Episode 017 – Spatial AI Megatrends

What this episode covers

In this episode, we list out the range of ‘megatrends’ and emerging patterns in the field of spatial AI that we’re are closely monitoring. Key topics include the rise of large multimodal AI models, the potential of agentic AI systems with specialized agents, the emergence of ‘mixture of experts’ architectures, the integration of spatial data and sensors with natural language processing, and the contrasting trends of high-end immersive technologies versus low-sensory ‘dumb phone’ experiences. We show the widening gap between open-source and commercial AI models on our spatial.space AI/ML model timeline, speculating on factors driving this divergence and potential future developments. Overall, the discussion centres on identifying and analysing cutting-edge advancements in Spatial AI, with a focus on their implications for a range of industries and applications. We’re also publishing this episode on YouTube, if you’d like to watch along, and see the outlinks & screensharing *with your own two eyes*: https://www.youtube.com/watch?v=UYDI4qfAreA Chapters 00:02:09 – Defining the Field of Spatial AI We talk about the need to define the field of spatial AI, which encompasses spatial computing, geospatial data analytics, depth perception, and AI techniques. We showed spaitial.space – a 3D graph/map showcasing the interconnected fields related to spatial AI – acknowledging the amorphous and rapidly evolving nature of this domain. 00:08:28 – Emerging AI Model Architectures William discussed three leading contenders in the design patterns of AI models: large multimodal models (eg GPT-4), agentic systems with specialized agents, and ‘mixture of experts’ architectures. He analyzed the potential advantages and trade-offs of each approach, emphasizing their accessibility to developers and potential for enabling new use cases. 00:23:05 – Integration of Spatial Data and Natural Language Processing Violet shared an example of a company (Architect AI) working on translating sensor data and physical world information into natural language. The discussion explored the potential for integrating spatial data, sensors, and natural language processing to enable better understanding and control of real-world environments. 00:28:11 – High-End vs. Low-Sensory Experiences We discuss contrasting trends in user experiences – with high-end immersive technologies like Apple’s Vision Pro on one end, and the ‘dumb phone’ movement emphasizing low-sensory, context-aware experiences on the other. The potential implications of these divergent approaches for spatial AI were explored. 00:32:55 – Open-Source vs. Commercial AI Models The widening gap between open-source and commercial AI models was examined, discussing factors such as the high computational costs of training large models, potential government-backed open-source initiatives, and the potential for specialized, domain-specific models to emerge alongside generalist models. Transcript and Links ABWell g’day and welcome to SPAITIAL. This is Episode 17 coming to you, I think, more towards the middle of April. Time will tell. The reason why time will tell is that it takes, well, this will be episode 2 of our video feed, which takes a little bit more than just the regular lunch break to get out to the world. So, surprisingly, it’ll probably be hitting you around the middle of April. That’s okay, should be great. We’ve got with us today, we have Mirek, and Violet, and William, and we’re going to go on a fairly gnarly topic this morning, today? Time zones, I must say, are hitting us quite hard. It’s now not daylight savings in Australia, it is in North America, so times are all around the world. Right now, we’re going to be talking about, on