A conversation with Ashish Arora about how and why the interlocking American institutions that support technological change have evolved over time, their current strengths and weaknesses, and how they might change in the future.

In this episode I talk to Venkatesh Narayanamurti about Bell Labs, running research organizations, and why the distinction between basic and applied research is totally wrong. Venkatesh has led organizations across the research landscape: he was a director at Bell Labs during its Golden Age, a VP at Sandia National Lab, the Dean of Engineering at UC Santa Barbara and started Harvard’s engineering school.

In this episode I talk to Adam Marblestone about technology roadmapping, scientific gems hidden in plain sight, and systematically exploring complex systems.

In this episode I talk to Jude Gomilla about distributed innovation systems focused especially around the bottom-up response to the coronavirus crisis. Jude is a physicist, founder and CEO of the knowledge compilation platform Golden, and a prolific angel investor. He’s also been in the thick of the distributed response to the coronavirus response from day one.

Intro In this episode I talk to Joel Chan about cross-disciplinary knowledge transfer, zettlekasten, and too many other things to enumerate. Joel is an a professor in the University of Maryland’s College of Information Studies and a member of their Human-Computer Interaction Lab. His research focuses on understanding and creating generalizable configurations of people, computing, and information that augment human intelligence and creativity. Essentially, how can we expand our knowledge frontier faster and better.  This conversation was also an experiment. Instead of a normal interview that’s mostly the host directing the conversation, Joel and I actually let the conversation be directed by his notes. We both use a note-taking system called a zettlekasten that’s based around densely linked notes and realized hat it might be interesting to record a podcast where the structure of the conversation is Joel walking through his notes around where his main lines of research originated. For those of you who just want to hear a normal podcast, don’t worry - this episode listens like any other episode of idea machines. For those of you who are interested in the experiment, I’ve put a longer-than normal post-pod at the end of the episode. Key Takeaways Context and synthesis are two critical pieces of knowledge transfer that we don’t talk or think about enough. There is so much exciting progress to be made in how we could generate and execute on new ideas. Show Notes More meta-experiments: An entry point to Joel’s Notes from our conversation - Wright brothers - Wing warping - Control is core problem - Boxes have nothing to do with flying - George Vestral - velcro - scite.ai - Canonical way you’re supposed to do scientific literature - Even good practice - find the people via the literature - Incubation Effect - Infrastructure has no way of knowing whether a paper has been contradicted - No way to know whether paper has been Refuted, Corroborated or Expanded - Incentives around references - Herb Simon, Allen Newell - problem solving as searching in space - Continuum from ill structured problem to well structured problems - Figuring out the parameters, what is the goal state, what are the available moves - Cyber security is both cryptography and social engineering - How do we know what we know? - Only infrastructure we have for sharing is via published literature - Antedisciplinary Science - Consequences of science as a career - Art in science - As there is more literature fragmentation it’s harder to synthesize and actually figure out what the problem is - Canonical unsolved problems - List of unsolved problems in physics  - Review papers are: Hard to write and Career suicide - Formulating a problem requires synthesis - Three levels of synthesis 1. Listing citations 2. Listing by idea 3. Synthesis - Bloom’s taxonomy  - Social markers - yes I’ve read X it wasn’t useful - Conceptual flag citations - there may actually be no relation between claims and claims in paper - Types of knowledge synthesis and their criteria - If you’ve synthesized the literature you’ve exposed fractures in it - To formulate problem you need to synthesize, to synthesize you need to find the right pieces, finding the right pieces is hard - Individual synthesis systems:       - Zettlekasten       - Tinderbox system       - Roam - Graveyard of systems that have tried to create centralized knowledge repository - The memex as the philosopher’s stone of computer science - Semantic web - Shibboleth words - Open problem - “What level of knowledge do you need in a discipline” - Feynman sense of knowing a word - Information work at interdisciplinary boundaries - carol palmer - Different modes of interdisciplinary research - “Surface areas of interaction”   - Causal modeling the Judea pearl sense - Sensemaking is moving from unstructured things towards more structured things and the tools matter

In this episode I talk to Anna Goldstein about how the ARPA (Advanced Research Projects Agency) model works and what makes it unique. We focus on ARPA-E: the department of Energy’s version of DARPA that funds breakthrough energy research.

In this episode I talk to Jason Crawford about his work on the history of progress, funding and incentivizing inventions, ideas behind their time, and more. Jason is the author of the Roots of Progress blog, where he focuses on telling the story of human progress in an amazingly accessible way.

In this episode I talk to Eli Velasquez about creating startup ecosystems, commercializing research, especially when it's not necessarily venture-backable, and how the US government thinks about startups.

In this episode I talk to Bill Janeway about previous eras of venture capital and startups, how bubbles drive innovation, the role of government in innovation. Bill describes himself as "theorist-practitioner": he did a PhD in Economics, was a successful venture capitalist in the 80's and 90's with the firm Warburg Pincus and is now an affiliated faculty member at Cambridge and the member of several boards.

In this episode I talk to Mark Hammond about how Deep Science Ventures works, why the linear commercialization model leaves a lot on the table, and the idea of venture-focused research. Mark is the founder of Deep Science Ventures, an organization with a fascinating model for launching science-based companies. Mark has many crisply articulated theses about holes in the current system by which research becomes useful innovations and what we might do to fill them. Key Takeaways: There are many places where innovation is slow and incremental because everybody is focused on individual pieces: batteries are a great example here. The perception that deep/frontier/hard tech companies are riskier and take longer to provide returns may in fact be more grounded in popular perception than fact The factors that make translational research so expensive may not be inherent but instead driven by administrative overhead and the fact that much of it is pointed in the wrong direction. Resources Deep Science Ventures Mark on Twitter (@iammarkhammond) Systematised ‘quant’ venture in the sciences. LifeSciVC on biotech returns