Getting Smarter Faster with AI
Getting Smarter Faster with AI
I enjoyed Limitless (2011) starring Bradley Cooper for many reasons.
His grasp of information and ability to create opportunities with it (while fending off risks) captivated me - and whenever I rewatch it reminds me of the importance of keeping valuable information in my head.
Unfortunately for me, the fields I have chosen to love move very quickly.
Hundreds of pages of AI papers are published to arXiv.org each day — 154 unique papers were published on May 31, 2024 alone.
Reading all of these isn’t possible, and even when you follow X accounts who summarize the top papers or you use aggregator websites, you’re lucky to see 1% of what’s being developed and explored.
This is where AI comes to the rescue.
NotebookLM
NotebookLM gives you a personalized AI collaborator that helps you do your best thinking.
After uploading your documents, NotebookLM becomes an instant expert in those sources so you can read, take notes, and collaborate with it to refine and organize your ideas.
How Does It Work?
You link your data into notebooks based on a topic — something you are learning, a problem you are engineering your way through, a book you are writing… anything.
Data is fed to NotebookLM as a Source.
A source is a static copy of a Google doc, a PDF file, or text that is copied and pasted directly into the app. When using NotebookLM, the model will use the sources you upload to answer your questions or complete your requests.
When you upload a new source the system creates a source guide that summarizes the document and offers key topics and questions to ask. Think of it as a research assistant that helps you better understand the source material.
This is way beyond the millions of “Ask The PDF” applications that we saw built atop GPT-X immediately upon launch.
Let’s explore how this helps us learn more rapidly and build more competently.
Notebook
NotebookLM organizes projects into Notebooks.
Notebooks are similar to how folders organize the files on your computer. Each notebook is totally separate, so NotebookLM can’t access information from multiple notebooks at the same time.
This is good for controlling context when engaging with the AI.
Citation
A specific block of text and/or image quoted from your source document that is considered relevant to your question and was used by NotebookLM to build the response for you.
This is incredibly useful because it gives the breadcrumb trail required to deep dive into the right section of the paper(s).
Noteboard
A space within a notebook to copy and paste AI responses and key passages from your sources or to write down thoughts related to your notebook. Each notebook may contain multiple notes.
Source Overview
A summary of a document that you upload or paste into NotebookLM. Source overview are automatically generated and also offer key topics or questions to ask that will help you better understand the source material.
Suggested action
Dynamic suggestions that NotebookLM makes based on the content you’ve selected. This is a powerful feature for knowledge discovery and information integration.
Different suggested actions are displayed based on what you have selected. For example, the Combined notes action is only applicable when you’ve selected multiple notes, and the Summarize to note action is only relevant when you highlight a block of text in a source document — these are the actions I have encountered so far in my testing:
Combine to a single note
Gather all your notes into a single unified note with one click.
Critique
Ask for constructive feedback on your argument
Summarize
Create a concise, easy-to-read overview of the content of multiple notes.
Create outline
Convert your selected notes into an outline, organized around topics.
Create study guide
Create an on-the-fly study guide based on your notes, including key questions and a glossary.
Suggest related ideas
NotebookLM can prompt you with related ideas from your sources based on the content of the selected notes.
You can see how this accelerates learning and building.
Limitless Learning
The best way to learn is by doing.
Pull down your favorite PDF, or a brand new research paper, and load it into NotebookLM here. This is an Experimental-stage product from our friends at Google.
Get started by opening a New Notebook
And then upload your paper to begin the accelerated learning process.
I’m going to share some of the different families of questions including specific queries I use:
Summarization Questions:
"What are the key findings of this paper?"
"Can you summarize the main contributions of this research?"
"What are the limitations or potential weaknesses identified in this study?"
Conceptual Clarification Questions:
"What is the core problem this paper aims to address?"
"Can you explain the methodology used in this research in simpler terms?"
"How does this work relate to previous research in the field?"
Comparative Analysis Questions:
"How does this paper differ from [another paper on a similar topic — make sure its included in the Source]?"
"What are the advantages and disadvantages of the approach proposed in this paper compared to existing methods?"
"Which of these two papers presents a more compelling argument/solution?"
Critical Evaluation Questions:
"Do you identify any potential biases or limitations in the methodology or data used in this study?"
"Are there any ethical implications of the research presented in this paper?"
"What future research directions does this paper suggest?"
Application-Oriented Questions:
"How could the findings of this paper be applied in a real-world scenario?"
"What are the potential practical implications of this research?"
"Can you identify any industries or sectors that might be most impacted by this work?"
Connection Questions:
"Are there any other papers that cite this research, and what are their findings?"
"Can you find research that contradicts or challenges the conclusions of this paper?"
"Are there any relevant datasets or code repositories associated with this research?"
Specific Topic Questions:
"What are the latest advancements in [specific AI subfield] discussed in this paper?"
"How does this paper contribute to our understanding of [specific AI concept]?"
"What are the current challenges and open questions in [specific AI research area] mentioned in this paper?"
Glossary Questions:
"Can you define the term [technical term from the paper]?"
"What is the difference between [term 1] and [term 2] as used in this paper?"
Creative Questions:
"Can you generate a list of potential research questions inspired by this paper?"
"If you were to design a follow-up study to this research, what would you investigate?"
Presentation Questions:
"Can you create an outline or summary of this paper for a presentation or lecture?"
"What would be the most impactful visuals to use when presenting the findings of this paper?"
The more specific and focused your questions are, the more insightful and relevant the responses from NotebookLM will be.
Start by experimenting with different types of questions to maximize your learning and understanding of AI research papers.
I take 3 to 5 of the most talked about papers from each week, create a Notebook for that week and start asking questions.
Creating a Python Jedi Academy
This system has been a great place for me to focus on specialities of Python and fine-tune my skillset.
As I build out the academy, I store Notes with implementation concepts, code and other ideas.
In a world overflowing with information, the ability to learn effectively is the key that unlocks a brighter future tools like harness the power of AI to cut through the noise, extract knowledge, and accelerate our learning journey.
Consider the possibilities: imagine being able to effortlessly stay abreast of the latest AI research, extracting key insights and connecting ideas across a vast landscape of information. Confidently discussing complex AND cutting-edge concepts, armed with a deep understanding gleaned from your personalized AI research assistant. Envision collaborating seamlessly with colleagues, sharing knowledge, and pushing the boundaries of innovation together.
Accelerating learning isn't just about staying ahead in our chosen fields; it's about personal growth, expanding horizons, and unlocking our full potential. By mastering new skills and knowledge, you open doors to new opportunities, both professionally and personally. You become a more valuable asset to your team, a more engaging conversationalist, and a more informed citizen of the world.
A better builder of a brighter tomorrow — with a deeper understanding of the world as well.
In the age of AI, the tools for accelerated learning are at your fingertips. NotebookLM is just one example of how AI is transforming the way we learn, making knowledge more accessible and empowering individuals to take control of their intellectual growth.
So, why settle for "Limitless" as a mere cinematic fantasy?
With NotebookLM and the power of AI, you can unlock your own limitless potential, accelerating your learning and creating a future filled with endless possibilities. The choice is yours – embrace the tools at your disposal and embark on a journey of lifelong learning, growth, and fulfillment.
A life without limits.
Life in the Singularity.
👋 Thank you for reading Life in the Singularity. I started this in May 2023 and technology keeps accelerating faster ever since. Our audience includes Wall St Analysts, VCs, Big Tech Data Engineers and Fortune 500 Executives.
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Thank you again!!!