Who’s Not Learning From Mistakes — and Why?

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One sector of the entertainment industry appears particularly poor at learning from its mistakes, according to a recently published study, and for reasons that are increasingly an element of contemporary business:

Movie production.

Circus performance.

Stand-up comedy.

Read on: Movie production crews scatter to different projects the moment filming wraps, long before box-office results are in, and with no collective learning from mistakes. In many companies these days, teams with disparate skills are rapidly assembled to tackle projects, and then move on to new teams handling something else.

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In which setting are users of AI realizing greater productivity gains than those seen in the corporate world?

Municipal government.

Home owner associations.

Households.

Read on: How to fix your leaky faucet; tax-filing advice; vacation planning. Household activities turned over to AI suggest efficiency gains of 500% to 1,000%, far greater than seen in a corporate setting.

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Twin brothers sitting on sofa, facing each other, smiling

It turns out that venture capital people — they’re mostly men — prefer to fund early-stage businesses with founders who look like the VC guy. How’s that go?

Oddly, the financial returns from placing VC bets on one’s look-alike are fabulous, above industry norms.

The returns are roughly the same as on early-stage VC investments, generally.

Not good, reducing the likelihood of a successful exit — such as an IPO or being acquired.

Read on: A researcher used facial recognition technology to analyze more than 93,000 potential deals between VC funders and startup founders. In early-stage investing — where hard data on a startup’s prospects is scarce — a significant (as defined in the paper) facial similarity between investor and founder boosts the odds of a startup getting funded by about 10%.

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Blank faded social security card on white background top view

News reports warn that Social Security will soon “run out” of money, “go broke” and faces imminent “insolvency.” All untrue. A trust fund that holds excess money to balance inflow and outflow is projected to be exhausted in 2035, but money from payroll deductions will continue to pour in. Rather than going broke, barring legislation (as was done in the 1980s) to adjust Social Security’s finances, benefit checks could be reduced to:

A third of what retirees and other beneficiaries were promised.

Half.

Three-quarters or so.

Read on: To correct misimpressions, research suggests showing people how money flows through Social Security, rather than a static picture of the trust funds, which are but one element of the pay-as-you-go system.

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On any given workday, about 1 in 5 of us gets annoyed enough to become angry. Having aggrieved workers explain their feelings to an AI bot, trained to express empathy, in an experiment:

Made them twice as angry, but not as angry as having to tell a human.

Made them somewhat less angry, about the same as telling a human that doesn’t express empathy.

Read on: Humans expressing empathy are preferred. But an AI bot doing so helped reduce anger and the research suggests injecting some humanness into bots that are designed to coach humans could help build trust.