Transfer LearningHow new technology is enabling Natural Language
Understanding for multilingual text, domain jargon, and slang.
Dr. Catherine Havasi, LuminosoIlluminate the unknown in your data
www.luminoso.com
Artificial intelligence seems poised to change every aspect of our world.
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The future of user interfaces is
conversational.
Siri and other systems have opened the
door.
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Advances have transformed language understanding.
1960sPattern Matching
& Keywords
1990sRule Sets & Ontologies
2000sUnsupervised
ML (Bayes)Word Embeddings(“Deep Learning”)
2010s
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Photo by: Johnny One Speed, CC BY-SA 3.0.
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Accenture projects that the market value of the artificial intelligence industry will
rise to $2.1 trillion in Japan by 2035.
Accomplishments of deep learning systems have shown the business and end users the power and ease of automation that artificial intelligence can
bring.
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Deep learning focuses on finding patterns in its input data
at a sophistication and scale that hasn’t been done before.
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In 2013, Google popularized a type of machine learning called word embeddings that applied the power of deep
learning to natural language understanding.
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Could this be the keyto language understanding?
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For natural language understanding to be successful, it must be:
Multilingual &Global
Explainable Unbiased &Ethical
Adaptable Automated
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The flaw in deep learning is that it requires more data, time, and compute than is
practical in most circumstances.
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Training a deep learning system can take
hundreds of millions or billionsof points of data.
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This data is needed because the model must learn from scratch each time it learns anything.
This isn’t how people work.
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Imagine understanding the way the public & experts tasters talk about chocolate.
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There would not be enough data for a deep learning system to understand chocolate.
This is worse for financial services, healthcare, and other jargon-heavy fields.
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Transfer learning helps apply what they learned in one context
to another context.
Photo by: Bill Evans/United States Air Force Academy.
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“Transfer learning will be … the next driver of
ML commercial success.”
– Andrew Ng 2016
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Transfer learning is what powers the app on your phone that makes it look like your cat
was painted by Picasso.
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Photo by: Chris Rodley
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Or imagining what would happen if a book of dinosaurs was painted with flowers
instead of scales.
Transfer learning helps self-driving cars learn from simulation or generalize from
dangerous situations.
Photo by: Google Research Blog
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We’ve used transfer learning to help computers understand under-
resourced languages.
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It’s beginning to work for voice and video…
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And its future implications are unpredictable!
Why does domain specificity matter for business?
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CPG: Colors Matter
CASE STUDY
Finding the right color of a consumer toiletry product drove sales and customer brand loyalty –
even for men.
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Technology: Language of Gaming
CASE STUDY Learning the ever-changing
language of online gaming
enables prioritization of bug fixes in new
releases.
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What do we do at Luminoso?
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We are transforming the contact center using our experience with transfer learning in the
enterprise.
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We work in the technology/mobile, consumer finance, retail, and marketing verticals.
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Photo by: Colin CC BY-SA 3.0.
We can automate discovering the reasons people call a contact center with 80-90% alignment with people…
…in 15 minutes instead of 4 months.
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We have built building models of how people think about the world in 70 languages – called ConceptNet.
Luminoso’s machine learning lets computers not start from scratch and bring a digital intuition of ConceptNet to natural
language understanding.
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We’ve been using transfer learning in production for seven years to help brands and agencies do better market research.
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Transfer learning is often referred to as “emerging technology”.
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Our transfer learning is so accurate, it can outperform Facebook’s Spanish deep
learning model with no Spanish training data.
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(This claim is externally verified.)
Our software works natively in Japanese. We also do Chinese, Korean, and ten additional languages.
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Thank you so much for listening to my talk!
I’m here with my colleagues who would love to talk to you about your business problems or about machine learning on
Japanese text!
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