Interaction Design for the New Computer

The next generation of the personal computing is an AI system.

In recent years, developments in large language models (LLMs) have revolutionized the field of artificial intelligence, placing AI at the center of personal computing. These advancements have enabled AI systems to understand and generate human-like text, process and analyze audio and video data, and interact seamlessly with various software tools and platforms. As a result, AI is now integral to many aspects of modern computing, offering enhanced capabilities and transforming how we interact with technology.

A multimodal AI system can be effectively compared to the hardware of a traditional personal computer by examining how both systems handle input, processing, storage, and output. In the diagram provided, the LLM (large language model) serves as the central processing unit (CPU) of the multimodal AI system. This parallels the role of the CPU in a traditional computer, which processes instructions and manages the flow of information throughout the system. The LLM, with its context window functioning similarly to RAM, handles real-time processing of diverse types of input, including text, audio, and video, enabling the AI to perform complex tasks by interpreting and generating various data forms.

The peripheral devices in a traditional computer, such as keyboards, mice, and monitors, can be compared to the video and audio inputs/outputs in the multimodal AI system. These peripheral devices facilitate interaction with the user by allowing input and displaying output. In the multimodal AI system, video and audio components serve a similar function, providing channels through which the AI can receive visual and auditory data and generate corresponding outputs. This enables the AI to engage in tasks requiring visual recognition or audio processing, expanding its capabilities beyond text-based interactions.

Storage in a traditional computer is managed by the file system, where data is saved on disk drives. In the multimodal AI system, a file system also exists to store embeddings and other data necessary for the AI's operations. This allows the AI to retain information between sessions and access stored data quickly when needed, much like a computer's hard drive stores and retrieves files. The inclusion of embeddings helps the AI maintain context and enhance its understanding and responses based on previously processed data, akin to how a computer uses cached data to speed up processes.

Finally, the Ethernet connection in a traditional computer provides access to the internet and network resources, similar to how the multimodal AI system connects to the browser and other LLMs. This connectivity enables the AI to fetch real-time information, collaborate with other models, and integrate external data sources into its processing, mirroring the way a personal computer uses network connections to enhance its functionality. The browser and other LLM connections allow the AI to expand its knowledge base and improve its response accuracy, just as internet connectivity allows a traditional computer to access vast amounts of information and computational resources.

The transformational aspects of interaction design for AI—Human interfaces aren't in the physical interface.

The next generation of personal computing is set to be defined by AI systems, which promise to revolutionize how we interact with technology. These AI-driven systems will offer unparalleled personalization, learning from users' behaviors and preferences to provide tailored experiences. Instead of manually configuring settings or searching for information, users will benefit from AI's ability to anticipate their needs and streamline tasks, leading to a more intuitive and efficient computing experience. This shift will mark a departure from the traditional, static user interfaces to dynamic, adaptive environments.

Furthermore, AI systems in personal computing will significantly enhance productivity and creativity. By leveraging advanced machine learning algorithms, these systems can automate routine tasks, manage schedules, and even draft content, freeing users to focus on more strategic and creative endeavors. For instance, AI can assist in generating ideas for projects, providing insights from large datasets, or even composing music and art based on user inputs. This symbiotic relationship between human creativity and machine efficiency will unlock new potentials and drive innovation across various fields. Security and privacy will also see transformative improvements with the integration of AI in personal computing. AI systems can continuously monitor for threats, adapt to new attack vectors, and provide robust defense mechanisms against cyber threats. Additionally, these systems can help manage personal data more effectively, ensuring that sensitive information is protected and used ethically. As AI becomes more embedded in our daily lives, it will be crucial to balance its capabilities with considerations for privacy and security, fostering a safer and more trustworthy computing environment.