1. HUMAN VS. MACHINE
2014 SXSW PROPOSAL VISUAL
MARC RUXIN - TASTEMAKERX
FORMAT - PANEL

2. HUMAN VS. MACHINE: THE MUSIC CURATION FORMULA
 RECREATING HUMAN RECOMMENDATIONS IN THE DIGITAL SPHERE AT SCALE IS A
PROBLEM WE’RE ACTIVELY SOLVING ACROSS VERTICALS BUT NO ONE QUITE HAS
THE PERFECT FORMULA. THE VERTICAL WHERE THIS ISSUE IS ESPECIALLY
UBIQUITOUS IS MUSIC.
 WHERE WE CURRENTLY STAND IS SOLVING THE INTEGRATION OF HUMAN DATA
WITH MACHINE DATA AND ALGORITHMS TO GENERATE PERSONALIZED
RECOMMENDATIONS THAT MIRRORS THE NUANCES OF HUMAN CURATION. THIS
FORMULA IS THE HOLY GRAIL.

3. “ ALGORITHMS CAN TELL YOU WHAT ARE THE MOST POPULAR TRACKS IN
ANY GENRE, BUT AN ALGORITHM MIGHT NOT KNOW THAT "YOU DON'T MISS
YOUR WATER" WAS SORT OF THE FIRST CLASSIC, SOUTHERN SOUL BALLAD
IN THAT PARTICULAR TIME SIGNATURE AND THAT IT BECAME THE TEMPLATE
FOR A DECADE'S WORTH OF PEOPLE DOING THE SAME THING," SAYS QUIRK.
"THAT’S SORT OF ARCANE HUMAN KNOWLEDGE.“
– TIM QUIRK, GOOGLE PLAY

4. ACROSS VERTICALS…
 MEDIUM: DELIVERS HIGH QUALITY CURATED AND
COMMISSIONED WRITING.
 CIRCA: USES PAID EDITORS TO PICK OUT NEWS,
SUMMARIZE IT, AND DELIVER IT THROUGH A MOBILE APP.
 FANHATTAN: TV AND FILM DISCOVERY FUELED BY FRIENDS
AND EXISTING FAVORITES.
 GOODREADS: DISCOVER NEW TITLES AND GET
RECOMMENDATIONS THROUGH OTHER READERS.
 GOOGLE PLAY ALL MUSIC ACCESS: RECOMMENDS MUSIC
BY USING A HYRID OF MACHINE LEARNING AND HUMAN
INTUITION TO EVOLVE ITS UNDERSTANDING OF A USERS
MUSICAL TASTE.

5. MUSIC
 TASTEMAKERX: A HUMAN POWERED SOCIAL MUSIC DISCOVERY PLATFORM
 TWITTER MUSIC: SCANS THE ACTIVITY OF THE TWITTER ECOSYSTEM AT LARGE TO
HELP USERS FIND THE MUSIC PEOPLE ARE TALKING ABOUT.
 PANDORA: INTERNET RADIO EMPLOYING A RECOMMENDATION ENGINE FUELED BY A
HUMAN SOURCED DATA BASE, GENOME PROJECT.
 PROJECT DAISY: CURRENTLY IN PROGESS, PROJECT DAISY AIMS TO DEEPLY
INTEGRATE “CULTURAL CONTEXT” AND “EMOTIONAL CONNECTION” INTO MUSIC
CURATION.

6. PANEL DISCUSSION POINTS
 Q 1: WHAT MECHANISMS + METHODS ARE YOU CURRENTLY USING TO
OPTIMIZE CURATION?
– HUMAN, MACHINE, OR WIZARDRY [BOTH] ?
– CROSS LEARNING: WHAT HAVE WE LEARNED FROM EACH OTHER?
– HUMANS + BIG DATA: FINDING DATA WIZARDS AND IMPLEMENTING
ALGORITHMS
 Q 2: HOW DO YOU SOLVE PREDICTIBILITY WITH MACHINES?
– MACHINE LISTENING : COMPUTERS THAT LISTEN AND ARE ABLE TO
PICK OUT QUALITIES + DETAILS
– MACHINES TRYING TO KEEP UP WITH THE SPEED OF NEW MUSIC
 Q 3: HOW DO YOU SOLVE SCALE AND DIVERSITY WITH HUMAN CURATION?
– MANAGING HUMAN GENERATED DATA
– THE HUMAN ABILITY TO DETECT VARIOUS NUANCES
– THE HUMAN CAPABILITY OF FACTORING MUSICAL AND CUTURAL
HISTORY
 Q 4: RECOMMENDING BY GENRE IS EASY. HOW DO YOU CURATE AND
RECOMMEND ON THE TRACK LEVEL?
 Q 5: THE FUTURE: WHAT ARE SOME THINGS WE CAN ANTICIPATE IN THE
NEAR FUTURE AND WHAT ARE YOUR PREDICTIONS?

7. THANK YOU!