Mar 15–18, 2020

At a Loss for Words: How ML Bridges the Creative Language Gap

Dan Gifford (Getty Images)

Who is this presentation for?

Data scientists or analysts

Level

Intermediate

Description

One of the most thrilling advancements in AI in recent years was the algorithm AlphaGo and its successors that competed against and beat the top Go players in the world. Another exciting area centers around the generation of synthetic images using Generative Adversarial Networks. Both of these advancements show a mastery in creative spaces thought previously to reside only with human intelligence. This means that our creative nature is more structured and contains more patterns than we previously admitted. While the goal posts for true intelligence have been forced to move once more, these fresh achievements mirror new areas of opportunity for creatives who work with visual imagery. Specifically, how AI can act as a translator between the visual inputs and how humans describe it.

In this session, Dan Gifford, Senior Data Scientist, will share how Getty Images scientists are using machine learning to help creatives discover images based on subjective visual concepts. For instance, “What makes an image authentic?”. As a consequence, the audience will see why coming up with a natural language-only approach to image search is destined to fall short of expectations and more creative approaches are often necessary. Solving these types of creative problems means data scientists will need to collaborate with creatives directly. While these two groups often approach problems differently, this session will include tips on how to work together, including success stories and techniques for building training sets and new visual models.

Attendees will learn:
1) How creative AI is changing the way we think about the limits of human language;
2) Why not all exciting problems are purely objective in nature;
3) Tips for how data scientists and creatives can make one another better;
4) How to build training sets when you have subject experts but a subjective problem; and
5) Why watching for biases early can save you pain later.

Prerequisite knowledge

1) Basic understanding of training AI algorithms 2) Knowledge of, and experience with, data set management

What you'll learn

Attendees will learn: 1) How creative AI is changing the way we think about the limits of human language; 2) Why not all exciting problems are purely objective in nature; 3) Tips for how data scientists and creatives can make one another better; 4) How to build training sets when you have subject experts but a subjective problem; and 5) Why watching for biases early can save you pain later.
Photo of Dan Gifford

Dan Gifford

Getty Images

Dan Gifford is a Senior Data Scientist responsible for creating data products at Getty Images in Seattle, Washington. Dan works at the intersection between science and creativity and builds products that improve the workflows of both Getty Images photographers and customers. Currently, he is the lead researcher on visual intelligence at Getty Images and is developing innovative new ways for customers to discover content. Prior to this, he worked as a Data Scientist on the Ecommerce Analytics team at Getty Images where he modernized testing frameworks and analysis tools used by Getty Images Analysts in addition to modeling content relationships for the Creative Research team. Dan earned a Ph.D. in Astronomy and Astrophysics from the University of Michigan in 2015 where he developed new algorithms for estimating the size of galaxy clusters. He also engineered a new image analysis pipeline for an instrument on a telescope used by the department at the Kitt Peak National Observatory.

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