Meetup #3 - Practical Advice for Responsible AI

AI for the rest of us is on a mission to make AI easier to understand.
AI is ready for everyone. No matter who you are. No matter what your role.
We promise no hype, no jargon and no confusing terminology. We meet you where you are, to get you where you want to be!

Join us for our next in-person meetup in London on Thursday February 27th!
Every conversation you have about AI will be peppered with concerns. These concerns are often valid. How do we justify the energy consumption? How do we make our solutions more efficient? How do we control and monitor these autonomous systems? How do we ensure we have sufficient governance?

The theme for this event is Practical Advice for Responsible AI and we'll be learning about Green AI with Charles Humble (Freelance consultant, author and podcaster) and AI Governance with Jovita Tam (Business-focused Data/AI Advisor & Attorney (England/NY))

Of course, we'll also have plenty of time for the most important part of any community - meeting and learning from each other! Massive thanks to [Narus.ai](http://narus.ai/) for hosting us again and to [Mindgard.ai](http://mindgard.ai/) and Causaly for their support and sponsorship!

Where and When?

• Thursday,February 27th
• 18:00 - 21:00
• Talks start at 19:00
• Adaptavist, 28 Scrutton St, London EC2A 4RP

Talk 1 - Responsible AI: Wrangling Robots with AI Governance With Jovita Tam

As AI systems become more integrated into our daily lives, the need for AI governance has never been greater. In this talk, we’ll explore the role of AI Governance in Responsible AI. We will uncover how thoughtful governance enables us to enjoy the benefits that AI brings as a technology while enabling continuous innovation.

Discover how you can play a pivotal role in responsibly shaping an AI-driven future. Are you ready?

Talk 2 - Green AI: Making Machine Learning Environmentally Sustainable with Charles Humble

After considering the significance of the carbon footprint of AI, Charles will offer practical strategies to reduce environmental impact at each stage of the AI lifecycle. These strategies include using smaller datasets, leveraging transfer learning, employing model compression techniques, and considering edge computing.

Code of Conduct
This event has a code of conduct that you can review here.