Link: Research: For Better Brainstorming, Tell an Embarrassing Story

we found that the “embarrassment” teams generated 26% more ideas spanning 15% more use categories than their counterparts.

Candor led to greater creativity. Thus, we propose a new rule for brainstorming sessions: Tell a self-deprecating story before you start. As uncomfortable as this may seem, especially among colleagues you would typically want to impress, the result will be a broader range of creative ideas, which will surely impress them even more.

Source: Research: For Better Brainstorming, Tell an Embarrassing Story

Programming your organization for loonshots

Here’s an an idea for a formula for figuring out how much innovation an organization will do. I never know how good math is for this kind of thing, but it adds structure to programming an organization to be innovative, rather than career advancement seeking:

In organizations, the competing forces can be described as “stake in outcome” versus “perks of rank.” When employees feel they have more to gain from the group’s collective output, that’s where they invest their energy. When they feel their greatest rewards come from moving up the corporate ladder, they stop taking chances on risky new ideas whose failure could harm their careers.

Also, getting people to do new things by manipulating their desire to try new “tricks”:

Companies usually invest in training employees with the goal of better products or higher sales. Send a device designer to a technical workshop, and you’ll probably get better pacemakers. Send a salesperson to a speaking coach, and his pitch delivery might improve. But there’s another benefit: A designer who has learned new techniques will want to practice them. Training encourages employees to spend more time on projects, which reduces time spent on lobbying and networking.

FDA wants to regular off-label use of ML-driven devices

Obtaining FDA approval can be a difficult and long process. “The traditional paradigm of medical device regulation was not designed for adaptive AI or ML technologies, which have the potential to adapt and optimize device performance in real – time to continuously improve healthcare for patients,” the report said.

“The highly iterative, autonomous, and adaptive nature of these tools requires a new, total product lifecycle (TPLC) regulatory approach that facilitates a rapid cycle of product improvement and allows these devices to continually improve while providing effective safeguards.”

For example:

If a manufacturer decides that its device that studies retinal scans for diabetic retinopathy can also measure if a patient has high blood pressure or not, it’ll have to contact FDA officials to check if the device can be used for that purpose.

Meanwhile, we can’t even figure out how to figure out the ethics of AI.

This is probably a good space for that Talebian thinking that goes “start with what’s worked for thousands of years, and probably don’t stop.”

Source: Not so fast AI Doctor, the FDA would like to check how good you really are at healthcare

Link: Exploring the map – Wardley Maps

Wardley’s take on riding the diffusion or understand curve:

The uncharted space is where no-one knows what is wanted which forces us to explore and experiment. Change is the norm here and any method that you use must enable and reduce the cost of change. In this part of the map, I tend to use an Agile approach that has been cut right back to the core principles, a very lightweight version of XP or SCRUM.

Of course, as a component evolves and we start to understand it more then our focus changes. Sometime during the stage of custom built we switch and start to think about creating a product. Whilst we may continue to use underlying techniques such as XP or SCRUM, our focus is now on reducing waste, improving measurements, learning and creating that first minimal viable product. We start to add artefacts to our methodology and the activity has more permanence about it as it undergoes this transition. We’ve stopped exploring the uncharted space and started concentrating on what we’ve found. Today, Lean tends to rule the waves here though back in 2005 we were struggling to find something appropriate. The component however will continue to evolve becoming more widespread and defined as it approaches the domain of industrialised volume operations. Our focus again switches but this time to mass production of good enough which means reducing deviation. At this point, Six Sigma along with formalised frameworks such as ITIL then start to rule the waves. Any significant system will have components at different stages of evolution. At any one moment in time, there is no single method that will fit all.
Original source: Exploring the map – Wardley Maps

Link: Media Availability with Secretary Mattis at DIUx, transcript

“And one of the ways you make certain that you don’t have bad processes eat up good peoples’ ideas is you make certain that you remove the bad processes and organize for success.”
Original source: Media Availability with Secretary Mattis at DIUx, transcript

Link: What Your Innovation Process Should Look Like

“Once a list of innovation ideas has been refined by curation, it needs to be prioritized. One of the quickest ways to sort innovation ideas is to use the McKinsey Three Horizons Model. Horizon 1 ideas provide continuous innovation to a company’s existing business model and core capabilities. Horizon 2 ideas extend a company’s existing business model and core capabilities to new customers, markets or targets. Horizon 3 is the creation of new capabilities to take advantage of or respond to disruptive opportunities or disruption. We’d add a new category, Horizon 0, which refers to graveyards ideas that are not viable or feasible.”
Original source: What Your Innovation Process Should Look Like

Link: How to make innovation programs deliver more than coffee cups

‘“a lack of connection between innovation teams and their parent organization. Teams form/and are taught outside of their parent organization because innovation is disconnected from other activities. This meant that when teams went back to their home organization, they found that execution of existing priorities took precedence. They returned speaking a foreign language (What’s a pivot? Minimum viable what?) to their colleagues and bosses who are rewarded on execution-based metrics. Further, as budgets are planned out years in advance, their organization had no slack for “good ideas.” As a result, there was no way to finish and deploy whatever innovative prototypes the innovators had developed – even ones that have been validated.”’
Original source: How to make innovation programs deliver more than coffee cups

Book Review: Technological Revolutions and Financial Capital

What you get from Technological Revolutions and Financial Capital is an explanation of how new technologies and the resulting changes in society and business (esp. finance) create cyclical 40-60 year boom, bust, and the thriving cycle.

The valuable part isn’t pointing out that there’s a cycle, but rather collecting together the lengthy cause and effect chain of various cycles of time. The account of how financing (that is, how money “seeks” to fund innovation and then “make money from money,” or “rent-seeking” as the kids like to say) is especially interesting. There’s a distinction between “production capital” (money used by companies to create profit by creating/doing some business in goods or services) and “financial capital” (“banks” and investors who seek to “make money from money,” not by producing something) that’s an especially helpful framing to pound into your head. For example, in this later passage, you get a sense of when each type of money “wants” to invest in business:

In the economy, the interrelations between financial and production capital determine the rhythm and the direction of each technological revolution. Financial capital enables the succession of surges. When production capital at the end of a surge becomes conservative, due to having so much investment and experience tired to it, financial capital will break loose and end up either helping the initial big-bang of the next revolution or following it up by backing the new entrepreneurs in spreading it. When financial capital, during the person of installation of a new paradigm, take the economy on a frenzied ride up a paper-wealth bubble, the new modernized production capital will be ready to take over and lead a more orderly growth process, in the “golden age” that sees the full deployment of that revolution.

As with any pattern/model, I have a dangerous urge to fractal down the cycles from 40-60ish year cycles to 10 years or less as a way to model point innovations. It’s probably a bad idea if precision is desired, but as far as describing what otherwise could look like chaos, it might be handy.

All that said, this book is hard as shit to read. I had to start over ¼ through because I’d let too much time elapse between readings.

The last passage is from June 2002, and I’d be eager to see the author’s current WTF analysis on the world economy.