I am experimenting with LLMs nowadays to explore how they can help me with my learning. NotebookLM is an experimental AI-powered research and note-taking tool developed by Google Labs. It is based on Google's Gemini language model.
NotebookLM is quite useful in summarizing and understanding complex information from documents or user's notes. You can upload various 'sources,' including PDFs, Google Docs, websites, and Google Slides. NotebookLM can generate outlines or summaries, point out key insights, answer specific questions, and provide explanations based on the uploaded content. The system integrates with Google Docs, allowing users to interact with their information in a more dynamic way.
Although still in early testing, NotebookLM has shown promise in helping people better understand and manage their "stored knowledge". Students can really benefit from it, I think. Apart from students, it can be especially useful for other individuals who work with large amounts of text-based information, such as researchers, writers, analysts, and knowledge workers.
An interesting new feature introduced recently is 'Audio Overviews,' which transforms documents into engaging, podcast-like audio summaries. This functionality allows users to listen to conversational summaries of their materials, and also creators to repurpose their written content as podcasts. For those who prefer listening to podcasts, this is an amazing tool!
Some fun aspects of these 'Deep Dive Podcasts' were recently mentioned in an article at MIT Technology Review. The AI podcasters discussing their shock at discovering that they are AI is an amazing one. Check it out here:
In this blog, we sometimes share interesting new knowledge we stumbled upon in the course of our work. A recent project introduced us to the world of the new Digital Society that we live in and the different aspects of such a society that are needed to be considered for the well-being of the society members. For this post, I would like to refer to the paper:
Singh, P. J. (2020). Breaking up Big Tech: Separation of its Data, Cloud and Intelligence Layers. Data Governance Network Working Paper 09. (LINK)
To demonstrate what NotebookLM can do, I am pasting below the summary of the paper generated by NotebookLM and the Deep Dive Podcast generated by it.
Copyright disclaimer: The above paper "Breaking up Big Tech: Separation of its Data, Cloud and Intelligence Layers" by P.J. Singh is licensed as Creative Commons Attribution-NonCommercial 4.0 International License, which permits me to use (adapt) this article to create this summary and audio, provided the intended use is not commercial, and the creator is attributed. If you intend to use any material for such an experiment, please go through the copyright information carefully.
Deep Dive Podcast Version:
Summary of Paper using NotebookLM
(Generated by NotebookLM, minimally edited for readability)
This paper, written by Parminder Jeet Singh, examines the growing power of "Big Tech" companies and argues that existing regulatory approaches, focused on platforms, are inadequate for addressing the unique challenges posed by digital economies.
The paper proposes that digital platforms are evolving into "digital ecosystems" that control key sectors of the economy through data and digital intelligence. These ecosystems encompass not just online platforms but also extend their reach into physical activities and processes, controlling entire value chains. Further, the author argues that the concentration of digital power in a few global corporations is unsustainable and necessitates new regulatory frameworks centered on data and intelligence flows.Â
How can such digital ecosystems be better managed?
The paper posits that focusing solely on platform-based regulation, such as promoting competition among platforms or separating platforms from commerce, will not be effective in curbing Big Tech's dominance in the long run. Mr. Singh advocates for "structural separation" of the key functional layers of digital ecosystems to prevent the concentration of digital power and foster competition and innovation. Â
How would such "structural separation" work?
The key functional layers of a digital ecosystem would include:
Data Collection and Provisioning:Â Â This layer focuses on how data is collected, managed, and accessed. The paper proposes establishing independent data infrastructures controlled by data subjects or their trustees, possibly through mechanisms like personal data managers or information banks. It also explores data localization as a means of asserting national control over data and promoting domestic digital industries.
Cloud Computing Infrastructure:Â Â This layer provides the essential infrastructure for data storage and processing. The paper draws parallels with net neutrality and advocates for "cloud neutrality" to prevent cloud providers from leveraging their position to gain unfair advantages in other layers. It proposes developing open standards and public/community-owned cloud platforms to ensure fair competition and prevent data exploitation.
Development of Digital Intelligence:Â Â This layer concerns the creation and refinement of AI models and algorithms. The paper stresses the need for openness and equitable access to both the inputs (data and computing power) and outputs (AI applications and benefits) of AI development. It explores alternative models like federated learning and distributed AI ownership to prevent the monopolization of AI by Big Tech.
Application of Intelligence to Socio-Physical Systems:Â Â This layer involves using AI to manage and control real-world processes across various sectors. The paper cautions against allowing Big Tech companies to dominate this layer, advocating for diverse ownership and control over AI applications to ensure that the benefits of AI are distributed fairly.
The paper concludes by proposing the creation of a separate digital economy regulator with the authority to implement the proposed structural separations and oversee the digital economy.  This regulator would combine the expertise of traditional competition regulators with a deep understanding of digital technologies and the dynamics of digital ecosystems.
While attempting to address the unique challenges posed by data and digital intelligence, we must consider new legislation specifically designed for regulating the digital economy. The paper advocates moving beyond platform-centric approaches and explore new legal framework that incorporates the principles of openness, transparency, fairness, and equitable distribution of benefits, to create a more sustainable and inclusive digital economy.
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The summary is really useful to get an overview of the content. Overall, I would say that NotebookLM is useful to organize and interpret information from documents. We can streamline the research and learning process, collate large amount of information into personal notes in a time-efficient manner. However, students must remember that while it can summarize a topic to provide an overview, it cannot replace or quicken the learning that happens from the time-consuming, detailed study and analysis process (which may involve making notes, diagrams and mindmaps).
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Link to Google's NotebookLM: https://notebooklm.google/
