Conviction

Advice offered to Harvard Business School students (via Quiet, p. 47):

“Speak with conviction. Even if you believe something only fifty-five percent, say it as if you believe it a hundred percent.”

It drives me nuts when people do this in meetings. It’s dishonest and inauthentic. The fact that it’s being taught at the leading business schools fills me with sadness.

Speak to me with false conviction, and it will have little effect except to shatter my trust in you.

On the importance of models

“Data, no matter how big, can only tell you what happened in the past. Unless you’re a historian, you actually care about the future — what will happen, what could happen, what would happen if you did this or that. Exploring these questions will always require models. Let’s get over ‘big data’ — it’s time for ‘big modeling’.”

-Bret Victor (link)

This reminds me of a similar point I made in 2008, about Graph Sketcher.

Making up stories

“In the absence of data, we will always make up stories. … Meaning making is in our biology, and our default is often to come up with a story that makes sense, feels familiar, and offers us insight into how best to self-protect.”

“Unfortunately, we don’t need to be accurate, just certain.”

-Brené Brown (Rising Strong, p. 79)

Apple Pencil

“There are clearly things you can do sketching and writing on the iPad which you could never dream of doing in the analogue world.” -Jony Ive (“The story of the Apple Pencil” via MacRumors)

Drawing apps that replicate the effects of physical tools such as pencils and paintbrushes are a good start, but what I’m most interested to see in the years ahead are software tools that use the Apple Pencil input mechanism to produce output that is altogether new. Living paint. Fractal brushes. Music and sound creation. Maybe even math and data analysis. Tools which simply could not exist in the physical world.

Spreadsheet Errors

“All in all, the research done to date in spreadsheet development presents a very disturbing picture. Every study that has attempted to measure errors, without exception, has found them at rates that would be unacceptable in any organization. These error rates, furthermore, are completely consistent with error rates found in other human activities. With such high cell error rates, most large spreadsheets will have multiple errors, and even relatively small “scratch pad” spreadsheets will have a significant probability of error.

“Despite the evidence, individual developers and organizations appear to be in a state of denial. They do not regularly implement even fairly simple controls to reduce errors, much less such bitter pills as comprehensive code inspection. One corporate officer probably summarized the situation by saying that he agreed with the error rate numbers but felt that comprehensive code inspection is simply impractical. In other words, he was saying that the company should continue to base critical decisions on bad numbers.”

-Raymond Panko,
“What We Know About Spreadsheet Errors” (via Eve)

Work-life balance II

“Much has been written about finding work life balance. My 2 cents are simple. You do not reach balance by reducing work. You reach balance by finding a passion that draws you out of work. Of course, family comes first on this ladder, but we often need some other passion.”

-Alon Halevy, “A Decade At Google

32-hour Workweek

I’m so excited that someone’s tried this! A video from The Atlantic describes the case for the 32-hour workweek:

Since 2006, Ryan Carson, the CEO of Treehouse, has maintained a four-day workweek for his employees. “There’s no rule that you have to work 40 hours, you have to work more to be successful,” says Carson. “We’ve proven that you can take it from an experiment into something that’s doable for real companies and real people in highly competitive markets.”

Learning about learning

“People need more structured ways to talk and think about the learning of skills. Contemporary language is not sufficiently rich in this domain, [and] the field of education research has not worked in the direction of developing such formalisms. But another research community, that of computer scientists, has had (for its own reasons) to work on the problem of descriptive languages and has thereby become an unexpected resource for educational innovation.

“Getting a computer to do something requires that the underlying processes be described with enough precision to be carried out by the machine. Thus computer scientists have devoted much of their talent and energy to developing powerful descriptive formalisms. [Some of these] are exactly what are needed to get a handle on the process of learning.”

-Seymour Papert, Mindstorms (p. 98-100)