Sony believes that AI and Robotics technology can activate and augment human capabilities and abilities. In 1999, Sony introduced AIBO to the consumer market. This was a four-legged autonomous robot with pseudo instincts and emotions that could interact with people like pets. The responses of the non-verbal robot was interpreted by humans with some degrees of freedom and it was this freedom of the interpretation that contributed to the smoothness of the interactions.
Then in 2010, Sony developed a robot prototype called Rudy, which focused on more specifically physical interaction between humans and robots This feature was achieved by a special force-controlled actuator (VA) and a method called generalized inverse dynamics (GID) calculation, which enabled Rudy to physically interact with objects and humans in the real world safely. For example, it was able to assist a physically handicapped patient to control Rudy with a simple GUI to fetch a towel on a shelf. This was an example of augmenting human's physical capability.
The VA and GID was then also applied for bilateral-controlled manipulator to augment the human hand's skills and capability. Using micro-end effector equipped with dedicated force sensors, we achieved to augment and improve the accuracy of the force sensitivity and position of the human hand by 10 times..
Now as we consider the AI and Robotics in the field of Gastronomy, we are exploring the possibilities of augmenting the chef's imagination and creativity for new recipe generation, and the Chef's skills in the actual cooking process.
In this talk, I will explain the activation and augmentation of these various human abilities, and the behavior control architecture behind them.

Speaker:
Masahiro Fujita - VP, Senior Chief Researcher, Sony Group Corporation / Director, Sony AI

Scientific research is led by the hypothesis - the supposition or proposal that forms the basis for further investigation. Traditionally, such hypotheses are formed by the researcher and their team, taking into account vast amounts of previous scientific research. However, this task is becoming more difficult, as the volume of data and research has grown exponentially. We formulate this hypothesis generation problem as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. We propose an inductive edge (node-pair) embedding method to utilize both the graphical structure and node attribute to encode the temporal node-pair relationship.

Speaker:
Dr. Uchenna Akujuobi - Research Scientist, Sony AI

Speaker info:
Scientific research is led by the hypothesis - the supposition or proposal that forms the basis for further investigation. Traditionally, such hypotheses are formed by the researcher and their team, taking into account vast amounts of previous scientific research. However, this task is becoming more difficult, as the volume of data and research has grown exponentially. We formulate this hypothesis generation problem as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. We propose an inductive edge (node-pair) embedding method to utilize both the graphical structure and node attribute to encode the temporal node-pair relationship.

This talk will cover some of the steps Sony AI is taking to operationalize AI ethics and some of the key research questions Sony AI will be exploring around fairness, transparency, and accountability. Given that AI ethics is an area with many competing values and the need for highly contextualized solutions, research will play a key role in bridging the gap between the goals of AI ethics and their operationalization. This talk will in particular discuss some of the challenges of addressing both privacy law and technical fairness issues in the computer vision context.

Speaker:
Alice Xiang - Senior Research Scientist, Sony AI

Speaker info:
Alice Xiang is a Senior Research Scientist at Sony AI and the Head of the AI Ethics Office for Sony Group. In these roles, Alice leads teams of AI ethics researchers and practitioners who work closely with business units to develop more ethical AI solutions. Alice previously worked as the Head of Fairness, Transparency, and Accountability Research at the Partnership on AI. She also served as a Visiting Scholar at Tsinghua University's Yau Mathematical Sciences Center. Core areas of Alice's research include algorithmic bias mitigation, explainability, causal inference, and algorithmic governance. She has been recognized as one of the 100 Brilliant Women in AI Ethics. Alice is both a statistician and lawyer, with experience developing machine learning models and serving as legal counsel for technology companies. Alice holds a Juris Doctor from Yale Law School, a Master's in Development Economics from Oxford, a Master's in Statistics from Harvard, and a Bachelor's in Economics from Harvard.

Video streams:
Tuesday, 24 August, 2021 (22:30pm EDT - Room Green 2)
Thursday, 26 August, 2021 (14:40pm EDT - Room Red 3)

Posters:
Tuesday, 24 August, 2021 (23:00pm EDT - Room Green)
Thursday, 26 August, 2021 (15:10pm EDT - Room Red)

Authors:
Jesse Thomason - University of Southern California
Aishwarya Padmakumar - Amazon
Jivko Sinapov - Tufts University
Nick Walker - University of Washington
Yuqian Jiang - UT Austin
Harel Yedidsion - UT Austin
Justin Hart - UT Austin
Peter Stone - University of Texas at Austin and Sony AI
Raymond Mooney - Univ. of Texas at Austin