Can life be created?

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Between life and non-life

A number of books have been published on the subject of between life and non-life. Between Life and Non-Life, Isaac Asimov; Between Life and Non-Life, Norimasa Kobayashi; Between Life and Non-Life, Peter Crawford; Between Life and Non-Life, Shinichi Fukuoka.

This subject is a very interesting question in the field of the origin of life and will be an area where many researchers have proposed theories on how life originated from non-life and what ‘miracles’ occurred on Earth.

Some of the main theories on the origin of life include

Chemical evolution theory (primordial soup hypothesis)
- Proposed by Alexander Oparin and John Holden.
- In the primitive earth environment, inorganic molecules (e.g. methane, ammonia, water) formed organic molecules (e.g. amino acids) when exposed to lightning and UV energy.
- Miller-Urie experiments in 1953 confirmed the formation of amino acids, providing evidence for this theory.
Deep-sea hydrothermal vent hypothesis
- The generation of life using chemicals and thermal energy at hydrothermal vents (black smokers) in the Earth’s deep sea.
- Organic molecules may have been formed in a high-temperature, high-pressure environment, catalysed by inorganic substances such as sulphides.
Panspermia theory.
- Assumed origin of life from extraterrestrial sources.
- Meteorites and comets may have brought organic matter and precursors of life to Earth.
RNA world hypothesis
- Theory that RNA molecules capable of self-replication appeared before modern DNA and proteins.
- RNA may have played an important role in the origin of life, both for the storage of genetic information and for its catalytic function.

Considering these mechanisms, the question of whether the origin of life was an inevitable phenomenon or a highly fortuitous event, the so-called ‘Did a “miracle” happen on Earth?’ This question is an important one. The ‘inevitability theory’ postulates that specific conditions on Earth (liquid water, moderate temperatures, stable chemical environment) led to the inevitable emergence of life, in which case life would have occurred universally throughout the universe and the universe would be full of life.

The ‘coincidence theory’ postulates that the emergence of life requires extremely rare conditions, which the Earth has ‘miraculously’ met. This includes, for example, the existence of the moon, which contributed to tides and climate stabilisation on Earth, and asteroid impacts, which changed the chemical environment, among other coincidental factors. Standing on this coincidence theory, the probability of life forms other than ourselves appearing in the universe is reduced.

In addition, when considering ‘between life and non-life’, the following philosophical perspectives can be identified

Emergence perspective: the view that life is a phenomenon that emerged through the self-organisation of complex systems and that there is no clear boundary between non-life and life.
Ambiguity of definition: the question ‘what is life? is itself a subject of debate, and entities such as viruses, for example, are considered to be somewhere between non-life and life.

The theme of ‘between life and non-life’ is a very interesting question, which goes to the origins of life and its nature, and this question is one that is debated in many fields, including science, biology, philosophy and astronomy.

Can life be created?

Given this ‘inevitability’ or ‘accidental’ ‘creation of life’, it is natural that the next step would be to consider whether humans are capable of doing so. While advances in science and technology have improved our understanding of the origins of life and its reproducibility, the question of what it means to ‘fully create’ life is still being debated. These are discussed below.

1. creating artificial life through synthetic biology: Synthetic biology is the field of research into creating new organisms or systems with life-like functions by recombining genes or synthesising artificial DNA, and scientists have already artificially created bacteria with ‘minimal genes’ and confirmed that these can sustain basic biological activities (growth and metabolism). In 2010, a research team led by Craig Venter created a bacterium called Mycoplasma mycoides with an artificially synthesised genome and showed that it can carry out life activities within cells. Although this is not the creation of a completely new life form from scratch, but the incorporation of artificial DNA into an existing life form, it is considered a major step towards artificial life.

2. creation of self-replicating molecules: one of the characteristics of life is ‘self-replication’, and according to the theory of the RNA world hypothesis, life originated in self-replicating RNA molecules, and it is thought that early life forms may have been created by RNA catalysing (functioning as ribozymes) and repeatedly self-replicating It is thought that this may have been the case. In the laboratory, self-replicating molecules such as RNA and DNA have been partially produced, demonstrating the process of self-replication, but whether this alone can be called complete life is still a matter of debate.

3. artificial reproduction of cellular functions: another feature of life would be the ability to metabolise, obtain energy and respond to the environment within a cell surrounded by a membrane. Experiments called artificial cells (protocells) are also underway, in which attempts are being made to incorporate metabolic and signalling functions into small structures surrounded by lipid membranes to make them behave in a life-like manner. For example, artificial metabolic systems have been developed that generate ATP to supply energy, and protocells that behave like mitotic cells have been realised. However, these artificial cells are very simple and do not replicate the complex regulatory functions found in natural life forms.

4. philosophical questions about the definition of life: even if it is scientifically possible to create life, there is also the philosophical question of what defines ‘life’. If self-replication is the minimum requirement for life, then a biological system starting from self-replicating molecules might be called life. However, some believe that if we take life to include elements such as intelligence, consciousness and the possibility of evolution, then we should call something with a higher level of complexity ‘life’.

At present, it is technically impossible to create life completely from scratch, but it is possible to artificially create systems with some of the functions of life (e.g. self-replication and metabolism), and there are multiple aspects to recreating life, and more discoveries and technologies are needed to recreate complete life. However, advances in synthetic biology are increasing our understanding of the nature of life, and it is possible that artificial life could be created in the future.

What is the difference between living and non-living organisms?

Assuming that there is a clear boundary separating life and non-life, living organisms can be considered to have life by fulfilling certain conditions, while non-living organisms can be considered lifeless because they do not fulfil these conditions. The characteristics of these conditions are considered below.

1. self-replication: organisms are able to pass on their own genetic information to the next generation. For example, all organisms, from bacteria to humans, have self-replication mechanisms that enable them to pass on genetic information to their offspring. Non-living organisms, on the other hand, lack the ability to self-replicate and cannot replicate themselves to leave future generations.

2. metabolism and energy utilisation: living organisms take in nutrients, convert them into energy and undergo chemical reactions (metabolism) in their bodies. Metabolism gives organisms the energy they need to grow, repair and adapt to their environment. Non-living organisms do not consume energy to grow or metabolise. For example, stones and metals do not need energy and do not use their own energy to change.

3. growth and development: living organisms have processes by which they grow and develop in response to their environment. Growth involves cell division and changes in organisation, increasing in size and complexity. Non-living organisms can also change shape due to physical factors, but not growth and development. For example, minerals can weather and change shape, but this is not based on life activity and cannot be called growth.

4. response to stimuli: Organisms respond to external stimuli. For example, plants grow towards light, while humans and animals take the actions necessary for survival by responding to danger, food, etc. Non-living organisms do not respond to stimuli. For example, stones cannot voluntarily respond to heat, light or sound stimuli.

5. homeostasis: living organisms have a regulatory function (homeostasis) to maintain a constant internal environment. This enables them to maintain a constant body temperature, blood glucose level, etc., even when the external environment changes. Non-living organisms do not have such a regulating function and only change under the influence of the environment, e.g. the temperature of water only changes as it is according to the external environment and does not try to maintain a constant temperature on its own.

6. evolutionary potential: organisms have the potential to evolve through successive generations. Evolution is the process of gradually accumulating changes in genetic information and adapting to the environment, which can result in new traits and species. Non-living organisms do not evolve because they do not have genetic information. For example, minerals and chemicals do not evolve over time to become new species.

Organisms can be considered to be distinguished from non-living organisms by features such as self-replication, metabolism, growth, response to stimuli, homeostasis and evolution. However, there are some things, such as viruses, that lie on the borderline between living and non-living organisms and provide an interesting example for discussing the definition of ‘life’ in biology. Viruses self-replicate, but they are dependent on host cells and cannot metabolise or grow on their own. Therefore, there is still no complete agreement on the definition of life, which continues to be studied and debated.

Can computers make them happen?

In the following, we consider the aforementioned features in terms of their reproduction using computers. Thinking about these can lead to approaches for building a strong AI.

1. self-replication: computers can make copies of programmes and data and can reproduce basic aspects of self-replication. Computer viruses are an example of self-replication and can be said to possess certain ‘life-like’ properties. However, it is difficult to reproduce complex self-replicating systems with evolution and adaptation like organisms in nature, and AI and algorithms that self-replicate in digital space need evolutionary functions to respond flexibly to environmental changes. Although there is currently some research on self-evolution and self-adaptation in some algorithms, it is not as good as the self-replicating function of nature.

2. metabolism and energy utilisation: living organisms use energy to carry out vital activities, whereas computers use electricity as an energy source to carry out digital processing. Energy consumption within computers is simply for computation, which is different from the complex chemical processes and energy exchange of life’s metabolism. However, AI systems and robots can introduce mechanisms to self-regulate their computational resources and power consumption, and research is underway to ensure that this works similarly to metabolism. Although different from actual metabolic functions, a certain degree of self-regulation in terms of energy management would be possible.

3. growth and development: just as organisms grow and develop, computers and AI may appear to grow through the accumulation of experience and learning data. Machine learning and deep learning algorithms appear to ‘grow’ by processing large amounts of data, accumulating new knowledge and increasing their application capabilities. However, growth in the same sense as the change in the physical form of an organism or the evolutionary development of the organism’s internal structure has not been achieved by computers.

4. response to stimuli: Modern AI and robots can respond to stimuli from the environment through sensors, cameras, voice recognition, etc. and take specific actions. For example, autonomous robots can sense their surroundings, avoid obstacles and follow commands. This has enabled computers to respond to stimuli, but only to a limited extent, as it is difficult to fully reproduce the instinctive and emotional responses of humans and animals.

5. homeostasis: some computer systems use auto-regulating functions to maintain a state similar to ‘homeostasis’. For example, servers adjust by activating cooling systems when the temperature rises too high, or by increasing resources to stabilise the system when the load increases. However, this is also programme-defined behaviour and differs from the flexible and complex self-regulating functions of living organisms.

6. evolutionary potential: computer algorithms can mimic evolutionary processes using genetic algorithms described in “Overview of genetic algorithms, application examples, and implementation examples” and evolutionary programming, as described in ‘Overview of evolutionary algorithms and examples of algorithms and implementations’. Genetic algorithms are algorithms that ‘evolve’ to find an optimal solution, and are adaptively improved from generation to generation. However, this evolution is based on goals set by humans and is not an adaptive evolution as unpredictable and diverse as biological evolution in the natural environment.

At this stage, computers can partially reproduce features of living organisms, but these are based on programmes and set algorithms, and unlike the flexible and self-regulating life activities of living organisms, reproduction as ‘complete life’, including elements such as emotions and consciousness, has not yet been achieved It has not yet been possible to reproduce it as ‘complete life’, including elements such as emotions and consciousness. In order to ‘fully reproduce’ life on a computer, new technologies and theories are needed to understand the complex and multi-layered functions of living organisms and to construct them.

To realise these, multi-agent technology as described in ‘Artificial life and agent technology’, distributed algorithms such as swarm intelligence, environmentally applicable algorithms such as cellular automata and genetic algorithms, as well as generative algorithms using GANs and generative machine learning neural networks as described in ‘Overview of GANs, their various applications and implementation examples’. Generative algorithms using GANs and generative machine learning neural networks described in ‘About GANs’, and causal inference approaches described in ‘Considerations towards causal inference and the realisation of strong AI’ are also important.

implementation example

Examples of computer implementations that mimic some features of living organisms are described. These examples will be technologies that are either in actual use or being considered for application in the fields of AI and robotics.

1. self-replication: self-replicating programmes

Abstract: A typical example of a self-replicating programme is a computer virus, which multiplies by self-replication and spreads to multiple systems.
Example implementation: as a self-replicating programme, a simple self-replicating script can be written in Python as follows. This programme copies itself and generates replicas.

import shutil
import os

def replicate():
    script_name = __file__
    destination = f"{os.path.splitext(script_name)[0]}_copy.py"
    shutil.copy(script_name, destination)
    print(f"Created a copy: {destination}")

replicate()

When this script is run, it replicates itself and a copy is created.

2. metabolism: resource self-management

Abstract: The autoscaling function of a server mimics part of metabolism, adding new instances when resource usage increases and reducing instances when the load decreases.
Example implementation: auto-scaling settings in a cloud environment (e.g. AWS) allow the system to increase or decrease computing resources according to load, while AWS Lambda and Google Cloud Functions dynamically manage processing resources according to CPU and memory usage. This enables the system to.

3. growth and development: reinforcement learning agents

Abstract: Reinforcement Learning (RL) is the process of learning from experience and improving one’s own behaviour, which is similar to growth and development in living organisms.
Example implementations: reinforcement learning agents can be implemented in Python to create programmes that learn within a maze or simulation, and the following is a simple Q-learning example.

import numpy as np

# Setting up a simple environment (number of states, number of actions, rewards)
state_size = 5
action_size = 2
q_table = np.zeros((state_size, action_size))
learning_rate = 0.1
discount_factor = 0.9

def choose_action(state):
    return np.argmax(q_table[state, :])

def update_q_table(state, action, reward, next_state):
    best_next_action = np.argmax(q_table[next_state, :])
    q_table[state, action] += learning_rate * (reward + discount_factor * q_table[next_state, best_next_action] - q_table[state, action])

# Tentative learning process
for _ in range(100):  # 100 episodes.
    state = np.random.randint(0, state_size)
    action = choose_action(state)
    reward = np.random.choice([-1, 0, 1])  # Temporary remuneration
    next_state = (state + action) % state_size
    update_q_table(state, action, reward, next_state)

The code is a simple Q-learning agent, which improves its behaviour policy based on rewards while acting in the environment.

4. responding to stimuli: response systems in voice assistants

Abstract: Voice assistants such as Siri and Alexa respond by responding to the user’s voice input (stimuli). Systems that use speech recognition to follow commands also mimic the basic ‘response to stimuli’.
Example implementation: a response system using speech recognition and speech synthesis can be easily implemented in Python.

import speech_recognition as sr
import pyttsx3

recognizer = sr.Recognizer()
synthesizer = pyttsx3.init()

with sr.Microphone() as source:
    print("Say something:")
    audio = recognizer.listen(source)

    try:
        text = recognizer.recognize_google(audio)
        print("You said:", text)
        synthesizer.say(f"You said: {text}")
        synthesizer.runAndWait()
    except sr.UnknownValueError:
        print("Could not understand audio")
    except sr.RequestError:
        print("Could not request results")

This programme, which recognises and responds to speech, produces an output (speech response) to an input (speech).

5. homeostasis: system self-adjustment

Summary: Server cooling and load balancing systems self-adjust in response to temperature and load. For example, in data centre server temperature management, cooling systems are set to automatically activate when the heat rises.
Implementation example: it is possible to create an automatic shutdown programme in Python based on CPU temperature.

import psutil

def check_cpu_temp():
    # Check CPU temperature (hardware dependent in practice)
    temp = psutil.sensors_temperatures()['coretemp'][0].current
    if temp > 70:  # If it's above 70 degrees.
        print("CPU temperature too high! Shutting down...")
        # If you actually want to shut down, enable the following
        # os.system("shutdown -h now")
    else:
        print(f"Current CPU temperature: {temp}°C")

check_cpu_temp()

It plays a role in maintaining homeostasis by alerting the CPU when its temperature exceeds a certain level or by performing a shutdown.

6. evolutionary potential: genetic algorithms

Abstract: Genetic Algorithm (GA) is an algorithm inspired by biological evolution, in which candidate solutions adaptively evolve through ‘generations’. In a sense, it replicates the process of evolution in nature.
Example implementation: it is possible to implement code in Python that uses a simple genetic algorithm to find the best solution.

import random

def fitness(individual):
    return sum(individual)

def mutate(individual):
    index = random.randint(0, len(individual) - 1)
    individual[index] = 1 - individual[index]

population = [[random.randint(0, 1) for _ in range(10)] for _ in range(20)]

for generation in range(100):
    population = sorted(population, key=fitness, reverse=True)
    next_generation = population[:5]  # 最良5個体を維持
    while len(next_generation) < 20:
        parent1, parent2 = random.sample(population[:10], 2)
        offspring = parent1[:5] + parent2[5:]
        mutate(offspring)
        next_generation.append(offspring)
    population = next_generation

このコードは、遺伝的アルゴリズムを用いて個体群の「適応度」が高まるように進化させている。

Examples of applications for common solutions

These implementation examples have a wide range of potential applications. They are described below.

1. application examples of self-replicating programmes

Deploying cloud systems: in a cloud environment, the self-replicating functionality of servers and containers is used to scale new instances and distribute the load as traffic increases. This ensures smooth service delivery without system downtime.
Disaster recovery (disaster recovery): self-replicating systems automatically replicate back-up systems and data in the event of a disaster to speed up recovery. The replication of back-up data reduces the risk of service outages in the event of physical failures.

2. applications of resource self-management

Load management systems (autoscaling): autoscaling is used in web applications and mobile apps to automatically add servers when user traffic increases. This enables server overloads to be avoided during periods of increased load or during events.
Energy efficiency management systems: in data centres and smart grid systems described in “Electricity storage technology, smart grids and GNNs“, autonomous resource management is used to optimise energy consumption and adjust power consumption as required.

3. applications of reinforcement learning agents

Robotics: using reinforcement learning, robots learn to optimise their behaviour and avoid obstacles. For example, picking robots in automated warehouses use reinforcement learning to handle goods quickly and efficiently, improving their efficiency.
Game AI: In game development, reinforcement learning is used to enable non-player characters (NPCs) to learn strategies in real-time in response to player behaviour, providing a more dynamic gaming experience.

4. voice assistant response systems applications

Customer support: voice assistants can be integrated into customer support systems, allowing users to enter questions in natural language and automated response systems to provide answers. Efficient support can be achieved in corporate customer service.
Smart devices for the home: smart speakers and home automation control and schedule appliances in response to the user’s voice commands. This increases convenience in the home.

5. applications of system self-adjustment

Data centre temperature management: data centres use AI to monitor temperature and humidity in real time and automatically adjust cooling systems to maintain a constant operating temperature. This ensures equipment safety while minimising energy consumption.
Risk management in financial systems: in stock markets and financial systems, AI is used to automatically adjust risks in response to sudden fluctuations and risks. It maintains constant returns and reduces risk by adjusting the balance of portfolios based on market conditions.

6. examples of applications of genetic algorithms

Manufacturing optimisation: genetic algorithms are used to optimise manufacturing processes to maximise resource and time efficiency in complex processes. For example, in automobile assembly lines, they optimise the placement of parts and the movement of workers.
Optimising marketing strategies: genetic algorithms are used for targeting marketing campaigns and optimising the allocation of advertising spend to automatically find the best strategy. For example, genetic algorithms may be used to optimise advertising based on user attributes and past behaviour.

By mimicking the characteristics of living organisms, these applications enable more autonomous and efficient behaviour than conventional systems and are often used in practice in various industries, for example, to improve the cost efficiency of the cloud through automatic scaling or to reduce energy consumption through self-regulating functions In addition, there are Thus, the application of the ‘adaptability’, ‘self-management’ and ‘learning ability’ of living organisms to technology is likely to make further progress in the future.

reference book

Describes books on the theory and practice of realising biological features in computers.

1. reference books on self-replication and self-regulation
– 『Towards open-ended evolution in self-replicating molecular systems』
– 『Energy Harvesting Autonomous Sensor Systems: Design, Analysis, and Practical Implementation』
– 『Self-replicating Robotic Systems』

2. reference books on reinforcement learning and robotics applications
– 『reinforcement learning』
– 『Deep Reinforcement Learning Hands-On』
– 『Introduction to robotics』

3. reference books on the application of evolutionary computation (genetic algorithms)
– 『Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications』
– 『Genetic Algorithms in Search, Optimization, and Machine Learning』
– 『Introduction to evolutionary algorithms』

4. reference books on self-management systems and energy efficiency management
– 『Optimization and Energy Management in Smart Grids』
– 『Self-Organizing Complexity in Psychological Systems』
– 『Self-Organizing Networks: Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE』

5. reference books on AI voice assistants and natural language processing

– 『自然言語処理の基礎』
– 『Speech and Language Processing』
– 『ディープラーニングによる自然言語処理』

『生命とは何か』 (エルヴィン・シュレーディンガー著)