Apple’s Vision Pro: A Threat or an Opportunity for the Future of AI and Design?

Apple has recently launched its new Vision Pro headset. This mixed reality device claims to be "a revolutionary spatial computer that seamlessly blends digital content with the physical world, while allowing users to stay present and connected to others." The device also features a ChatGPT-like generative AI assistant that can understand natural language and create anything the user imagines.

The Vision Pro is not the first device of its kind but arguably the most advanced and ambitious one. It competes with other AR and XR headsets from companies like Google, Meta, and Vuzix, aiming to provide users with immersive and interactive experiences. However, Apple's device stands out for its design, performance, and integration with its hardware, software, and services ecosystem.

The Vision Pro could have a significant impact on the future of AI and design, as it offers a new way of accessing and creating digital content that is more intuitive, engaging, and creative than ever before. It could also enable new possibilities and opportunities for various fields and industries, such as education, entertainment, gaming, health care, engineering, and architecture.

However, the Vision Pro could also pose some challenges and risks for the future of AI and design, as it raises some ethical, social, and technical questions that must be addressed. It could also create competition and compatibility issues with other platforms and devices and disrupt existing markets and practices.

How does Apple's Vision Pro enhance AI and design?

Apple's Vision Pro enhances AI and design in several ways. First, it leverages AI technology to improve user experience and creativity. The device uses a ChatGPT-like generative AI assistant to understand natural language commands and user queries and generate digital content accordingly. For example, a user could say, "I want to build a house," the AI assistant would create a 3D model of a house that the user could customize and place in their virtual world.

The AI assistant can also learn from user feedback and preferences and provide suggestions and recommendations based on them. For example, a user could say, "I like this color" or "I don't like this shape," the AI assistant would adjust the content accordingly. The AI assistant can also access various sources of information from the internet or Apple's services, such as Wikipedia or iCloud, to provide relevant facts or data to users.

The AI assistant can collaborate with other users or devices, such as Vision Pro headsets or iPhones, to create shared or multiplayer experiences. For example, a user could say, "I want to play chess with my friend," the AI assistant would create a virtual chess board that both users could see and interact with.

Second, Apple's Vision Pro enhances AI and design by providing a new interface and medium for accessing and creating digital content. The device uses a spatial operating system called VisionOS, which offers a three-dimensional interface that releases applications from the boundaries of traditional screens and brings them into real-world spaces. The device also uses eye tracking, hand tracking, and voice input as the main modes of interaction with the device.

The device also uses high-resolution displays and spatial audio to create realistic, immersive visuals and sounds that blend with the physical world. The device also uses video passthrough technology to allow users to see their surroundings while wearing the headset.

These features enable users to access and create digital content that feels more natural, intuitive, and engaging than conventional devices such as laptops or smartphones. Users can create more complex, detailed, and expressive content than traditional tools such as keyboards or mice.

How does Apple's Vision Pro challenge AI and design?

Apple's Vision Pro challenges AI and design in several ways. First, it challenges the ethical implications of using AI technology to create digital content. The device uses a generative AI assistant to make anything the user imagines. Still, it needs to be clarified how it decides what to start or how it verifies the accuracy or quality of its creations. For example, a user could say, "I want to see a picture of my grandmother," the AI assistant would generate an image based on available data or information. But how does it know if the idea is accurate or respectful? How does it handle sensitive or controversial topics? How does it deal with misinformation or manipulation?

The device also challenges the social implications of using AI technology to create digital content. The device uses a generative AI assistant to make anything the user imagines. Still, how it affects the user's sense of agency, identity, and creativity needs to be clarified. For example, a user could say, "I want to write a poem," the AI assistant would generate a poem based on the user's input or preferences. But how does it affect the user's sense of authorship, ownership, and originality? How does it affect the user's emotional and cognitive engagement with the content? How does it affect the user's social and cultural context and values?

Second, Apple's Vision Pro challenges the technical implications of using AI technology to create digital content. The device uses a generative AI assistant that can create anything the user imagines, but it needs to be clarified unclear how it handles its creation's complexity and scalability. For example, a user could say, "I want to build a city," the AI assistant would create a 3D model of a city that the user could explore and interact with. But how does it handle the computational and storage requirements of such large and dynamic content? How does it address the performance and reliability issues of such complex and interactive content? How does it address the security and privacy issues of such personal and sensitive content?

Third, Apple's Vision Pro challenges the competitive and compatibility implications of using AI technology to create digital content. The device uses a generative AI assistant to make anything the user imagines. Still, how it interacts with other platforms and devices that also use AI technology to create digital content needs to be clarified. For example, a user could say, "I want to see what Meta's headset can do," the AI assistant would show a comparison or a demonstration of Meta's headset features. But how does it handle the interoperability and integration issues of such different and diverse platforms and devices? How does it address the innovation and collaboration issues of similar rival platforms and devices? How does it address the regulation and governance issues of such powerful and influential platforms and devices?

How does Apple's Vision Pro compare to other AR and XR headsets?

Apple's Vision Pro is not the only AR or XR headset in the market, but it is one of the most advanced and ambitious. It competes with other devices from companies like Google, Meta, and Vuzix, which also offer immersive and interactive experiences for users. However, each device has strengths and weaknesses, different target audiences, and use cases.

Google Glass was one of the first AR headsets introduced to the public in 2013. Still, it failed to gain popularity due to its high price, limited functionality, privacy concerns, and social stigma. Google then focused on enterprise applications, such as manufacturing, health care, logistics, and education. Google Glass Enterprise 2 was launched in 2019, offering improved performance, battery life, camera quality, and design. Google Glass Enterprise 2 is mainly used for hands-free access to information, instructions, communication, and collaboration.

Meta (formerly Facebook) is one of the leading players in the VR space, with its popular Oculus Quest 2 headset that offers standalone wireless VR gaming and entertainment. Meta also has ambitions in the AR space, with its Project Aria research initiative aiming to develop AR glasses that can enhance human perception and connection. Meta has not revealed many details about its AR glasses yet, but it has hinted that they will be lightweight, stylish, and affordable. Meta's AR glasses are expected to launch in 2024.

Vuzix is one of the pioneers in the AR space, with its Blade Upgraded intelligent glasses that offer hands-free access to information, communication, navigation, and entertainment. Vuzix Blade Upgraded uses waveguide optics to project a full-color display onto the user's right eye. It also features an 8MP camera, noise-canceling microphones, touchpad controls, and voice commands. Vuzix Blade Upgraded is mainly used for remote access, support, and training.

What are some historical examples of AI and design?

AI and design have a long history of interaction and collaboration, dating back to the origins of both fields. Here are some historical examples of AI and design:

• In 1843, Ada Lovelace wrote about the potential of Charles Babbage's Analytical Engine (a proto-computer) as a symbol manipulator that could automate the calculation and creative tasks such as composing music.
• In 1950, Alan Turing proposed his famous test for machine intelligence (the Turing test), which involved a human judge conversing with a human and a machine through text messages. He also suggested that machines could learn from experience and improve their performance over time.
• In 1956, John McCarthy coined the term "artificial intelligence" at a conference at Dartmouth College, where he invited researchers from various disciplines (such as mathematics, psychology, engineering, and logic) to discuss "the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." He also introduced the idea of Lisp, a programming language for AI.
• In 1957, Frank Rosenblatt invented the perceptron, a model of a neuron that could learn from data and perform binary classification tasks. The perceptron was one of the earliest examples of artificial neural networks, which are the basis of modern deep learning.
• In 1962, Douglas Engelbart demonstrated his "oNline System" (NLS), a computer system that enabled interactive manipulation of text, graphics, and video. NLS also featured a mouse, a keyboard, a display, and a network connection. NLS was among the first human-computer interaction and graphical user interface design examples.
• In 1968, Marvin Minsky and Seymour Papert published their book Perceptrons, which analyzed the limitations and capabilities of perceptrons and neural networks. They showed that perceptrons could not solve some simple problems, such as the XOR problem, and argued that more complex architectures and learning algorithms were needed for AI.
• In 1972, Richard Stallman created the first Emacs editor, a text editor that users could extend and customize using Lisp. Emacs was one of the first examples of user-generated content and open-source software.
• In 1975, John Holland introduced genetic algorithms, a class of optimization techniques that mimic natural evolution. Genetic algorithms use a population of candidate solutions that are randomly initialized and then iteratively improved by applying operators such as selection, crossover, and mutation. Genetic algorithms are widely used to solve complex design problems without known analytical solutions.
• In 1984, Terry Winograd and Fernando Flores published their book Understanding Computers and Cognition, which criticized the overall symbolic approach to AI and advocated for a more situated and embodied view of intelligence. They also proposed a new paradigm for a design that emphasized human communication and interpretation rather than formal logic and rules.
• In 1987, John Koza invented genetic programming, a variant of genetic algorithms that evolves computer programs rather than fixed-length strings. Genetic programming can automatically discover novel and valuable solutions to various problems, such as symbolic regression, classification, control, and art.
• In 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov in a six-game match. Deep Blue was a specialized computer system that used brute-force search and evaluation functions to explore millions of possible moves per second. Deep Blue was one of the first examples of AI surpassing human performance in a specific domain.
• In 2001, Philip Rosedale launched Second Life, a virtual world allowing users to create and explore various environments and interact with others. Second Life is one of the first examples of user-generated virtual reality and social media.
• In 2006, Geoffrey Hinton and his colleagues introduced deep belief networks, a deep neural network that can learn from unlabeled data using a greedy layer-wise pre-training technique. Deep belief networks revived the interest in deep learning and sparked a new wave of research and applications in AI.
• In 2010, Jeff Han demonstrated his Perceptive Pixel technology at TED, which enabled multi-touch interaction on large-scale displays. Perceptive Pixel technology is one of the first examples of natural user interface design and gesture recognition.
• In 2011, IBM's Watson defeated two human champions in Jeopardy!, a quiz show that requires natural language understanding and general knowledge. Watson was a general-purpose computer system that used natural language processing, information retrieval, knowledge representation, and machine learning to answer questions from various domains.
• In 2012, Andrew Ng and his colleagues trained a deep convolutional neural network to recognize objects in images using millions of labeled examples from ImageNet, a large-scale image database. They achieved state-of-the-art results on several image recognition benchmarks, surpassing previous methods by a large margin. This breakthrough demonstrated the power and potential of deep learning for computer vision.
• In 2014, Ian Goodfellow and his colleagues introduced generative adversarial networks (GANs), a generative model that can learn to produce realistic images or other types of data from noise. GANs consist of two neural networks: a generator that tries to fool a discriminator that tries to distinguish between real and fake data. GANs can generate novel and diverse content such as faces, landscapes, or artworks.
• In 2016, Google's AlphaGo defeated world Go champion Lee Sedol in a five-game match. AlphaGo was a specialized computer system that used deep neural networks and reinforcement learning to learn from human and self-play games. AlphaGo was one of the first examples of AI surpassing human performance in a complex and intuitive domain.
• In 2017, Adobe launched Adobe Sensei, a collection of AI and machine learning services that power various features and functions across Adobe's products. Adobe Sensei enables users to enhance their creativity and productivity by automating or assisting tasks such as image editing, video production, document analysis, and content creation.
• In 2018, OpenAI and DeepMind released ChatGPT and BERT, respectively, two pre-trained language models that generate coherent and diverse text on various topics and tasks. ChatGPT and BERT use deep neural networks and large-scale text corpora to learn natural language's statistical patterns and structures. ChatGPT and BERT can perform natural languages processing tasks such as text generation, summarization, translation, question answering, and sentiment analysis.
• In 2019, Nvidia launched Nvidia Omniverse, a platform that enables users to create and collaborate on 3D simulations and virtual worlds. Nvidia Omniverse uses Nvidia's RTX technology to render realistic graphics and physics and Nvidia's Isaac Sim technology to simulate intelligent agents and robots. Nvidia Omniverse also integrates with various design tools and frameworks such as Maya, Blender, Unreal Engine, and Unity.
• In 2020, Microsoft launched Microsoft Mesh, a platform that enables users to create and experience shared holographic experiences across devices and locations. Microsoft Mesh uses Microsoft's Azure cloud computing and AI services to provide spatial mapping, rendering, synchronization, and collaboration. Microsoft Mesh also supports various mixed reality devices such as Microsoft's HoloLens 2, Meta's Oculus Quest 2, and smartphones.
• In 2021, Apple launched its Vision Pro headset. This mixed reality device claims to be "a revolutionary spatial computer that seamlessly blends digital content with the physical world, while allowing users to stay present and connected to others." The device also features a ChatGPT-like generative AI assistant that can understand natural language and create anything the user imagines.

What are some future trends and scenarios for AI and design?

AI and design constantly evolve and influence others, creating new trends and scenarios for the future. Here are some possible future trends and strategies for AI and design:

• AI will become more accessible and democratized, enabling more people to use and create AI applications without requiring technical expertise or coding skills. Platforms such as Google's Teachable Machine, IBM's Watson Studio, or Microsoft's Power Platform will allow users to train and deploy AI models using simple interfaces or natural language commands. Users also remix their AI creations through online communities or marketplaces.
• AI will become more creative and generative, enabling users to create novel and diverse content that goes beyond existing data or examples. GANs, ChatGPT, or Adobe Sensei will allow users to generate realistic images, videos, texts, or sounds on demand or based on their preferences or emotions.
• AI will become more collaborative and social, enabling users to interact with other users or intelligent agents in immersive and engaging ways. Technologies such as Microsoft Mesh, Nvidia Omniverse, or Meta's Horizon will allow users to create and experience shared holographic experiences across devices and locations.
• AI will become more ethical and responsible, enabling users to understand the impact and implications of their AI applications on themselves and others. Technologies such as IBM's AI Fairness 360, Google's Model Cards, or Microsoft's Responsible AI Toolkit will allow users to measure and mitigate the potential biases, risks, or harms of their AI applications on various dimensions, such as fairness, privacy, security, or sustainability.
• AI will become more adaptive and personalized, enabling users to customize and optimize their AI applications according to their needs and contexts. Technologies like Google's Federated Learning, Amazon's Personalize, or Apple's Core ML will allow users to train and update their AI models using their data or devices without compromising privacy or performance.

Conclusion

Apple's Vision Pro is a groundbreaking device that could change how we access and create digital content using AI technology. It could also significantly impact the future of AI and design, as it offers new possibilities and opportunities for various fields and industries and new challenges and risks for multiple stakeholders. As Apple says, "Vision Pro is years ahead," but it remains to be seen how it will shape the years ahead.

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