Jack Dorsey admits mistakes at Twitter, says site still has problems

Twitter CEO Jack Dorsey addresses students during a town hall at the Indian Institute of Technology (IIT) in New Delhi, India, November 12, 2018.

Anushree Fadnavis | Reuters

Twitter co-founder and CEO Jack Dorsey didn’t mention Elon Musk by name. But in a blog post on Tuesday, he made it clear that the company he once led still had significant problems then and now.

Dorsey said he was adding his voice to discussion around the “Twitter Files,” which Musk started releasing last week to support his claims that prior management was biased against conservatives in its handling of content moderation.

At the beginning of his post, Dorsey said he’s come to believe in three principles. Social media must withstand “corporate and government control,” the author is the only person who can remove content they produce, and “moderation is best implemented by algorithmic choice.”

“The Twitter when I led it and the Twitter of today do not meet any of these principles,” Dorsey wrote.

Musk, who closed his $44 billion acquisition of Twitter in October, has rolled back many of the old moderation policies. He’s also welcomed back former President Donald Trump, who was permanently kicked off the site under Dorsey’s leadership after the Jan. 6 attack on the U.S. Capitol.

Dorsey didn’t level any specific criticism at Musk. He said he personally abandoned his efforts to push the company in the right direction after activist firm Elliott Management got involved with the company over two years ago.

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“This is my fault alone,” Dorsey wrote. “I completely gave up pushing for them when an activist entered our stock in 2020.”

Regarding Twitter’s decision to suspend Trump, Dorsey said he believes “there was no ill intent or hidden agendas, and everyone acted according to the best information we had at the time.”

Still, he said that “mistakes were made” and Twitter would be in a better position today if the company “focused more on tools for the people using the service rather than tools for us.”

Dorsey said that in general social messaging

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Dynamic Programming: An Approach to Solving Computing Problems

Sometimes in computer science, you will run into problems. You can divide these into subproblems, which can, in turn, be divided into smaller subproblems. If the smaller subproblems overlap, then you can save the result in memory for future reference. This way, you don’t need to compute the same result multiple times, thus increasing the efficiency of the program substantially. This way of solving these problems is referred to as dynamic programming.

Dynamic Programming – Learn to Solve Algorithmic Problems & Coding Challenges. | Video: freeCodeCamp.org

In this article, you will learn what dynamic programming is. I will also show how to compute Fibonacci numbers, which is a simple problem that dynamic programming can solve. I will compare the dynamic programming solutions to the naive solution that uses recursion. These examples are written in Python syntax. Finally, I’ll also give some general pointers to keep in mind when attempting to solve problems using dynamic programming

Dynamic programming

Dynamic programming is an efficient method for solving computing problems by saving solutions in memory for future reference. When you have overlapping subproblems, you can apply dynamic programming to save time and increase program efficiency. 

More From Artturi Jalli: Python Cheat Sheet: A Handy Guide to Python

 

What Types of Problems Can Dynamic Programming Solve?

Dynamic programming is typically a way to optimize solutions to certain problems that use recursion. If a recursive solution to a problem has to compute solutions for subproblems with the same inputs repeatedly, then you can optimize it through dynamic programming. As mentioned earlier, in this case, you would simply save the result of the computation for use later if and when it’s needed. This optimization can reduce the time complexity of an algorithm from exponential time to polynomial time. This means that the number of computations n scales like a polynomial expression instead of scaling like an exponential expression as n increases. In general, polynomial expressions grow much slower than exponential expressions.

There are two conditions that need to be satisfied to use dynamic programming:

  1. Overlapping subproblems
  2. Optimal substructure property

 

What Are Overlapping Subproblems?

I alluded to

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Search, Play, Ads: Google’s many antitrust problems

This story is part of a Recode series about Big Tech and antitrust. Over the last several weeks, we’ve covered what’s happening with Apple, Amazon, Microsoft, Meta, and Google.

There’s a new Big Tech antitrust bill in town, and this one is especially painful for Google.

A group of lawmakers led by Sen. Mike Lee (R-UT) introduced the Competition and Transparency in Digital Advertising Act on Thursday. This bipartisan and bicameral legislation would forbid any company with more than $20 billion in digital advertising revenue — that’s Google and Meta, basically — from owning multiple parts of the digital advertising chain. Google would have to choose between being a buyer or a seller or running the ad exchange between the two. It currently owns all three parts, and has been dogged by allegations, which it denies, that it uses that power to unfairly manipulate that market to its own advantage.

“This lack of competition in digital advertising means that monopoly rents are being imposed upon every website that is ad-supported and every company — small, medium, or large — that relies on internet advertising to grow its business,” Sen. Lee said in a statement. “It is essentially a tax on thousands of American businesses, and thus a tax on millions of American consumers.”

Google said in a statement that this is “the wrong bill, at the wrong time, aimed at the wrong target,” and that its ad tools produce better quality ads and protect user privacy.

You can add the new legislation to the growing pile of Google’s antitrust woes. While the media has given more attention to the antitrust issues of rivals Apple and Meta, Google is potentially in more trouble than any other Big Tech company. State and federal governments have filed four antitrust cases, all within a year of each other. In October 2020, the Department of Justice and 14 state attorneys general sued Google over alleged anti-competitive practices to maintain its search engine and search ad monopoly. That December, 38 other state attorneys general filed a separate, similar case. If you

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