In these slides Hayim Makabee presents several applications of Machine Learning and their impact on our lives, including Recommender Systems and Autonomous Vehicles.
4. Examples of Clustering
Applications
• “How to group customers for targeted
marketing purposes?”
• “Which neighborhoods in a country
are most similar to each other?”
• “What groups of insurance policy
holders have high claim costs?”
• “How to group the products in a store
based on their attributes?”
• “How to group pictures based on their
description?”
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6. Examples of Classification
Applications
• “Which category of products is most
interesting to this customer?”
• “Is this movie a romantic comedy,
documentary, or thriller?”
• “Is this review written by a customer
or a robot?”
• “Will the customer buy this product?”
• “Is this email spam or not spam?”
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8. Examples of
Recommendation
Applications
• “Which movies should be
recommended to a user?”
• “If the user just listened to a song,
which song would he like now?”
• “Which news articles are relevant for
a user in a particular context?”
• “Which advertisements should be
displayed for a user on a mobile
app?”
• “Which products are frequently
bought together?”
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9. Failures of Machine
Learning
• In May 2015 Flickr released an
automatic image tagging capability
that mistakenly labeled a black man
for an ape.
• Soon afterwards, Google came up with
a photo labeling tool similar to Flickr,
which made similar mistakes. Black
men were tagged as gorillas.
• A recent Carnegie Mellon University
study showed that Google displayed
ads in a way that discriminated based
on the gender of the user.
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12. The Netflix Challenge
On October 2006 Netflix offered a challenge to the data science community:
improve our recommendation algorithm by 10% and win a million dollars. In
September 2009 the winners were announced.
13. Are recommendations
effective?
• 35% of Amazon’s revenue is
generated by its
recommendation engine
• 60% of video clicks on
YouTube came from home
page recommendation
• 80% of movies watched on
Netflix came from
recommendations
17. Autonomous Vehicles
• Car-as-a-Service
• End of ownership
• Less vehicles
• End of parking lots
• End of taxi drivers
• End of gas stations
• Public transportation?
• Insurance industry
• Banking industry
• Symbol of status
• Form vs Function
• Less accidents