Apple has been working of Artificial Intelligence and Machine Learning for decades, but Apple-watcher Ben Bajarin believes that Apple is more cautious about publicly touting its own AI prowess in light of the recent controversies surrounding Microsoft’s Bing AI and Google’s Bard ChatGPT competitor.

Ben Bajarin for Fast Company:

Apple’s largest computing-platform competitors—Microsoft and Google—have been showing their cards around their bets in generative AI. We can assume that, beyond their current applications, they will likely add AI to their Windows and Android operating systems. How Apple might compete is an open question.

But Apple has some core differentiators when it comes to anything it does in AI, including generative AI.

• SILICON
• PRIVACY
• DEVELOPERS
• USER DATA

Many companies have considered mixing third-party large language models with smaller models trained on their own proprietary data. Apple could use this approach, so that its customer could still use the general-purpose large language models but also get the benefit of personal language models that are unique for each user. If this happens, you can imagine that no two Apple customers would have the same generative AI experience. Each personal model would be uniquely customized to the user’s workflows, language preferences and style, personality, and more.

All of this is interesting to ponder, and this is my best hunch at how Apple could approach generative AI in a way that offers something unique compared to its competition.

Each bullet point is expanded upon in the full article here.

MacDailyNews Take: We agree with Bajarin that until myriad trust and safety issues are resolved, we don’t expect Apple to integrate generative AI technology into its software.

That said, Apple has made significant strides in the fields of Artificial Intelligence and Machine learning over the years and its efforts are accelerating. Some of Apple’s notable achievements include:

• Core ML: Apple’s Core ML is a framework that allows developers to integrate machine learning models into their iOS apps. Core ML supports a variety of popular machine learning tools and techniques, such as neural networks and decision trees.

• Face ID: Apple’s facial recognition technology, Face ID, uses AI and machine learning to recognize a user’s face and authenticate their identity. Face ID is used on the iPhone and iPad, and is considered one of the most secure forms of biometric authentication.

• Siri: Apple’s voice-activated personal assistant, Siri, is one of the company’s most well-known AI applications. Siri uses natural language processing (NLP) and machine learning to understand and respond to spoken requests from users.

• Apple Neural Engine: Apple’s custom-designed Neural Engine is a chip that is used in some of the company’s devices, including the iPhone and iPad. The Neural Engine is designed specifically for machine learning tasks, and is used to power features like Face ID and Siri.

• Machine Learning Research: Apple also conducts research in the field of machine learning, with a particular focus on developing techniques that can be used to improve user privacy. For example, Apple has developed a technique called “differential privacy,” which allows data to be analyzed without revealing individual users’ personal information.

Overall, Apple has made significant contributions to the field of artificial intelligence, and continues to invest in AI research and development.

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