Introduction

As the largest company in the world by market value, Apple aims to leverage the power of the AI technology that has dominated the industry in the current year. Google, Microsoft, and Meta can’t stop talking about artificial intelligence “generative AI” but Apple stays silent and only rumours indicate that something is going on. In the Apple earnings call held in May, CEO Tim Cook mentioned AI only on two occasions, both in response to questions. Throughout Apple's extensive two-hour software launch event in June, the term was not even used; however, the event introduced numerous novel features driven by artificial intelligence. Let’s investigate rumours together to see what Apple is doing in the background.

Genesis of Generative AI: the Origins of Creative Machines

Generative AI, short for Generative Artificial Intelligence, refers to a subset of artificial intelligence focused on creating new and original content, such as images, text, music, and even videos, by learning patterns and structures from existing data. It involves the development of algorithms and models that can generate content that is similar in style or format to the data they've been trained on. This branch of AI is closely tied to creative processes and is used to produce novel outputs that often exhibit human-like qualities. Generative AI has found applications in various domains, including art, entertainment, design, and content creation. It has been used to generate realistic images, compose music, write text, enhance creativity, and even assist in tasks like drug discovery and protein folding. While generative AI holds enormous potential for creative and practical applications, it also raises ethical considerations, such as the potential for creating misleading content or deep fake videos.

Let's embark on a journey through time to understand how generative AI emerged and evolved:

• Early Concepts (1950s-1960s): The foundations of generative AI were laid during the inception of AI research. Pioneers like Alan Turing (English mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist) and John McCarthy (an American computer scientist and cognitive scientist) proposed the idea of machines simulating human intelligence.
• Birth of Machine Learning (1980s-1990s): Machine learning gained prominence as researchers explored algorithms that allowed computers to learn from data.
• Rebirth of Neural Networks (2000s): Neural networks, especially deep learning architectures, regained attention with increased computational power and larger datasets.
• Introduction of GANs (2014): The introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow (an American computer scientist, engineer) and his colleagues in 2014 marked a significant milestone. GANs consist of two neural networks, a generator, and a discriminator, that work in tandem. GANs could generate incredibly realistic data, from images to text, by learning from the interplay between these networks.
• Rise of Variational Autoencoders (2014): Around the same time, Variational Autoencoders (VAEs) emerged. VAEs are probabilistic models that learn underlying representations of data and could generate new samples by manipulating these representations.
• Attention Mechanisms and Transformers (2017): Attention mechanisms, initially used in tasks like machine translation, gained attention for their generative capabilities. The advent of the Transformer architecture, notably showcased in models like GPT (Generative Pre-trained Transformer), revolutionised natural language generation tasks.
• Applications and Advancements (Present): Today, generative AI has taken the spotlight across various industries. Text generation, image synthesis, music composition, and even video creation are just a few areas where generative AI is making a substantial impact.

Unveiling Apple's AI Secrets

In June, Apple introduced novel iPhone keyboard software, employing the identical architecture of transformers seen in GPT. This move underscores the significant in-house advancement of AI models. However, Apple's tendency is to withhold discussion about products that have yet to hit the market, a strategy aimed at building investor excitement. This information can confirm the news from the most recent Blooberg report in which Apple was developing a ChatGPT-like language model internally.

In early June, Apple introduced a few minor enhancements to the iPhone user experience, including the Photos app's ability to recognize not only people but also animals. Imagine that your phone can recognise your beloved dog from other dogs. Additionally, AirPods are set to become smarter with new features powered by AI. AirPods will now use “Adaptive Audio” to analyse sound around you and adjust accordingly. For example, your Airpods might automatically lower the volume of your music when you start talking and then raise it when you stop. There's also an upgrade in the spelling software that checks both spelling and sentence context. Apple has stated that this software will excel at adapting itself to our typing habits and predicting our upcoming words and phrases. When you ask a question to ChatGPT, you're tapping into the same extensive language model stored in the big cloud, which is accessible to everyone else as well. In contrast, the significantly smaller and personalised language model fueling the new autocorrect will be housed directly on your iPhone.

Apple employed terminology like "machine learning" and "transformer language model," delving into more technical discourse. The company has been so quiet about the generative AI that it has been accused of falling way behind its competitors in this area. Especially, when keeping in mind Siri which can’t do much more than setting a morning alarm. The focus on user privacy remains a significant aspect, an area that Apple has consistently prioritised. Since the company is employing an "on device" model, it might present a comparatively lower privacy concern in contrast to extensive cloud-based models like ChatGPT.

What does Siri think about the new Apple AI?

AI can be categorised into two distinct groups: strong AI and weak AI. Voice assistants such as Siri and Alexa belong to the weak AI category. Also known as narrow AI, this form of AI creates the semblance of human-like intelligence but lacks the capacity for independent thought.

Apple users are asking for Siri to become more proactive. Siri ould to predict what actions you're likely to take and offer suggestions accordingly. If you often check traffic before leaving for work, Siri might proactively provide traffic updates during your usual departure time. If you're planning a trip, Siri could help you research flights, hotels, and local attractions all in one command. If you often forget to take your medication after breakfast, Siri could proactively remind you around that time.

What can we expect? There are a couple of breakthroughs in AI which can significantly improve our dear old friend Siri:

• Natural Language Processing (NLP): ability to have a natural chat
• Emotion Recognition: recognising human emotions giving empathetic interactions
• Machine Learning (ML): ML is a technique that educates AI using data and experience
• Contextual Understanding: giving the user more accurate and relevant answer and action
• Interpretable AI: the ability to analyse scenarios, the best for decision making processes
• Automated Awareness: control multiple devices directly
• Predictive Analysis: in the future, Siri will be able to analyse data and predict future events
• Visual Interpretation: the ability to interpret and understand visual data, such as images or video
• Self-Reliant Services: This involves integrating with robotics or automated systems (such as drone delivery, lawn maintenance, vacuum cleaning, pool upkeep, etc.) and third-party services to enhance the home's efficiency.

Apple's AI: the Path to a Future of Possibilities

As we journey through the ever-evolving landscape of AI technology, it's evident that Apple's foray into the realm of AI is a symphony of innovation and aspiration. With each iteration, from Siri's initial debut to the intricate interplay of machine learning and personalised experiences, Apple continues to create a future where technology not only serves us but truly understands us.

As we eagerly await the next chapter in Apple's AI odyssey, one thing remains certain: the intersection of human ingenuity and artificial intelligence holds the promise of a future where our devices become more than just tools.