FreakTakes

FreakTakes explores the evolution and strategies of historical and modern research organizations, emphasizing the blend of basic and applied research, innovation, and practical technology applications. It draws lessons from past successes like Bell Labs and DARPA for today's research orgs and deep tech VC, aiming to guide new generation research orgs.

Historical Research Organizations Innovation and Technology Development Deep Tech Venture Capital Applied vs. Basic Research Research Organization Strategies Computer Technology and AI Parallel Computing Research Funding and Management Industrial R&D Lessons

The hottest Substack posts of FreakTakes

And their main takeaways

Lessons Answer.AI can learn from history’s greatest R&D labs

11 implied HN points • 26 Jan 24
🕹 Technology Research & Development Industrial Funding Innovation
  1. Learn from the past: Draw lessons from historical R&D labs like Edison's Menlo Park Lab and the Early GE Research Laboratory.
  2. Focus on application: Aim to build practical, impactful technology rather than just pursuing theoretical breakthroughs.
  3. Embrace innovation: Be bold, think long-term, and run your own race free from competition.

MOSIS

13 implied HN points • 05 Nov 23
🕹 Technology Innovation Research Infrastructure Success Lessons Learned
  1. MOSIS project facilitated cheaper and faster access to computer chips for researchers in the 1980s.
  2. The program encouraged modularity, open interfaces, and coordination to enhance research feedback cycles.
  3. MOSIS played a key role in advancing semiconductor design by enabling unbundling of design and production tasks.

An Alternative Approach to Deep Tech VC

30 implied HN points • 13 Jul 23
💼 Business Venture Capital Technology Innovation Financial Strategy
  1. It's important to consider the original goals of organizations, like Y Combinator, which focused on providing fast experimentation and proof-of-growth.
  2. Deep tech VC can benefit from a model inspired by mid-20th century research organizations that focused on profitable problem-solving and spinoff companies.
  3. Collaboration with applied research professors at universities and shaping projects for future VC investment can enhance the quality and viability of deep tech investments.

ILLIAC IV and the Connection Machine

10 implied HN points • 03 Nov 23
  1. DARPA funded ILLIAC IV and Connection Machine for parallel computing, following different strategies.
  2. ILLIAC IV faced cost overruns and delays due to innovative component development, while Connection Machine, guided by gray coding, focused on gradual progress.
  3. TMC succeeded in proving viability of parallel computing, but struggled commercially; lessons include aligning funding sources and goals, focusing on market needs.

How did places like Bell Labs know how to ask the right questions?

30 implied HN points • 20 Apr 23
🕹 Technology Engineering Research Innovation Telecommunications
  1. New science orgs should aim to combine the positive aspects of both applied and basic research.
  2. Applied and basic research distinctions are sometimes arbitrary, with some projects blurring the lines between the two.
  3. Institutions like Bell Labs successfully managed research by selecting profitable courses that satisfied both basic and applied research needs.
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The Past and Present of Computer-Augmented Hypothesis Generation

11 implied HN points • 10 Aug 23
🔬 Science Chemistry Machine Learning Neural Networks
  1. Computer-augmented hypothesis generation is a promising concept that can help uncover new and valuable ideas from existing data.
  2. Looking at old research in a new light can lead to significant breakthroughs, as seen with Don Swanson's and Sharpless' work in different fields.
  3. Tools like LLMs can assist researchers in finding connections between disparate data points, potentially unlocking new avenues for scientific discovery.

Coming Soon...Diving into ARPA History

6 implied HN points • 11 Oct 23
💾 History
  1. The author plans to write administrative histories focusing on specific DARPA/ARPA projects throughout history.
  2. The project aims to cover both successful projects and those known more for lessons learned than scientific outputs.
  3. The goal is to produce materials that help those managing research funding portfolios and labs do their jobs better.

Strategic Computing's Machine Acquisition Program

1 implied HN point • 05 Nov 23
🕹 Technology Computing AI Research Hardware
  1. The Machine Acquisition Program helped DARPA researchers acquire expensive machines for AI research at a lower cost through negotiation with manufacturers.
  2. The program had successful cost savings but faced challenges due to rapid technological changes, making some purchased machines obsolete within a few years.
  3. Lessons learned included the importance of adapting to evolving technology, weighing risks of investing in rapidly changing fields, and considering long-term impacts of equipment purchases.

Bonus: More details on how Bell Labs operated

3 implied HN points • 20 Apr 23
🕹 Technology Innovation Engineering Development Research Systems
  1. Mervin Kelly emphasized the three key groups at Bell Labs: Research and Fundamental Development, Systems Engineering, and Specific Systems and Facilities Development.
  2. Research and Fundamental Development focused on pushing research frontiers, with a balance between research and basic technology.
  3. Systems Engineers played a vital role in integrating new knowledge with existing systems, ensuring efficiency, and guiding the application of research ideas into profitable projects.