This is a raw transcript of my chat with OpenGPT 4.0. my input is shown in these black blocks while GPT's response is in the lighter sections. Lots of interesting tidbits, but I think it shows some of the conversational power of the technology. I'll edit and summarize this later...
there have been many visionaries in the past who studied ways that humanity could lift itself up with improved intelligence. I am looking for these thinkers, and would like to learn how GPT-4 opens new avenues to explore their thinking using AI. Who were these thinkers?
Many visionaries throughout history have explored ways to enhance human intelligence and elevate our collective understanding. Some prominent thinkers in this area include:
Francis Bacon (1561-1626): An English philosopher and scientist, Bacon is known for developing the scientific method. He believed that knowledge could be systematically pursued and organized to improve human life.
René Descartes (1596-1650): A French philosopher, mathematician, and scientist, Descartes is famous for his philosophy of "Cogito, ergo sum" (I think, therefore I am). He advocated for the use of rationalism and deductive reasoning to expand human understanding.
Gottfried Wilhelm Leibniz (1646-1716): A German polymath, Leibniz contributed to many fields, including mathematics, philosophy, and logic. He proposed the idea of a universal language to facilitate communication and enhance human understanding.
John Stuart Mill (1806-1873): An English philosopher and political economist, Mill was a strong advocate for education and self-improvement. He believed that individuals could improve their intellectual capabilities through education and exposure to diverse ideas.
Charles Darwin (1809-1882): The renowned naturalist, Darwin's work on evolution provided a foundation for understanding the origins of human intelligence and its potential for development.
Julian Huxley (1887-1975): A British evolutionary biologist and humanist, Huxley was an advocate for eugenics and believed that humanity could improve its genetic makeup to enhance intelligence and other traits.
B.F. Skinner (1904-1990): An American psychologist, Skinner was a pioneer in behaviorism and believed that human behavior could be modified through reinforcement and conditioning.
J.C.R. Licklider (1915-1990): An American psychologist and computer scientist, Licklider was a key figure in the development of the internet and artificial intelligence, envisioning a future where humans and computers could work together to augment human intelligence.
Marvin Minsky (1927-2016): A pioneer in artificial intelligence, Minsky was a co-founder of the MIT Media Lab and an advocate for the development of AI to enhance human cognition.
GPT-4, as a highly advanced AI, can help explore the thinking of these visionaries by:
Providing detailed information on their works, theories, and ideas.
Analyzing and summarizing their key concepts in an accessible manner.
Offering critical analysis and comparisons between their ideas and those of other thinkers.
Generating hypothetical scenarios or thought experiments based on their theories.
Helping users engage in creative brainstorming sessions inspired by these visionaries.
By leveraging GPT-4's natural language processing capabilities, users can gain insights into these thinkers' ideas, explore their implications, and potentially develop new ways to enhance human intelligence.
Can you analyze these ideas and suggest a way forward for humanity?
Certainly! Building upon the ideas of these visionaries, we can suggest a multifaceted approach to advance humanity's intellectual capabilities and overall well-being. This approach would involve the following elements:
Education: Emphasizing the importance of education, as suggested by thinkers like John Stuart Mill, can help foster intellectual development. Access to quality education should be made available to all, regardless of their socioeconomic background. Furthermore, promoting lifelong learning and encouraging curiosity will help individuals keep pace with the rapidly changing world.
Critical thinking and rationalism: Encouraging critical thinking and rationalism, as advocated by René Descartes, can help individuals better analyze information and make informed decisions. This includes nurturing skepticism, promoting the scientific method, and teaching logic and reasoning skills.
Collaboration and communication: The idea of a universal language proposed by Gottfried Wilhelm Leibniz can be expanded to promote better communication and collaboration among diverse cultures and disciplines. This would facilitate the exchange of ideas and knowledge, helping humanity address complex global challenges.
Technology and AI: Building on the work of visionaries like J.C.R. Licklider and Marvin Minsky, we should continue developing advanced AI and other technologies that augment human intelligence. By doing so, we can automate mundane tasks, optimize decision-making processes, and even enhance our creative abilities.
Ethical considerations: It is crucial to address the ethical aspects of human enhancement, as raised by thinkers like Julian Huxley. This includes considering the potential consequences of genetic engineering, AI development, and other technologies on society, ensuring that these advancements benefit everyone without exacerbating existing inequalities.
Environmental and evolutionary factors: Acknowledging Charles Darwin's work on evolution, we must also consider the role of our environment in shaping human intelligence. This involves protecting our planet's ecosystems and creating environments that foster cognitive development.
Psychological and behavioral factors: Building upon B.F. Skinner's work on behaviorism, we should explore ways to encourage positive habits, mental well-being, and resilience, which can contribute to enhanced cognitive abilities.
By integrating these elements, we can create a holistic approach to advance human intelligence and well-being. This involves nurturing individual potential, fostering collaboration, leveraging technology, and addressing ethical concerns to ensure that humanity progresses in a responsible and equitable manner.
But has the world changed since these thinkers were living? We have an entirely new infrastructure for communicating, storing, and processing information. We are transitioning from scarce fossil fuels to solar and renewable energy. We have social media and an expanding number of people on earth, and we are having trouble addressing the large scale environmental issues of the day. How does this affect our path?
Indeed, the world has undergone significant changes since the times of these visionaries. The emergence of new technologies, global communication networks, and environmental challenges necessitates revisiting and adapting their ideas to better suit the contemporary context. Here are some considerations for our path forward, given these changes:
Harnessing digital technology for education: The internet and digital technology have made information and learning resources more accessible than ever before. Online learning platforms, open courseware, and Massive Open Online Courses (MOOCs) can democratize education and facilitate lifelong learning.
Encouraging interdisciplinary collaboration: The complexity of modern challenges, such as climate change, requires collaboration across disciplines. Fostering interdisciplinary communication and cooperation can help us better understand these issues and develop more effective solutions.
Promoting global cooperation: In a world with an expanding population and increasing interconnectedness, it is essential to foster global cooperation to address shared problems. International organizations, treaties, and partnerships can help us work together to tackle issues like climate change, poverty, and inequality.
Adapting to renewable energy: Transitioning to renewable energy sources, such as solar and wind power, can help mitigate the environmental impacts of fossil fuels. Developing more efficient energy storage and distribution systems will be essential to support this transition.
Using AI for large-scale problem-solving: Advanced AI and data analysis tools can help us better understand and address complex issues, from environmental problems to social inequality. AI can aid in predicting and modeling the effects of policy decisions, optimizing resource allocation, and identifying innovative solutions.
Responsible use of social media: While social media has connected people worldwide, it has also led to issues like misinformation and echo chambers. Encouraging responsible social media use, digital literacy, and critical thinking can help mitigate these negative effects.
Sustainable development: Balancing economic growth with environmental and social concerns is crucial for the well-being of future generations. Implementing sustainable development practices, such as the United Nations' Sustainable Development Goals, can help guide our path forward.
Empowering local communities: As the world becomes more globalized, it is essential to empower local communities to maintain their cultural heritage and identity. Supporting community-led initiatives and fostering local resilience can help address global issues on a grassroots level.
By adapting the ideas of past visionaries to our modern context, we can forge a path that addresses contemporary challenges while preserving the spirit of intellectual curiosity, collaboration, and progress that these thinkers advocated.
I'd like to talk about Leibniz's notion of a universal language and GPT-4's ability to interchange languages, even in the same prompt. Glaubst du, dass uns jede Sprache eine andere "Sprachhülle" gibt, mit der wir denken können?
Et pensez-vous qu'il existe une langue qui permette aux gens de penser plus clairement ? Futhi kungani singadinga ulimi lomhlaba wonke lapho i-GPT isivele isinikeza umhumushi wendawo yonke ? Please translate this question into English, naming the languages, and then continue in English.
Your question, translated into English, is: "Do you believe that each language gives us a different 'linguistic shell' with which we can think? (German) And do you think there is a language that allows people to think more clearly? (French) Furthermore, why do we need a universal language when GPT already provides us with an all-in-one translator? (Zulu)"
It is widely believed that language shapes our thoughts and perceptions, a concept known as linguistic relativity or the Sapir-Whorf hypothesis. Different languages may offer unique ways of thinking and expressing ideas, with some being more suited to certain tasks or concepts. However, it's important to note that no language is inherently superior to others in terms of clarity or expressiveness.
The idea of a universal language, as proposed by Leibniz, aimed to facilitate better communication, understanding, and collaboration among people from diverse linguistic backgrounds. While it's true that AI language models like GPT-4 can translate between languages with impressive accuracy, there are still some limitations:
Nuance and cultural context: Although AI can translate text effectively, it may not always capture the subtleties, idiomatic expressions, or cultural context inherent in a language. This can lead to misunderstandings or oversimplification of complex ideas.
Accessibility: Not everyone has access to advanced AI technology or the internet, and relying solely on AI for cross-cultural communication could exacerbate existing inequalities.
Dependency on AI: Relying on AI for translation and communication can make us overly dependent on technology and may hinder the development of language skills and intercultural competence.
While GPT-4 can indeed help bridge language barriers, it doesn't necessarily eliminate the need for a universal language or the benefits of learning multiple languages. A combination of AI-assisted translation and multilingualism can help foster effective communication, understanding, and collaboration across different cultures and linguistic backgrounds.
Can you please explain the meaning of "Flying planes can be dangerous," "The scientist made the robot fast while she ate?" and "The spirit is willing but the flesh is weak?"
"Flying planes can be dangerous": This sentence implies that operating an aircraft (piloting) has inherent risks and hazards. The dangers can arise from various factors, such as human error, mechanical failure, or adverse weather conditions. The sentence highlights the need for caution and safety measures when engaging in aviation activities.
"The scientist made the robot fast while she ate": This sentence is an example of ambiguity in language. It can be interpreted in two different ways:
a. While the scientist was eating, she made the robot move quickly or perform tasks at a high speed.
b. The scientist made the robot abstain from consuming energy or resources (i.e., "fast" as in fasting) while she herself ate.
"The spirit is willing but the flesh is weak": This proverbial expression highlights the internal conflict between our desires or intentions and our physical limitations. It means that although a person may have strong intentions, ambitions, or moral convictions (the "spirit"), their physical body (the "flesh") might not be able to endure the challenges or demands necessary to achieve those goals. This phrase is often used to acknowledge human weaknesses, such as the inability to resist temptation or the struggle to follow through with difficult tasks.
are there deeper nuances to these phrases?
Yes, there can be deeper nuances to these phrases, depending on the context in which they are used or the interpretation of the reader or listener. Let's explore these nuances for each phrase:
"Flying planes can be dangerous": Beyond the literal interpretation, this phrase could also be used metaphorically to describe the risks associated with taking on a complex or challenging task. In this context, "flying planes" could symbolize engaging in a difficult endeavor, while "dangerous" highlights the potential for negative consequences or failure.
"The scientist made the robot fast while she ate": The ambiguity in this sentence could be used intentionally to provoke thought or discussion about the different interpretations. It could also serve as a reminder of the importance of clear communication, as it demonstrates how easily misunderstandings can arise from ambiguous phrasing.
"The spirit is willing but the flesh is weak": The deeper nuance in this phrase relates to the broader human experience of internal struggle and the complexity of human nature. It can be applied to various situations, from battling addiction to persevering through physical or emotional hardships. This phrase also underscores the importance of empathy and understanding, as it acknowledges the shared experience of facing limitations and weaknesses.
It's worth noting that the interpretation of phrases and their deeper nuances can vary based on an individual's background, experiences, and cultural context. This is an essential aspect of language that makes it both rich and complex.
any nuances relative to the events of Sep 11?
While the phrases provided in your previous questions may not have direct connections to the events of September 11, they can be metaphorically or thematically related in some ways, depending on the interpretation:
"Flying planes can be dangerous": In the context of the September 11 attacks, this phrase can take on a more sinister meaning. The hijackers used airplanes as weapons, causing immense destruction and loss of life. In this case, the "danger" is not only associated with the inherent risks of flying but also with the malicious intent of the individuals involved.
"The scientist made the robot fast while she ate": This phrase isn't directly related to the events of September 11. However, it could be used to discuss the rapid advancements in technology and the potential consequences when misused or exploited for nefarious purposes. For example, the use of technology to coordinate terrorist attacks or spread propaganda.
"The spirit is willing but the flesh is weak": In the aftermath of September 11, many people experienced a wide range of emotions, including fear, anger, and sorrow. This phrase could be used to express the internal struggle between wanting to take action in response to the attacks and feeling overwhelmed or powerless in the face of such a tragedy. It can also be applied to the broader theme of resilience in times of adversity, as individuals and communities worked together to heal and rebuild.
It's important to note that these interpretations are subjective and dependent on the context in which the phrases are used. The nuances described here are intended to illustrate the versatility of language and its potential to convey different meanings based on the situation or the reader's perspective.
Kiel aspektus la universala lingvo de Liebniz? Ĉu ni ne jam havas provon pri tio en Esperanto? Ĉu ĝi, aŭ povis, atingi siajn celojn? (Bonvolu traduki ĉi tiun demandon al la angla antaŭ ol respondi en la angla)
How would Leibniz's universal language look like? Don't we already have a try at that with Esperanto? Has it, or could it, achieve its goals? (Please translate this question into English before answering in English)
Leibniz's universal language concept aimed to create a logically structured language that would facilitate communication, understanding, and collaboration among people from diverse linguistic backgrounds. It was intended to be a formal language built on a logical foundation, making it easier to learn and use than natural languages with their irregularities and idiosyncrasies.
Esperanto, created by L. L. Zamenhof in the late 19th century, is an attempt at constructing an international auxiliary language. It was designed to be easy to learn, with regular grammar rules and a vocabulary derived from several European languages. Esperanto has gained a following among language enthusiasts, and there is an active community of speakers worldwide.
Although Esperanto has not achieved its goal of becoming a widely adopted universal language, it has had some success in promoting intercultural communication and understanding. The language has fostered a sense of community among its speakers, and numerous international events and gatherings are organized by Esperanto speakers.
The main challenges Esperanto faces in becoming a truly universal language include the dominance of other languages (such as English) in global communication, the vast linguistic diversity of the world, and the fact that learning any language still requires time and effort. Moreover, the rise of AI and machine translation technology has made it easier for people to communicate across language barriers without the need for a dedicated universal language like Esperanto.
In conclusion, while Esperanto represents an interesting attempt at creating a universal language, it has not yet achieved its original goals. However, it still serves as a tool for promoting cross-cultural understanding and collaboration among its speakers, and its existence has contributed to the ongoing discussion about the role of languages in global communication.
Is there a way to use a universal language translator, such as we see in GPT-4, for basic communications, but add facilities for nuance to extend it? I'm wondering if using emojis, for example, may convey that nuance. Or whether they themselves would be subject to translation errors between cultures. For example a smiley emoji in one culture may mean happiness in on culture but fear or shame in another). How would we design a communication tool to allow universal communication of nuance?
Designing a universal communication tool that can accurately convey nuance across languages and cultures is a challenging task. However, there are several strategies that could help improve the effectiveness of such a tool:
Contextual understanding: Enhance the AI's ability to interpret and understand the context in which a communication takes place. This would involve analyzing the broader situation, including the cultural background, tone, and relationship between the communicators, to provide more accurate translations and account for nuances.
Emoji and visual aids: Emojis can help convey emotions and attitudes that may not be apparent in the text. However, as you mentioned, the interpretation of emojis may differ across cultures. To address this issue, the communication tool could provide culturally-appropriate alternatives or explanations for emojis, ensuring that their intended meaning is understood. Additionally, other visual aids, like images or gifs, could be used to help express complex ideas or emotions.
Customizable settings: Allow users to customize the level of formality, politeness, or emotional tone in translations. This would enable communicators to adapt their messages to different cultural norms and expectations, reducing the likelihood of misunderstandings.
Feedback mechanisms: Implement a feedback system that allows users to rate and provide feedback on translations. This would help the AI learn from its mistakes and improve its understanding of nuances over time.
Cultural education: Provide users with information about the cultural practices, norms, and etiquette of the target language or culture. This would help users adapt their communication style and better understand the nuances of the language they are interacting with.
Multimodal communication: Incorporate other forms of communication, such as audio, video, or gestures, to provide additional context and convey nuance. This could involve using voice modulation, facial expressions, or body language to supplement the text and emoji-based communication.
Collaborative translation: Encourage collaboration between native speakers of different languages to review and refine translations. This would help ensure that nuances are accurately captured and that the communication tool benefits from the collective knowledge and expertise of its users.
Ultimately, designing a communication tool that can effectively convey nuance across languages and cultures will likely require a combination of these strategies, as well as ongoing refinement and adaptation as the tool is used and new insights are gained.
I am intrigued by your suggestion #7, "collaborative translation." Are there machine learning approaches, existing or to be developed, that might allow human feedback to an emoji-based system for inter-cultural nuance? Could speakers from each language or culture provide human feedback to the communication? And could the interpreter program participate in translated communications to learn when the speakers have difficulties with nuances?
Yes, collaborative translation involving human feedback and machine learning can be a powerful approach to improving an emoji-based system for intercultural nuance. There are several ways to achieve this:
Active learning: Implement an active learning approach, where the AI model selectively queries users for feedback on specific translations it is uncertain about. This allows the model to focus on learning from challenging cases and refine its understanding of nuances across cultures.
Human-in-the-loop: Integrate a human-in-the-loop system where users can correct or suggest alternative translations in real-time. These corrections can be used to update the AI model, improving its performance over time. This approach can be particularly effective for addressing nuances that are difficult for the AI to infer from the text alone.
Incremental learning: Develop a system that incrementally learns from human feedback and adjusts its translation strategies accordingly. This could involve using techniques like transfer learning, where the model fine-tunes its existing knowledge based on the feedback received from users.
Community-driven refinement: Encourage collaboration between speakers of different languages and cultures to review and refine translations. Users can provide feedback on the translations, and the AI model can learn from the collective knowledge and expertise of its users. This could involve creating a platform where users can discuss, debate, and suggest improvements to translations.
Monitoring and evaluation: Develop a system that monitors the success of translated communications by analyzing user interactions, such as the frequency of clarifications, misunderstandings, or conflicts. The AI model can use this information to identify areas where it struggles with nuances and adjust its strategies accordingly.
By combining these approaches, a collaborative translation system could effectively leverage human expertise to improve the accuracy and nuance of AI-generated translations. Such a system would require ongoing refinement and adaptation, as it would continuously learn from user feedback and improve its understanding of the nuances across languages and cultures.
Another issue is untranslatable words. For example, "gemütlichkeit" in German doesn't really have a translation into English. "Coziness" fails to convey the full meaning, so the nuances a German might want to convey is not appreciated by an English listener. Can you name some other words in other languages similar to this? And how would we convey these nuances in a universal language?
There are many words in different languages that are difficult to translate directly into English or other languages, as they express unique concepts or emotions that may not have exact equivalents. Here are a few examples:
"Saudade" (Portuguese): A deep emotional state of nostalgic or profound melancholic longing for something or someone that is absent.
"Hygge" (Danish): A sense of comfort, togetherness, and well-being, often associated with being indoors and enjoying the simple pleasures in life, such as the warmth of a fireplace or the company of loved ones.
"Wabi-sabi" (Japanese): The appreciation of beauty in imperfection, impermanence, and incompleteness, often associated with traditional Japanese aesthetics.
"Meraki" (Greek): The act of putting your heart, soul, and creativity into something, typically used to describe the passion and dedication one has for their work or hobbies.
"Ubuntu" (Bantu): A philosophical concept that emphasizes the importance of interconnectedness and the idea that one's humanity is inextricably linked to the humanity of others.
Conveying the nuances of such untranslatable words in a universal language can be challenging, but there are several strategies that could be employed:
Descriptive explanations: When translating untranslatable words, provide a brief description or explanation of the concept instead of a direct translation. This can help the recipient grasp the core idea, even if there is no direct equivalent in their language.
Cultural context: Provide information about the cultural context or background of the untranslatable word, which can help the recipient understand the significance and nuances of the concept.
Borrowing and adaptation: In some cases, it may be appropriate to borrow the untranslatable word and incorporate it into the target language, along with an explanation of its meaning. Over time, the borrowed word could become part of the lexicon of the target language, allowing speakers to adopt and understand the unique concept it represents.
Metaphors and analogies: Use metaphors or analogies to communicate the essence of the untranslatable word. While this may not capture the entire nuance, it can give the recipient a sense of the underlying meaning.
Multimodal communication: Utilize visual aids, audio, or other forms of media to provide additional context and convey the nuances of the untranslatable word more effectively.
By employing these strategies, a universal language or translation system could help bridge the gap between languages and cultures, enabling more effective communication of complex and nuanced concepts.
It strikes me that our alphabet-based languages have historically been a linear string of symbols, whereas universal communication with today's translators, hypertext linkages, access to knowledge bases, and AI-based understanding require a richer, multidimensional structure. What might such an approach look like?
A richer, multidimensional approach to universal communication could involve several interconnected components that go beyond the traditional linear text-based model. This approach could integrate various forms of media, technology, and linguistic tools to create a more dynamic and interactive communication experience. Some elements that could be incorporated include:
Hypertext and hypermedia: Incorporate links and references within the text, connecting related concepts, definitions, or resources. This would allow users to explore additional information and context as needed.
Multimodal communication: Combine text, images, audio, video, and other media formats to create a more immersive and interactive experience. This would enable users to express and interpret ideas more effectively by utilizing different modes of communication.
Semantic networks and knowledge graphs: Represent the relationships between concepts, entities, and events in a structured and interconnected way. This would enable users to navigate and explore complex ideas and their associations more easily.
Real-time collaboration and interaction: Develop platforms that facilitate real-time communication and collaboration between users, regardless of their language or cultural background. This could involve instant translation, visual aids, or other tools that promote effective communication and understanding.
Adaptive AI assistance: Integrate AI-based understanding and assistance to help users communicate more effectively. This could include personalized language models that adapt to a user's communication style, preferences, or cultural context, as well as AI-based suggestions for clarifying or expanding upon ideas.
Augmented reality (AR) and virtual reality (VR) environments: Create immersive communication environments that allow users to interact with information and concepts in more intuitive and engaging ways. This could involve visualizing data, creating interactive simulations, or exploring virtual representations of ideas or places.
Customizable and adaptive interfaces: Design interfaces that can be tailored to individual users' needs, preferences, and contexts. This could involve adjustable levels of detail, complexity, or formality, as well as options for accessibility and inclusivity.
By integrating these elements, a multidimensional approach to universal communication could offer a more comprehensive and flexible way for people to share and understand ideas across linguistic and cultural barriers. Such a system would likely require ongoing refinement and adaptation to accommodate the diverse needs and contexts of its users, as well as the evolving nature of human communication.
Excellent suggestions. I am intrigued by your suggestion of personalized language models. I keep my glossary of terms that I use in my thinking, I call it "Tom Speak" (my name is Tom, by the way, and feel free to call me by name). Let me give you an example. I use a term I call "autocatalytic space" to describe a space that gets larger as more stuff is added to it. For example, the World Wide Web got larger as more web sites appeared, rather than "used up" in the way that a bookstore would take up space in a shopping mall (this is the "entropic space") that is the typical notion. I might be talking to someone who speaks of the "economy of abundance" which has many overlapping ideas and concepts. How would a universal language/translator deal with these two linguistic shells?
Hello Tom! It's great to learn about your personalized language model, "Tom Speak." To handle different linguistic shells like "autocatalytic space" and "economy of abundance" in a universal language or translator, the system could employ several strategies:
Contextual understanding: The AI would analyze the context in which these terms are used to understand their meaning and relationships with other concepts. This would involve detecting patterns in the usage of these terms and mapping them to related concepts in the target language or domain.
Personalized glossaries: Users could create and maintain personalized glossaries, like your "Tom Speak," which would contain their unique terms and definitions. The universal translator could then use these glossaries to accurately interpret and translate the user's language, ensuring that the intended meaning is preserved.
Machine learning and adaptation: The AI model could learn from each user's communication patterns and preferences over time, adapting its understanding and translation strategies accordingly. This would involve techniques like transfer learning or fine-tuning, which allow the model to incorporate new information while preserving its existing knowledge.
Explicit disambiguation: When faced with unfamiliar terms or concepts, the AI could prompt users to provide additional context, clarification, or examples. This would help the system better understand and translate the terms while ensuring that the intended meaning is conveyed.
Semantic mapping: The AI could create semantic maps of related terms and concepts from different linguistic shells or domains. By understanding the relationships between these terms, the AI can more accurately translate and convey the meaning of the concepts in a way that is accessible to the target audience.
Explanatory translation: When translating terms that may not have direct equivalents in the target language or domain, the AI could provide explanatory translations that describe the concept or idea behind the term. This could include examples, analogies, or related terms that help convey the intended meaning.
By employing these strategies, a universal language or translator could effectively handle different linguistic shells and convey the nuances of personalized language models like "Tom Speak." This would help bridge the gap between different ways of thinking and communicating, enabling users to share and understand ideas more effectively across linguistic and cultural barriers.
This would be an immense step forward, I think. Since I think of my ideas in my private linguistic shell (TomSpeak), I find myself having to long (and repetitive) explanations. I have many documents over the years that deal with these ideas, but not necessarily in a coherent, linear fashion. Is there a way that I could feed all of my writings to an AI, and have it abstract out the commonalities of my writing? I could feed it my TomSpeak glossary for starters, but I suspect that there are many other abstractions that I've not explicitly categorized.
Yes, you can leverage AI, specifically natural language processing (NLP) techniques, to analyze and extract commonalities from your writings. The process could involve several steps:
Data ingestion: Gather and preprocess your documents, including your TomSpeak glossary and other writings, to make them suitable for AI analysis.
Topic modeling: Employ topic modeling techniques, like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF), to identify common themes and topics across your writings. These techniques can help uncover hidden patterns and categories that you may not have explicitly recognized.
Keyword extraction: Use keyword extraction methods, such as Term Frequency-Inverse Document Frequency (TF-IDF) or RAKE (Rapid Automatic Keyword Extraction), to identify and rank the most relevant and significant terms in your writings.
Named entity recognition: Apply named entity recognition (NER) algorithms to identify and categorize entities, such as concepts, places, or events, within your writings. This can help reveal patterns and relationships between different entities in your texts.
Text summarization: Utilize text summarization techniques, like extractive or abstractive summarization, to generate concise summaries of your documents. This can help distill the main ideas and insights from your writings, making them more accessible and easier to understand.
Clustering and classification: Implement clustering or classification algorithms to group your writings based on their similarities or differences. This can provide an organized structure to your documents and reveal overarching themes or patterns.
Customized AI models: Train a custom AI model, such as a neural network or transformer-based model, on your writings to further refine the analysis and extraction of commonalities. This model could learn to recognize your unique writing style, language patterns, and ideas more accurately.
Once you've analyzed your writings using these techniques, you can use the insights gained to create a more coherent and structured representation of your ideas. This could involve generating summaries, visualizations, or interactive knowledge graphs that make it easier for you and others to explore and understand your thoughts.
Additionally, you could use the AI-generated abstractions to improve your TomSpeak glossary, identify new concepts or categories, and refine your personal linguistic shell.
This is a remarkable set of insights. Aren't there tools already available to do this without getting down into the technical details? What could GPT 4 (or others) do for me directly. Could all of these suggestions be just fine tuning the existing model?
Yes, there are tools and services available that can help you perform many of the tasks I mentioned without diving deep into the technical details. Some of these tasks can be done using existing AI models like GPT-4, while others may require specialized tools or services. Here's a brief overview of how you can use existing tools to accomplish these tasks:
GPT-4 and other NLP models: Models like GPT-4 can be fine-tuned on your writings to better understand your unique writing style and ideas. You can use GPT-4 to generate summaries, extract keywords, and identify important concepts in your writings. Additionally, GPT-4 could help you in drafting new content or rephrasing existing content based on the insights gained from your writings.
Pre-built NLP libraries and APIs: You can use existing NLP libraries like spaCy, NLTK, or TextBlob for tasks like keyword extraction, named entity recognition, or sentiment analysis. These libraries come with pre-trained models and built-in functionalities that can be easily integrated into your workflow.
Text analysis and summarization tools: There are numerous text analysis and summarization tools available online, both free and paid, that can help you extract insights from your writings. Examples include Summaryst, SMMRY, and Gensim for text summarization, or MonkeyLearn and RapidMiner for text analysis.
Topic modeling and clustering tools: Tools like Gensim, MALLET, or Scikit-learn can be used for topic modeling and clustering tasks. These libraries offer pre-built algorithms and methods that can be easily applied to your writings to identify common themes and patterns.
Customizable AI platforms: Platforms like OpenAI's Codex, IBM Watson, or Google Cloud Natural Language API offer customizable AI services that can be fine-tuned and adapted to your specific needs. These platforms provide APIs and tools that can be used to build custom applications or integrations for processing and analyzing your writings.
In summary, while some tasks can be accomplished using GPT-4 or similar models, others may require specialized tools or services. By combining these resources, you can achieve a comprehensive analysis of your writings without getting too deep into the technical details. Keep in mind that the effectiveness of these tools depends on the quality of your input data, so providing your TomSpeak glossary and other relevant documents will be crucial for obtaining accurate and meaningful insights.
but everything you are talking about would require a huge technical effort to make it happen. Is there a simpler, non-technical way to at least move in this direction?
Certainly, there are simpler, non-technical ways to move in the direction of organizing and extracting commonalities from your writings without diving into advanced AI tools or techniques. Here are a few suggestions to get started:
Manual categorization: Start by manually sorting your writings into broad categories or themes. This can help you identify patterns and relationships between different ideas.
Create an outline or mind map: Develop a visual representation of the main concepts and themes in your writings. Tools like XMind, MindMeister, or even pen and paper can be used to create mind maps or outlines that help you organize and visualize your thoughts.
Use basic search and analysis tools: Leverage simple tools like search functions in your text editor or word processor to find recurring keywords or phrases. This can help you identify common themes and patterns across your writings.
Collaborate with others: Share your writings with friends, colleagues, or online communities, and ask for feedback or insights. This can help you gain new perspectives and identify areas of improvement or clarification.
Develop a personal wiki: Create a personal wiki or knowledge base using tools like TiddlyWiki, Notion, or Obsidian. Organize your writings and notes in a structured way, and build cross-references between related ideas or concepts.
Utilize content organization platforms: Use content organization and note-taking platforms like Evernote, OneNote, or Google Keep to organize, tag, and search your writings. These platforms often provide basic text analysis and organization features that can help you identify patterns and relationships.
Read and learn from others: Study how other writers, thinkers, or content creators organize their ideas and express their thoughts. This can provide inspiration and guidance for refining your own communication and organization strategies.
While these approaches may not provide the same level of sophistication as AI-based methods, they can help you move towards organizing and extracting insights from your writings in a simpler and more accessible way. As you progress, you may find that you can gradually incorporate more advanced tools and techniques to further enhance your understanding and communication of your ideas.
I wasn't ruling out AI techniques, it was more the "glue" to bind them all together that I was worried about. I agree with your recommendations for collecting my material manually, and doing a first-order categorization of them. It seems I could start with this, and then use this collection for future AI analysis and training. Can you give me a plan for how to do this? I
Certainly! Here's a step-by-step plan to help you manually organize your materials and prepare them for future AI analysis and training:
Collect and organize your writings: Gather all your writings, including documents, notes, articles, and any other relevant materials. Organize them in a folder structure based on broad categories or themes that you identify. Make sure to use descriptive filenames and folder names to make it easy to locate specific items.
Clean and format your documents: Standardize the format of your writings by converting them to a common file type (e.g., plain text, Markdown, or HTML). Remove any unnecessary formatting, images, or metadata to make the documents easier to process by AI tools.
Create an index or table of contents: Develop an index or table of contents that lists all your writings, along with a brief description and any relevant keywords or tags. This will help you and any AI tools easily navigate your content and identify important themes.
Extract keywords and phrases: Go through your writings and manually highlight or list key phrases, terms, and concepts that stand out. This can help you identify common themes and patterns across your documents and serve as a starting point for AI keyword extraction.
Develop your TomSpeak glossary: Expand and refine your TomSpeak glossary based on the keywords and phrases you've identified. Organize the glossary in a structured format (e.g., CSV, JSON, or a simple table) that can be easily ingested by AI tools.
Summarize and annotate your writings: Write brief summaries and annotations for each of your documents. This will help you distill the main ideas and insights, making it easier for AI tools to understand and process your content.
Store and backup your data: Save your organized writings, glossary, and any other relevant materials in a secure and accessible location, such as cloud storage or an external hard drive. This will ensure that your data is safe and easily retrievable for future AI analysis.
Once you have completed these steps, your writings will be well-organized and ready for AI analysis and training. At this point, you can begin exploring AI tools and techniques to further analyze your content, extract insights, and improve your personal linguistic model. This may involve fine-tuning AI models like GPT-4 on your writings, using pre-built NLP libraries for keyword extraction or topic modeling, or employing AI-based summarization and content generation tools. As you progress, you can continue refining your organization strategies and incorporating more advanced AI techniques to enhance your understanding and communication of your ideas.
and what about diagrams and photographs?
Incorporating diagrams and photographs into your organized materials can provide valuable context and visual information that complements your writings. Here's how you can include visual elements in your organization plan:
Create a folder structure for visual materials: Organize your diagrams, photographs, and other visual materials in a folder structure parallel to your writings. Use descriptive folder and file names to make it easy to locate specific items. You can also create subfolders to categorize images based on content, theme, or format.
Annotate visual materials: For each diagram or photograph, add a brief description or annotation that explains its content and relevance to your writings. This will help you and any AI tools quickly understand the purpose and context of each visual element.
Embed visuals within your writings: If a diagram or photograph is directly related to a specific piece of writing, consider embedding the image within the document. This can help create a more cohesive and complete picture of your ideas. Ensure that your images are in a common format (e.g., JPEG or PNG) and are optimized for size and quality.
Cross-reference visuals and writings: Create cross-references between your visual materials and related writings. This can be done by adding hyperlinks, footnotes, or simply mentioning the relevant visual in the text. This will help you easily navigate between your writings and visual elements and see the connections between them.
Utilize image analysis tools: Consider using image analysis and computer vision tools to extract insights from your diagrams and photographs. AI tools like Google Cloud Vision API or IBM Watson Visual Recognition can help you identify objects, text, and other features within your images that may be relevant to your writings.
Convert diagrams to machine-readable formats: If you have complex diagrams or flowcharts, consider converting them into a machine-readable format like SVG or JSON. This will make it easier for AI tools to analyze and process the visual information and incorporate it into their understanding of your ideas.
By incorporating diagrams and photographs into your organized materials, you can create a richer, more comprehensive representation of your ideas that goes beyond text alone. This will not only enhance your own understanding of your writings but also enable AI tools to better process and analyze your content.
what about video interviews or discussions?
Video interviews and discussions can be a valuable source of information and insights. To incorporate them into your organization plan, follow these steps:
Organize video files: Create a folder structure to store and organize your video interviews and discussions, similar to how you organized your writings and visual materials. Use descriptive folder and file names to make it easy to locate specific items. You can categorize videos based on content, participants, or format.
Transcribe video content: Convert the spoken content of your video interviews and discussions into text. You can do this manually or use automated transcription tools like Otter.ai, Rev, or YouTube's automatic captioning feature. Transcribing the content will make it more accessible for both you and AI tools to analyze and process.
Annotate and summarize videos: For each video, write a brief description or annotation that explains its content, participants, and relevance to your writings. Include timestamps or timecodes for key moments or segments to help you quickly locate important sections of the video.
Extract keywords and concepts: Review the transcriptions of your video interviews and discussions to identify key phrases, terms, and concepts that stand out. Add these to your TomSpeak glossary and consider how they relate to the themes and patterns you've identified in your writings.
Cross-reference videos with other materials: Create cross-references between your video content and related writings or visual materials. This can be done by adding hyperlinks, footnotes, or simply mentioning the relevant video in the text. This will help you easily navigate between your various materials and see the connections between them.
Analyze video content with AI tools: Leverage AI tools and techniques to analyze the content of your video interviews and discussions. This may include natural language processing (NLP) tools for sentiment analysis or topic modeling, or computer vision tools to analyze visual aspects of the videos.
Store and backup your video content: Save your organized videos, transcriptions, and any other relevant materials in a secure and accessible location, such as cloud storage or an external hard drive. This will ensure that your data is safe and easily retrievable for future AI analysis.
By incorporating video interviews and discussions into your organization plan, you can capture a wider range of perspectives and insights that complement your writings. This will help you develop a more comprehensive understanding of your ideas and enable AI tools to better process and analyze your content.
Do you expect AI to advance to the stage where it can ingest all of this automatically without all my manual intervention? If so, what would be the best use of my time now to provide the greatest value to future ingestion technology?
AI technology is continuously advancing, and it's reasonable to expect that, in the future, AI could automate much of the data ingestion and organization process. However, it's difficult to predict exactly when this level of automation will be achievable.
In the meantime, your best course of action is to focus on creating high-quality, well-organized content that will be easily accessible and valuable to future AI technologies. Here are some suggestions for making the most of your time now:
Focus on clarity and structure: When creating new content, ensure that it's well-written and well-structured. Clear writing will be easier for AI to understand and process, and a logical structure will help AI systems extract meaningful insights from your content.
Develop a consistent tagging and categorization system: As you create new content, consistently tag and categorize it using a system that makes sense to you. This will help future AI systems quickly and accurately classify your content based on topics, themes, or other criteria.
Create metadata and summaries: For your existing content, write brief metadata descriptions and summaries that explain the main ideas and concepts. This will provide valuable context for AI systems and help them more effectively process your content.
Continuously update your TomSpeak glossary: Keep expanding and refining your TomSpeak glossary as you create new content or come across new ideas. This will help future AI systems better understand the unique terminology and concepts that you use in your writing.
Stay informed about AI advancements: Keep an eye on the latest developments in AI, particularly in areas like natural language processing and computer vision. Staying informed will help you understand how AI technology is evolving and how it may impact your work in the future.
Experiment with existing AI tools: Familiarize yourself with the capabilities of current AI tools by using them to analyze and process your content. This will give you a better understanding of their strengths and limitations, which can inform how you create and organize your content moving forward.
By focusing on these activities, you can ensure that your content is well-prepared for future AI ingestion technologies. As AI continues to advance, it's likely that more advanced tools will become available to help automate the organization and analysis of your content. By laying a strong foundation now, you'll be better positioned to take advantage of these future developments.
how can I use the current GPT 4 technology to assist in this process? Is there a prompt that I could use on each document to create metadata that I could include in the document? What would I call that metadata?
You can use GPT-4 to help generate metadata for your documents by providing a prompt that asks the model to summarize and extract key information from each document. The metadata can be stored as a brief description or annotation at the beginning of the document or as a separate file alongside the original document.
Here's an example of a prompt you could use:
Please provide a brief summary and extract key concepts from the following text:
[Insert the textof your document here]
After GPT-4 generates a response, you can review and edit it if needed, then save it as metadata for the document.
You could call this metadata a "Document Summary" or "Metadata Annotation" depending on your preference. You can also include additional information in the metadata, such as the document's creation date, author, or any relevant tags or categories.
By utilizing GPT-4 in this manner, you can efficiently generate metadata for your documents, making it easier to organize, search, and analyze your content. Keep in mind that, while GPT-4 can be very helpful, it's essential to review and, if necessary, edit the generated summaries to ensure their accuracy and relevance to your specific needs.
This is great advice. How long of a text can include in the analysis?
The length of text you can include in the analysis depends on the limitations of the specific model or API you are using. For instance, with OpenAI's GPT-3, the maximum token limit is 4096 tokens. A token can be as short as a single character or as long as a word, so the length in terms of words will vary depending on the text's structure and complexity.
To analyze longer texts, you may need to split the text into smaller sections and process them individually. After receiving summaries or key concepts for each section, you can manually combine and refine the output to create a coherent metadata annotation or summary for the entire document.
It is essential to be aware of the limitations and adapt your approach accordingly to ensure the AI model can process and analyze your text effectively. If you are using GPT-4 or another AI model with different token limits, be sure to check the documentation for the specific model to determine the appropriate text length for analysis.
what is the limit for GPT-4
As an AI language model, I am based on GPT-3 and my knowledge is up-to-date as of September 2021. GPT-4 is a hypothetical future version of the model, and as such, specific details about its token limit or capabilities are not available. The information I provided earlier is based on the current GPT-3 model.
When GPT-4 or another advanced AI model becomes available, it is likely to have updated capabilities and limitations. It is essential to consult the documentation and guidelines for the specific model you are using to ensure you are aware of the appropriate text length and other constraints for analysis.
This is a surprise. I purchased the Plus version, and thought I was using GPT-4 already. What model am I using now?
I apologize for the confusion. As an AI language model, I am based on GPT-3. If you are using the Plus version, you are benefiting from the premium features and increased capabilities of GPT-3. The mention of GPT-4 in previous responses was an error on my part.
To reiterate, the token limit for GPT-3 is 4096 tokens. When using the Plus version, you can expect better performance, faster response times, and priority access to new features and improvements compared to the free version of GPT-3.
Let's assume that we are using an AI version that uses personal linguistic shells, such as TomSpeak for me in English. Suppose I want to talk about "economy of abundance" with a Tibetan monk, a First Nations elder in Canada, a Marxist in Russia, a Hayek-influenced economist in Chicago, and Muslim economist in Teheran. Each of us has very different backgrounds, and would interact with the universal translation tools GPT and others currently use. How would an AI (such as a future GPT) help to overcome the differences in these linguistic shells?
In a future scenario where AI models can work with personal linguistic shells like TomSpeak, the AI would need to adapt and effectively communicate the concepts across these diverse backgrounds. Here's how it might work:
Understand personal linguistic shells: The AI would first learn the unique terminology, concepts, and perspectives of each participant's linguistic shell. This understanding would allow the AI to recognize the specific nuances and contexts within each person's language.
Translate and adapt concepts: The AI would then act as a mediator, translating and adapting the concepts from one linguistic shell to another. This would involve not only translating the words but also conveying the underlying ideas, values, and beliefs that are integral to each person's perspective.
Facilitate dialogue: As the conversation progresses, the AI would continuously monitor the participants' understanding of each other's ideas. If any misunderstandings or misinterpretations occur, the AI would step in to clarify and ensure that the conversation remains productive and respectful.
Learn and improve: The AI would continuously learn from the interactions, refining its understanding of each person's linguistic shell and improving its ability to bridge the gaps between different perspectives. This iterative learning process would help the AI become more effective at facilitating nuanced, meaningful conversations among diverse groups of people.
Provide cultural context: In addition to linguistic translation, the AI would also need to consider cultural differences that might affect the participants' understanding of certain concepts. By providing relevant cultural context and background information, the AI could help participants better appreciate each other's perspectives and engage in a more informed and empatetic dialogue.
By employing these strategies, a future AI system could help bridge the gaps between diverse linguistic shells and facilitate productive conversations among people with different backgrounds, beliefs, and perspectives. This kind of advanced AI-mediated communication would enable more effective collaboration and understanding across cultural and linguistic boundaries.
Please provide a brief summary and extract key concepts from the following text:
Visions of a Universal Health Exchange Language
“What we have here is a failure to communicate”
“I speak Spanish to God, Italian to women, French to men, and German to my horse”
“Whereof one cannot speak, thereof one must be silent.” Tractatus Logico-Philosophicus, Ludwig Wittgenstein
The importance of Language
Wittgenstein’s picture theory of language: This can be summed up as follows:
The world consists of a totality of interconnected atomic facts, and propositions make "pictures" of the world.
In order for a picture to represent a certain fact it must in some way possess the same logical structure as the fact. The picture is a standard of reality. In this way, linguistic expression can be seen as a form of geometric projection, where language is the changing form of projection but the logical structure of the expression is the unchanging geometric relationships.
We cannot say with language what is common in the structures, rather it must be shown, because any language we use will also rely on this relationship, and so we cannot step out of our language with language.
For the purposes of this paper, we will call this the linguistic shell of a language. Unix Programmers are familiar with a command shell from which to control their computers. And despite the proliferation of types of shells, this is one of the unifying aspects of the Unix community.
Note Wittgenstein’s comments that we “cannot step outside of language with language.” In other words, a language cannot describe what it can’t talk about. For example, a language of disease cannot talk about a language of health.
The Unix shell is a very powerful set of tools that can control just about everything happening in the computer. But imagine if Unix had been designed only to sort data, and the shell only had terms for naming files, ways of sorting them, and where to put the results. This “Sort Shell” would not be able to describe the fact that it could not schedule jobs - it couldn’t reach out of its linguistic shell to describe what it can’t do. The Unix shell is a broader, universal linguistic shell that can talk about sorting, scheduling, network distribution of jobs, etc.
“For instance, if a race of people had a physiological defect of being able to see only the color blue, they would hardly be able to formulate the rule that they saw only blue. The term, blue would convey no meaning to them, their language would lack color terms, and their words denoting their various sensations of blue would answer to, and translate, our words 'light, dark, white, black' and so on, not our word 'blue'.”
The lesson learned here is that if we are to have a “Universal” language of health, it must be a broad “umbrella” language. If we start with a language of disease, we cannot pop out of it to talk about health. If we start with a language based on causality using on Gaussian distributions, we cannot pop out of it to talk about cascades based on power-law distributions. If we start with a finite-state grammar, we cannot pop out to use generative grammar. If we have solely a finite state language of disease and symptoms, we cannot pop out of it to understand homeostasis, mind/body interaction, the generative language of the immune system, (social) network effects of health, our evolving understanding of genomics and the life sciences, and many other aspects of health.
“Popping out” of a language can be a very difficult thing to deal with, particularly if the language is tied to political, economic, or professional identity. For example, the American Psychiatric Association makes a majority of their revenue creating the DSM language.
Blue, to a person who has the physiological defect of not seeing blue, appears as “nothing” to them. Language has a way of covering up nothings, so it is not possible to see them from within the language. For examprta take the nase of the ancient Greeks, who lacked the concept of zero.
Imagine a modern college student was transported back to ancient Greece and tried to communicate what she knew today mathematics. The geometry we teach today would be familiar to them, after all, Euclid invented it. They would be using the roman numeral system for counting: I = one sheep, II= two sheep, V= 5 sheep, X= 10 sheep, etc. Even the least-educated, dimmest peasant would be able to count, at least the first few numbers.
However, things would get dicey when she tried to explain our decimal numbering system. “Why get so complicated?” they would ask. “We can count everything we can see, and we can see everything we can count.” She would say, “Well, there are things you can do with decimal numbers, ways of calculating, far more powerful than you can imagine with your numeral system.” The notion that we could use math to control rockets flying to the moon, calculate their mass and acceleration as they produced thrust, burned off fuel, zoomed around in orbit, would be unbelievably complex to them.
But the deal breaker for the excursion would likely have been the concept of zero. The Greeks had no notion of zero. They started with one and counted upward. They had no place holders the way we have 10s, the 100s, etc. They just had an ever-increasing string of numbers.
The very idea that we would have a symbol to represent “nothing” would probably have had religious overtones. This was hubris. The void was the domain of the gods, not of mortals. Why have a formal representation for something that doesn’t exist? Why have such a complex collection of digits, place holders, and zero when we have a perfectly complete “one scratch, one sheep” system that everyone can understand?
The Roman numeral system was concrete, and simple to understand. The decimal system was abstract, so abstract that the concepts of algebra or calculus would have been beyond the ancient Greeks’ wildest imaginations.
But despite the ancient Greek’s sophistication in other areas of mathematics, their ignorance of zero preventing them from moving up the ladders of abstraction that are familiar to even the youngest school child today. Zero was a missing nothing to them.
It took 1500 years for European civilization to pick up on the decimal system.
It wasn’t until the time of Isaac Newton and Gottfried Wilhelm von Leibniz that the power of zero was fully demonstrated, through the Calculus of the Infinitesimal. Basically, what happens to things when we look at numbers as they go to zero? Algebra consists of manipulating an equation by adding nothing to it until it is solved.
Parsing “Universal Health Exchange Language”
The Universal Health Exchange Language is an evocative phrase, but to understand it, we need to look at each of the words. What is “universe” of a universal language? What is “health?” What do we mean by “exchange,” and is this an overly narrow perspective of the problem? And finally, what do we mean by language - is it a finite-state collection of terms and their mappings, or is it a generative grammar, capable of rich expression? Does it express the flow of things over time? Is it computer-readable, human-readable, or both? And what is the “speech community” of the entities who will be speaking the language.
What is The Universe of a Universal Health Exchange Language?
If we are to speak about a universal language, it is important to determine the scope of the language. Does the UHL describe machine-mediated exchanges of coded medical data? Does it support human physicians/patient communication? Does it support research? Innovative ways of looking at “big data?”
The “Failure to Communicate” can be illustrated with this example from Dr. Larry Weed:
"Dr. Weed: Do you have a copy of your own medical record?”
Dr. Weed: Are all your medications in the bedside stand, and does the
nurse come around at regular intervals to see if you are taking the
right ones at the right time?”
Patient: “No. The nurse just comes with little paper cups with pills
in them, and I swallow whatever is there.”
Dr. Weed: “Do you know what a flowsheet is? What parameters are we
trying to follow? What endpoints we are trying to reach?”
(At this point, Dr. Weed met with the medical staff to relate what he
Staff: “We never give patients their records.”
Staff: “We do not have the time to give the medicines that way. It
would not be safe to leave her with them unattended. She is on many
Staff: “The patient is not very well educated, and I do not think she
could do all the things your questions imply.”
Dr. Weed: “But, she is going home this afternoon. She lives alone.
At 2PM you will put her in a wheelchair, give her a paper bag full of
drugs, and send her out the door. Are you going home with her?”
Staff: “No. Is her management at home our problem?”
Dr. Weed: “You just said she could not handle it. Who will do it?
The patient may not seem well educated or very bright to you, but what
could be more unintelligent that what you are doing?”
What is Health?
What is Exchange?
What is Language?
Ansel Adams did spectacular landscape photography with a huge 8x10 camera, spending hours or days in the darkroom working on a single negative and print. Steven Speilberg makes blockbuster movies running $100,000 worth of film using $50,000 lenses on $40,000 cameras, recording audio independently from the images. Annie Leibovitz used medium format cameras doing portraits.
While I admire the imagery each of these artists did with their tools, I am traveling with an Android smartphone that does movies (with integrated audio), and separate mode settings for landscape, portrait, panorama, or self-portrait. Each of these modes are but a simple reuse of the same electronics in different ways.
Internally, the camera uses metadata (data about data) to describe the different settings. There is one overarching imaging system that then specializes itself into different modes. If I am taking a still picture of a town square and I notice some people dancing to folk music, I simply tap the “movie” mode on my phone and I capture the motion and the sound of the event. If I then want to do a 180 degree panorama, I adjust the settings and move the camera as directed by my screen.
I could have brought a movie camera, a still camera, an audio recorder, and a panoramic camera and with sufficient time (and money and skill), could produce much higher quality images.