Companies in and out of EdTech give demonstrations time and again in order to provide a clear and exciting vision for investors and potential users. It is not yet an exact science and success is not guaranteed, but it is possible, and most of the time the right step can be easily distinguished from the wrong one. Hint: It’s usually the one that makes you yawn or groan. Going through the hurdles of materializing a sustainable path for that vision must become a discipline or a principle for your team. It will not ensure success, but doing the opposite is a recipe for failure.
Let’s take a look at two recent examples of great vision affected, sometimes lethally, by failure to do the daily grunt work.
The only one who can tell if you are doing things right is (most often) yourself. Excitement about your idea can bring on a healthy dose of skeptics ready to poke your work. But it can also hush them, especially if your idea is intimidatingly innovative. Consider Theranos, a promising lab testing startup whose premise was so captivating it “suspended all skepticism.” Some argue signs of its inevitable downfall were plainly visible, but perhaps the most interesting bit is how the success of the story curtailed critical thinking, eventually leading to “massive” fraud charges. While the case against the company and its founder Elizabeth Holmes proceeds, it is still difficult to think her malfeasance was part of the original plan.
Plan for a success as little “hellish” as possible. A look at the design and development process at Tesla by Bloomberg Businessweek reveals in Elon Musk a passionate leader, deserving (for better or worse) of his cult-like status. A pioneer in many fields, which unfortunately include disregard for proper risk management and possibly occupational safety. If once again he defied critics by achieving a stunning weekly output of vehicles —at last—, it seems doubtful engineers can withstand the exhausting conditions it reportedly takes, never mind all the company-procured Red Bull. Pressure deserves credit as a source of “eureka moments” in industrial history, as much as it does for its ability to destroy lives, families and long-term prospects. Chances are your world-changing idea can take one more quarter to complete.
Bonus: When is “Transfer Learning” not cheating? In the recently published “State of AI” report, Venture Capitalist Nathan Benach mentions how current artificial intelligence optimization techniques involve some circumstance of the machine watching a human perform a task as training (and eventual replacing) exercise. So far so dandy. It becomes a problem when the company claims a software does it when it is a human (patent “pseudo-AI”), or a human does it for the basis of training, when all signs point that is not the case. It worsens when the task involves putting sensitive information in the hands of casual freelancers. ■
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