. Grammatical, natural language search
3 full life cycle SAP R/3 implementations
Carry out wound (pressure ulcer) assessment, recommend treatment…
SOX compliancy weekly internal auditing
Perform investment performance and attribution analysis
support* near (CEO or CFO or CTO or CIO or "C‐Level" or chief*)
config* near juniper near router*
“created access database”~7
4. Implied skills, experience and responsibilities
Data center migrations possibility of SAN, and possibly EMC specifically
Working at Equant possibility of PeopleSoft experience
5. Human‐reviewed and classified
Candidate records (resumes, profiles, etc.) that have been identified, analyzed and labeled
Tagging
Using human capital data for talent discovery and identification
5 Levels
Straight lexical matching of job titles and required skill terms
Deep understanding of positions not necessary Buzzword bingo Superficial match “Once and done” Easily outsourced and automated
Beyond literal lexical matching
Involves: Interpretive analysis of the need (explicit/implicit) Synonymous terms and concepts The analysis of the relevance of the initial search
results and the adaptive process of learning from the results to creatively refine searches
Awareness of candidates excluded/not found
Searching for and identifying what isn’t explicitly mentioned
Involves: Understanding of intrinsic limitations of
resumes and profiles Skill that can only be developed over time from
observation and experience
Searching for responsibilities and capabilities, not keywords
Involves: Semantic search – specifically targeting what
people DO, not just what they SAY
Searching for the “wrong” people to find the “right” people
Involves: Targeting under/overqualified professionals Targeting people who likely work with or know
the professionals you need