Apple’s no-code Trinity AI platform handles complex spatial datasets


The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!


Apple has been slowly but surely creating a name for itself in the low-code/no-code movement. This July, the Cupertino-based company announced the launch of Trinity AI, a no-code platform for complex spatial datasets. Trinity enables machine learning researchers and non-AI devs to tailor complex spatiotemporal datasets to fit deep learning models.

Back in 2019, Apple revealed SwiftUI, a programming language that required much less coding than the Swift language. With the release of Trinity, Apple doubles down on its effort to significantly lower the threshold for non-devs and non-ML devs.

Fusemachines CEO Sameer Maskey, who also teaches AI as an adjunct associate professor at Columbia University, sees Trinity as a great way for developers to use machine learning in their apps. “Initially, I see Trinity being used by devs who already create apps for iOS, but who don’t know machine learning, so they can incorporate spatial datasets in their work,” Maskey told VentureBeat.

We asked Maskey to give VentureBeat his take on Apple’s platform and what it means for the future of AI and low-code/no-code industry. This is a literal transcription of the interview.

VentureBeat: What makes Trinity different from other no-code AI platforms?

Sameer Maskey: It’s not so groundbreaking, really. By creating a similar system, the difference is that it’s more focused on geospatial data, like maps and moving objects. A lot of people are trying to build apps with geospatial data, for a phone. If you don’t know machine learning, but if you have a background building apps, now you can do it with Trinity.

Let’s say you’re trying to build an app that recommends the best places to eat in an area. Let’s also say you have access to how many people are going to that specific spot. Before, you’d have to collect all the data and stream the collected data and build it on a server or whatever system you were using. With neural networks, you experiment with many different models. For example, you find a model that predicts what are the best food places; you’d need to…

Source…