Tag Archives: Mitchell Waldrop

The Dream Machine: JCR Licklider and the Revolution that Made Computing Personal by Mitchell Waldrop

Summary

  1. Licklider was far ahead of his generation in seeing the potential for computers – for making them humane and individual, in democratizing access to information, creating a symbiosis between man and machine. It was his work in the Pentagon along with many other visionaries who made this possible – that allowed for the standalone computer with a mouse and a graphical user interface to come into existence. His desire to understand how the brain worked as a system fueled his curiosity. Lick went on to form the ARPA Information Processing Techniques Office in 1962 and started the research funding for interactive computing and pervasive worldwide networks that has resulted in most of the technology we use today and also fueled the next generations of computing researchers – many of whom became the founders and mainstays of Xerox PARC. When computers were a short step removed from mechanical data processor, Lick’s treatises on human/computer symbiosis shifted our understanding of what computers were and could be.

Key Takeaways

  1. Lick’s goal was to forge ahead with the human/computer symbiosis and create an interconnected, self-perpetuating system into a single computer network. An electronic medium to connect everyone – the ARPA net. Today it is known as the internet and everything we now associate with it
  2. JCR Licklider may be one of the most intuitive geniuses of all time. He simply saw in his head how information flowed, and how people, things, and ideas are interconnected
  3. Lick, while humble and nice, hated sloppy work, glib answers, and never took anything for granted. He was mischievous and a little anarchical. He was never satisfied with the ordinary and always pushed the limits. His grounding in psychology was essential for his later work with computers as he always tried to design the computer and how it functioned to best meet the needs of the humans operating it. Lick approached every problem as a systems problem rather than a detailed or individual problem
  4. The first high-profile project he worked on was related to acoustics for the war and his boss had a simple mantra: hire the best people, buy them the best machines money can buy, inspire them to no end, and work them 14 hours a day. With this formula they achieve nearly everything they set out to
  5. Norbert Wiener was a prodigious character at MIT. He was a genius in multiple ways, especially mathematics where he was able to use his intuition and form physical models in his head of the problem rather than merely manipulating symbols on the page. He had the hologram in the head 
  6. Alan Turing didn’t like seeing what others had accomplished before him. He preferred to reinvent the wheel and figure things out for himself. He wasted a lot of time and reinvented the wheel but he came to understand things deeply.
  7. Johnny Von Neumann’s stored program concept created software and changed computing, opening up the potential that we associate with computers today
  8. Claude Shannon thought of information through a 5 part framework: source, transmitter, communication medium, receiver, destination. This simple framework helped him think through the purpose of information and not get bogged down in details. Information ought to measure how much you learn from a given message. If you knew everything in a given message, the information content is zero. However, information and meaning is separated as it relates to computers. Shannon also proved that it is possible to get a message through with perfect fidelity no matter how much static or distortion or how faint the signal. It’ll eventually get too slow and the codes too long but it is possible to overcome noise. This is the fundamental theorem of information theory. Shannon didn’t like how information and meaning could be too easily confused so he had Von Neumann come up with a new name and he came up with one immediately: entropy. Information is entropy. It has the same formula as the physicists formula for entropy. A mathematical variable related to the flow of heat. Information is everywhere and in everything it is as old as time and ties together the mind-body problem, computation, communication, and more
  9. Lick was interested in every domain and was always pulling in new ideas from different fields. He loved novel ideas and would always push himself and others to think about things differently in order to gain new or deeper insights. While Lick has high expectations for his team, he was extremely devoted and his team knew it – he had built a tribe more than a research group. Lick optimized for creativity and productivity so cared very little for credit. He would give his ideas and insights away for others to work on and publish so that he could get more done 
  10. Understanding how our brain works brought together information theory, logic, communication, cognitive science, behavioral psychology, and much more. Two key breakthroughs were understanding chunking and that it matters tremendously how our neurons fire and are organized – not just the raw number of neurons we have
  11. When Lick was brought on to head up the new ARPA project there was no budget, no mandate, no charter. This was perfect as they could simply talk about and work on the most important questions and topics as they came up, not being pigeonholed or sucked into a specific purpose but able to adjust and adapt to everything new that was happening
  12. A key realization for Lick was that if all his visions where to come true, he had to create a self-reinforcing and self-sustaining community between all the different groups who are contributing to this project. Without this focus and insight, many of these dreams might have been lost, forgotten, or not achieved for some other reason
  13. Corvado created the first open source system which led to the software boom and the PC. Controversial at the time, he followed the dictum that if you create something useful people, will use it. This was significantly different from other utilities of the past because rather than value flowing just one way (like electricity to users), value flows two ways now: from software to user and user back to software. This had tremendous implications
  14. Lick give people plenty of space as long as they’re doing something interesting and living up to his high standards. However, if not, he can be ruthless and shut down programs that weren’t performing
  15. For all of Lick’s strengths, he was terrible administratively. Frustrating his colleagues and friends as they had to badger him for weeks or months to get anything done. And, when everything is funded by ARPA, this was a huge deal 
  16. Lick at ARPA and Bob Taylor at Xerox Parc had to learn how to find a way to get their groups all to move together, to give their groups a sense of cohesion and purpose without crushing their spontaneity and creativity. They had to set things up and create an environment where they would follow their own instincts and self-organize. This is the fundamental to dilemma of management. Bob Taylor spent years traveling and getting to know the cultures of different high performing groups and he took the time to speak to the youngest people there. Not only tp pick up their ideas but to understand what their values were and how he could cater to them.  Taylor’s style of research can be summed up as don’t just invent the future, go live in it. Don’t worry about the cost for now but whatever you invent, make sure to use it and then show others how to use it and why it’s helpful. The only mandatory program was a once weekly discussion from the program leaders about what they were doing and for an hour the other people would have at him. This created a sense of cohesion and purpose and also flushed out ideas before going too far along the wrong path. These meetings often got heated and Taylor would help turn them from “class 1” to “class 2” meetings, meaning they would go from yelling at each other to having to explain the other side‘s position to their satisfaction. This worked amazingly well to flush out ideas and improve communication.
  17. Xerox PARC’s main vision was to create the digital office, an integrated symbiosis between working man and machine. Broadly, it was split into two groups – one focused on hardware and the other on applications. Low cost, high performance and high quality graphics was a thread which ran through everything they were trying to do. Moore’s Law was just beginning to take hold and this who were still sold on time sharing began to be able to see the possibility of an individual, high powered machine for everybody
    1. There was this thread that ran through Vannevar Bush, Licklider, Doug Engelbart, Alan Kay, and others. It was the ascent of man, it was like the Holy Grail. PARC would rationalize it according to what Xerox needed but whenever they could phrase an idea to align with this path everybody’s eyes would light up, hitting a sort of resonance frequency. 
      1. Engelbart’s “Mother of All Demos” – showing off technology which set fire to the vision of the future and what could be
  18. Alan Kay was one of the key members of PARC’s team and was a prodigy from a young age. He learned to read by the age of three and read hundreds of books before going to school. By that young age he knew that a lot of what the teachers were telling him was wrong or at least that there were multiple points of view. The teachers did not like this. He never distinguished art from science and was one of the key pioneers in this field. 
  19. Good names are incredibly important for prototypes – they have to be familiar, easy to spell, easy to use, easy to understand, have a broad theme, and conjure up pleasant feelings. 
  20. Alan Kay mentions that in the history of art, it is not the adults who actually invent the new medium who do amazing things, but the first generation of kids to grow up with it who do
  21. Xerox was growing so quickly in the late 1960s and 1970s that they almost choked on their own growth. In order to survive, they had to bring in management, marketing, and finance types – mostly from IBM and Ford.  While this helped them survive their amazing growth, it also reinforced some bad lessons – that nothing exists or is useful unless it could be shown and captured on the spreadsheet and eventually this led to the demise of Xerox PARC and that era of research and innovation. Jim O’Neil became the numbers guy and shut down much of the spontaneous generation and innovation because if it didn’t meet his numbers he couldn’t “see it” and wouldn’t buy into it. When sales and finance make all the shots, the company is on a downward spiral as they are not able to innovate or think long term
  22. Xerox PARC was an Eden in many ways but what allowed them to flourish was the vision, the people, and an abundance mentality. The fact that they had money to spend and didn’t have to jump through hoops to get it. When there is scarcity you don’t have a community, you just have a bunch of people trying to survive. In 1975 Xerox’s printer and copier business was being threatened and this was their cash cow. The instinct is to keep pouring money into this in order to save it but sometimes that isn’t appropriate. You must know when to cannibalize or disrupt yourself 
  23. You always got the sense that Lick was playing. He was like a kid in a candy store. His exploratory and curious child-like mind never went away. He was not suited to be an administrator or manager but was a visionary and community builder. He encouraged people and showed them what was possible, what they were really working towards 
  24. DEC took advantage of the open architecture and was able to foster creativity and uses for their machines that they never would’ve been able to come up with. Many people loved the ability to tinker, upgrade, or personalize what they bought rather than buying a finish package from an IBM for example. Roberts and his Altera machine would follow DEC‘s lead and make it an open architecture which unleashed a wave on entrepreneurialism and garage start ups by the hundreds – filling all sorts of niches and launching some of the world’s biggest and most successful companies (such as Microsoft)

What I got out of it

  1. An incredibly fun read – detailing not only the people and the history behind the computer revolution, but the atmosphere, thinking, and optimism which fueled it

Complexity: The Emerging Science at the Edge of Order and Chaos by Mitchell Waldrop

Summary
  1. Explanations of complexity, self-organization, emergence, order and chaos and some of the pioneers behind this work. It also details the history of the Santa Fe Institute
Key Takeaways
  1. Complex systems – many individual agents interacting and outcomes difficult to predict
    1. Complexity is the science of emergence
  2. Spontaneous self-organization (organization with no central conductor) found all over nature
    1. Complex systems all over nature have somehow acquired ability to bring order and chaos into a special kind of balance – the edge of chaos. The components of the system never lock into place yet never dissolve into turbulence either. the edge of chaos is where life has enough stability to sustain itself and enough creativity to deserve the name of life. The edge of chaos is where new ideas and innovative genotypes are forever nibbling away at the edges of the status quo and where even the most entrenched old guard will eventually be overthrown; where eons of evolutionary stability suddenly give way to wholesale species transformation. the edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive and alive.
    2. Self-organization is the most powerful force in biology and living systems operate at the edge of chaos
      1. Evolution always seems to lead to the edge of chaos
  3. Them that has, gets – domino effect once tipping point hits leads to cascades and often winner-take-all systems
  4. The crucial skill is insight. The ability to see connections
  5. At some fundamental level that Brian Arthur didn’t yet understand, the phenomena of physics and biology are the same
    1. Self-organization found everywhere! – positive feedback, increasing returns, lock-in (more niches dependent on a technology, the harder it is to change that technology until something vastly better comes along), unpredictability, tiny events that have immense consequences all seem to be a re-requisite for life itself
  6. Must look at world how it is, not as some elegant theory says it ought to be
  7. Essence of science lies in explanation more than prediction
  8. Increasing returns prominent when marginal cost is minimal (software for example)
  9. Nearly everything and everybody caught up in non-linear web of incentives, constraints and connections
  10. Innovations never happen in a vacuum and often come from someone who is outside the field
  11. Catalysis everywhere and life wouldn’t be possible without it – molecules could have catalyzed the formation of other molecules so that those in the web would have taken over. The web would keep growing and would have catalyzed its own formation, it would become an autocatalytic set – order for free
    1. Autocatalytic set can bootstrap its own creation and evolution by growing more and more complex over time and will also experience booms and busts from small changes
  12. Complex adaptive systems – characterized by perpetual novelty; dispersed, hierarchical, learn / adapt / evolve, anticipate the future
    1. Can never get to equilibrium as new opportunities are always being created by the system – always unfolding, always in transition
  13. Emergence is hierarchical – building blocks at one level combining into new blocks at a higher level. Hierarchies are one of the fundamental organizing principles of the world. Found everywhere because a well-designed hierarchy is an excellent way of getting some work done without any one person being overwhelmed or having to know everything. Also, utterly transforms a system’s ability to learn, evolve and adapt – can reshuffle building blocks and take giant leaps. Can describe a great many complicated things from relatively few building blocks
  14. Adaptive agents always playing game with environment for fitness requires feedback and prediction
    1. In order to learn, must be able to take advantage of what the world is trying to tell it
  15. Implicit expertise – a huge, interlocking set of standard operating procedures that have been inscribed on the nervous system and refined by years of experience
    1. Competition much more essential than consistency
    2. Competition and cooperation may seem antithetical but at some very deep level, they are two sides of the same coin (leading to symbiosis across nature, tit for tat strategy)
  16. Self-reproduction requires medium to be both data and instructions (DNA)
    1. von Neumann and cellular automata
  17. Spectrums:
    1. Dynamical systems: Order – Complexity – Chaos
      1. Complexity is emergent, dynamical, characterized by phase transitions
      2. Interesting things always happen at the edge of chaos
    2. Matter: Solid – Phase Transition – Liquid
      1. First and second order phase transitions – sharp and precise phase transitions (molecules forced to make either or choice between order and chaos) compared to second order which is much less common in nature – much less abrupt because molecules don’t need to make an either-or choice, they combine order and chaos (fluid with pockets of solid or vice versa)
    3. Computation: Halting – “Undecidable” – Nonhalting
    4. Life: too static – “life / intelligence” – too noisy
  18. Life is based to a great degree on its ability to process and store information and then mapping it out to determine proper action
  19. Always ask, “optimal relative to what?
  20. Artificial life – effort to understand life by synthesis, putting together simple pieces to generate lifelike behavior in man-made systems. Its credo is that life is not a property of matter per se, but the organization of that matter
    1. ‘Aliveness’ lies in the organization of the molecules and not the molecules themselves
    2. Fact that simple rules leads to unpredictability is reason trial and error (Darwinian natural selection), although somewhat crude and ‘wasteful’ is the best strategy in nature and evolution
    3. If organization determines life, it shouldn’t matter what it is made of if properly organized
    4. Complex, life-like behavior is the result of simple rules unfolding from the bottom up
  21. Emergence – somehow, by groups of agents cooperating and seeking self-accommodation, they manage to transcend themselves and become something more where the whole is greater than the sum of the parts
  22. Power truly lies in connections – exploitation (improving what you already have) vs. exploration (taking big risk for big reward)
  23. Edge of chaos – found right in between order and chaos, aka complexity
    1. Stable enough to store information but evanescent enough to transmit it
    2. Observe systems in terms of how they behave instead of how they are made
    3. Systems which are too controlled, too stagnant, too locked in will perish
    4. Healthy economies and societies must balance order and chaos via feedback and regulation while leaving room for creativity, change and response to new conditions – “evolution thrives in systems with a bottom-up organization which gives rise to flexibility”
    5. Information has to flow from the bottom-up and from the top-down
    6. Learning and evolution move agents along the edge of chaos towards ever greater complexity, sophistication and functionality
      1. One of the greatest questions and mysteries is why life gains ‘quality’ and becomes more complex over time. It is also one of the most fascinating and profound clues as to what life is all about
  24. Complex phenomena of life only associated with molecular scale due to variety and reactivity
  25. Tao of complexity – there is no duality between man and nature, we are all part of this interlocking network
    1. Once this is realized, conversation changes from optimality to co-adaptation and accommodation – what would be good for the system as a whole
    2. You keep as many options open as possible and go for what’s workable, rather than what’s ‘optimal’
    3. Optimization isn’t well defined anymore. Rather, what you’re trying to do is maximize robustness, or survivability, in the face of an ill-defined future
What I got out of it
  1. Ties together a lot of fascinating concepts and drew some more light on phase transitions and complexity for me