3 Ways Apple is Using AI in Its New iPhones and Smartwatch

As an iPhone lover, you may already be accustomed to AI in trading or AI that invests for you. With Apple’s new iPhones and smartwatch, a powerhouse of AI innovations is coming your way, designed to make your life easier and more enjoyable. 

Apple has always been at the forefront of cutting-edge technologies and the company’s latest gadgets are no exception. It’s interesting to note that Apple didn’t shout “artificial intelligence” from the rooftops when they unveiled the new products at the company’s annual iPhone launch event on September 12. However, the company has subtly integrated AI into their new iPhones and smartwatches in the following ways:

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Enhanced User Interactions with AI

The introduction of improved semiconductor designs, powered by AI, has transformed device interaction. For example, with the Series 9 Watch, users can answer or end phone calls, pause music, or access weather information simply by double-tapping with their watch hand. This innovation caters to scenarios such as when your non-watch hand is occupied with walking your dog or taking a drink. The new chip and machine learning detects subtle finger movements and changes in blood flow, making these interactions feel intuitive and seamless.

AI-Enhanced Photography 

Apple has taken its renowned photography capabilities up a notch. While the company’s “portrait mode” has been a big hit, it required users to remember to activate it. Now, thanks to AI, the camera automatically recognizes when a person enters the frame and collects the data it needs to create background-blurred photos at a later time. This means you can focus on capturing the perfect shot, while AI takes care of the background work. 

AI-Powered Siri

The Series 9 Watch’s new chip also significantly enhances the performance of Siri, Apple’s voice assistant. With 25 percent more accuracy, you can rely on Siri to assist you with even more precision.

Conclusion

Instead of aiming for radical transformations like some of its competitors, Apple is focused on using AI to refine basic functionalities in its gadgets, making them more intuitive and user-friendly. Ultimately, this provides users with a more enjoyable experience.

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Jeff Sekinger
Jeff Sekinger | Wealth Strategies

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Bingham Zhou, CFA, has over 15 years of experience as a quantitative researcher. His expertise spans systematic equity strategies, CTA trend-following, and interest rate proprietary trading in both U.S. and Asian markets. He holds advanced degrees from MIT, Carnegie Mellon, and Yale.

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Greg doscher

Greg Doscher was a CFO for many years who built out many quantitative strategies and investment tools to manage and enhance risk adjusted returns in the company’s pension plan. Prior to joining Nurp, he consolidated his skills in coding and discretionary trading to develop a comprehensive and fully automated algorithmic trading system deployed across 200+ futures markets and cryptocurrencies that encompassed all of the trading strategies he had honed over the last 22 years in finance

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Marcin Borratynski

Marcin was Head of Quant IT at the USD 4bn+ CERN Pension Fund, where he spent nearly a decade building quantitative asset allocation systems and implementing algorithmic investment strategies for a multi-asset institutional portfolio.Before joining Nurp Marcin was also Senior Quant Strategist at Evooq, a Swiss-based fund managing four strategies across equities, gold, and equity derivatives.Marcin holds a degree in Computer Science an MBA from the University of Geneva and the Certificate in Quantitative Finance (CQF).

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Abhayjit Anand

Abhay has worked with Nurp since 2022. As a Product Strategist, he focuses on building, refining, and commercializing algorithmic trading strategies. He brings seven years of experience in financial trading – combining macro research, technical analysis, quantitative strategy development, and market psychology. Alongside his work at Nurp, Abhay also serves as an Investment Analyst at Orca Capital. Before entering financial markets professionally, he spent eight years at IBM, including three years in the AI & data division as a Delivery Lead managing complex implementation projects.