Robot Scientists Arise! [Part 1]
Welcome to the age of empirical computation
Agents of innovation
We’re entering an age in which humans will no longer be the sole agents of innovation.
Instead, new knowledge, technologies and sophisticated real-world products will be invented by smart robotic platforms called empirical computation engines (ECEs).
To many in the tech community, it’s a given that empirical compute will cause a seismic shift in the status quo. To everyone else, the question most commonly voiced is “empirical what?”
I’ve written this piece to provide the wider community with a comprehensive overview of empirical computation and the future that lies ahead.
In this first instalment, we’ll be exploring the concept of solution space. So buckle up and enjoy the ride!
Nothing is invented – only discovered.
It’s Christmas, you’re 5 years old and the proud recipient of 18 shiny red LEGO bricks and an accompanying manual.
“To hell with the manual,” you cry! “I’m going to invent something new.”
You diligently set to work and before long you’ve presented an underwhelmed but supportive mother with 3 entirely novel LEGO sculptures (each built from 6 pieces).
“How pedestrian,” Older Brother remarks as he leafs through the discarded LEGO manual before smugly pointing out each of your ‘entirely novel’ designs. With abject horror, you find that the manual describes all 915,103,765 theoretically possible 6 piece assemblies.
In a dizzying moment of clarity, you realise that everything that can exist physically already exists theoretically.
Any problem has an infinite number of solutions which exist (albeit theoretically) irrespective of whether they’ve been manifested in the real world. If you can’t get on board with this, don’t read any further.
Ok, so you’re still with me. Good!
For a given problem, the corresponding set of possible solutions live in a hypothetical place called ‘solution space’. Every problem has its own solution space which in turn has its own unique topology. If, like me, you’re a visual thinker, you can picture the topology of this space as a fitness landscape where the best solutions are found at peaks and the worst in troughs.
Now, here’s the interesting bit. If you can map a solution space’s topology, you can rationally search it. As you might expect, the better your search algorithm, the more likely you are to discover a high-performing solution.
After I first adopted this mental framework, I started to realise that all problems are tractable provided you can build a search algorithm capable of intelligently traversing its solution space.
What’s incredible, is that many of these search algorithms already exist and have done so for billions of years! It turns out that our universe has birthed and subsequently been shaped by a menagerie of autonomous problem-solving algorithms. In fact, you and I are both the direct product of one of those algorithms (Darwinian evolution) but more about that later.
Every one of these search algorithms works by recursively generating and evaluating hypotheses. Any given search algorithm can be categorised based on whether it generates and tests hypotheses virtually (e.g. in a brain or on a computer) or empirically (i.e. in the real world).
Type 1 search algorithms
Darwinian evolution is a purely empirical (Type 1) search algorithm in that it both generates and tests hypotheses in the real world. Every new genome that arises through mutation or recombination can be thought of as a new genetic hypothesis. Once formulated, a genetic hypothesis is compiled by cellular machinery and its impact on the corresponding organism’s fitness is measured as a function of reproductive success. As a process, Darwinian evolution has been operating continuously for the past 3.8 billion years and has been responsible for autonomously filling all biological niches with high performing genetic designs.
Type 1 search algorithms are powerful but suffer from several limitations. To exemplify this, consider Darwinian evolution. Firstly, as a process, it never learns from its mistakes. For us, this means that the same harmful genetic mutations arise (de-novo) over and over again. Secondly, Darwinian evolution is unable to design solutions from scratch and instead has to incrementally improve what currently exists. This means that we have to endure a body replete with bizarre and sub-optimal design features. Take for example, the fact that we have to risk choking with every meal because both air and food enter the body through the same opening. Thirdly, the rate of Darwinian evolution (at the population level) is slow because it’s coupled to an organism’s generation time.
Type 2 search algorithms
Biological Type 2 search
The human brain is an organic super-computer capable of both generating and testing hypotheses virtually. We exercise this capability every day when we imagine, run thought experiments and plan. Every time we execute on an idea generated through virtual hypothesis testing, we’re running a Type 2 search.
Think back to the last time that you had a difficult or important conversation ahead of you. If you’re anything like me, you’ll have run through several dozen simulations of the conversation in advance so you were able to best represent your perspective in the face of anticipated counterarguments.
Type 2 search algorithms are fast and resource efficient but they also suffer from a couple of major limitations.
Firstly, the sophistication of rationally formulated hypotheses is constrained by the intelligence of the underlying hypothesis-generator.
Secondly, the ability to evaluate a hypothesis virtually is determined by the accuracy of the underlying real world model. Building accurate virtual models of highly complex and poorly characterised real world systems is immensely challenging. To illustrate just how terrible mental models are, just think how often we experience surprise.
I’m fairly confident that the brain instinctively knows that Type 2 search algorithms are often incorrect and that the emotional response we experience when surprised encourages us to refine the underlying world model. This phenomenon perhaps provides a mechanistic explanation for why we enjoy music and humour.
Artificial Type 2 search
Like the human brain, computers are also able to both generate and test hypotheses virtually. As a platform for Type 2 search, the computer has been breathtakingly successful.
The major weakness for this type of Type 2 search lies in hypothesis evaluation. This is once again because it’s incredibly challenging to computationally simulate highly complex and poorly characterised real world systems.
Type 3 search algorithms
For a Type 3 search algorithm, hypotheses are generated virtually (e.g. in a brain or computationally) and then tested in the real world (i.e. empirically). We tend to call the virtual world ‘the world of bits’ and the real world ‘the world of atoms’.
Biological Type 3 search
The most successful entrepreneurs that I’ve met build and optimise their businesses using Type 3 search. This involves relentlessly maturing and re-formulating world models (hypotheses) based on experience (data). This dramatically contrasts the Type 2 search approach (simulate and execute) that dominated the era of the 30 page business plan.
Artificial Type 3 search
An empirical computation engine (ECE) is an artificial system capable of recursively executing a Type 3 search. ECEs have the following properties:
(1) Hypothesis generation — virtually generated hypotheses must have the capacity to accurately capture the dynamics of the underlying real world system.
(2) Hypothesis testing — the empirical test must accurately capture the dynamics of the system of interest.
(3) Intelligence — the data generated with each iteration must inform the next round of hypothesis generation.
(4) Autonomy — the system must have the capacity to run continuously without human involvement.
What’s up next
I hope you enjoyed this first instalment. Next time we’ll take a deep dive into how ECEs are built and the problem spaces in which they’re being most successfully applied.
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