Capturing Knowledge: From Cave Art to Artificial Intelligence Models
Reading time: 12 minutes
Key takeaways: Traditional note-taking methods can be time-consuming and inefficient, particularly when transcribing from physical books to digital formats. Raven Notes, an AI-powered system, addresses this problem by automating the note-taking process, extracting key points, and organizing notes for easier future reference.
Retaining knowledge, particularly complex or vast amounts of it, is a hurdle humanity has been trying to overcome for centuries. The human brain, as remarkable as it is, has limitations when it comes to remembering and retrieving information. As a result, we've always been on the hunt for methods and tools to aid us in capturing, recalling, and making sense of knowledge.
Capturing Knowledge
The saga of our struggle and innovation in capturing knowledge traces back to the earliest days of human civilization. From the primal etchings on cave walls where our ancestors chronicled their lives and the world around them, to the scribbled marginalia by monks and scholars in the pages of ancient texts, we've always sought to not just consume, but also record and remember.
The advent of the Renaissance witnessed scholars turning their books into interactive knowledge repositories, complete with their personal insights, thoughts, and observations. As the Enlightenment period rolled around, scholars had adopted the practice of diligently copying informative passages into notebooks for future reference, arguably one of the earliest instances of formal note-taking.
Contemporary Note-Taking Systems
Fast forward to modern times, and our methods have evolved and become more systematic. We've seen the birth of the Cornell Method, which encourages active engagement with learning material through distinct sections for cues, notes, and summaries. The visually appealing mind maps, which graphically represent interrelated concepts to aid comprehension and recall, have also become a mainstay. There's also outlining, a more structured approach that fosters logical organization of notes to improve understanding.
Yet, as effective as these methods may be, they all share one common drawback – they are incredibly time-consuming. While these detailed processes can enhance learning and recall, they demand a significant investment of time and patience, something not everyone can afford. And even for those who can make the trade-off, they often find themselves torn between spending more time diving deeper into new information or revisiting and better organizing their existing notes.
Technological Solutions
With the advent of the digital age, digital note-taking tools have become increasingly popular, aiming to harness technology to make the process more efficient and organized. From basic tools like Microsoft OneNote or Google Keep to more specialized apps like Evernote or Notion, digital note-taking methods offer exciting advantages, including easier editing, faster searching, and the ability to store and organize vast amounts of information.
However, while these digital solutions have made note-taking more convenient in many respects, they still pose challenges. Most notably, these digital solutions can still be quite time-consuming. In order to make the most out of these systems, users often need to invest considerable time in learning how to use them effectively and subsequently organizing their notes in a way that best suits their needs.
Moreover, a significant issue arises when reading physical books. If you're not reading digitally, then using these digital note-taking systems can be cumbersome. Readers often resort to scribbling notes in the book margins or on separate pieces of paper, which they later have to manually transcribe into their digital note-taking system. This process is not only tedious but also disrupts the flow of reading and can significantly extend the time it takes to finish a book.
Digital Search
Furthermore, even once all the information is digitized, searching remains a formidable task unless a complex tagging system is in place. The current digital search functionalities require you to pinpoint the exact term used in the text. So, if an alternate word was used, your search may miss the intended note entirely.
Adding insult to injury, even when you manage to search with the right term, navigating through the multitude of notes can be overwhelming. When you're dealing with a large volume of notes, each search can return an extensive list of results. The only viable option then becomes to painstakingly read through each note that came up in the search, to find the specific information you're seeking. This once again leads to a significant drain on your time.
The Ideal System
While digital note-taking tools have undoubtedly improved the organization and searchability of our notes, they're not without their limitations, and they haven't fully addressed the challenges of time and effort commitment, especially when dealing with physical books. There remains a significant need for a solution that can bridge the gap between the physical and digital worlds, making note-taking efficient, effective, and seamlessly integrated into our reading process, regardless of the format of our reading material. The perfect solution would address all these challenges.
This system would cater not only to the digital readers but also to those who prefer physical books, eliminating the taxing task of digitization. Current technologies like Optical Character Recognition (OCR) systems, while impressive, are not without flaws. They do help in digitizing the text, but this process can be time-consuming. OCRs are prone to errors and often output poorly formatted text, causing yet another layer of work for the user - proofreading and reformatting.
The dream of such an intuitive, seamless, and accurate system has seemed like a fantasy. As a devoted reader myself, I have grappled with these limitations and frustrations for years. I have tried every method and system available, from traditional note-taking to the latest digital tools. Each time, however, I've run into the same stumbling block: the time and effort required to maintain these systems is substantial, detracting from the very process of learning and absorbing information from the books I love to read.
But if I didn’t make those systems, then it was almost impossible to effectively retain the knowledge from the books I was reading. Even if I highlighted sections or made notes, the lack of categorization made searching through them an incredibly tedious process. A very frustrating dilemma!
Machine Learning
Only in the last couple of years has technology advanced enough to bring us closer to this elusive ideal. The dawn of machine learning has opened up new possibilities, presenting us with solutions that were unthinkable just a few years ago. But even so, creating a system that harmoniously combines all these elements remains daunting.
Even though I'm now immersed in the world of AI, I must admit that I've always approached the field with a degree of skepticism. With a background in psychology, cognitive science and philosophy, I'm all too aware of the profound complexity of the human mind and the vast gulf that separates even the most advanced AI from human intelligence.
The history of AI is littered with overblown predictions and missed deadlines. A famous example comes from Marvin Minsky, a pioneer of AI research, who in 1966 assigned an undergraduate student the "summer project" of solving computer vision. Minsky assumed that the problem was simple enough to be solved within a couple of months. Today, over half a century later, despite major advancements, computer vision still isn't fully solved.
Perhaps no one encapsulated this skepticism of AI more than philosopher and cognitive scientist Hubert L. Dreyfus. In his book "What Computers Can't Do: A Critique of Artificial Reason", he argued that AI's reliance on formal symbolic manipulation, removed from the full context of the real world, was a dead end. He suggested that human intelligence couldn't be reduced to formal rules and procedures.
Dreyfus's critique was heavily influenced by the philosophical tradition of phenomenology, which emphasizes the importance of embodied experience and context in understanding human cognition. In a nutshell, Dreyfus argued that computers lack the necessary 'worldliness' and embodied existence to truly understand or replicate human thought.
Recently, Erik J. Larson echoed and expanded upon Dreyfus's arguments in "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do". Larson argues that the current approach to AI is fundamentally flawed because it underestimates the complexity of human cognition and overstates the capabilities of machines. He contends that computers, as they currently exist, lack the ability to genuinely understand, make judgments, or engage in creative thought.
I share these concerns, which I find very valid and with few good counter-arguments. However, my skepticism was soon to be changed. Not necessarily by new philosophical foundations, but by reframing the topic and focusing on the pragmatic utility of machine learning.
Large-Language-Models
Despite my skepticism towards AI, the advent of Large Language Models (LLMs) from Open AI has drastically altered my perspective. To be clear, I still hold many of the critiques discussed earlier. LLMs don't exhibit 'intelligence' in the same sense humans do. They don't 'understand' text or engage with the world in a genuinely conscious way. However, they are excellent at one thing: processing and generating human-like text based on vast amounts of data. And they do it so convincingly that, for many practical purposes, it almost stops mattering whether it's 'real' intelligence or not.
Each new version of GPT represented a quantum leap in the model's capacity to generate human-like text. These models were trained on vast amounts of text data, learning to predict what word is likely to come next given a particular context. As a result, they became remarkably good at mimicking human writing styles, understanding context, and providing coherent and relevant responses to a wide array of prompts.
The power of these models is nothing short of astonishing. We're witnessing an exponential growth in AI's capabilities. I genuinely feel like we're seeing decades of progress compressed into a few months. The potential of LLMs is vast, and I believe we're just scratching the surface of what they can achieve.
Ever since I discovered Chat-GPT, I couldn’t stop using it. It was just too good. Not perfect, and sometimes horrible. But most of the time, it was an incredible tool. It is used more and more, and every single day. Whenever GPT was down, I felt as if electricity went down. I either couldn’t do what I needed to do, or now I had to resort to what it felt like an “stone-age” method that took me 5 times as long.
Overtime, I learned what it was good for and wasn’t, how to optimize my prompts, and what to expect with a given content. I was completely hooked, and I currently have hundreds of hours of using GPT, along with other LLMs I found along the way, each with their strengths and limitations.
The Magic Solution Was Found
With this newfound potential, I began to realize that LLMs could offer the solution to my long-standing frustration with traditional note-taking methods. By harnessing the power of AI, we could create a system that simplifies note-taking, making it easy to capture, categorize, and index notes in a digital environment. This was the birth of Raven Notes.
I recognized that LLMs held the key to resolving the age-old problem of note-taking. By integrating this technology into a user-friendly platform, we could automate the most time-consuming aspects of note-taking, making the process more efficient and less labor-intensive.
A Revolution in Note-Taking
Raven is an innovative AI-powered note-taking system that revolutionizes the way we interact with knowledge from our books. It leverages the power of LLMs to provide an intuitive, efficient, and most importantly, effortless note-taking experience, bridging the gap between physical and digital worlds.
Working with Raven is simple. Whether you're reading a physical book or a digital one, you just need to highlight the passages you're interested in and send them to Raven. If you're dealing with a physical book, you can snap a photo of the highlighted section and send it to the platform. The inbuilt OCR system in Raven is highly effective in converting these images into digital text, and use AI to clean up any mistakes.
This is where the magic begins. Raven, powered by LLM, processes the highlighted sections, extracts the key points, and structures the notes in a coherent and concise format. It doesn't just dumbly transcribe your highlights; it intelligently digests the information and summarizes it in a way that is easy to understand and remember.
Raven goes beyond mere summarization. It uses its advanced AI to categorize the notes, making them easy to search and cross-reference later. By identifying the core concepts and ideas in your notes, it organizes your notes to find exactly what you're looking for with just a few clicks. This is something no other existing note-taking system can do, and it drastically cuts down the time you spend organizing and searching through your notes.
Raven is also capable of cross-referencing your notes across multiple books. This is especially useful when you're studying a particular topic from multiple sources. Raven's AI understands the relationships between ideas and concepts and can connect the dots between different notes, giving you a more holistic understanding of the subject matter.
The Holy Grail
In essence, Raven is an innovative tool that revolutionizes the note-taking process. It solves the problem I just discussed by transforming an otherwise labor-intensive task into an effortless, seamless process. By bridging the divide between the physical and digital worlds, Raven allows for efficient note-taking, irrespective of whether your source material is a physical book or a digital one.
This remarkable advancement enables us to concentrate on what truly matters: delving into and learning from the rich knowledge presented in our chosen books.
An Evolutionary Leap in Learning
With Raven, we usher in a new era in note-taking, finally offering a tangible solution to the longstanding challenge of wanting to make comprehensive notes without the associated time drain.
Raven is far more than just a mere upgrade to current methods. It symbolizes an entirely new approach to capturing and interacting with our notes. This shift has been made possible due to the tremendous strides we have taken in AI and LLMs. Although the quest for the perfect note-taking system continues, I firmly believe that with Raven, we've taken a significant step towards that coveted goal.
Getting Started with Raven
As of now, Raven is in its development phase and is not available for public use. However, you can sign up for early access, and we'll notify you when the beta version rolls out.
If you're interested in revolutionizing your note-taking experience, visit www.ravenotes.com and join our early access list. Together, let's make the process of learning more efficient, effective, and enjoyable!
