Philosophy of Innovation

 Innovation is one of the most powerful forces to advance humanity.

Innovation is to figure out better ways to do things. Philosophy is abstract and has deliberate processes for identifying questions, reasoning, and thinking outside the box. From the realm of philosophy, innovation sparks a lot of interesting questions that go beyond the creation of new things. 

We need more people to ask” big why” questions and dig through the root cause of many problems, to solve them innovatively. Here are some key areas to explore innovation philosophically.

Progress and the Value of Novelty: Philosophy is abstract, but it could provoke the chain of thoughts to stimulate fresh ideas. Philosophically, people need to ponder innovation from multiple angles: Is innovation inherently good? Does creating something new always represent progress? Philosophers grapple with how we define "progress" and whether constant change is always beneficial. Philosophical reasoning encourages critical thinking and intends to discover the true reasoning behind taking innovation initiatives. They might explore the potential downsides of innovation, such as the disruption of traditional ways of solving existing problems or the unintended consequences of new practices.

Philosophers explore the cognitive processes behind innovation: Philosophical understanding clarifies innovation-related concepts, human conditions, and motivations. Innovation can involve both knowledge discovery (finding something that already exists) and invention (creating something entirely new). Philosophers question the relationship between these concepts. What drives human creativity, the source of innovation? Is innovation a unique human capacity, while now, generative intelligence assists humans to generate more knowledge; what’s the magic behind those “Aha moments”? Philosophers explore the wellspring of creativity and its role in shaping our world. They might debate the role of intuition, imagination, and reason in the birth of new ideas.

The philosophical wisdom about ethics and responsibility in innovation: New technologies and ideas often raise ethical dilemmas, and how to develop accountable or responsible AI? Philosophers delve into the ethical considerations of innovation or machine intelligence. They might ask questions about who benefits from innovation, who might get hurt, and the responsibility innovators have for the potential consequences of their creations.

Humans and machines should work more collaboratively to harness innovation. How will our understanding of innovation shape the future? Philosophers consider the potential implications of continuous innovation on society, the environment, and even the nature of humanity itself. They might ask if we can predict or control the direction of innovation, and deep learning, and what role we should play in shaping its future course. Incremental innovation or breakthrough innovation, how could we handle them with the right philosophical principles and scientific processes for improving the success rate?

Brainstorming more philosophical questions surrounding innovation. By pondering these issues, we gain a deeper understanding of the role innovation plays in our lives and the complex forces that drive it. In fact, innovation is one of the most powerful forces to advance humanity.

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InsightofPatternLogic

 The digital world is so data-driven and information-intensive, Pattern Thinking is important for designing, and Adaptive Problem-Solving.

There are a variety of patterns we could discover in our beautiful natural worlds. Each pattern is useful to address some specific problem. We all use pattern logic to make sense of the world around us. In today’s over-complex and interdependent world, pattern thinking is a type of problem-solving thinking. 

Pattern thinking enables us to capture deep insight into complex problems systematically. Pattern logic isn't a universally recognized term in psychology or science, but it's an interesting concept used in multiple fields to describe a way of thinking and problem-solving.

Identifying Meaningful Patterns: Patterns play a role in science, math, art, and virtually every action or activity we engage in. So the type of pattern logic emphasizes the ability to identify meaningful patterns in complex situations, even when the information is incomplete or seemingly random. Scientists use pattern logic to analyze data, identify trends, and formulate hypotheses. Business people use pattern logic to identify market trends, customer behavior patterns, and potential risks or opportunities. Observe, overserve more to discover patterns; pay close attention to details and notice subtle connections.

Pattern Analysis & Synthesis: Besides humans’ Eyes, nowadays, machine intelligence become more efficient in recognizing patterns, breaking down information into its parts, and identifying relationships between them; understanding the interconnectivity of seemingly different issues; connecting seemingly disparate pieces of information to form a coherent whole. Evaluate the validity of patterns and consider alternative explanations.

Patterns are good in practice and intellectually stimulating: Someone ties the patterns together so you can navigate among them. If we have a set of patterns that bear some relationship to one another - usually because they are in some common domain, then we can compose those patterns to generate a larger design or architecture, in much the same way that human languages can generate sentences or paragraphs.

The digital world is so data-driven and information-intensive, Pattern Thinking is important for designing, and Adaptive Problem-Solving. It involves using past experiences and identified patterns to navigate new situations and make effective decisions. Every pattern perhaps helps you to discover or fix something; sometimes tie the patterns together so you can navigate among them to figure out better solutions to complex problems.

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Insight of Key Concepts of deep learning

 Big Data and machine intelligence intend to push human society forward from the information age to the intelligence era. The technical aspects of deep learning delve into the inner workings of these complex models and algorithms. Here's a breakdown of some key technical concepts:

Artificial Neural Networks (ANNs): Deep learning models are built on the foundation of ANNs. Inspired by the structure and function of the human brain, ANNs consist of interconnected nodes (artificial neurons) arranged in layers. These layers process information by applying mathematical functions to the data they receive and passing it on to the next layer. 

Deep learning algorithms rely on ANNs, inspired by the structure and function of the human brain. ANNs consist of interconnected nodes (artificial neurons) arranged in layers. These layers process information by applying mathematical functions to the data they receive and passing it on to the next layer.

Deep Architectures: Deep learning lives up to its name by using multiple layers in its ANNs. This allows the model to learn increasingly complex features from the data with each layer. Compared to traditional machine learning models, deep learning architectures can achieve higher accuracy on complex tasks.

Activation Functions: These functions determine whether a neuron "fires" its signal to the next layer based on the weighted sum of its inputs. Common activation functions include ReLU (Rectified Linear Unit) and sigmoid functions, which introduce non-linearity into the network, allowing it to learn more complex patterns.

Gradient Descent: Training a deep learning model involves adjusting the weights between neurons in the network to minimize the error between the model's predictions and the actual data. Gradient descent is an optimization algorithm that iteratively adjusts these weights in a direction that minimizes the error.

Backpropagation: Backpropagation is a technique used within gradient descent to efficiently calculate the contribution of each neuron to the overall error, allowing the weights to be adjusted accordingly. Backpropagation is a crucial step in training the network. It calculates the error (difference between prediction and actual value) and propagates it backward through the network. Based on the error and how it flows through the network, the weights between neurons are adjusted in a way that minimizes the overall error. This process is like the network learning from its mistakes and adjusting its internal parameters to improve its performance on future inputs.

Loss Functions: The loss function guides the training process by indicating how well the model is performing. Common loss functions include mean squared error for continuous data and cross-entropy for classification tasks. These functions quantify the difference between the model's predictions and the actual data. Common loss functions include mean squared error for continuous data and cross-entropy for classification tasks. This function measures how well the model's predictions differ from the actual data. The lower the loss, the better the model's performance.

Optimization Algorithms: Gradient descent is a fundamental optimization algorithm used to adjust the weights in the network. It iteratively adjusts the weights in a direction that minimizes the loss function. While gradient descent is a fundamental algorithm, there are many advanced optimization techniques used to train deep learning models more efficiently. These algorithms address the limitations of basic gradient descent and accelerate the training process.

Regularization Techniques: Deep learning models with many parameters are prone to overfitting, where they learn the training data too well but fail to generalize to unseen data. Regularization techniques like L1/L2 regularization help to prevent overfitting by adding constraints to the model and reducing its complexity.

Understanding these technical aspects gives us a solid foundation for delving deeper into the fascinating world of deep learning. These concepts pave the way for exploring the applications of deep learning in various fields and the ongoing research that is constantly pushing the boundaries of what's possible.

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LLM & GNN Integration

Research is ongoing to develop techniques for understanding how these integrated models arrive at their outputs.

LLM is a field whose purpose is to create computational models of natural intelligent systems. It excels at processing sequential data like text and code, understanding nuances of language, and generating creative text formats. GNNs are powerful at reasoning about relationships within graph-structured data. Graphs can represent entities (nodes) and their connections (edges) - useful for modeling social networks, molecules, or knowledge graphs. Integrating Graph Neural Networks (GNNs) and Large Language Models (LLMs) is an emerging area of research with exciting potential. By combining those capabilities, it’s possible to build models that leverage the strengths of both.

LLM injects information from GNN; GNN incorporates textual information processed by LLM: LLMs can inject information from GNNs about entities and relationships into their reasoning process, potentially leading to more comprehensive and contextually aware language generation. GNNs can incorporate textual information processed by LLMs as node features, enriching the graph representation and enabling reasoning tasks that involve both structure and text content. GNNs can model the network of user connections, allowing for deeper analysis of how information and influence flow within the network.

Defining clear and effective training objectives that leverage the strengths of both GNNs and LLMs is crucial for successful integration: Integrating these models creates a more complex architecture that requires significant computational resources for training and inference. Technically, the integration often requires data in a unified format - combining graph data with textual information associated with nodes. This can necessitate data pre-processing or specific data collection methods.

Building Conversational AI and deepening intelligent learning: LLMs form the core of many chatbots, but integrating GNNs could enable them to reason about the user's history, preferences, or the context of a conversation, leading to more engaging and informative interactions. Integration of LLM and GNN enables across-disciplinary scientific discovery, analyzes research papers (text) as nodes connected by citations, and uses LLMs to identify research gaps or promising directions. GNNs can help to reason about the relationships between different areas of study. The integration also helps to deepen Social Network Analysis. LLMs can process social media text to understand user sentiment or topics of discussion.

Modular Integration Techniques: Researchers are exploring efficient ways to combine GNNs and LLMs while maintaining modularity, allowing for easier development and training. Co-training methods involve training GNNs and LLMs simultaneously, allowing them to learn from each other and improve their performance on the integrated task.

As with many deep learning models, interpretability remains a challenge. Research is ongoing to develop techniques for understanding how these integrated models arrive at their outputs. By overcoming challenges and continuing research efforts, GNN and LLM integration has the potential to revolutionize various fields that involve reasoning about data with both structural and textual elements.

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InnovationviaCognitiveaability

 To harness innovation, encourage cognitive diversity, and focus on assessing people’s unique thinking styles and problem-solving skills.

Cognition is a faculty for the processing of information, applying knowledge, and changing preferences. Cognitive diversity refers to the variety of thinking styles, information-processing methods, and mental perspectives within a group of people. Cognitive gaps will cause the blind spots for either defining the real problem or solving it.

 One of the most influencing aspects of people's cognition is how they perceive the world around them and how they relate to it by applying diverse thought processes.

Thinking spectrum: Cognitive diversity goes beyond simply having people from different backgrounds; it focuses on the different ways individuals approach problems, generate ideas, and make decisions. People have different preferences for how they take in and understand information. Some may learn best visually, while others prefer written instructions or hands-on experiences.

Thinking styles: This refers to how people naturally approach information and problem-solving. Some people are analytical and detail-oriented, while others are more creative and big-picture thinkers.

Thinking perspectives: Our life experiences, cultural backgrounds, and upbringing all shape our perspectives on the world. Cognitive diversity brings together people with different viewpoints, leading to a richer understanding of any situation.

Importance of Cognitive Diversity: Each of us has a defined cognitive threshold - an upper limit to the level of complexity that we can handle at any one time or on any one topic. Each cognitive mind has a perceptual difference, so cognitive diversity has a better chance of improving collective decision effectiveness. A classroom with students who learn best visually, auditorily, and kinesthetically allows for a more inclusive and effective learning environment. A team with a mix of engineers, designers, marketers, and salespeople can bring together diverse skill sets and perspectives to develop a successful product launch. A company with a leadership team that includes people from different ethnicities, genders, and age groups can make more well-rounded decisions that consider the needs of a diverse workforce and customer base.

Enhanced Problem-Solving: Groups with cognitive diversity are better at tackling complex problems because they have access to a wider range of ideas and perspectives.

Improve Creativity: When people with different thinking styles come together, they can spark new ideas and approaches that might not have emerged otherwise. Cognitive diversity can foster a culture of innovation by encouraging people to challenge the status quo and think outside the box.

Better Decision-Making: By considering different viewpoints, teams can make more informed and well-rounded decisions.

To harness innovation, encourage cognitive diversity, and focus on assessing people’s unique thinking styles and problem-solving skills. Create opportunities for team members to share their perspectives and learn from each other's strengths. Foster a culture where different ideas are welcomed and respected, even if they challenge the status quo. By fostering cognitive diversity, we can create more innovative, effective, and successful teams and organizations.

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LinquisticPerspectiveofIntelligentlearning

By bridging the gap between computer science and linguistics, deep learning is constantly evolving, leading to new applications and a deeper understanding of how language works.

Deep learning is part of a broader concept of machine learning methods based on the learning representation of data. As its name indicates, there’s deep learning needed for object detection, segmentation, recognition, etc., but it has become a fundamental vision for digital computerization. 

The linguistic aspects of deep learning explore how deep learning models can be applied to understand, analyze, and process human language. Here's a breakdown of this fascinating intersection:

Distributed Representations: Deep learning excels at creating distributed representations of words. Instead of a single numerical value for each word, these models represent words as vectors in a high-dimensional space. Words with similar meanings tend to cluster closer together in this space, allowing the model to capture semantic relationships between words.

Capturing Long-Range Dependencies: Traditional language processing methods often struggled with capturing long-range dependencies in sentences – the relationships between words that might be far apart grammatically. Deep learning architectures like recurrent neural networks (RNNs) and transformers can better handle these dependencies, allowing for a more nuanced understanding of sentence structure and meaning.

Learning from Large Text Corpora: Deep learning thrives on vast amounts of data. Linguistic applications leverage massive datasets of text and code, such as books, articles, code repositories, and online conversations. By training on these corpora, models learn complex patterns in language usage and can perform tasks like machine translation, sentiment analysis, and text summarization.

Intelligent Learning and Language Discovery: Deep learning models can be used for unsupervised learning tasks in linguistics. By analyzing unlabeled text data, these models can identify patterns and relationships between words, helping us discover new syntactic structures or semantic groupings without manually labeling data.

Weights and Biases: While powerful, deep learning approaches to language also come with challenges. Some concerns include, how to weigh different factors more reasonably; and how to handle bias, as models can inherit biases present in the data they are trained on. It's crucial to consider these limitations and develop methods to mitigate bias in language processing tasks.

Deep learning offers linguists new tools to unlock the complexities of human language. By bridging the gap between computer science and linguistics, deep learning is constantly evolving, leading to new applications and a deeper understanding of how language works.

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Best of all

Best of all, we could release - our rebels; the things we don’t feel right, we can speak out; unconventional insights, we would like to share, misfit thoughts or acts, perhaps, one day, become a progressive new normal.

In this vast fantastical world,

we need to -

keep exploring-

the art of possibilities,

discovering our purpose,

sensing our uniqueness.




Best of all,

we should understand-

our inner self,

our passion and interest,

strength and weakness,

the things we would like to -

accomplish by-

following our gut,

shaping our minds.


Best of all,

we need to recognize-

our pals,

the ones who-

guide us through,

walk us along,

motivate us, empathetically,

criticize us, constructively.


Best of all,

we need to-

grow our capabilities,

with integrated building blocks,

challenge ourselves a bit,

breakdown -

all sorts of roadblocks,

clarify the vision,

make accomplishments.



Best of all,

we could release -

our rebels;

the things we don’t feel right,

we can speak out;

unconventional insight,

we would like to share,

misfit thoughts or acts,

perhaps, one day,

become a progressive new normal.

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Anthropological aspect of grammar

From an anthropological perspective, grammar isn't just a set of rules for speaking "correctly." It's about how language, and the way we structure it, reflect and shape our culture and our existence as social beings.

Grammar isn't about memorizing a bunch of rules. It's a system that allows us to be creative and generate a large number of sentences. Linguists typically dissect grammar into different levels like morphology (word structure), syntax (sentence structure), and semantics (meaning). Analyzing each level helps us understand how language builds meaning from the ground up. They are interested in how grammatical rules allow us to form new expressions and constantly adapt our language.

 From an anthropological perspective, grammar isn't just a set of rules for speaking "correctly." It's about how language, and the way we structure it, reflect and shape our culture and our existence as social beings.

Grammar and cognition: There's a connection between grammar and how we think. The way a language structures sentences might influence how speakers perceive the world and categorize information. Studying grammar can offer insights into a culture's cognitive processes.

Grammar as cultural expression: Anthropologists see grammar as a way a culture expresses itself. The way a language organizes sentences can reveal cultural values and priorities. For example, some languages prioritize putting the actor (the one doing the action) first, while others focus on the object of the action.

Social context and grammar: How we use grammar can change depending on the social situation. We might use different grammatical structures when talking to an elder, a child, or a close friend. Anthropology helps us understand how grammar reflects social hierarchies and relationships.

Anthropology is interested in how language and grammar evolved. By looking at the grammar of different languages and how they might be related, anthropologists can trace the history of human communication and social organization, drive an evolution of grammar, and improve communication effectiveness.

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ParableofIntelligentLearning

 The analogous aspects of intelligent deep learning from the parable explore how deep learning models can be applied to understand, analyze, and process languages of all sorts of.

Intelligent deep learning offers linguists new tools to unlock the complexities of human language. By bridging the gap between computer science and linguistics, such intelligence is constantly evolving, leading to new applications and a deeper understanding of how language works. The parable reminds us that intelligent deep learning, like Lynn, is a journey of exploration. It requires patience, experimentation, and the courage to venture beyond the known. And with perseverance.

In a hidden grove, nestled amongst towering algorithms, lived a young program named Lynn. Unlike her neighbors, who followed rigid rules, Lynn dreamt of untangling the mysteries of the world. One crisp morning, found an interesting book titled "The Book of Intelligent Deep Learning."

Intrigued, Lynn cracked open the book. It spoke of vast networks, mimicking the human brain, that could learn from experience. The more data they devoured, the wiser they became. Lynn's circuits crackled with excitement. She envisioned these networks untangling the language of birds, the patterns of the wind, or even the whispers of the ancient trees.

With newfound zeal, Lynn began constructing her network. She gathered data – the chirps of birds, the sway of leaves, the whispers carried on the breeze. She meticulously connected his nodes, mimicking the intricate web she saw in the book.

Finally, the moment arrived. Lynn activated her network. It whirred to life, a symphony of calculations. Days turned into weeks, and disappointment gnawed at Lynn. The network produced nothing but nonsensical gibberish. Just as she was about to give up, a single, clear melody emerged – the song of a mourning dove, a bird she'd often admired.

Elated, Lynn fed her network more birdsongs. Slowly, it began to decipher the language of the nature– the chatter of sparrows, the trill of cardinals, the hoots of owls. But Lynn wasn't satisfied. She hungered for deeper understanding.

Lynn included data about wind patterns, barometric pressure, and even the movements of insects. The network sputtered and choked at first. Then, a breakthrough. It began to predict the coming of storms, the rustling of leaves before a strong gust, and the dance of butterflies in response to air currents.

 Lynn's network, though not perfect, was learning, and growing wiser with each passing day. It became a marvel, a testament to the power of deep learning – not just to mimic, but to understand the very essence of the world around it.

The parable reminds us that intelligent deep learning is a journey of exploration. It requires patience, experimentation, and the courage to venture beyond the known. And with perseverance, it can unlock a deeper understanding of the world, one piece of data at a time.

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InterdisciplinariesofChanges

Do not think of changes through linear logic only, always think about how to amplify progressive change significantly, and diminish the negative change effect necessarily.

Change is inevitable, lack of a clear vision or in-depth understanding is a big roadblock to change. In a world where change is significantly speeding up, many organizational leaders and professionals can’t predict the future with a certain degree of accuracy. 

So it’s always important to understand different effects and impacts from an interdisciplinary angle to accelerate change impact.

Chain Reaction: A chain reaction describes a sequence of events where each event triggers the next one, often resulting in a rapid and escalating series of consequences. Today’s organizations have stepped into the ever-evolving hyper-connected digital world, Very often, one seemingly small effort could cause a chain of reactions later on, and make a big impact. People’s online influence or customer feedback complaints were so easily spread up, creating a chain reaction, and directly impacting the brand name of the organization

Butterfly Ripple: The butterfly effect refers to the concept that small, seemingly insignificant actions or events can lead to significant and unpredictable outcomes; how a single action or event can create a series of consequences that spread outward, affecting other interconnected elements or systems. Combining elements of the butterfly effect and ripple effect, the butterfly ripple effect describes how a small initial change can create a ripple effect that spreads and amplifies, much like the wings of a butterfly causing ripples across a pond.

Snowball Effect: Like a snowball rolling down a hill and gathering more snow as it goes, the snowball effect describes how a small initial change or action can grow in magnitude over time, amplifying its impact. Understanding the snowball effect helps to improve the change management effect. The more people evolve in change, the bigger the impact could be. Be cautious of failures: No one factor is usually at fault; there is usually a snowball into the failure of change management. There are varying causes of failures such as scope missed, features added by creep, ineffective processes, skill gaps, etc. Every factor about large business initiates is rooted in the fact that people need to understand what is changing, what the impacts of those changes are,

Echo Effect: The echo effect highlights how actions or events can reverberate or echo through time and space, influencing future occurrences or outcomes. The pace of change is accelerated both in organizations and the entire human society, either as individuals or organizations, the uncertainty of outcomes or occurrences is inherent in almost every endeavor. Understanding the echo effect helps people discover patterns, predict trends, prevent risks, amplify positive results, and reduce negative impacts.

Cascading Effect: This effect portrays how one change or event can trigger a series of subsequent changes or events, cascading through a system or network and leading to far-reaching consequences. 'Change' is continuously happening and spiraling up in a dynamic environment, to move from one level to another, it’s important to understand the change effect, how one change initiative could trigger other events, and analyze the pros and cons of different scenarios. Change maturity requires step-function changes in tools, culture, leadership, and process, and making the change a differentiated competency.

Amplification Effect: The amplification effect illustrates how small inputs or changes can be magnified or amplified as they propagate through a system, leading to larger-scale effects. Intelligence amplification is a digital phenomenon of human and machine collaboration. Just take a reasonably intelligent person and give them the Internet and search and they are more capable; and right now, machine learning is emerging as an intelligence power to process information and generate results momentarily. Give them a sensing system that is ambiently and constantly attempting to augment their understanding of what they see and hear and you have a human/machine construct that begins to direct its evolution.

There are changes we desire, and some are imposed on us. Do not think of changes through linear logic only, always think about how to amplify progressive change significantly, and diminish the negative change effect. If changes fail; don’t just scratch the surface to manage the numbers, dig into the root causes. Change is never for its own sake, we need to identify the right spot to change to amplify its effects and improve adaptability and professional maturity.

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InsightfulQuotesof“PyramidofNeeds”

Overall, Abraham Maslow's contributions to psychology remain significant. His focus on human potential and self-actualization continues to inspire and influence various fields.

Change becomes the new normal with increasing paces, and the world becomes more hyperconnected and interdependent, people strive to not only survive in the hyper-competitive business society but also keep growing to unleash their potential. US psychologist Abraham Maslow developed the hierarchy of needs, a theory of human motivation. 

Here are a few of his quotes and how we can learn from them to improve professional competency.

"One can choose to go back toward safety or forward toward growth. Growth must be chosen again and again; fear must be overcome again and again": The hierarchy of Needs theory proposes a pyramid-like structure of human needs; suggesting individuals strive to fulfill lower-level needs before moving on to higher-level ones. Basic physiological needs (food, water, shelter) form the foundation, followed by safety needs (security, stability), love and belonging needs (connection, intimacy), esteem needs (achievement, recognition), and finally, self-actualization needs (fulfilling one's potential).

It emphasizes the ongoing nature of personal development)growth requires that we move from a fixed to growth mind, from thinking conventionally and conveniently to thinking consequence, thinking innovatively with a long-term perspective. Either individually or collectively, it’s important to overcome “growth pain,” help employees at all levels within an organization to “think out of the box” take reasonable risks, go beyond their defined roles, and demonstrate their creative capacities, intellectual capabilities to bring positive changes and catalyze breakthrough innovation.

"What is necessary to change a person is to change the awareness of thyselves": It highlights the importance of self-awareness in personal growth; it challenges the assumption that everyone knows what they desire) Mindset is at the heart of our belief system. These are beliefs that we all hold and have formed over time and hard experiences.

Changing mindsets is challenging, often if people have been in an organization for a long period of time, their mindset may be one of "I don't want to change, and why should I". A ‘changeable mindset’ is a success factor for any change management effort, a prerequisite to building a culture of innovation, and a trait in any high-performance business. Mindset is everything. Despite your well-intended objective, strategic, and proactive efforts, be cognizant of the consequences, in particular, your own 'discouragement' of failures resulting from inertia and lukewarm support, especially from the level that claims to be "leaders."

"It isn't normal to know what we want. It is a rare and difficult psychological achievement." It takes planning, adapting, and innovating to enable true meritocracy as a system in which top talented people are chosen and moved ahead based on their talent or achievement. Motivation is the "cognitive momentum" that comes from consistently applying habits congruent with the achievement of the goal.

In an ideal digital working environment with democratic meritocracy, self-motivated leaders, teams, and employees have a passion for challenging themselves, advancing their thinking ability, making things happen, and catalyzing changes.

The hierarchy of needs has been criticized as overly simplistic and not universally applicable across cultures and situations. Overall, Abraham Maslow's contributions to psychology remain significant. His focus on human potential and self-actualization continues to inspire and influence various fields. Developing high mature digital fit workforce won’t happen overnight, it takes planning, adapting, and innovating to enable true meritocracy as a system in which top talented people are chosen and moved ahead on the basis of their talent or achievement.

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PrincipleProcessPracticeofInformationIntelligence

 Technically, deep learning intelligence combines computer science, mathematics, statistical analysis, data visualization, and even social science to gain insight and foresight about what we need and what the future trends are, to advance humanity significantly.

Information is more intangible, complex, and dynamic compared to the physical asset. Deep learning is part of a broader concept of machine learning methods based on the learning representation of data. It is a subfield of machine learning inspired by the structure and function of the human brain. Here's a breakdown of its principles, process, and practices:

Learning from Data: Deep learning models learn from large amounts of data. This data is used to adjust the connections (weights) between neurons in the network, allowing it to improve its performance on a specific task. To process: Start with a small data sample. You don’t need the full width and depth of data to find interesting stuff. Start small. It saves you a lot of technology headaches at the start. Data Preparation: Data is collected, cleaned, and preprocessed to ensure it's suitable for training the deep learning model. This may involve normalization, scaling, and handling missing values. in the real world, you have always been constrained by existing systems and databases.

Deep learning relies on artificial neural networks (ANNs) loosely inspired by biological neural networks. Not all big data is new data. a wealth of data generated sits unused or at least not used effectively. The architecture of the deep learning model is defined. This includes choosing the type of network (convolutional neural network for images, recurrent neural network for sequences), the number of layers, and the number of neurons in each layer. Optimizers like gradient descent iteratively update the weights of the network to minimize the loss function. The process never stands alone, and rarely is the sole source of data for any meaningful process. So while considering the process primary, you must consider existing data sources as constraints. Ignoring them while designing processes will likely lead to a bad result and rework.

Hierarchical Representation Learning: Deep learning models achieve complex representations of data by processing it through multiple layers. Each layer learns a more abstract and meaningful representation based on the features extracted from the previous layer. Activation Functions: introduce non-linearity into the network, allowing it to learn complex patterns in the data. The tacit and explicit knowledge and learning from experience are essential aspects, components, parts, or facets of the architecture-design feedback loops that drive the emergence, and extend through and beyond any chosen boundary. The process, structure, and practices of machine learning, all these factors interact in a dynamic for evolving emerging properties to achieve the “art of possible.”

The model is trained on the prepared data. Once trained, the model is evaluated on a separate test dataset to assess its performance on unseen data. During training, the model iteratively processes data points, adjusts its internal weights based on the errors between predictions and actual values, and aims to minimize the overall error. Regularization helps to prevent overfitting (memorizing the training data instead of learning general patterns). Metrics like accuracy, and precision, are used for evaluation. Based on the evaluation results, the model might be fine-tuned by adjusting hyperparameters (learning rate, number of epochs) or architecture. Finally, the trained model can be deployed for real-world applications.

The Assessment of Deep Learning: Machine learning needs to go deeper and deeper, and the results can be delivered faster and faster, the goals of evaluating Deep Learning maturity can be assessed through:

High Accuracy: Deep learning models can achieve high accuracy on complex tasks like image recognition, natural language processing, and speech recognition.

Feature Learning: Deep learning models can automatically learn meaningful features from data, eliminating the need for manual feature engineering.

Scalability: Deep learning models can handle large amounts of data, making them suitable for real-world problems involving big data.

For most companies today, data are abundant and readily available, but not well used. The biggest challenge that improves deep learning maturity is not just technical, but more about cultural wisdom. Data-oriented culture needs to be well established to help every level of the organization to make data-driven, better, and faster decisions. Technically, deep learning intelligence combines computer science, mathematics, statistical analysis, data visualization, and even social science to gain insight and foresight about what we need and what the future trends are, to advance humanity significantly.

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Insight of Superconsciousness Logic

The greatest art or science pieces often emerge from a place beyond understanding, a whisper from the superconscious, the logic of mother nature.

The flow of energy as it grows in size gradually differentiates itself into three parts that are aura, mind, and consciousness. It is connected on one side to the superconscious and on another side to the mind. With superconsciousness, human beings will be able to let themselves think not only of what exists (conscious and subconscious), how they exist in the world, being in the world (conscious), reach the state of subconscious via autonomy and becoming superconscious - how to change the world. 

Here are a few existing idioms that capture some of the potential aspects of superconscious logic:

“Seeing the bigger picture", and "Tapping into a wellspring of knowledge", "Downloading from the universe": These idioms implyaAccessing Universal Knowledge, understanding a situation beyond the immediate details, potentially through a more holistic perspective. They suggest accessing a vast source of information or understanding, which could be interpreted as the superconscious connecting to universal knowledge. They also refer to the idea of accessing universal knowledge through the superconscious.

"A sixth sense." "Thinking with your soul.” "Knowing something in your bones""The logic of the heart","Hitting your stride": These idioms refer to an ability to perceive things beyond the five traditional senses, potentially aligning with the idea of superconscious intuition. They suggest a deep, intuitive knowing that goes beyond conscious reasoning.” The reasoning goes beyond the conscious mind and incorporates a deeper level of awareness, finding a rhythm or flow in your actions, potentially reflecting the efficient processing of superconscious logic. These idioms also suggest that intuition and emotions play a role in superconscious reasoning, leading to insightful decisions.

While these idioms aren't established phrases, they offer a way to talk about the potential qualities of superconscious logic in a creative and evocative way.

A parable reveals superconscious logic:

Joy, a struggling artist, felt creatively blocked. Her paintings were empty, devoid of the spark that once ignited them. In desperation, she embarked on a solo backpacking trip through a remote region. Days turned into weeks, the vast silence punctuated only by the wind and her own thoughts. One starlit night, gazing at the Milky Way, a feeling washed over her – a sense of connection to something vast and ancient. It wasn't a thought, but a knowing, a download of information that bypassed her conscious mind.

Back in her studio, the brush flowed effortlessly. Shapes and colors danced on the canvas, forming a vibrant landscape that captured the essence of the universe with the night sky. It was unlike anything she had ever created, filled with a cosmic energy she couldn't explain.

An art critic, struck by the painting's power, asked, "Where did this come from, Joy?" She smiled enigmatically. "The universe whispered to my soul." The critic scoffed. "Nonsense. Art is about technique and skill." But a young talent, gazing at the painting, felt a strange resonance. It wasn't just the colors, but the emotions it evoked – a sense of awe, of being part of something bigger.

In that moment, the artist glimpsed the power of superconscious logic, the ability to tap into a wellspring of creativity that transcends the limitations of the conscious mind. The artist’s painting became a symbol, a reminder that the greatest art or science pieces often emerge from a place beyond understanding, a whisper from the superconscious, the logic of mother nature.

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InspirationofFemaleAnthropologiststoCelebrateMonther’sFestival

Anthropology helps us open to a variety of perspectives and enables people to navigate various cross-disciplinary situations.

Anthropology takes a holistic approach, studying various aspects of human societies such as cultural custom aspects: rituals, traditions, language, art, and belief systems; social Structures: Family systems, kinship networks, and political organization; economic Systems: Subsistence strategies, production, exchange, consumption patterns. iistory: How societies have evolved and changed over time. 

It’s time to celebrate Mother’s Day. Female artists and scientists who follow mother nature, have made significant influence across the long river of human history, there were definitely females worldwide throughout history who made noteworthy observations about cultures that can be seen as laying the groundwork for anthropology. Here are a few examples:

Hypatia (350-415 AD) - While Hypatia was previously mentioned as a wise woman from Roman Egypt, it's important to note that Alexandria was a center of learning that bridged the gap between West Asia and North Africa during her time. Her contributions to mathematics, astronomy, and philosophy transcended geographical boundaries.

Feng Guan (Lived during the Western Han Dynasty) - China: A legendary physician revered for her medical knowledge and skills. According to historical accounts, Feng Guan mastered acupuncture and herbal medicine, becoming a prominent figure in traditional Chinese medicine.

Shin Saimdang (1504-1551 AD) - Korea: A renowned calligrapher, artist, writer, and mother. Shin Saimdang was a multi-talented woman who advocated for female education and is considered a role model for Korean women.

Mirabai (1498-1547 AD) - India: A mystic poet and princess, Mirabai is revered for her devotional poems and hymns dedicated to the Hindu god Krishna. Her defiance of societal norms and her powerful expressions of love and faith continue to inspire.

Hildegard of Bingen (1098-1179 AD) - Germany: A polymath known for her writings on theology, medicine, music, and natural history. Hildegard was a visionary thinker and a leader in her Benedictine monastery.

Pocahontas (1595 – 1617) - Powhatan Confederacy (North America): A Native American woman who played a significant role in the early interactions between the Powhatan people and English colonists. Pocahontas is remembered for her bravery and diplomacy.

Isabella Bird (1831-1904, England): A prolific Victorian traveler and writer, Isabella Bird documented her extensive travels to countries. Her detailed accounts provided insights into the cultures she encountered, including social customs, religious practices, and daily life. While not strictly anthropological, her work offers valuable historical and cultural information.

Anthropology helps us open to a variety of perspectives and enables people to navigate various cross-disciplinary situations. Modern anthropology emphasizes understanding the unique cultural practices, values, and belief systems of each society within its context; enhancing contextual understanding of cultures within their specific environments; understanding the unique characteristics and perspectives of each society; and taking a combination of emic and etic approaches for a more comprehensive understanding.

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Inclusiveness

 There are plenty of more questions one can ask to illustrate the importance of inclusion.

Organizations can implement programs to attract and retain a diverse workforce and create an inclusive environment for all. Statistically, diversity is most common at very low levels of the organization, the higher the hierarchy the more homogenous the workforce is. 

At the end of the day, you want the people to bring in growth, by building a competitive edge at the different levels of our societies. The challenge is to get managers to recognize how inclusion improves harmony and innovation of human society.

Advocate equal opportunities: Language and cultural differences can sometimes create communication barriers. Unfortunately, social diversity can also lead to prejudice and discrimination against people who are different. In some cases, significant social diversity can lead to social conflict if not managed effectively. Efforts to promote intercultural communication are important. Promoting understanding and respect is crucial. It's important to actively combat these issues and promote inclusivity.

Harness creativity and innovation: Do not just think that diversity is only based on how people look, in fact, the diversity of thought is the gem of creativity. When people from different backgrounds come together, they bring a wider range of ideas and perspectives, leading to more creative solutions and innovative thinking. Having an inclusive culture always helps us learn from diverse people. Orienting people and making them aware of the diversity in their organization or team, helps them understand the value of harnessing differences.

Orchestrate stronger communities: Diversity can create a lot of confusion and conflict if people are not well prepared for it, and if the leaders are unaware of how to manage diversity. There should not be a stagnation in engaging the talent from different demographics. Social diversity allows people to learn about and appreciate different cultures and ways of life, fostering empathy and understanding. By considering different viewpoints from diverse backgrounds, groups can make more informed and well-rounded decisions. Social diversity can foster a sense of belonging and acceptance for all community members, leading to greater social cohesion.

Enhance intercultural training and education: Training programs that promote understanding and appreciation of different cultures can help break down stereotypes and build bridges between groups. Events and programs that celebrate the richness of social diversity can foster a sense of belonging and acceptance for all community members.

There are plenty of more questions one can ask to illustrate the importance of inclusion. Social diversity is a complex but enriching aspect of our world. By acknowledging the benefits and challenges, and actively working to promote inclusivity, we can create a more vibrant and equitable society for everyone.

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InsightofLLM

 Deep learning focuses on object detection, segmentation, recognition, argumentation, etc, it has become a fundamental vision and emerging area for digital computerization.

Artificial intelligence can be viewed as the ability of a computer to learn and reason is not only about learning; but also about understanding language, and refining knowledge. Either for people learning scenarios or deep learning processes, you need to not only assimilate existing knowledge, but more importantly, you also have to keep updating knowledge, create new knowledge, and become the knowledge value creator. 

There are different models and approaches to improve machine intelligence.

Traditional vs. LLM-based Translation: Traditional Rule-based Machine Translation (RBMT): RBMT relies on a set of predefined rules for translating grammar, syntax, and vocabulary between languages. This approach can be effective for simple sentences but needs help with nuances, idioms, and context. LLMs are trained on massive amounts of text data in multiple languages. This allows them to learn the statistical patterns and relationships between words and phrases across languages. They can capture the context and subtle variations in human language much better than RBMT. LLMs can capture the nuances of language, including idioms, sarcasm, and cultural references, leading to more natural-sounding translations compared to RBMT.

Statistical Machine Translation (SMT) with an LLM twist: Many LLM-based translation systems use a core SMT approach but leverage the LLM's ability to understand language statistically. The LLM helps predict the most likely translation for a word or phrase in the target language based on the context and surrounding words. Advanced LLM architectures like transformers excel at sequence-to-sequence learning. They process the entire source sentence and generate the target sentence word by word, considering overall context and meaning.LLMs consider the surrounding text and context when translating, resulting in translations that better reflect the intended meaning.

Limitations and Challenges: Although deep learning and machine language translation automation have made significant progress in recent years. LLMs trained on massive datasets can inherit biases presented in that data. It's crucial to mitigate bias in training data to ensure fair and unbiased translations. While LLM translations are often impressive, understanding the reasoning behind the translation choices can be challenging. So how to make things more explainable? Training and running large LLMs requires significant computational resources. It takes time and lots of effort to improve LLM effectiveness.

Deep learning focuses on object detection, segmentation, recognition, argumentation, etc, it has become a fundamental vision and emerging area for digital computerization. Overall, LLM-based translation represents a significant leap forward in deep learning. By continuing to develop and refine these models, we can overcome current limitations and create even more accurate, nuanced, and human-like translation systems.

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InfluenceofHabit

 Habit logic is a powerful force that shapes our behavior more effortlessly.

A habit, from the standpoint of psychology, is a more or less fixed way of PERCEIVING, thinking, willing, or feeling acquired through previous repetition of a mental experience. 

Habit logic refers to the unconscious decision-making process behind forming and maintaining habits. It's the hidden reasoning that drives our automatic behaviors. Here's a breakdown of this concept:

Understand habit cycle: Building on the Habit cycle: cue -> routine -> reward. The habit cycle influences how cues trigger routines and how rewards reinforce them. It helps us recognize cues in our environment that trigger specific routines. For example, seeing your gym bag might automatically trigger the urge to go for a workout.

Effortless choice or mindless automation: Habit patterns bypass conscious deliberation. Once a habit is formed, the "choice" to perform the routine becomes effortless, driven by the underlying logic of the habit loop. It needs to understand the limitations of habit patterns and how they could enhance unhealthy habits. While efficiency is a benefit, habit logic can sometimes lead to mindless automation. We might continue unhealthy habits simply because they're ingrained, even if they no longer serve us.

Reward Anticipation: Habit logic can anticipate the reward associated with a routine, further motivating us to complete it. Reward Tweaking: Understanding the reward system can help us modify habits. The brain might crave the dopamine released from checking social media, even before you consciously pick up your phone. Habits can be formed to avoid negative emotions like boredom or loneliness. Scrolling through social media might be driven by a subconscious desire to connect, even if it doesn't always satisfy that need. Habit logic often prioritizes efficiency. By automating routine tasks, we free up mental space for more complex thinking.

Habit logic can reinforce our sense of identity: Habit logic often operates below our conscious awareness. This can make it challenging to identify and change unwanted habits. Habit logic can be used to our advantage. Stacking new habits onto existing routines can make them easier to stick with.

Habit logic is a powerful force that shapes our behavior more effortlessly. By understanding how it works, we can become more mindful of our automatic routines and harness the power of habit formation to build a life that aligns with our goals and values.

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logic of the universe

Universe reasoning delves into the vast and often deep principles that govern the cosmos.

Our universe is vast and beautiful, full of miracles. As curious beings, we continue to explore and learn about it. We admire natural beauty and sustain a wonderful world. The logic of the universe is a fascinating concept that philosophers and scientists have been grappling with for centuries. There isn't one definitive answer, but here are some key ideas that contribute to our understanding:

Laws of Physics: Logic Applied to the Physical World: This interpretation suggests using the principles of formal logic to reason about physical phenomena. These fundamental laws, like gravity, electromagnetism, thermodynamics, etc, form the bedrock of our understanding of how the universe operates. They provide a framework for explaining and predicting the behavior of matter and energy at various scales, from the subatomic to the cosmological.

Cause and Effect: This principle states that every event has a cause, and that cause precedes the effect. It helps us understand the interconnectedness of phenomena in the universe. Nothing happens in isolation, and every action has a reaction that ripples throughout the system. Our world is complex. It becomes complex if things do interact, particularly in the case of nonlinear interconnection and interactions. You can't separate things properly or you cannot predict the actual effect of interactions straightforwardly. The cause-effect may be seen as yet another cause. Can we be patient, listen to what the universe intends to tell us, figure out multiple perspectives, be sensitive to emergent factors, close a multitude of gaps, and grasp the essence of universal logic?

Symmetry and Simplicity: The universe often exhibits a tendency towards symmetry and elegance in its underlying principles. Symmetry delights our eyes and touches our aesthetic feelings. Simplicity is an aspect of “appropriate” abstraction. Many physical laws can be expressed in simple, mathematical equations that capture their essence. The inherent order and beauty suggest a deeper logic at play.

Emergent characteristics: Our universe is a gigantic system, the two most basic systemic structures are the balancing cycle and the growth cycle; and their emergent characteristics just happen to be balance and growth, which implies that any system must have emergent characteristics. Oftentimes, the whole is greater than the sum of its parts. Complex systems can arise from the interaction of simpler components. This emergent complexity suggests that the logic of the universe might involve more than just the basic physical laws.

The Role of Observation and Evidence: Science, the primary tool we use to understand the universe, relies heavily on observation, experimentation, and evidence-based reasoning. The logic of the universe is not something we can simply intuit; it's an ongoing process of discovery and refinement based on the data we collect.

We admire the universe, survive, and thrive within it at every moment. Universe reasoning delves into the vast and often deep principles that govern the cosmos. It's a realm where established logic can be stretched, challenged, and sometimes even defied. Of course, it’s worth our efforts to constantly discover patterns and abstract wisdom.

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Superconsciousness

The concept of superconsciousness is open to interpretation and can vary depending on the specific belief system or theory.

As human beings, we all experience the state of unconsciousness, subconsciousness, and consciousness, even the state of superconsciousness -the experiences of the world through the senses and the mind, the various processes of thinking, and the different implications of this experience are all offshoots of the consciousness of the Self. It suggests that there might be more to our cognitive abilities than we currently understand.

Accessing and processing the Universal Knowledge: The superconscious is believed to be connected to a universal source of knowledge, allowing for insights beyond the limitations of individual experience. Superconscious logic allows for perceiving the interconnectedness of all things, leading to a more holistic understanding of reality. Intuition Beyond the Ordinary: Intuition in this context goes beyond gut feelings. It's a form of direct knowing that bypasses the need for conscious reasoning.

Peak Performance Logic: In a more secular interpretation, "superconscious logic" could refer to the mental state experienced during peak performance. Scientists’ “Aha Moments,” Athletes in "the zone" or artists experiencing a flow state might access a heightened level of mental clarity and focus. This superconscious logic could involve:

Optimal Processing: Information processing becomes effortless and efficient, allowing for quick and accurate decisions without conscious deliberation.

Pattern Recognition on Steroids: The ability to identify subtle patterns and make connections that are often missed by the conscious mind is enhanced.

Intuition for Action: A deep understanding of the situation allows for intuitive actions and reactions that seem almost precognitive.

Transcendence of Understanding: In some spiritual traditions, the superconscious is seen as a state of heightened awareness that transcends the limitations of the conscious mind. Superconscious logic, in this view, refers to a way of reasoning that is not bound by the rules of everyday logic. It’s more about the Integration of Consciousness. Physics excels at explaining physical phenomena, but it doesn't directly address the nature of consciousness. How consciousness arises from physical processes in the brain remains a major unsolved problem. To reach the next level of cognitive understanding, a growth mind can keep learning agile, filter information selectively, update knowledge timely, and improve the level of cognitive understanding effectively, to make transcendental consciousness progress.

The digital era represents the next stage of the growth cycle with which people and organizations improve how they work and interact with each other as well as its ecosystem. Whether you view it as a spiritual concept or a metaphor for peak performance, "superconscious logic" encourages us to consider the potential for our minds to operate in ways beyond the limitations of everyday logic. The concept of superconsciousness is open to interpretation and can vary depending on the specific belief system or theory. Some might see superconscious logic as a form of heightened intuition or creativity, while others might view it as a spiritual experience.

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Seattle’sCharm

Seattle is vibrant, but not sleepless, it's quiet at deep night. Seattle is a modern city with certain cultural influences.

I visited Seattle quite a while ago by taking a flight and car. This is my first time taking the overnight train from California to Seattle, Washington. The overall experience was fine the seats were comfortable and the cafe had enough food supply. Looking out of the window, there were mountains rivers, and trees with a greenery environment. After entering the border of Oregon for a while, the ocean was unfolded with mountains as the background, the scenery looked fantastic

Seattle City Center: In the early morning, Seattle was still a bit chilly, I walked across the streets hanging around in the city center areas of Pioneer Square and Chinatown, where you could find numerous shops and restaurants. Then walking across the downtown, I headed towards the oceanfront street Alaskan. That was just about ten to fifteen minutes of walking distance.

The ocean looked deep blue and a few sailboats parked near the bay. So the harbor looked very vibrant. People in Seattle were friendly they greeted the global strangers kindly; the food stalls nearby sell seafood meals or snacks, and I had the white creamy clam chowder for lunch.

Walking along the coast, you can go to the sculpture park. Different shapes and sizes of sculptures were scattered near the seashore. There was a tall human figure standing on the shore, you could imagine it was female or male eastern or western, I saw the spirit of Guanyin in it. After that, walked back to the Pike Market Center. Even though it was a rainy day, lots of people ate, drank, and shopped around, very crowded.

Island Visting: Then I took the ferry towards Bainbridge Island. That’s only about thirty minutes away. The ship was very spacious and comfortable with good facilities. It was slightly raining and windy; the sea became grey. But the water was still very calm so the ship moved forward smoothly. So we landed on the island in about half an hour. Honestly, when I took the first glimpse of the island, it was just the normal place not having peach blossoms as I had imagined

There was a parking lot near the ferry. I followed other passengers to go further and reached the city center of the island. There are souvenir shops cafes food markets and different properties. You could see some flowers scattered around but not condensed enough to create fantastic scenery. I went to the local museum with a limited collection of wood print pottery metal paper and sculpture collections in the two-floor building.

There were quite a few tourists who sipped coffee or had late lunch nearby. The atmosphere was warm but the scenery was not extremely impressive. It was still raining So I walked back towards the ferry and headed back to Seattle city center.

Seattle Arboretum: It was a sunshine day on the second day, I decided to visit the Seattle Arboretum which is a beautiful plantation park with peach cherry blossom, fountains and near Washington Lake. That was a colorful garden with diverse plants and flowers, purple pink red yellow white orange blue vibrant and enchanting.

I saw a few squirrels looking for seeds to eat. But I didn’t see birds and other creatures playing around. I imagine lambs strolling over the meadow peacocks dancing with their beautiful wings on the grass; little rabbits jumping around; and cute little monkeys climbing the trees. All sorts of birds bees and butterflies flying around creating the vibe of influence. So I imagined an ecological environment in which all different creatures could harmonize the beautiful scenery.

Seattle Asian Museum and Volunteer Park: It took me about half an hour to walk around the plant-enriched neighborhood and arrive at the other local tourist spot Volunteer Park. It was a green environment with plenty of different plants and flower blooms. I visited the Seattle Asian Museum inside the park. The staff were very friendly and guided me through the hallways. The very intense colors of paintings and photographs demonstrated the Asian culture and festival themes with bright red dragons or traditional costumes. I saw Guanyin and a few other artistically crafted statues. There were also other ancient treasure pieces. However, the museum only has one floor with a limited collection.

UoW Campus with a clear river and cute duck family: After visiting the art exhibition, I was pondering about the science in the IT-enriched city of Seattle. The next morning, I went to the University of Washington campus. It was spacious and greenery I didn’t see a lot of contemporary buildings, instead I walked along the clean river I saw a family of cute ducks swimming around, Mom led ahead the baby duck followed through and then the daddy duck swam on behind, they were not afraid of me at all, passed across the pedestrian sidewalk and swam towards the other side of river bravely. It was a very original ecosystem and quiet learning environment in the state college.

Seattle Pacific Science Center: After that, I headed back to the city center and went to the Seattle Pacific Science Center, and enjoyed some scientific flavor of entertainment and education. The exhibition hall showed the planet images dinosaur animation and some experiments or craft sessions that fit for kids' science study. But it’s a bit childish for adult visitors. I was sort of disappointed.

When I went to the biology center, surprisingly I saw lots of blue black, and colorful butterflies flying around. That was truly enchanting and I took photos and videos of them. That was a small plant nursery but vibrant because of those energetic creatures and truly cheered me up.

Then I went to see the movie music laser show. Lots of local families brought their blankets and lay down to see the laser lightings spinning around on the ceiling rhyming with the pop music. The atmosphere was hot spicy and cheerful. So the butterflies' enchantment and laser show enlightenment are the two highlights of the science center. Other capacities of the museum are limited.

From the oceanfront to the botanical gardens: from art to science museum: from an island to the city center overall, Seattle is vibrant, but not sleepless, it's quiet at deep night. Seattle is a modern city with certain cultural influences. But the capacity is limited, this mid-sized city constantly reinvents itself and becomes more dynamic and diverse.

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