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AI Hardware and Its Impact on Education

Updated: May 30

Introduction


Listening to a recent podcast from A16z Remaking the UI for AI sparked some important thoughts worth sharing. The focus on AI over the last 18-24 months has moved towards powerful models like large language models (LLMs), which generate content and perform complex analyses across just about any topic. This blog post is meant to push the conversation a bit further in a bid to spark conversation on the fact that these are the early days and as AI evolves so will the platforms that we use to engage it. This evolution is set to reshape how we engage with technology across just about every aspect of our lives including in educational settings.


From Command Lines to Chatbots


Historically, advancements in computer reasoning have paralleled developments in hardware and user interfaces. The early days of computing required users to communicate with machines directly via command-line interfaces. Over time, we developed new systems to enable greater accessibility first with interfaces that allowed us to use a mouse and keyboard on graphical user interfaces (GUIs), culminating in the interfaces we know as smartphones. This transition reflects the trend that as computers become more intuitive and capable of reasoning, they enable new forms of interaction and unlock new possibilities for their use.


Next-Gen AI


While we can be assured that there will be more advancements around the capability of AI models, we have yet to see new forms of technology that enable rich human-like interactions. We're reaching the point where AI models are becoming increasingly efficient and accurate at processing visual inputs through cameras and auditory inputs through microphones, creating multimodal interaction capabilities. This means we can interact with machines in ways that more closely mimic human-to-human communication, potentially transforming educational environments.


Current intelligent devices like Siri or Alexa, while helpful, still require unnatural interaction prompts like "Hey, Siri" or "Alexa." The next generation of hardware aims to integrate more naturally into our daily lives. Similar to how smartphones fit seamlessly into our routines with intuitive touch interfaces, future devices will likely depend heavily on visual and auditory cues.


We are starting to see early attempts built around this shift in an attempt to redesign the user experience by leveraging AI's new capabilities. Newcomers to this segment include AI-powered pins (Humane AI), AI-powered handhelds like the Rabbit R1, smart glasses like Rayban's collab with Meta , and even direct brain interfaces such as Neuralink. These innovations represent the first wave of hardware that can effectively leverage AI to create more responsive and interactive educational tools.


Shifting from Compute to Inference


As AI models mature, we may see a shift from relying on models that rely on huge computational power to more specialized smaller models designed for specific tasks. This shift could lead to the development of specialized chips or interfaces dedicated to specific tasks like image generation, video creation, or text synthesis. If we think about smartphones, we can see how this might play out in reality.


The phones themselves handle some basic functions, calls, text, and web searches, but rely on applications to power specific functions. Optimizations could make advanced AI tools more accessible and efficient, further embedding them into educational contexts. Apple Vision Pro shows us some of the benefit of moving inference onboard the device. AI embedded in the device allows the user to leverage faster interactions with the AI and helps maintain a level of privacy that using a web-based chatbot like ChatGPT can't offer.


Privacy and Personalization


With advancements in open-source AI, we can anticipate that more inference processes will be handled directly on devices, enhancing privacy by keeping personal data local rather than in the cloud. This approach is promising for creating intelligent devices that better understand and support the unique needs of individual learners. By keeping personal information on-device, we can safeguard privacy while still delivering personalized educational experiences.


Implications for Education


One of the questions that we are routinely asking at Ed3 DAO is So what does all this mean for education? In the Ed3 World Newsletter, Vriti Saraf details some of the major updates and changes coming to Google's Gemini and Open AI's ChatGPT, both poised to enter the K-12 marketplace on the software front. Directionally, things are less clear when it comes to AI-powered hardware, however, when those breakthroughs come, rest assured it will have implications for education.


Here is what it might look like:

  • Personalized Learning: AI can provide highly personalized educational experiences, adapting to individual learning styles and needs. This becomes increasingly possible as AI moves onboard devices removing some concern around privacy of data.

  • Enhanced Accessibility: New interfaces can make learning more accessible to all learners, offering tailored support through speech, vision, and touch.

  • Interactive Learning Environments: Classrooms equipped with AI-powered devices can offer immersive and interactive learning experiences, making education more engaging.

  • One-on-One Support: With AI models acting as personal tutors, learners can receive one-on-one support that is scalable and available around the clock, addressing individual learning needs more effectively than a single teacher managing an entire classroom.


A Look Ahead


As AI continues to evolve, so too will the ways we interact with these powerful systems. The convergence of advanced AI models and innovative hardware interfaces will power new educational possibilities, making learning more personalized, accessible, and engaging.


sync up soon!

Mike

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