Annual General Meeting and Technical Lecture
About
Annual General Meeting Agenda:
1 Finance status as of 30 September 2024
2 Review of current and future activities in 2024
3 Budgetary plan and the events for 2025
4 Appointment of committee members for 2025
5 Other subjects
Technical Lecture:
This lecture will include discussion of the state of some of the current developments regarding on-device generative AI and Multimodal Language Models (MLLMs), looking at how this can bring enhanced perception to machines that interact with humans along with enabling better and more useful responses.
This involves compact implementation of MLLMs, taking input from various sensors of image, sound and other types, along with contextual information to deliver superior perception based on data fusion. This then providing the analysis needed to generate more useful and accurate responses in support of a machine’s human interlocutors.
Various use cases of Human Behavioural AI for machine perception will be discussed, and though there will be some focus on mobility applications such as Cars with Personality, the technologies involved can be applied to a wide range of applications providing these with greatly improved human machine interaction.
The advantages of moving from high level to low level fusion will be briefly discussed, and also the potential of neuromorphic technology to deliver higher performance at lower power consumption and in a smaller footprint. Further a new technology, Temporal Event Neural Networks will be introduced.
2
Continuing Professional Development
This event can contribute towards your Continuing Professional Development (CPD) hours as part of the IET's CPD monitoring scheme.
25 Oct 2024
6:00pm - 7:30pm
Programme
Organized by IET Japan Local Network
Annual General Meeting
Time: 18:00 to 18:30 Japan Time
Chair: Dr. Isao Iyoda
Language: English and Japanese as applicable
Technical Lecture Meeting
Time: 18:45 to 19:30 Japan Time
Speaker: Mr. Colin Mason
Language: English
Title: Multimodal (S/L) LMs at the Embedded Edge for Machine Perception Applications