Longest Common Substring distance: Minimum number of symbols that have to be removed in both strings until resulting substrings are identical.
Source: https://stackoverflow.com/questions/1916218/find-the-longest-common-starting-substring-in-a-set-of-strings
<span class="kwd">function</span><span class="pln"> sharedStart</span><span class="pun">(</span><span class="pln">array</span><span class="pun">){</span> <span class="kwd">var</span><span class="pln"> A</span><span class="pun">=</span><span class="pln"> array</span><span class="pun">.</span><span class="pln">concat</span><span class="pun">().</span><span class="pln">sort</span><span class="pun">(),</span><span class="pln"> a1</span><span class="pun">=</span><span class="pln"> A</span><span class="pun">[</span><span class="lit">0</span><span class="pun">],</span><span class="pln"> a2</span><span class="pun">=</span><span class="pln"> A</span><span class="pun">[</span><span class="pln">A</span><span class="pun">.</span><span class="pln">length</span><span class="pun">-</span><span class="lit">1</span><span class="pun">],</span><span class="pln"> L</span><span class="pun">=</span><span class="pln"> a1</span><span class="pun">.</span><span class="pln">length</span><span class="pun">,</span><span class="pln"> i</span><span class="pun">=</span> <span class="lit">0</span><span class="pun">;</span> <span class="kwd">while</span><span class="pun">(</span><span class="pln">i</span><span class="pun"><</span><span class="pln">L </span><span class="pun">&&</span><span class="pln"> a1</span><span class="pun">.</span><span class="pln">charAt</span><span class="pun">(</span><span class="pln">i</span><span class="pun">)===</span><span class="pln"> a2</span><span class="pun">.</span><span class="pln">charAt</span><span class="pun">(</span><span class="pln">i</span><span class="pun">))</span><span class="pln"> i</span><span class="pun">++;</span> <span class="kwd">return</span><span class="pln"> a1</span><span class="pun">.</span><span class="pln">substring</span><span class="pun">(</span><span class="lit">0</span><span class="pun">,</span><span class="pln"> i</span><span class="pun">);</span> <span class="pun">}</span>
Engagement Algorithms Work off of Emotions like Fear, Anger, Jealousy
violence is a part of human nature, too, but the technology of the atom bomb multiplies the danger for us all. So too with these newsfeed algorithms, which favor engagement above everything else, no matter how base and degraded the content is.
I’ve been thinking a lot about this interview with Jaron Lanier, and I’ll just share an excerpt because I think it provides some insight: “The problem, however, is that behind the scenes there are these manipulation, behavior modification, and addiction algorithms that are running. And these addiction algorithms are blind. They’re just dumb algorithms. What they want to do is take whatever input people put into the system and find a way to turn it into the most engagement possible. And the most engagement comes from the startle emotions, like fear and anger and jealousy, because they tend to rise the fastest and then subside the slowest in people, and the algorithms are measuring people very rapidly, so they tend to pick up and amplify startle emotions over slower emotions like the building of trust or affection.” https://lareviewofbooks.org/article/delete-your-account-a-co…!
What Medicare Could Learn From Netflix
The company offered a $1 million prize to improve its algorithm. Why not do that for risk adjustment?
Netflix ran a world-wide contest to improve Cinematch, its proprietary algorithm for predicting how users would rate films they’d never seen. The grand prize was $1 million. Within a year, over 2,000 separate teams from 150 countries had submitted more than 13,000 algorithms. Eventually, the winning team, which included researchers from AT&T Labs, Yahoo and an Austrian consulting firm, improved the algorithm by more than 10%. For a tiny cost, Netflix got a huge amount of computer-science research that even its highly skilled employees could not perform.
.. Medicare should do the same: create a contest open to anyone in the world who can beat its current risk-adjustment model. To ensure fairness and encourage competition, administering it should be outsourced to the X Prize Foundation or a similar group. The winner should be able to use objective patient data to account for at least 45% of the spending variation caused by disease. Medicare could award a $10 million grand prize and several million for second and third place. It also should be required to adopt one of the top methods. (There may be practical reasons not to pick the winner.)
How YouTube Drives People to the Internet’s Darkest Corners
Google’s video site often recommends divisive or misleading material, despite recent changes designed to fix the problem
YouTube engineered its algorithm several years ago to make the site “sticky”—to recommend videos that keep users staying to watch still more, said current and former YouTube engineers who helped build it. The site earns money selling ads that run before and during videos.
The algorithm doesn’t seek out extreme videos, they said, but looks for clips that data show are already drawing high traffic and keeping people on the site. Those videos often tend to be sensationalist and on the extreme fringe, the engineers said.
.. The same search in YouTube and Google can produce strikingly divergent results.
.. Google spokeswoman Crystal Dahlen said that Google improved its algorithm last year “to surface more authoritative content, to help prevent the spread of blatantly misleading, low-quality, offensive or downright false information,” adding that it is “working with the YouTube team to help share learnings.”
.. In October, YouTube tweaked its algorithm to return more mainstream sources on breaking-news queries after searches about the deadly Las Vegas shooting yielded videos claiming the government was involved.
.. Since then, the Journal’s tests show, news searches in YouTube return fewer videos from highly partisan channels.