How to Speak Machine – Book Highlights

Below are a selection of my top Readwise highlights from John Maeda’s book How to Speak Machine. Also see my takeaways from reading the book on the blog.

Key highlights

  • Machine learning expert Andrew Ng describes the problem not as one of questioning whether a computer will eventually become awakened as a superior life form, but instead of: “If you’re trying to understand AI’s near-term impact, don’t think ‘sentience.’ Instead think ‘automation on steroids.”” Automation on steroids means we’re living in an era where looping, infinitely large computing machines have provoked a kind of living material into action that excels at behaving like a zombie that never tires, AI is a mindless robot that does our bidding based upon patterns that its brain has been exposed to. (Page 78)
  • Described more cynically by the late David Foster Wallace, as being left alone to “the freedom to be lords of our own tiny skullsized kingdoms, alone at the centre of all creation.” (Page 88)
  • The high expectations set for achieving intelligence with the primitive computers of that era eventually resulted in major funding cuts to research in the 1970s, known as the “AI winter.” (Page 94)
  • Data scientists’ most basic, universal skill is the ability to write code. This may be less true in five years’ time, when many more people will have the title “data scientist” on their business cards. More enduring will be the need for data scientists to communicate in language that all their stakeholders understand-and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or-ideally-both. (Page 148)
  • Data science often doesn’t mean just writing computer programs. It means throwing out bad data that’s collected, bringing stakeholders along so they can make sense of the data, and ultimately deciding on what data to collect in the first place. (Page 149)
  • As user research expert and design legend Erika Hall puts it:
    The best way to assess a functional design is through a combination of quantitative and qualitative methods. The numbers will tell you what’s going on, and the individual people will help you understand why it’s happening. (Page 151)
  • The science attempts to answer the question, and the humanism makes you ask why it’s relevant to people. (Page 151)
  • Technologist = I do, because I can.
    Humanist = I do, because I care. (Page 165)
  • If and when computers fully outpace the intelligence of the entire human race, there will always be certain things that machines will not be able to beat us at doing, and it’s our job as humans to figure them out. (Page 165)
  • This is the lesson of good ethnography: to understand a cultural phenomenon, you need to get as close as possible to “first-source” information, instead of relying on second- or thirdhand information. Furthermore, to truly understand first-source information, you need to invest time in knowing and understanding the cultural context that surrounds it. (Page 177)
  • Cultural anthropologist Clifford Geertz defined the ultimate goal of ethnography as “thick description,” as opposed to “thin description.” Thin description focuses merely on the superficial details, whereas thick description goes much deeper than immediate observations and attempts to capture the many layers beneath just the surface. (Page 177)
  • So when presented with quantitative data, it’s important to de mand what tech ethnographer Tricia Wang calls “thick data” in contrast with “big data.” Gathering thick data takes time, and interpreting it well can take even longer. You need to marinate in the thick data that you gather to fully capture the many contexts d your fellow human beings, or else there will be little benefit from your added investment. The allure and ease of quantitatively processing big data will constantly pull you away from the time conmitments required to comprehend thick data. (Page 178)
  • Keep in mind the three traps that have always gotten me in trouble:
    1. Thinking like a classical engineer, and believing there’s got to be only one way to build it right.
    2. Thinking like a classical designer, and believing your solution is the one that all will bow down to and adapt.
    3. Thinking like a senior leader, and believing that what worked well in the past is obviously applicable yet again. (Page 181)
  • Holmes’s three design principles for addressing imbalance are simple enough to put into practice, and yet deep enough to spend a lifetime trying to master. They are:
    1. “Recognize exclusion.” Make a conscious effort to notice when someone or a group of people is being excluded.
    2. “Learn from human diversity.” Go thick, and go into neighborhoods and cultures that are unlike your own.
    3. “Solve for one, extend to many.” Construct solutions that break your biases and help you find new markets. (Page 187)