Wasswa  Meagan

Wasswa Meagan

1624465260

Algorithms Gone Wrong- A Terrifying Future

Algorithms will usher in an era that will be ever more efficient, and ever more terrifying

  • We use them all the time, but what exactly are algorithms?
  • When algorithms are used for good
  • The horrifying consequences of algorithms gone wrong
  • What can we do to make things better?
  • The ‘computer-says-no’ dilemma
  • A final observation

Algorithms are a major part of our everyday lives. Most of the time, we aren’t even aware that we use them, nor why. But the reality is that we couldn’t even function without them anymore. The internet runs on algorithms. Emails reach their destination because of algorithms. All our online searches are accomplished via algorithms and our smartphone apps wouldn’t work without them. And although algorithms have been created with good intentions – to improve our lives – they are increasingly causing major issues. They make mistakes, are biased, and can be used for criminal purposes. And to make matters worse, there are no regulatory or supervisory bodies (to speak of) to protect us from algorithms going wrong – yet. To add insult to injury, directors and decision makers don’t always have the right knowledge to base their decisions on – and civil servants and employees often leave decisions to algorithms that don’t function as they should. Proper supervision, and therefore protection, is lacking. Are we at the mercy of the ‘gods’?

We use them all the time, but what exactly are algorithms?

An algorithm is basically a set of rules or steps that can be implemented in various ways in order to achieve a certain objective. The steps in a recipe that you follow in order to create a meal, for instance, are an algorithm. And computer algorithms are the invisible mechanisms that determine, for instance, the recommendations we see on social media, Netflix, or on a fashion website. Algorithms solve problems. One example is the apps on our smartphones that help us find the most efficient route to a destination, and sometimes they also need to connect with other databases to gather real-time traffic information. Algorithms can predict the weather and help us with our next move on the stock market. They can help discover illnesses such as breast cancer, or identify fake news. They make sure our smart devices respond to our voice commands and recognise our fingerprints or our faces. They are used to track our every move and monitor which articles we prefer reading. They extract important data from these actions, so that they can offer reading suggestions. Algorithms can even be ‘instructed’ to discourage or prevent us from seeing certain types of information.

When algorithms are used for good

As mentioned before, computer algorithms have been created to assist us with a myriad of tasks and are intended to be used for good. They enable incredible levels of speed and efficiency, and lead to increased creativity and enhanced self expression. They help us crunch databases and can extract knowledge much faster than humans will ever be able to, and make decision-making, purchasing, transportation, and all kinds of other important tasks more efficient. In short, “If every algorithm suddenly stopped working, it would be the end of the world as we know it”, says trendwatcher and futurist Richard van Hooijdonk.

#ai & machine learning #algorithms #artificial intelligence #future

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Algorithms Gone Wrong- A Terrifying Future
Wasswa  Meagan

Wasswa Meagan

1624465260

Algorithms Gone Wrong- A Terrifying Future

Algorithms will usher in an era that will be ever more efficient, and ever more terrifying

  • We use them all the time, but what exactly are algorithms?
  • When algorithms are used for good
  • The horrifying consequences of algorithms gone wrong
  • What can we do to make things better?
  • The ‘computer-says-no’ dilemma
  • A final observation

Algorithms are a major part of our everyday lives. Most of the time, we aren’t even aware that we use them, nor why. But the reality is that we couldn’t even function without them anymore. The internet runs on algorithms. Emails reach their destination because of algorithms. All our online searches are accomplished via algorithms and our smartphone apps wouldn’t work without them. And although algorithms have been created with good intentions – to improve our lives – they are increasingly causing major issues. They make mistakes, are biased, and can be used for criminal purposes. And to make matters worse, there are no regulatory or supervisory bodies (to speak of) to protect us from algorithms going wrong – yet. To add insult to injury, directors and decision makers don’t always have the right knowledge to base their decisions on – and civil servants and employees often leave decisions to algorithms that don’t function as they should. Proper supervision, and therefore protection, is lacking. Are we at the mercy of the ‘gods’?

We use them all the time, but what exactly are algorithms?

An algorithm is basically a set of rules or steps that can be implemented in various ways in order to achieve a certain objective. The steps in a recipe that you follow in order to create a meal, for instance, are an algorithm. And computer algorithms are the invisible mechanisms that determine, for instance, the recommendations we see on social media, Netflix, or on a fashion website. Algorithms solve problems. One example is the apps on our smartphones that help us find the most efficient route to a destination, and sometimes they also need to connect with other databases to gather real-time traffic information. Algorithms can predict the weather and help us with our next move on the stock market. They can help discover illnesses such as breast cancer, or identify fake news. They make sure our smart devices respond to our voice commands and recognise our fingerprints or our faces. They are used to track our every move and monitor which articles we prefer reading. They extract important data from these actions, so that they can offer reading suggestions. Algorithms can even be ‘instructed’ to discourage or prevent us from seeing certain types of information.

When algorithms are used for good

As mentioned before, computer algorithms have been created to assist us with a myriad of tasks and are intended to be used for good. They enable incredible levels of speed and efficiency, and lead to increased creativity and enhanced self expression. They help us crunch databases and can extract knowledge much faster than humans will ever be able to, and make decision-making, purchasing, transportation, and all kinds of other important tasks more efficient. In short, “If every algorithm suddenly stopped working, it would be the end of the world as we know it”, says trendwatcher and futurist Richard van Hooijdonk.

#ai & machine learning #algorithms #artificial intelligence #future

A greedy algorithm is a simple

The Greedy Method is an approach for solving certain types of optimization problems. The greedy algorithm chooses the optimum result at each stage. While this works the majority of the times, there are numerous examples where the greedy approach is not the correct approach. For example, let’s say that you’re taking the greedy algorithm approach to earning money at a certain point in your life. You graduate high school and have two options:

#computer-science #algorithms #developer #programming #greedy-algorithms #algorithms

Tia  Gottlieb

Tia Gottlieb

1596427800

KMP — Pattern Matching Algorithm

Finding a certain piece of text inside a document represents an important feature nowadays. This is widely used in many practical things that we regularly do in our everyday lives, such as searching for something on Google or even plagiarism. In small texts, the algorithm used for pattern matching doesn’t require a certain complexity to behave well. However, big processes like searching the word ‘cake’ in a 300 pages book can take a lot of time if a naive algorithm is used.

The naive algorithm

Before, talking about KMP, we should analyze the inefficient approach for finding a sequence of characters into a text. This algorithm slides over the text one by one to check for a match. The complexity provided by this solution is O (m * (n — m + 1)), where m is the length of the pattern and n the length of the text.

Find all the occurrences of string pat in string txt (naive algorithm).

#include <iostream>
	#include <string>
	#include <algorithm>
	using namespace std;

	string pat = "ABA"; // the pattern
	string txt = "CABBCABABAB"; // the text in which we are searching

	bool checkForPattern(int index, int patLength) {
	    int i;
	    // checks if characters from pat are different from those in txt
	    for(i = 0; i < patLength; i++) {
	        if(txt[index + i] != pat[i]) {
	            return false;
	        }
	    }
	    return true;
	}

	void findPattern() {
	    int patternLength = pat.size();
	    int textLength = txt.size();

	    for(int i = 0; i <= textLength - patternLength; i++) {
	        // check for every index if there is a match
	        if(checkForPattern(i,patternLength)) {
	            cout << "Pattern at index " << i << "\n";
	        }
	    }

	}

	int main() 
	{
	    findPattern();
	    return 0;
	}
view raw
main6.cpp hosted with ❤ by GitHub

KMP approach

This algorithm is based on a degenerating property that uses the fact that our pattern has some sub-patterns appearing more than once. This approach is significantly improving our complexity to linear time. The idea is when we find a mismatch, we already know some of the characters in the next searching window. This way we save time by skip matching the characters that we already know will surely match. To know when to skip, we need to pre-process an auxiliary array prePos in our pattern. prePos will hold integer values that will tell us the count of characters to be jumped. This supporting array can be described as the longest proper prefix that is also a suffix.

#programming #data-science #coding #kmp-algorithm #algorithms #algorithms

Beth  Nabimanya

Beth Nabimanya

1624867080

Algorithm trading backtest and optimization examples

Algorithm trading backtest and optimization examples

Algorithmic trading backtests

Algorithm trading backtest and optimization examples.

xbtusd-vanila-market-making-backtest-hedge

xbtusd-vanila-market-making-backtest-hedge

#algorithms #optimization examples #algorithm trading backtest #algorithm #trading backtest

Genetic Algorithm (GA): A Simple and Intuitive Guide

Learn what are metaheuristics and why we use them sometimes instead of traditional optimization algorithms. Learn the metaheuristic Genetic Algorithm (GA) and how it works through a simple step by step guide.

#genetic-algorithm #algorithms #optimization #metaheuristics #data-science #algorithms