Too many metrics and the Otis Redding problem

The "Otis Redding problem" is "holding people, groups, or businesses to too many metrics: They can’t satisfy or even think about all of them at once."

The problem is not just that people don't really know what to do anymore. It's that many people, when faced with this, start doing things that reward themselves: "They end up doing what they want or the one or two things they believe are important or that will bring them rewards (regardless of senior management’s strategic intent)."

That quote is from Stanford Professor Bob Sutton's book Good Boss, Bad Boss, which somehow I hadn't read until recently. I've read all of Bob Sutton's other books too, they're all great reads.

This is just one tidbit from that book. There's lots more in there. On the Otis Redding problem, my read is that Bob's advice is to only pick a 2-3 simple, actionable metrics, but then frequently discuss whether they are achieving what you want and change them if they aren't.

By the way, the name the "Otis Redding problem" comes from the line in his song "Sitting on the Dock of the Bay" where he says, "Can’t do what ten people tell me to do, so I guess I’ll remain the same."

Superhuman AI in the game Go

For a few years now, AI achieved superhuman game playing abilities for Go.

It was quite a milestone for AI. When I was in graduate school, people used to joke that AI for Go was where careers go to die. The game has a massive search space, so had thwarted efforts for decades.

So AlphaGo and similar efforts that beat top-ranked Go players was a very big deal indeed when it happened back in 2016. But now, a amateur-level human player just beat a top-ranked AI at playing Go. He won 14 of 15 games.

Most of the reporting on this has been that the player used an exploit, one hole in the AI strategy, that will easily be closed. But I think this will be harder to fix than most people expect.

AlphaGo and similar techniques work by using deep learning to guide the game tree search, focusing it on moves used by experts. This result says you can't do that, that you need to consider more possible moves.

The human won here by doing moves the AI didn't expect, then exploiting the result. It's not that there is just one hole. It's that doing moves outside of what the AI expects, anything outside of what it has seen in the training data, can result in a bad playing by the AI, which can then be exploited by the human.

Solving that means considering more moves by the opponent, which explodes the game tree search, making the search massively exponential again. I suspect it's going to be hard to fix.

Huge numbers of fake accounts on Twitter

It seems like this should get more attention, "hundreds of thousands of counterfeit Twitter accounts set up by Russian propaganda and disinformation" that are "still active on social media today."

There has been widespread manipulation of social media, customer reviews, and trending, search ranker, and recommender algorithms using fake crowds.

All of these depend on wisdom of the crowds. They try to use what people do and like to help other people find things. But wisdom of the crowds doesn't work when the crowd isn't real.

Caroline Orr Bueno has some more details, writing that "this is the first we've heard of an ongoing campaign involving such a large number of accounts" and that it is clear this is at "a scale with the potential to mass-manipulate."

Orr Bueno also quotes former Twitter executive Yoel Roth as saying "it's all too cheap and all too easy." This is the core problem with misinformation and disinformation in the last decade.

If it is cheap, easy, and profitable to scam and manipulate using huge crowds of fake accounts, you will get huge numbers of fake accounts. The solution will have to be to make it more expensive, difficult, and unprofitable to scam and manipulate using fake accounts.

Details on personalized learning at Duolingo

There's a new, great, long article on how Duolingo's personalized learning algorithms work, "How Duolingo's AI learns what you need to learn".

An excerpt as a teaser:

When students are given material that’s too difficult, they often get frustrated and quit ... [Too] easy ... doesn’t challenge.

Duolingo uses AI to keep its learners squarely in the zone where they remain engaged but are still learning at the edge of their abilities.

Bloom’s 2-sigma problem ... [found that] average students who were individually tutored performed two standard deviations better than they would have in a classroom. That’s enough to raise a person’s test scores from the 50th percentile to the 98th

When Duolingo was launched in 2012 ... the goal was to make an easy-to-use online language tutor that could approximate that supercharging effect.

We'd like to create adaptive systems that respond to learners based not only on what they know but also on the teaching approaches that work best for them. What types of exercises does a learner really pay attention to? What exercises seem to make concepts click for them?

Great details on how Duolingo maximizes fun and learning while minimizing frustration and abandons, even when those goals are in conflict. Lots more in there, well worth reading.

Massive fake crowds for disinformation campaigns

The Guardian has a good article, "'Aims': the software for hire that can control 30,000 fake online profiles", on fake crowds faking popularity and consensus to manipulate opinion.

Misinformation and disinformation are the biggest problems on the internet right now. And it's never been cheaper and easier to do.

Note how it works. The fake accounts coordinate together to shout down others and create the appearance of agreement. It's like giving one person a megaphone. One person now has thousands of voices shouting in unison, dominating the conversation.

Propaganda is not free speech. One person should have one voice. It shouldn't be possible to buy more voices to add to yours. And algorithms like rankers and recommenders definitely shouldn't treat these as organic popularity and amplify them further.

The article is part of a much larger investigative report combining reporters from The Guardian, Le Monde, Der Spiegel, El Pais, and others. You can read much more starting from this article, "Revealed: the hacking and disinformation team meddling in elections".