Comparing naturally evolved and engineered solutions

AI Impacts

This seems like an interesting way of comparing the results of different types of design solutions.

One important thing to consider is that evolution was under a lot of additional constraints compared to engineers when 'designing' organisms. For instance, reactions occur at room temperature and with organic chemistry, many organisms are self-replicating and self-assembling, energy and materials are usually limited to what an organism can collect itself. And rather than optimising for any specific parameter, evolution is just aiming for an organism to survive and reproduce - so few solutions will be optimal in terms of performance/efficiency unless there was a strong evolutionary pressure for them to be so.

My experience with bio-inspired design is that it is usually best to look to biology for high-efficiency solutions as resource scarcity is a constant in most environments. High-performance biology is seen in microscopic structures, which probably still out-perform engineered solutions in many areas.

Motivation

Engineers and evolution have faced many similar design problems. For instance, the problem of designing an efficient flying machine. Another instance of a design problem that engineers and evolution have both worked on is designing intelligent machines. We hope that by looking at other instances of engineers and evolution working on similar problems, we will be able to learn more about how future AI systems will compare to evolved intelligences.

Methods

We will collect examples of optimization problems that engineers and evolution would perform better on if they could. Here are some candidate examples of such problems:

Flying

Hovering

Swimming

Running

Traveling long distances

Traveling quickly

Jumping

Balancing

Height of structure

Piercing

Applying compressive force

Striking

Tensile strength

Pumping blood

Breathing

Liver function

Detecting light

Recording light

Producing light

Detecting sound

Recording sound

Producing sound

Heat insulation

Determining chemical composition of a substance

Detecting chemical composition in the air

Adhesiveness

Picking heavy things up

Joint activation

Elasticity

Toxicity

Extracting energy from sunlight

Storing energy

We will then collect the best solutions we can readily find to these design problems, made by human engineers and by evolution respectively, and quantitative data on their performances. We will try to collect this over time, for engineered solutions.

Analysis

We will use the data to answer the following questions for different design problems:

How long does it take engineers to half, match, double, triple, etc. the performance of evolution’s current best designs?

What does the shape of engineers’ performance curve look like around the point where engineers’ solutions first match evolution’s?

How efficient (in terms of performance per energy or mass used) are the first solutions that match evolution’s performance compared to evolution’s best solutions?

How long does it take engineers to find a more efficient solution after finding an equally good solution in terms of absolute performance?

From a design perspective, how similar are engineers’ first equally good solutions to evolution’s best solutions?

We will use patterns in the answers to these questions across technologies to make inferences about the answers for natural and artificial intelligence.

In general, the more similar the answers to these questions turn out to be across design problems, the more strongly we will expect the answers for problems addressed by future AI developments to fit the same patterns.

We expect to make the data publicly available, so that others can check our conclusions, investigate related questions, or use it in other investigations of technology and evolution.