Machine Learning Artificial Intelligence Natural Language Processing Semantic Web Ontology Knowledge Information Processing Digital Transformation Probabilistic Generative Model Deep Learning Navigation of this blog
Modelling and human creativity
Modelling is an act closely linked to human creativity and provides a simple method of representing the real world and abstract concepts. In this process, physical objects, social dynamics, emotions and decision-making are modelled, enabling understanding, prediction and simulation. Creativity, on the other hand, is the ability to generate new ideas and solutions and plays an important role in various fields such as science, art and technological development.
Modelling embodies creativity and gives concrete form to abstract ideas, which can be shared with others and further developed. It simplifies complex real-world phenomena and provides new perspectives, which can lead to unexpected discoveries and innovations, while AI can be used to extract patterns from vast amounts of data and provide creative inspiration.
In particular, modelling techniques using AI and machine learning support the creative process through iterative learning and data analysis; AI-generated design and art can provide forms and patterns beyond human imagination and trigger new creations. In these processes, AI is not only a passive tool, but may also act as a creative partner.
In this way, modelling and creativity complement each other and enhance the creative process: modelling technologies such as AI and simulation tools are important support tools for visualising creativity and producing more innovative results. This makes the integration of creativity and technology key to opening up new possibilities.
Power of modelling people in philosophy, religion and literature
Modelling the person in philosophy, religion, literature and other areas is explored in different ways and with different objectives in each field, but the common approach is to deepen our understanding of human nature and existence.
<Person modelling in philosophy>
Modelling the person in philosophy becomes an attempt to explore human nature, behaviour, ethics and social relations from multiple perspectives. These perspectives provide a diverse framework within which philosophy attempts to understand human existence and behaviour, and to provide idealised images and paths for growth. The following are examples of modelling in representative philosophies.
1. ancient philosophy: Plato and Aristotle saw humans as beings who reason and pursue happiness in harmony with the order of the universe.
2. existentialism: Sartre saw man as a free and self-determining being, emphasising choice and responsibility
3. dialectics of Hegel: Hegel saw humans as beings who develop themselves through dialogue and conflict with others and deepen their self-awareness.
4. ethics: Kant advocated moral action based on reason, while Mill advocated the evaluation of behaviour by the ‘maximum happiness principle’.
5. structuralism and post-structuralism: Foucault et al. described humans as beings who construct themselves under the influence of social and cultural frameworks.
<Modelling the person in religion>
Religions draw up ideal images (models) of human beings, providing guidelines for morality and ways of life on the basis of the relationship between God and man. These ideal images promote moral and spiritual growth through the doctrines and values of each religion. The ideal images of man in each religion are as follows.
1. Christianity: as the ‘likeness’ of God, it embodies love, forgiveness and humility, and aims to restore one’s relationship with God.
2. Buddhism: idealises a Buddha-like being with the ultimate goal of enlightenment (nirvana), asceticism and compassion.
3. Hinduism: aims to realise one’s divinity and harmonise spiritual growth with social obligations while fulfilling karma and dharma.
4. Islam: the ideal is to follow God’s will, to have justice and mercy and to realise God’s teachings through faith and action.
5. Judaism: emphasises keeping the covenant with God and living in harmony with the community through charity and just behaviour.
6. Taoism: harmonises with nature and pursues inner growth and peace on the basis of ‘no action, no nature’.
<Modelling people in literature>
Literature serves as a means of delving deeply into human psychology and behaviour, portraying emotions and inner conflicts and promoting self-understanding. Through its various genres, literature plays an important role in exploring the many facets of the human person and examining behaviours and values in individuals and society. The following are the main approaches to ‘modelling the person’ in literary genres.
1. realism: depicting human complexity based on social and psychological realities (e.g. Dostoevsky, Crime and Punishment; Tolstoy, War and Peace)
2. modernism: emphasises stream of consciousness and inner exploration (e.g. Joyce’s Ulysses, Woolf’s Mrs Dalloway)
3. ontogeny: exploration of the meaning of human existence in a meaningless world, with themes of freedom and responsibility (e.g. Camus, The Gentiles; Sartre, Vomit).
4. feminist literature: depicts social oppression and the liberation of the ego from a woman’s perspective (e.g. Beauvoir, The Second Sex).
5. dystopian literature: depicts dehumanisation and conflicts under social conditions (e.g. Orwell’s 1984, Huxley’s The Wonderful New World).
Modelling with artificial intelligence technology
In contrast to this modelling in human activities, modelling with artificial intelligence (AI) technologies aims to predict human behaviour, decision-making, knowledge, emotions, social interactions, etc. AI-based modelling is used to understand, optimise and improve complex systems and phenomena. The following are some of the main approaches. Key approaches include.
1. machine learning predictive modelling: machine learning (ML) is a powerful tool for learning patterns based on data and predicting future events, and AI-based predictive modelling is used to predict unknown events based on historical data.
- Regression analysis and classification algorithms can be used to predict customer purchasing behaviour, the probability of disease outbreaks, traffic congestion forecasts, etc.
- Deep learning uses large amounts of data and complex models to make high-level predictions such as image recognition, natural language processing and speech recognition.
For example, health check-up data and genetic information are used to predict diseases, and modelling is used in automated vehicle systems to recognise the vehicle’s surrounding environment and predict optimal driving behaviour.
2. optimisation modelling through reinforcement learning: reinforcement learning (RL) is a method by which agents learn optimal strategies for interacting with their environment and maximising rewards; RL finds optimal solutions through actual trial-and-error and is therefore a It is a highly effective approach.
- Game strategy optimisation: for example, AlphaGo and AlphaZero used reinforcement learning to learn optimal strategies in board games (e.g. Go and Shogi).
- Robot control: reinforcement learning is also used to help robots learn and adapt themselves to their environment and optimise their behaviour to accomplish tasks. Examples include the operation of robots in self-driving cars and on production lines.
Reinforcement learning models how to efficiently achieve a goal by providing a reward signal to an AI agent and making it learn optimal behaviour based on it.
3. recognition and classification modelling with neural networks: neural networks (NNs) are models based on the structure of the human brain and perform particularly well in image and speech recognition. Using them, AI can ‘recognise’ the environment through vision and hearing, and perform classification and analysis.
- Convolutional neural networks (CNNs) are particularly powerful for image recognition and are used for tasks such as face and object recognition.
- Recurrent neural networks (RNNs) and their advanced forms, such as long short-term memory (LSTM) networks, are suitable for time series data and natural language processing, and are used for semantic analysis of text and speech transcription.
These technologies are used in modelling to form ‘perception’ and ‘understanding’ for AI to process input data, learn patterns, classify and make predictions.
4. natural language processing (NLP) for semantic analysis and dialogue modelling: natural language processing (NLP) will be the technology that enables AI to understand, generate and respond to human language. This will enable AI to interact more naturally with humans.
- Contextual understanding: uses NLP technology to understand the context of a sentence or dialogue and generate appropriate responses. For example, large-scale language models, such as the GPT series, have the ability to understand text and facilitate conversation.
- Emotional analysis: NLP can be used to analyse emotion and tone from text and to model human feelings. It is used in corporate customer support and social media analysis.
NLP will be a technology that allows AI to ‘interpret’ linguistic data and create models to understand meaning and emotions.
5. social and cultural modelling: social and cultural modelling with AI uses social network analysis and agent-based models to understand people’s behaviour, interactions and social tendencies.
- Agent-based modelling (ABM): a method that simulates how individual agents (e.g. individuals, organisations, social groups) interact to form overall dynamics. Used to simulate social behaviour and economic trends.
- Social network analysis (SNA): a technique for analysing people’s relationships and interactions to understand social structures. For example, it is used to analyse how people connect and influence each other on social networking sites.
AI is also used in social sciences, economics and political science as a tool for understanding people’s behaviour and group dynamics, helping to predict social phenomena and support decision-making.
6. ethical modelling and bias: modelling using AI technology also entails ethical issues. In particular, when AI models human behaviour and decision-making, it is necessary to avoid bias and bias.
- Fairness and transparency: when AI models human behaviour and social decisions, ethical guidelines must be followed to ensure that the models are fair; it is important to ensure that AI predictions and decisions do not have an unequal impact; it is important to ensure that AI models are fair and transparent; it is important to ensure that AI models are fair and transparent.
- AI biases: biases in the data may be reflected in AI learning, and AI models must therefore be designed to eliminate the effects of bias.
Modelling with artificial intelligence techniques can provide very diverse and powerful tools. Examples include the use of AI to understand, simulate and predict complex systems such as human behaviour, decision-making, social interactions, emotions and moral judgements.
implementation example
The following specific cases are examples of actual implementations of human modelling with AI technology. These will be examples of implementations where AI is used to mimic, predict and analyse human behaviour, emotions and decision-making.
1. emotional analysis (text analysis using NLP): emotional analysis involves the task of identifying emotions from user text data. For example, it is used to identify customer emotions in social media and customer support.
Example implementation (Python + Hugging Face Transformers).
from transformers import pipeline
# Loading the sentiment analysis pipeline
sentiment_analysis = pipeline('sentiment-analysis')
# Text to be analysed.
text = "I love this product! It's amazing."
# Perform an emotional analysis.
result = sentiment_analysis(text)
print(result)The code uses a pre-learned model of Hugging Face to analyse the emotion (positive or negative) of a given text.
2. optimising the behaviour of an autonomous agent using reinforcement learning: an example of using reinforcement learning to learn strategies for an agent to interact with its environment and maximise rewards. This is often used in robots and games.
Example implementation (Python + OpenAI Gym)
import gym
import numpy as np
# Create environment (CartPole).
env = gym.make('CartPole-v1')
# Q Define variables for learning
q_table = np.zeros([env.observation_space.shape[0], env.action_space.n])
learning_rate = 0.1
discount_factor = 0.99
epsilon = 0.1
# learning process
for episode in range(1000):
state = env.reset()
done = False
while not done:
if np.random.rand() < epsilon: # search
action = env.action_space.sample()
else: # Uses
action = np.argmax(q_table[state])
next_state, reward, done, _ = env.step(action)
# Q-value update
q_table[state, action] = (1 - learning_rate) * q_table[state, action] + learning_rate * (reward + discount_factor * np.max(q_table[next_state]))
state = next_state
if episode % 100 == 0:
print(f"Episode {episode}: Reward {reward}")This example uses the OpenAI Gym environment to learn how agents balance in a ‘CartPole’ environment, using Q-learning to learn strategies for selecting the best action based on state-action pairs.
3. modelling people through face recognition: an example implementation of a system that uses face recognition to identify individual people. This is used to improve surveillance systems and user interaction.
Example implementation (Python + OpenCV + Dlib)
import cv2
import dlib
# Load face detector.
detector = dlib.get_frontal_face_detector()
# Acquire video from the camera.
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
# face detection
faces = detector(frame)
for face in faces:
x, y, w, h = (face.left(), face.top(), face.width(), face.height())
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Face Detection", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()The code uses dlib and OpenCV to detect faces from camera footage in real-time and draw rectangles on the detected faces. This allows for next steps such as person identification and emotion analysis.
4. social behaviour modelling (agent-based modelling): agent-based modelling (ABM) to simulate social interaction and behaviour. This provides an understanding of how the behaviour of individual agents affects the overall social structure.
Example implementation (Python + Mesa)
from mesa import Agent, Model
from mesa.time import RandomActivation
from mesa.space import MultiGrid
from mesa.datacollection import DataCollector
class PersonAgent(Agent):
""" Class of social agents """
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
def step(self):
# Define agent behaviour here.
pass
class SocialModel(Model):
""" Class of social models """
def __init__(self, width, height, num_agents):
self.num_agents = num_agents
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
for i in range(self.num_agents):
a = PersonAgent(i, self)
self.schedule.add(a)
x = self.random.randint(0, self.grid.width - 1)
y = self.random.randint(0, self.grid.height - 1)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
agent_reporters={"Agent Position": "pos"}
)
def step(self):
self.datacollector.collect(self)
self.schedule.step()
# Model instantiation and step execution.
model = SocialModel(10, 10, 5)
for i in range(10):
model.step()
# Get agent location information.
data = model.datacollector.get_agent_vars_dataframe()
print(data)In this example, the Mesa library is used to simulate social agents. It is a simple social model where agents move randomly, but in practice complex social interactions and rules can be added here.
5. natural language interactive AI (chatbot): an example implementation of an AI that understands emotions and intentions and generates appropriate responses through interaction with the user.
Example implementation (Python + GPT-3)
import openai
openai.api_key = 'your-api-key-here'
def chat_with_ai(user_input):
response = openai.Completion.create(
engine="text-davinci-003",
prompt=user_input,
max_tokens=150
)
return response.choices[0].text.strip()
# Get user input.
user_input = "How are you today?"
response = chat_with_ai(user_input)
print(f"AI: {response}")The code will use OpenAI’s GPT-3 to carry out dialogue based on user input. It can analyse emotions and intentions to facilitate natural conversations.
reference book
The following are reference books for in-depth study of ‘human modelling’ by artificial intelligence. 1.
1. ‘’
– Abstract: This book covers a wide range of topics from the basic theory of artificial intelligence to its applications, allowing readers to learn about AI algorithms and modelling techniques, especially how to model human behaviour through reinforcement learning, emotion analysis and natural language processing (NLP).
2. ‘’
– Abstract: This classic reference on reinforcement learning describes how agents learn from their environment and make decisions. In particular, it provides detailed information on modelling autonomous behaviour and decision-making by AI.
3. ‘Deep Learning’ (authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville)
– Abstract: This classic book on deep learning provides a comprehensive introduction to neural networks, from the basics to applications. It provides a deeper understanding of how human emotion, cognition and language processing can be modelled using deep learning.
4. ‘’
– Abstract: This book explains the basics and applications of natural language processing (NLP), especially the design of AI systems for analysing emotions and decision-making based on text data. It focuses on techniques for analysing human emotions and intentions with AI.
5. ‘’
– Abstract: This comprehensive textbook on artificial intelligence provides a useful foundation for modelling and understanding human behaviour using AI technologies. In particular, it touches on topics related to decision theory, game theory and modelling social behaviour.
6. ‘Agent-based modelling and simulation’
– Abstract: A book that uses agent-based modelling (ABM) to understand how individual agents interact and shape the overall social dynamics. Useful for modelling social behaviour and relationships.
7. ‘’
– Abstract: Describes techniques for extracting emotions from textual data and provides a practical approach to modelling human emotions. It teaches how to implement and apply emotion analysis algorithms.
8. ‘’
– Summary: Discusses the social implications and ethical issues of AI technology modelling human behaviour, delving into how AI can mimic social behaviour and decision-making and cause ethical challenges.
9. ‘’
– Abstract: This book provides an introduction to the basics of modelling and simulation, in particular methods for simulating human behaviour and social interaction. Agent-based modelling and Monte Carlo methods are also covered.
10.
– Abstract: This book teaches techniques for analysing human posture and facial expressions using computer vision. It teaches techniques for modelling human non-verbal behaviour through face and gesture recognition.
コメント