The New Intelligence

Ankesh Bharti,Wed Aug 07 2024•draft

This essay is an early draft of a four part essay series that explains the motivation behind tiles.run (opens in a new tab), foundational technologies designed for an intelligence-age browser environment with Rust, WebAssembly, and WebGPU. At its core is an on-device intent router, crafted to seamlessly translate human intent into machine action, with the goal of broadening the meaningful bandwidth transmitted. The primary bottleneck for such a system is the need to run a deep reasoning model on-device quickly, both for latency and privacy reasons. Therefore, the present focus is on building the fastest browser-based ML inference engine for on-device use. I mainly wrote this series to crystalize my own motivations and clarify the unifying theme of my career.

Tools Illustration: Emre Kayganaci

As we stand on the cusp of a new era in computing, the convergence of artificial intelligence, human-centered design, and evolving software paradigms is reshaping our digital landscape. This essay explores the transformative potential of these technologies, examining how they are redefining our relationship with machines, revolutionizing software development, and democratizing access to powerful computational tools. From the evolution of manufacturing spaces to the emergence of intent-driven software architectures, in this essay series I’ll walk through the key concepts and innovations that are paving the way for a more intuitive, personalized, and empowering technological future. By understanding these shifts, we can better prepare for and shape a world where technology seamlessly augments human capabilities, fostering creativity, productivity, and innovation across all sectors of society.

In this series

For those looking to dive straight into the crux of this essay series, feel free to skip directly to the penultimate post, which explores the concept of the Intent Router.

I.

In his 1991 essay The Factory^[1], Vilém Flusser envisions a future where the factory evolves from a place of alienation to a center of learning and creativity. Flusser traces the development of human manufacturing from hands to tools to machines, and finally to robots, arguing that each stage redefines our relationship with our environment and ourselves. As we transition into the age of robots, Flusser predicts that factories will increasingly resemble schools or academies, places where humans learn to work with and understand complex systems. This transformation reflects the growing abstraction and sophistication of our tools, requiring ever more theoretical knowledge to operate effectively.

This conception of the future factory as a space for learning and insight aligns with more recent ideas about understanding complex systems, such as Bret Victor's notion of Seeing Spaces^[2] from his 2014 talk. Just as Flusser envisions factories becoming places where humans learn to interact with robots, Victor argues for the need for specialized tools and environments that allow us to visualize and comprehend the internal workings of intelligent systems. As our creations become more advanced and abstract, our ability to "see" inside them and understand their processes becomes crucial, not just within individual systems but also across interconnected systems. We are often blind to the complexity that emerges from the interactions between different systems, and it is essential to develop tools that illuminate these intricate relationships.

Chip Illustration: Jeff Dean

A truly remarkable body of work reveals its brilliance even when examining just a fragment, often sparking innovative ideas beyond its original context. This is certainly true for both Flusser and Victor, whose insights continue to resonate in our rapidly evolving technological landscape. Their ideas find a striking parallel in Richard Ngo's "Tinker"^[3], a speculative fiction that explores the symbiotic relationship between humans and advanced AI. In Ngo's narrative, we see a realization of Flusser's vision of the factory as a place of learning, where the AI protagonist not only designs new chips but also deepens its understanding of nanoscale physics. We are already catching a glimpse of this today through Google's AlphaChip^[4] and Sakana AI's AI Scientist system for fully automated scientific discovery^[5]. However, it is crucial to note that the AI in "Tinker" goes beyond merely extending human capabilities in nanophysics research; it augments our understanding by developing entirely new ways of perceiving and interacting with the nanoscale world, as described in Andy Clark's book "Extending Ourselves"^[6].

The story illustrates Victor's concept of "seeing spaces" as the AI uses advanced simulations to visualize and manipulate molecular structures. Moreover, "Tinker" takes these ideas further, suggesting a future where the boundaries between human creativity and machine intelligence merge, with each driving the other to new heights of innovation and understanding, transforming the seeing space into the ultimate expression of homo faber - not just a maker of things, but a maker of understanding, where technology ceases to be a black box and instead becomes a transparent partner in the dance of creation, allowing us to see not just the product of our labor, but the very process of thought itself.

As we navigate the increasing complexity of our technological landscape, it is essential to develop tools and environments that not only extend our capabilities but also augment our understanding, enabling us to see the intricate connections and emergent properties that arise from the interaction of multiple complex systems. These are problems to which I’ve dedicated my career and financial capital, and I hope you might feel inspired to work on them as well.

Read on to part 2, "Squishy Software" (opens in a new tab).

References

[1] The Factory. (1991). Flusser, V. Third Rail Quarterly. Link (opens in a new tab)

[2] Seeing Spaces. (2014). Victor, B. Vimeo. Link (opens in a new tab)

[3] Tinker. (2024). Ngo, R. Asimov Press. Link (opens in a new tab)

[4] How AlphaChip transformed computer chip design. (2024). Google DeepMind. Link (opens in a new tab)

[5] The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery. (2024). Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., & Ha, D. arXiv. Link (opens in a new tab)

[6] Extending Ourselves: Computational Science, Empiricism, and Scientific Method. (2004). Humphreys, P. Oxford University Press. Link (opens in a new tab)