Michael
Spranger

Profile

Michael Spranger is the COO of Sony AI Inc., Sony’s strategic research and development organization established April 2020. Sony AI’s mission is to “unleash human imagination and creativity with AI.” Michael is a roboticist by training with extensive research experience in fields such as Natural Language Processing, robotics, and foundations of Artificial Intelligence. Michael has published more than 70 papers at top AI conferences such as IJCAI, NeurIPS and others. Concurrent to Sony AI, Michael also holds a Senior Researcher position at Sony Computer Science Laboratories, Inc., and is actively contributing to Sony’s overall AI ethics strategy.

Publications

Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning

Nature, 2022
Pete Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Kompella, Hao Chih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead Amago, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block…

Logic Tensor Networks

Artificial Intelligence, 2022
Samy Badreddine, Artur d'Avila Garcez*, Luciano Serafini*, Michael Spranger

Attempts at combining logic and neural networks into neurosymbolic approaches have been on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists deep learning, which typically uses a sub-symbolic distributed representation, to learn and reason a…

Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

NeurIPS, 2021
Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environ…

RecipeBowl: A Cooking Recommender for Ingredients and Recipes using Set Transformer

IEEE Access, 2021
Michael Spranger, Kana Maruyama

Countless possibilities of recipe combinations challenge us to determine which additional ingredient goes well with others. In this work, we propose RecipeBowl which is a cooking recommendation system that takes a set of ingredients and cooking tags as input and suggests pos…

Extending Real Logic with Aggregate Functions

IJCLR, 2021
Samy Badreddine, Michael Spranger

Real Logic is a recently introduced first-order language where formulas have fuzzy truth values in the interval [0, 1] and semantics are defined concretely with real domains. The Logic Tensor Networks (LTN) framework has applied Real Logic to many important AI tasks through …

Assessing SATNet's Ability to Solve the Symbol Grounding Problem

NeurIPS, 2020
Michael Spranger, Oscar Chang*, Lampros Flokas*, Hod Lipson*

SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudoku puzzles visually from examples of puzzle digit images, and was heralded as an impressive achi…

Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation

NeurIPS, 2020
Uchenna Akujuobi, Jun Chen*, Mohamed Elhoseiny*, Michael Spranger, Xiangliang Zhang*

Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation (HG), which refers …

T-PAIR: Temporal node-pair embedding for automatic biomedical hypothesis generation

IEEE TKDE, 2020
Uchenna Akujuobi, Michael Spranger, Sucheendra K Palaniappan*, Xiangliang Zhang*

In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningfulimplicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted fromdatabases of biomedical publica…

Blog

February 10, 2022 | Sony AI

革新的なAI「Gran Turismo Sophy™(グランツーリスモ・ソフィー™)」を発表

ソニーAI初となる、革新的なAI「Gran Turismo Sophy(グランツーリスモ・ソフィー、以下GTソフィー)」が英国の総合科学誌「Nature」(2月10日号)の表紙に掲載され、同時にこのプロジェクトの発表ができることを大変うれしく思います。NatureGTソフィーは、PlayStation®4のドライビングシミュレーター『グランツーリスモSPORT』を学習した革新的なレーシングAIエージェントです。GTソフィーはトレーニングを通じ、世界最高峰の…

ソニーAI初となる、革新的なAI「Gran Turismo Sophy(グランツーリスモ・ソフィー、以下GTソフィー)」が英国の総合科学誌「Nature」(2月10日号)の表紙に掲載され、同時にこのプロジェクトの発表ができることを大変うれしく思います。Natu…

February 10, 2022 | Sony AI

Unveiling Gran Turismo Sophy™ : An AI Breakthrough

I’m thrilled to announce Sony AI's very first AI breakthrough - Gran Turismo Sophy which is featured on the cover of Nature magazine’s Feb 10th issue.NatureGran Turismo Sophy is a revolutionary racing AI agent that has learned to …

I’m thrilled to announce Sony AI's very first AI breakthrough - Gran Turismo Sophy which is featured on the cover of Nature magazi…

October 12, 2021 | Sony AI

Sony AI Values and Why They are Important for the Future of AI

Sony AI’s mission is to unleash human imagination and creativity with AI. This mission is only achievable if we follow three core values that underlie everything we do. We believe extraordinary innovation requires diversity in …

Sony AI’s mission is to unleash human imagination and creativity with AI. This mission is only achievable if we follow three cor…

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