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The AI Takeover in Hollywood by Yves Bergquist

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Data Con LA
Data Con LA

Abstract:- As the entertainment industry faces a landscape of exponential opportunities and threats, it is quietly turning to artificial intelligence to manage risk, develop operational efficiencies, and make more data-driven decisions. From developing cognitive solutions to assess why we think certain films and characters are more interesting than others, to isolating granular, scene-level story and character mechanics that drive better box office returns, Hollywood has fully caught up on other industries in leveraging high-end analytics methods and tools. _As the director of the Data & Analytics Project at USC's prestigious Entertainment Technology Center (created by George Lucas in 1993), Yves Bergquist sits at the center of this revolution. He and his team are developing next-generation AI tools and methods that are being deployed throughout the entertainment industry. Because his research is funded by all 6 Hollywood studios, and he personally answers to all the CTOs of those studios, Yves has unique and powerful insight into how Hollywood is quietly using machine intelligence to take its hit-making game to the next level. What the audience will learn: the audience will go behind the scenes to discover how precisely Hollywood studios are using data, analytics and AI to make better development, production and distribution decisions. Yves will draw from his and his team's research and use case studies to lift the veil on how AI, game theory, and neuroscience are transforming audience intelligence, film development, and distribution strategies.

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The AI Takeover in Yves Bergquist
ENTERTAINMENT TECHNOLOGY CENTER Data & Analytics Project Audience Intelligence Creative Marketing Data/ML/AI applied to Production Project Snowflake Operations Content Security AI Database
Based in LA Hong Kong Employees 47 NOVAMENTE Founded 2001 75% with PhDs
Leadership Yves Bergquist CEO • Data Scientist. • Director of the “Data & Analytics” Project, Entertainment Technology Center at USC. • Leading researcher on applying NLP to measure narrative intelligence and its impact on performance in marketing and film. • Built AI applications which understand human stories. • Leading AI researcher with hundreds of publications. • Creator of the OpenCog AGI project. • Chairman of the Artificial Brain Lab, Xiamen University. • Chairman of the AI Society. • Former Senior DARPA + IARPA Researcher. • PhD in Mathematics, Temple University. Dr. Ben Goertzel CTO Dr. Gian Gonzaga Chief Data Scientist • Former Head of Content Algorithms, Netflix. • Former Chief Scientist, eHarmony. • PhD in Psychology, UC Berkeley. Decades of applied AI experience
Deepest AI Expertise (1) The world’s only blueprint to human-level AI
Examples of our work We have built some of the most advanced AI platforms to date. We built the AI behind Hanson Robotics’ “Sophia” (the world’s most advanced humanoid robot) Sophia was interviewed by Charlie Rose on “60 Minutes” RoboticsMedia National Security An AI-driven application that identifies deep semantic narratives around brands in social media conversations and assesses their performance. Finance Entirely AI-powered hedge fund (no humans involved in trading). AI application modeling the human brain’s probabilistic decision- making process in the parietal cortex, to seed human-level cognition in geospatial analysis systems. Profiled in “Wired” Magazine
AI vs Machine Learning
Narrow AI vs AGI Narrow AI AGI Machine Learning Doesn’t generalize Machine Understanding Generalizes Across Domain
Our Unique Approach Deep Neural Nets + Integrative, Neural-Symbolic Cognitive Architectures Deep neural nets currently touch only a few of the numerous cognitive processes underlying human intelligence; Integrative neural-symbolic cognitive architectures attempt to embrace all of them From Machine Learning to Machine Understanding
OpenCog stores and manipulates knowledge in the form of weighed labeled hypergraphs called “The AtomSpace” OpenCog Features multiple cognitive algorithms, each acting on different sorts of knowledge within the common “AtomSpace” The world’s most extensive open source AI architecture OpenCog
Unique Challenges Cars Entertainment Much more Complexity Much more cognitive Much less data
The Supply / Demand Dilemma
Cognitive Approach AI & NEUROSCIENCE APPLIED TO AUDIENCE INTELLIGENCE Understanding Deep Human Drivers Detecting / Classifying Them in Natural Language A New Age of Behavioral Communication
Stories as Algorithms
Measuring “interestingness” (Schmidhuber) low compression rate high compression rate there is an ideal compression rate (ratio of known to new) that draws the brain to want to collect new training data familiar people, problems, characters, stories, emotions and situations unfamiliar people, problems, characters, stories, emotions and situations known new too known too newPERFECT RATIO Neuroscience & AI Research Application we built an application that mines social media data to extract and score how interesting a brand is based on the ratio of traditional / emerging themes around that brand Neuroscience R&D
Storygraphics Research Our CEO, Yves Bergquist, has been conducting research at USC’s Entertainment Technology Center on the performance of various narrative structures in feature length film, film trailers and video ads Sample Findings Feature film audiences like when a movie ends with a “collective community win” which end in a“collective win” vs a “personal win” • Stories focused on “internal states” perform poorly. • Main character mechanics that are visual in nature (revenge, obtaining, etc.) perform very highly. • Temporal elements (flashbacks, flash-forwards) perform highly. • 3-act structure does not work very well. • Acts 2 and 3 are where most film stories fail at the box office. • Audiences care about solving plot problems at the community level vs the individual level. • Audiences don’t care much about heroes obtaining what they set out to obtain. • Highest-performing MBTI main character types: INTP, ISFP, ENTJ. the algorithmic structure of story.
Overall Story Signpost 1 The Crucible, Donnie Darko, Apt Pupil Othello, The Babadook, Searching for Bobby Fischer, The House of Yes Beginning of the Movie My Big Fat Greek Wedding, The King's Speech, There's Something About Mary The Dark Knight, The Producers, How to Train Your Dragon Juno, Toy Story, Frozen In Bruges, Hamlet, The Importanc e of Being Earnest Charlotte's Web, Inside Man, Pride and Prejudice The Age of Innocence, The Silence of the Lambs
The AI Takeover in Hollywood by Yves Bergquist
CORTO
the first AI-based enterprise database. 1. Knowledge Representation: Rich semantic framework build on top of a custom hypergraph DB 2. AI tools: AGI Plugins (neural-symbolic architecture) 3. Interface: ChatBot, Query language, Graph Viz UI CortoDB: • Hypergraph DB: next step beyond graph DBs - best knowledge representation system for complex, dynamic data. • Powerful pattern matcher, to enable a very wide variety of user queries to be answered flexibly and efficiently. CortoAI: • AI, AGI, ML plugins integrated with CortoDB for data analysis, decision support and hypothesis formation. Uniquely powerful AI tools: • Evolutionary learning. • Probabilistic logic. • Neural nets. • Integrated with TensorFlow/Theano, etc., for deep learning-based data processing. • Natural language processing plugins. CortoChat: • Enterprise chatbots with customizable dialogue, and the ability to interact with the knowledge in CortoDB. SOLUTION | CORTO
Video Data social media data Production Data Financial Data Now • data in dozens of silos. • data in thousands of formats and taxonomies. Studio has few specialized experts and no unified framework for representing and analyzing its data. Data sits largely unused. Then • data is represented in a way that reflects its semantics. • relevant patterns are found by AI plugins. • interaction via query language, graphical UI or chatbot . All studio data is represented in a consolidated semantic hypergraph, analyzed using AI plugins and flexibly queryable through CortoChat Market Segmentation Data Post-Production Data CASE STUDY | FILM STUDIO
https://vimeo.com/215478217
yves@etcusc.org

More Related Content

The AI Takeover in Hollywood by Yves Bergquist

  • 2. ENTERTAINMENT TECHNOLOGY CENTER Data & Analytics Project Audience Intelligence Creative Marketing Data/ML/AI applied to Production Project Snowflake Operations Content Security AI Database
  • 3. Based in LA Hong Kong Employees 47 NOVAMENTE Founded 2001 75% with PhDs
  • 4. Leadership Yves Bergquist CEO • Data Scientist. • Director of the “Data & Analytics” Project, Entertainment Technology Center at USC. • Leading researcher on applying NLP to measure narrative intelligence and its impact on performance in marketing and film. • Built AI applications which understand human stories. • Leading AI researcher with hundreds of publications. • Creator of the OpenCog AGI project. • Chairman of the Artificial Brain Lab, Xiamen University. • Chairman of the AI Society. • Former Senior DARPA + IARPA Researcher. • PhD in Mathematics, Temple University. Dr. Ben Goertzel CTO Dr. Gian Gonzaga Chief Data Scientist • Former Head of Content Algorithms, Netflix. • Former Chief Scientist, eHarmony. • PhD in Psychology, UC Berkeley. Decades of applied AI experience
  • 5. Deepest AI Expertise (1) The world’s only blueprint to human-level AI
  • 6. Examples of our work We have built some of the most advanced AI platforms to date. We built the AI behind Hanson Robotics’ “Sophia” (the world’s most advanced humanoid robot) Sophia was interviewed by Charlie Rose on “60 Minutes” RoboticsMedia National Security An AI-driven application that identifies deep semantic narratives around brands in social media conversations and assesses their performance. Finance Entirely AI-powered hedge fund (no humans involved in trading). AI application modeling the human brain’s probabilistic decision- making process in the parietal cortex, to seed human-level cognition in geospatial analysis systems. Profiled in “Wired” Magazine
  • 7. AI vs Machine Learning
  • 8. Narrow AI vs AGI Narrow AI AGI Machine Learning Doesn’t generalize Machine Understanding Generalizes Across Domain
  • 9. Our Unique Approach Deep Neural Nets + Integrative, Neural-Symbolic Cognitive Architectures Deep neural nets currently touch only a few of the numerous cognitive processes underlying human intelligence; Integrative neural-symbolic cognitive architectures attempt to embrace all of them From Machine Learning to Machine Understanding
  • 10. OpenCog stores and manipulates knowledge in the form of weighed labeled hypergraphs called “The AtomSpace” OpenCog Features multiple cognitive algorithms, each acting on different sorts of knowledge within the common “AtomSpace” The world’s most extensive open source AI architecture OpenCog
  • 11. Unique Challenges Cars Entertainment Much more Complexity Much more cognitive Much less data
  • 12. The Supply / Demand Dilemma
  • 13. Cognitive Approach AI & NEUROSCIENCE APPLIED TO AUDIENCE INTELLIGENCE Understanding Deep Human Drivers Detecting / Classifying Them in Natural Language A New Age of Behavioral Communication
  • 15. Measuring “interestingness” (Schmidhuber) low compression rate high compression rate there is an ideal compression rate (ratio of known to new) that draws the brain to want to collect new training data familiar people, problems, characters, stories, emotions and situations unfamiliar people, problems, characters, stories, emotions and situations known new too known too newPERFECT RATIO Neuroscience & AI Research Application we built an application that mines social media data to extract and score how interesting a brand is based on the ratio of traditional / emerging themes around that brand Neuroscience R&D
  • 16. Storygraphics Research Our CEO, Yves Bergquist, has been conducting research at USC’s Entertainment Technology Center on the performance of various narrative structures in feature length film, film trailers and video ads Sample Findings Feature film audiences like when a movie ends with a “collective community win” which end in a“collective win” vs a “personal win” • Stories focused on “internal states” perform poorly. • Main character mechanics that are visual in nature (revenge, obtaining, etc.) perform very highly. • Temporal elements (flashbacks, flash-forwards) perform highly. • 3-act structure does not work very well. • Acts 2 and 3 are where most film stories fail at the box office. • Audiences care about solving plot problems at the community level vs the individual level. • Audiences don’t care much about heroes obtaining what they set out to obtain. • Highest-performing MBTI main character types: INTP, ISFP, ENTJ. the algorithmic structure of story.
  • 17. Overall Story Signpost 1 The Crucible, Donnie Darko, Apt Pupil Othello, The Babadook, Searching for Bobby Fischer, The House of Yes Beginning of the Movie My Big Fat Greek Wedding, The King's Speech, There's Something About Mary The Dark Knight, The Producers, How to Train Your Dragon Juno, Toy Story, Frozen In Bruges, Hamlet, The Importanc e of Being Earnest Charlotte's Web, Inside Man, Pride and Prejudice The Age of Innocence, The Silence of the Lambs
  • 19. CORTO
  • 20. the first AI-based enterprise database. 1. Knowledge Representation: Rich semantic framework build on top of a custom hypergraph DB 2. AI tools: AGI Plugins (neural-symbolic architecture) 3. Interface: ChatBot, Query language, Graph Viz UI CortoDB: • Hypergraph DB: next step beyond graph DBs - best knowledge representation system for complex, dynamic data. • Powerful pattern matcher, to enable a very wide variety of user queries to be answered flexibly and efficiently. CortoAI: • AI, AGI, ML plugins integrated with CortoDB for data analysis, decision support and hypothesis formation. Uniquely powerful AI tools: • Evolutionary learning. • Probabilistic logic. • Neural nets. • Integrated with TensorFlow/Theano, etc., for deep learning-based data processing. • Natural language processing plugins. CortoChat: • Enterprise chatbots with customizable dialogue, and the ability to interact with the knowledge in CortoDB. SOLUTION | CORTO
  • 21. Video Data social media data Production Data Financial Data Now • data in dozens of silos. • data in thousands of formats and taxonomies. Studio has few specialized experts and no unified framework for representing and analyzing its data. Data sits largely unused. Then • data is represented in a way that reflects its semantics. • relevant patterns are found by AI plugins. • interaction via query language, graphical UI or chatbot . All studio data is represented in a consolidated semantic hypergraph, analyzed using AI plugins and flexibly queryable through CortoChat Market Segmentation Data Post-Production Data CASE STUDY | FILM STUDIO