How evolution creates problem-solving machines | Michael Levin

Summary

The transcript discusses the skepticism engineers may initially feel towards evolution, as they understand the difficulty of creating complex systems through random changes. However, evolution's true power lies in its ability to produce problem-solving machines rather than specific solutions. It highlights the concept of biological systems being highly adaptable and capable of handling changes without detailed micromanagement. The speaker uses examples like 'Picasso Tadpoles' to illustrate how evolution leverages existing biological processes to create novel outcomes. The talk emphasizes the robustness of biological systems and the elegance of evolution's strategies in shaping the complex behaviors and structures we see in life today.

Takeaways

• 🤔 Initial skepticism among engineers about evolution due to the belief that random changes often lead to worse outcomes in engineered systems.
• 🧬 Evolution's true power lies in creating problem-solving machines rather than specific solutions to environmental challenges.
• 🛠️ Biological hardware is adept at problem-solving without strict assumptions about the environment or its cellular makeup.
• 🐸 'Picasso Tadpoles' demonstrate the system's competency by rearranging their facial features based on bioelectrical signaling.
• 👀 The ability to instruct cells to build an eye without specifying all the details showcases the system's inherent problem-solving capacity.
• 🔄 Evolution may search for behavior-shaping signals rather than just exploring all possible hardware configurations.
• 🌀 Individual parts of biological systems can maintain functionality despite changes, contributing to the overall robustness of biology.
• 🧠 Evolution leverages 'free lunches' or inherent properties (like mathematical laws) to reduce the complexity of adaptation.
• 📈 The process of evolution involves scaling up from simple metabolic problems to complex anatomical and physiological challenges.
• 🚀 The ultimate goal is to understand how evolution navigates various 'spaces' from physical to linguistic and social domains.
• 🏛️ Biology's architecture allows higher levels to shape the behavior of lower levels, creating a system that doesn't require micromanagement.

Q & A

Why might engineers initially react with incredulity when hearing about the theory of evolution?

Engineers may react with incredulity because their experience in creating complex systems suggests that random changes often lead to deterioration rather than improvement, which contrasts with the idea of evolution making things better over time.

How does evolution overcome the challenge of random changes potentially making things worse?

Evolution overcomes this challenge by producing problem-solving machines that are hierarchical and adaptable, capable of solving problems without needing specific, detailed instructions for every task.

What does the term 'hierarchical biological hardware' refer to in the context of evolution?

Hierarchical biological hardware refers to the complex, multi-level structures in organisms that are adept at solving problems without making strong assumptions about their environment, cell count, or genome copies.

How do 'Picasso Tadpoles' demonstrate the competency of biological systems?

Picasso Tadpoles rearrange their facial features in response to bioelectrical signals without the need for detailed eye-construction instructions, showing that biological systems can achieve complex outcomes through high-level directives.

What is the significance of the ability to build an eye anywhere in the body?

The ability to build an eye anywhere in the body demonstrates that biological systems can take advantage of existing processes and signals to create complex structures without needing to understand or control every detail of the construction.

How do the movements of organs in frog embryos illustrate the robustness of biological systems?

Even when organs are moved to incorrect positions in frog embryos, the resulting frogs are largely normal, indicating that biological systems can adapt and function effectively despite changes in initial conditions.

What does the concept of a 'free lunch' in evolution refer to?

The 'free lunch' in evolution refers to the advantageous use of inherent properties or laws, such as mathematical principles, that are not encoded in DNA but can be leveraged by biological systems to optimize their structures and functions.

How do simple systems contribute to the understanding of evolution?

Simple systems provide a foundation for understanding how evolution solves metabolic, physiological, and transcriptional problems, eventually leading to insights into more complex anatomical and multicellular challenges.

What is the role of collective intelligence in the evolution of movement and navigation?

Collective intelligence in evolution involves coordinating the actions of individual cells or muscles to achieve complex tasks like movement and navigation in three-dimensional space without micromanagement from higher levels.

How does the concept of competency in biology contribute to robustness?

Competency in biology refers to the ability of individual components to perform their functions effectively even when faced with changes or novel conditions, which allows the overall system to remain robust and adaptable.

Outlines

00:00

🤖 Evolution and Problem-Solving Machines

This paragraph discusses the initial skepticism engineers may feel towards the theory of evolution, given their experience in creating complex systems where random changes often lead to degradation rather than improvement. It contrasts this with evolution's ability to produce problem-solving machines, emphasizing the hierarchical biological hardware's capability to solve problems without strict environmental assumptions. The 'Picasso Tadpoles' experiment is highlighted as an example of how biological systems can respond to high-level directives without needing specific, detailed instructions. The paragraph suggests that evolution has optimized the use of inherent 'free gifts' or principles, such as mathematical laws, to shape the behavior of biological systems, allowing for robustness and adaptability in the face of change.

05:02

🌿 Robustness and Competency in Biological Systems

This paragraph emphasizes the robustness of biological systems, attributing it to the competency of each part to perform its function effectively despite changes or novel conditions. It suggests that the ability of individual components to maintain their roles in varied circumstances contributes to the overall resilience of biology. The paragraph implies that this inherent adaptability and robustness are key to the success and diversity of life, as it allows biological systems to navigate environmental shifts and developmental challenges without the need for exhaustive, specific instructions for every possible scenario.

Keywords

💡Evolution

Evolution is a fundamental biological concept referring to the process by which populations of organisms change over time through genetic variation and natural selection. In the context of the video, it is highlighted as a mechanism that produces problem-solving machines rather than just specific solutions to environmental challenges, emphasizing the adaptability and robustness of life forms.

💡Incredulity

Incredulity refers to the feeling of skepticism or doubt, often due to a lack of understanding or belief in a concept. In the video, engineers initially react with incredulity to the theory of evolution because they find it hard to believe that random changes can lead to improvements in complex systems like those they design.

💡Problem Solving

Problem solving is the process of finding solutions to difficult questions or tasks. In the video, it is used to describe the inherent ability of biological systems to adapt and overcome challenges without explicit instructions. This concept is central to understanding how evolution has shaped life into efficient problem solvers.

💡Hierarchical Biological Hardware

Hierarchical biological hardware refers to the complex, layered structure of biological systems where different levels of organization work together to perform tasks. In the video, this term is used to describe how evolution has created organisms with multiple levels of problem-solving capabilities, allowing them to function effectively in varied environments.

💡Bioelectrical Signaling

Bioelectrical signaling is the process by which living organisms communicate or transmit information through electrical signals. In the context of the video, it is the method used to instruct cells in a frog embryo to form an eye, demonstrating the sophisticated communication systems within biological organisms.

💡Competency

Competency refers to the ability of an individual or system to perform tasks effectively and efficiently. In the video, it is used to describe the inherent capability of biological systems to maintain functionality and adapt to changes, which is crucial for the robustness of life.

💡Phenotypic Space

Phenotypic space is the range of possible physical characteristics or traits that an organism can exhibit, which results from the interaction of its genotype with the environment. The video suggests that evolution may not only search through this space of physical traits but also look for simpler behavioral signals that guide development.

💡Free Lunch

In the context of the video, 'free lunch' is a metaphor for the inherent advantages or principles that can be leveraged without additional effort or cost. It refers to the fundamental laws or principles, like mathematical truths, that simplify the process of evolution by providing predictable outcomes.

💡Multicellularity

Multicellularity is the condition of being composed of multiple cells, which is a characteristic of most complex life forms. In the video, it is discussed as a significant stage in evolution where organisms developed the ability to coordinate complex structures and behaviors, leading to a new level of problem-solving.

💡Collective Intelligence

Collective intelligence refers to the shared knowledge and intelligence that emerges from the collaboration and collective efforts of many individuals. In the video, it is used to describe how biological systems, such as muscles and neurons, work together to perform complex tasks like navigating three-dimensional space, without needing detailed instructions from a central authority.

💡Robustness

Robustness in this context refers to the ability of biological systems to maintain their function and withstand changes or disturbances. The video emphasizes that the robustness of biology comes from the competency of its parts to perform their jobs effectively, even when faced with changes or novel conditions.

Highlights

Engineers' initial incredulity towards evolution due to the perceived negative impact of random changes on complex systems.

Evolution's ability to circumvent the problem of random changes leading to degradation rather than improvement in complex systems.

Evolution produces problem-solving machines rather than specific solutions to environmental challenges.

Biological hardware's hierarchical structure enables effective problem-solving without strict environmental assumptions.

The richness of evolutionary outcomes surpasses expectations based on tailored environmental adaptation.

Experiments with 'Picasso Tadpoles' demonstrate the system's competency without specifying intricate details.

Bioelectrical signaling allows for the manipulation of developmental processes without detailed knowledge of organ construction.

The concept of using high-level directives to reset cellular behavior, as opposed to engineering new circuits.

Evidence from frog face manipulations showing that organs can adapt to new positions without losing functionality.

Evolution's potential to search for behavior-shaping signals rather than just exploring hardware configurations.

The robustness of biological systems to changes due to the competency of individual parts to adapt and perform their roles.

Biology's use of 'free gifts' or inherent advantages like mathematical laws to optimize evolution.

The idea of evolution pivoting on universal principles, such as geometrical facts, to simplify the evolutionary process.

The scientific goal to uncover these evolutionary tricks from simple metabolic to complex anatomical and physiological systems.

The transition from direct cellular instruction to the use of collective intelligence in navigating three-dimensional space.

The remarkable architecture of biology where each level shapes the behavior of lower levels without micromanagement.

The concept of biological robustness due to the ability of parts to perform their jobs despite environmental novelty and change.

Transcripts

- When most engineers hear about the theory of evolution

for the first time, the reaction is one of incredulity.

And that's because anybody who's ever made anything

from writing a computer program

to building a complicated device,

you know that it's entirely likely

that random changes that you make to that system

are not gonna make things better,

they're gonna make it worse.

And so, the interesting thing about evolution is

trying to understand how life gets around that problem.

It's often thought that what evolution produces

are specific solutions to specific environmental challenges.

What I think is a more accurate way of putting

it is that what evolution actually gives us

are problem solving machines.

Hierarchical biological hardware that is very good

at solving problems without making strong assumptions

about what the environment is or how many cells it has

or how many copies of the genome it has.

Of course, there are ways to mess up that process

and we scientists have many ways of doing that,

but the default is amazingly much more rich

than you would expect, just from something that was tailored

to a specific environment.

So one set of experiments that really showed the importance

of not assuming the level of competency of a system

was our work with "Picasso Tadpoles,"

that rearrange their face.

We have a way of communicating to a bunch of cells

in the frog embryo that they should make an eye,

and this is done via some bioelectrical signaling

and we give them the signal,

and they go ahead and they build an eye.

This does not require us to know how to build an eye.

We don't specify all of the information needed

to make an eye.

All we do is provide that high-level directive

that resets the set point

for these cells, and they will go ahead

and they will build an eye wherever we want in the body.

So this isn't because we have done something new,

we haven't engineered any new circuits into the embryo;

this is only because we've taken advantage

of what the system already does.

This is how biology works.

It uses large-scale triggers

and ways to change the goal-directed behavior of these cells

and then lets the lower levels handle this.

And another example is what happens in the frog face.

If we move the various organs of the early embryo,

you still get largely normal frogs

because every organ will move around in novel paths

even despite the fact

that they start off in the incorrect position.

And so what you could imagine is

that evolution is not necessarily just searching the space

of all possible micro states of the hardware, right,

that whole phenotypic space,

but it might also be searching a much easier base

of behavior-shaping signals.

So that means that

because the individual parts can often get their job done,

even when things change, that competency-

the fact that small changes do not wreck the whole business

because the different parts have the ability

to get their job done even if the circumstances change-

that has huge implications for evolution because it means

that the material can make up for a lot of damage

and a lot of changes in the hardware.

So I think what biology does

and what evolution does is it basically finds machines

that, structurally, it builds machines via the genome

that optimally take advantage of these various free gifts,

what physicists might call a 'free lunch.'

For example, if you're evolution

and you want to evolve a particular kind of triangle,

you do a lot of work sorting through the population

and you get the first angle, and then you do a lot more work

and you get the second angle.

But now something amazing happens, you don't need to look

for the third angle because it's already a fact

that you know what the third angle has to be

because it's already a fact

that the angles have to add up to 180.

That is nowhere in the DNA, that comes to us

from wherever the laws of mathematics come from.

What I think evolution has done

is pivoted some of the same tricks.

And our goal as scientists is to discover

some of these tricks from very simple systems

that only solve metabolic problems

eventually to physiological

and then to transcriptional problems.

And eventually, when multicellularity comes on the scene,

a large-scale anatomical problem-

so this idea of starting out in a particular shape

but you need to get to a very different shape-

how much do you grow in what direction?

Which cells go where?

So making that journey and then once neurons

and muscles came on the scene,

then those same kind of strategies were used

to navigate three-dimensional space.

That collective intelligence instead of telling

all the cells what to do to make specific shapes,

they now tell the muscles what to do to move you

and I around in three-dimensional space.

And after that, who knows, right?

There are linguistic spaces

and maybe social spaces

and who knows what else after that.

What evolution has given us

is this remarkable architecture

where every level shapes the behavioral landscape

of the levels below,

and the levels below do clever

and interesting things that allow the levels above

not to have to micromanage.

This ability of each part to get its job done,

despite all kinds of novelty,

despite changes in its environment,

changes in where it starts off,

that is the competency

that makes biology so incredibly robust.