Modern Artificial Intelligence (AI) research is currently at the heart of a massive financial bubble. It might pop soon, and it might not. "AI" is seemingly everywhere, although its actual value is yet to be determined. I remember learning how to program GPUs in the period around 2010 for applications to computational biology, never thinking that an esoteric research topic could become elemental in propping up the tech economy.
This got me to thinking about what it would look like if we switched out one research area for another, just to highlight any potential absurdities of the situation. So imagine if the field of Chaos and Fractals, quite popular to the point of cliche in the 1980s, was the subject of a financial bubble. It is of note that chaos and fractals were definitely hyped in their time, being featured in movies such as Star Trek: the Wrath of Khan (the Genesis Effect scene) and Ian Malcolm's rhetoric in Jurassic Park. Interestingly, advances in chaos and fractals in particular relied on advances in computing power, initially with supercomputing, and later with GPUs, parallel computing, and quantum computing [1].
In the formative years of chaos, people such as the Eds (Ott and Lorenz) [2, 3] produced a paradigm shift in how complex systems were viewed. The visualization of chaos in the form of fractals were advanced by Benoit Mandelbrot [4]. Fractals were likewise a paradigm shift in how complex phenomena were visualized. In particular, fractals visualize various aspects of chaos using non-Euclidean geometries and relatively simple sets of equations. Their popularity was advanced by a convenient shorthand: visualizations that captured the imagination. While slogans and pretty pictures captured the imagination, the popular imagination got quite far ahead of methodological rigor. This is reminiscent of claims that ascribe properties like sentience or superintelligence to AI systems.
Is this science, or inspiration, or both? Please don't financialize this. COURTESY: Moss and Fog blog.
Eventually, enthusiasm for chaos and fractals regressed back into the fields of physics and mathematics, while also becoming specialized tools for fields like finance. In short, the field matured without the irrational influx of cash, roughly following a Gartner hype cycle. This is curious in light of the limits of AI that people discuss today: regardless of whether or not AI exhibits "true" intelligence, AI systems require intense computational resources to merely be evocative of biological intelligence. But what if there is not a missing component of the intelligence simulation, but of the way in which the underlying system is modeled? Chaos and fractals are not the product of reductionist relationships (as science had been done before), but rather the product of system dynamics, recursivity, and a sensitivity to initial condition. This was the main insight of chaos and fractals, but apparently those insights are not worth a large-scale financial bubble [5].
In their time, fractals were derided as "pretty pictures", and eventually, the pretty pictures could not keep up with methodological trends across the different sciences. But fractals did provide at least one serious insight: systems that look regular at one scale exhibit irregularities apparent at other scales. This has been popularized by the Powers of 10 idea, and further applied to ideas like the coastline paradox. What is particularly interesting to a person who likes complexity approaches to science is that standard hypothesis testing was exposed to many of the same criticisms as chose and fractals. This is despite much more serious consequences of the unaddressed issues with NHST, and has persisted as the scientific norm in spite of superior methods. Quite an interesting exercise in methodological inertia.
In the current era, AI has partially been driven by advances in methodology, but also by advances in hardware. Central to this has been NVIDIA and their GPU architecture. While GPUs have done much of the heavy lifting in the current AI summer, it is important to remember the origins of GPUs: as a graphical processing tool. This parallels how advances in computing and computational power suddenly opened up our ability to solve and plot the equations of fractal growth and other structures. Perhaps the experience of chaos and fractals will guide AI research after the bubble bursts.
References:
[1] Kaboudian, A., Cherry, E.M., and Fenton, F.H. (2019). Large-scale interactive numerical experiments of chaos, solitons and fractals in real time via GPU in a web browser. Chaos, Solitons & Fractals, 121, 6-29.
[2] Motter, A. and Campbell, D.K. (2013). Chaos at Fifty. Physics Today, 66(5).
[3] Viswanath, D. (2004). The fractal property of the Lorenz attractor. Physica D, 190, 115–128.
[4] Mandelbrot, B.B. and Blumen, A. (1989). Fractal Geometry: What is it, and What Does it do? Royal Society A, 423(1864), 3–16.
[5] Notice that I said "large-scale", which is the distinction between overeager commercialization and financialization. Perhaps financialization is a feature of 21st century popularity, but there does seem to be a difference that makes its way into scientific practice. Fractals are used extensively in attempts to understand the stochastic nature of markets, and have been commercialized in line with that expectation.
The connections between fractals, efficient markets, and to a lesser extent chaotic behavior is exemplified in books such as: Peters, E. (1994). Fractal Market Analysis. Wiley.
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