Will Else Will Apple Do to Support AI?

Apple is negotiating to use ChatGPT features in Apple’s iOS 18, according to a Bloomberg report. That raises the question of what else Apple might eventually do in the artificial intelligence area, since that rumored deal seems centered on adding chatbot features. 

Other approaches would be needed to support many of the anticipated iPhone use cases, ranging from speech-to-text to translation to camera functions or health and fitness features, for example. 

Some of those approaches will likely lead to more custom chips. Apple's A-series chips already power iPhones, iPads, and Macs, so moves to add on-board AI processing capabilities would be logical. Neural engines or co-processors also are possible avenues. 

Core ML is Apple's framework for developers to build and integrate AI features into their apps, and likely will be another avenue of development.

That points up a major difference between consumer and enterprise or business use cases: consumer use cases will favor on-device implementations; enterprise will favor remote processing. In the area of personalization, for example, enterprise processing typically can rely on remote processing, as presently is the case for most AI-related personalization efforts in the advertising and content areas. 

Many consumer use cases for personalization will be more contextual, based on location (typically on a smartphone, in a mobile context) and might require more local processing. The other area of activity trackers might often require real-time processing, which will lean towards on-board processing as a consequence. 

Likewise, facial recognition and other security features might need to be processed on board, rather than remotely, as will other image processing and text-to-speech or speech-to-text use cases. Real-time translation also will tend to work best on board. 

Feature	Consumer AI Examples	Enterprise AI Examples
Personalization	Recommendation engines in shopping apps, news feeds curated based on user preferences.	Targeted marketing campaigns, personalized customer service interactions.
Efficiency & Automation	Smart assistants for scheduling appointments, voice commands for device control.	Robotic Process Automation (RPA) for repetitive tasks, predictive maintenance in manufacturing.
Data Analysis & Insights	Activity trackers that analyze fitness data, sleep monitoring apps that provide personalized recommendations.	Customer sentiment analysis from social media, predictive analytics for inventory management and demand forecasting.
Security & Fraud Detection	Facial recognition for unlocking phones, spam filtering in email applications.	Fraud detection in financial transactions, anomaly detection in network security.

Even looking only at generative AI, to support content creation use cases, enterprise applications will tend to work when supported by remote processing, as many of the activities are not extremely latency dependent. 

source: McKinsey, Seeking Alpha