{"id":284678,"date":"2019-04-30T16:42:25","date_gmt":"2019-04-30T19:42:25","guid":{"rendered":"http:\/\/revistapesquisa.fapesp.br\/?p=284678"},"modified":"2019-04-30T18:33:44","modified_gmt":"2019-04-30T21:33:44","slug":"a-world-that-is-controlled-by-algorithms","status":"publish","type":"post","link":"https:\/\/revistapesquisa.fapesp.br\/en\/a-world-that-is-controlled-by-algorithms\/","title":{"rendered":"A world that is controlled by algorithms"},"content":{"rendered":"

Algorithms are everywhere. When share prices rise and fall, algorithms are typically involved. According to data that were released in 2016 by the Institute for Applied Economic Research (IPEA), investment robots that are programmed to instantly react to specified scenarios account for more than 40% of stock market transactions in Brazil. In the United States, this figure is 70%. The success of a simple Google search depends on these computer programming procedures, which can filter billions of web pages in mere seconds; the importance of a website, as defined by an algorithm, is based on the quantity and quality of other pages that link to it. At the frontier of automotive engineering research, sets of algorithms are used by autonomous cars to process information that has been captured by cameras and sensors to instantly make decisions at the wheel without human intervention.<\/p>\n

<\/a>Although they play a role in even the most mundane tasks, such as traffic avoidance via mobile applications, algorithms are often viewed as intangible by the general population, who feel their effects but do not know or understand what they are or how they work. An algorithm is nothing more than a sequence of steps that are used to automatically solve a problem or accomplish a task, regardless of whether a dozen or a million lines of programming code are required. \u201cIt is the nucleus of any computational process,\u201d says computer scientist Roberto Marcondes Cesar Junior, who is a researcher at the Institute of Mathematics and Statistics of the University of S\u00e3o Paulo (IME-USP).<\/p>\n\n \n \n \n <\/picture>058-065_Algoritmo_ING2019-1<\/span>\n

Consider the sequence of steps that are performed by the Facebook algorithm, for example. The choice of what to display in a user\u2019s news feed is based primarily on the set of posts that have been produced by or are circulating among the user\u2019s friends. The algorithm analyzes this information and discards posts that have been flagged as violent or inappropriate, posts that appear to be spam, and posts in which the wording is identified as \u201cclickbait\u201d\u2014a form of exaggeration that is used to encourage users to click a link. Finally, the algorithm assigns a score to each post that is based on the user\u2019s activity history and estimates how likely the user is to enjoy or share the information. The algorithm has recently been modified to reduce the reach of posts that have been made by news outlets.<\/p>\n

The development of an algorithm involves three steps (see the infographic<\/a><\/em>): The first is to accurately identify the problem and find a solution to it. In this phase, computer programmers work with professionals who understand the task that must be performed. They could be doctors, in the case of an algorithm that analyzes imaging exams; sociologists, if the objective is to identify patterns of violence in regions of a city; or psychologists and demographers in the development of a dating application. \u201cThe challenge is to show that a practical solution to the problem exists, that it is not a problem of exponential complexity, for which the time needed to produce a response can increase exponentially, making it impractical,\u201d explains computer scientist Jayme Szwarcfiter, who is a researcher at the Federal University of Rio de Janeiro (UFRJ).<\/p>\n

The second phase is also free of mathematical operations: it consists of describing the sequence of steps in normal language, for everyone to understand. Next, this description is translated into a programming language during phase three. Only then can the computer understand the commands\u2014which can be simple, mathematical operations or complex algorithms within algorithms\u2014all in a logical and precise sequence. During this stage, programmers are tasked with writing the algorithms. On complex projects, large teams of programmers work together and share tasks.<\/p>\n

Robots are responsible for 40% of the decisions that are made on the Brazilian stock market<\/p><\/blockquote>\n

At their origin, algorithms are logical systems that are as old as mathematics. \u201cThe expression comes from a Latinization of the name of Persian mathematician and astronomer Mohamed al-Khwarizmi, who produced famous works on algebra in the ninth century,\u201d explains computer scientist Cristina Gomes Fernandes, who is a professor at IME-USP. They gained new impetus in the second half of the last century alongside the development of the computer, with which it was possible to create work routines for the machines. There are two reasons why algorithms are now so widely used in the real world and why they have become the basis of most complex software development: First, the increased processing power of computers has increased the speed at which complex tasks can be executed. Second, the advent of big data has made it cheaper to collect and store huge amounts of information, thereby enabling algorithms to identify patterns that are imperceptible to the human eye in a wide range of scenarios. Advanced manufacturing, which is known as Industry 4.0, promises to increase productivity by using artificial intelligence algorithms to monitor industrial plants in real time and make decisions on stock control, logistics, and maintenance.<\/p>\n

One effect of the growing use of algorithms in computing was a boost to artificial intelligence, which is a field that was established in the 1950s and aims at developing mechanisms that are capable of simulating human reasoning. Through increasingly fast computations and the collection of data for statistical comparisons, computers can now modify their operations based on accumulated experience, thereby improving their performance in a process that mimics learning.<\/p>\n

Computers have proven capable of beating humans in many board games; this demonstrates how the field has evolved. In 1997, IBM\u2019s Deep Blue supercomputer succeeded for the first time in beating the world chess champion of the time, Gary Kasparov, who was from Russia. Capable of simulating approximately 200 million chess positions per second, the machine anticipated its opponent\u2019s decisions several moves ahead. However, this strategy was unsuccessful for Go<\/em>, which is a Chinese board game, because there are too many possible moves at any time to anticipate\u2014the number of possibilities exceeds the number of atoms in the universe. In March 2016, Go<\/em> was finally defeated: the AlphaGo program, which was created by DeepMind, which is a subsidiary of Google, beat world champion Lee Sedol, who was from South Korea.<\/p>\n\n \n \n \n <\/picture>058-065_Algoritmo_ING2019-2<\/span>\n

Instead of considering millions of possibilities, the program\u2019s algorithm used a more restricted strategy: By statistically analyzing data from previous matches between the game\u2019s best players, the program identified the most common and efficient moves, thereby resulting in a smaller set of variables, and was soon able to beat the human players. However, there was more to come. Last year, DeepMind developed a new program, namely, AlphaGo Zero, which outperformed the original AlphaGo. In the new program, the machine did not learn from humans, but from the previous versions of the program.<\/p>\n

There are a growing number of practical applications for this type of technology. Artificial intelligence algorithms that were developed by computer scientist Anderson de Rezende Rocha, who is a professor at the Institute of Computing of the University of Campinas (UNICAMP), have been used to facilitate police investigations. Rocha specializes in computer forensics and creates artificial intelligence tools for detecting subtle details in digital documents that are often imperceptible to the naked eye. \u201cThe technology can help the experts confirm that a particular photograph or video related to a crime is genuine, for example,\u201d says Rocha.<\/p>\n

One scenario in which the algorithms are being used is to automate investigations into images of child abuse. Police regularly seize large volumes of photographs and videos from the computers of suspects. If there are files that are related to child abuse, the algorithm helps find them. \u201cWe exposed the robot to hours of pornographic videos from the internet to teach it what pornography is,\u201d says Rocha. Then, to identify the presence of children, the algorithm needed to \u201cwatch\u201d the videos of child abuse that were seized by the police. \u201cThis stage was carried out by police officers. Nobody at UNICAMP had access to this material,\u201d he adds. Rocha says that these types of files were previously analyzed manually in most cases. \u201cAutomating the process makes it more efficient, giving the police more time and allowing them to examine more data.\u201d<\/p>\n

Programmers should be aware of the implications of their work, says Nick Seaver, from Tufts University<\/p><\/blockquote>\n

Many computer scientists use mathematical properties, theorems, and logic when working on algorithms, regardless of the immediate purpose of the application. In many scenarios, the only known algorithms are highly inefficient and do not perform well with large data volumes, for example, in the factorization of a number into its constituent primes (which is highly important in cryptography) or routing a welding robot through several weld points. There is little hope that efficient algorithms will be identified for these applications, which fall under unsolved problem of \u201cP versus NP,\u201d which is considered one of the greatest challenges in both computer science and mathematics.<\/p>\n

Although there is more programming involved than basic science in the development of many of the algorithms that are used in everyday life, advances in knowledge are essential if new applications are to be explored in the future. Marcondes Cesar, who is from USP, is working on computer vision, which is a type of artificial intelligence that extracts information from images to simulate human vision. The technique is being explored in various industries, particularly in medical diagnoses. \u201cComputer vision can detect anomalies more accurately and evaluate subtle details in magnetic resonance imaging, for example.\u201d<\/p>\n

The objective of the project, which is being carried out in partnership with the USP School of Medicine and the Children\u2019s Institute of the university\u2019s teaching hospital, is to develop a mathematical model that can provide a more accurate analysis of the liver and brain in newborns. The models that are used to interpret magnetic resonance images are typically based on white adult males and have been developed in other countries, which can lead to inaccurate diagnoses in newborn babies in Brazil. However, the project\u2019s success depends on several theoretical problems being solved first. \u201cWe do not yet know if we will be able to write an efficient algorithm. We are still studying properties based on graph theory,\u201d he says, referring to the branch of mathematics in which the relations between objects of a specified set are studied by associating them to one another via structures that are called graphs.<\/p>\n

\"\"

STR \/ AFP \/ Getty Images<\/span><\/a> Google\u2019s AlphaGo software beat South Korean Lee Sedol in a game of Go in 2016STR \/ AFP \/ Getty Images<\/span><\/p><\/div>\n

The impact of algorithms has also been analyzed in other fields of knowledge. \u201cAlgorithms are already playing a moderating role. Google, Facebook, and Amazon have an extraordinary amount of power over what we are exposed to in culture today,\u201d said Ted Striphas, who is a professor of the history of culture and technology at the University of Colorado, USA, and author of the book Algorithmic Culture <\/em>(2015), which examines the influence of these online giants. American anthropologist Nick Seaver, who is a researcher at Tufts University, USA, is currently conducting ethnographic research and interviews with the creators of music recommendation algorithms for streaming services. His interest is in how these systems are designed to attract users and draw their attention and he is studying the interface between areas such as machine learning and online advertising. \u201cThe mechanisms that control attention and its technical mediations have become a subject of great interest. The formation of interest and opinion bubbles, as well as fake news<\/em>, and political distractions, can be attributed to technologies designed to manipulate user attention,\u201d he explains.<\/p>\n

Recommendation systems that are based on algorithms have become key players in the online entertainment industry. In an article that was published in the journal ACM Transactions on Management Information Systems<\/em> in 2015, Mexican electronic engineer Carlos Gomez-Uribe described how the algorithms that are used by streaming service Netflix rank television series and movies according to the individual profile of each user. The objective is to encourage customers to select a TV show to watch within 90 seconds of logging on\u2014any longer than that and they tend to get frustrated and lose interest. The success of this ranking system gave Gomez-Uribe\u2019s career a boost and in 2017, he became head of algorithms and internet technology products at Facebook.<\/p>\n<\/div>

\n \n \n \n <\/picture>058-065_Algoritmo_ING2019-3<\/span><\/div>
\n

The influence and power of major internet companies do not depend solely on the creativity of their programmers. They are also linked to the huge volumes of data that have been accumulated and processed by their algorithms, which have generated highly valuable information. \u201cWhat prevents another company from developing an application like Uber? This has already been done, in fact. But the traffic and customer behavior data that Uber has accumulated over time belongs only to them, and it is valuable,\u201d says Marcondes Cesar, who is from USP.<\/p>\n

The recent Facebook user-data leak, which caused the value of the company to fall by US$49 billion last month, revealed a vulnerability that was thought to be uncommon\u2014algorithms that are used by Cambridge Analytica were able to access the behavioral data of 50 million Facebook users, which were subsequently used to influence political campaigns on social networks, including the Brexit vote and Donald Trump\u2019s ultimately successful bid to become president of the United States. The Facebook case is an example of the ethical challenges that are created by the widespread use of algorithms, although data misuse and abuse are only part of the problem. Data use has become as important for algorithms as the challenge of programming them. \u201cAnalyzing the characteristics of the data is fundamental to the construction of an algorithm; a mistake at this stage could lead to biases in the results,\u201d says Marcondes Cesar.<\/p>\n

\"\"

DLLU \/ Wikimedia Commons <\/span><\/a> Testing Uber\u2019s autonomous car prototype in San Francisco (USA)DLLU \/ Wikimedia Commons <\/span><\/p><\/div>\n

It is also common for algorithms to reproduce biases when they are based on human behavior. The Cloud Natural Language API, which is a tool that was created by Google that identifies the structure and meaning of texts through machine learning, has developed its own biases. A test by American website Motherboard demonstrated that when analyzing text to determine if it has a \u201cpositive\u201d or \u201cnegative\u201d sentiment, the algorithm classified statements such as \u201cI\u2019m a homosexual\u201d and \u201cI\u2019m a gay black woman\u201d as negative. \u201cProgrammers who create smart algorithms need to be aware that their work has social and political implications,\u201d says Nick Seaver, from Tufts University. Various undergraduate and graduate computer science programs already offer classes that address computer ethics, including USP in Brazil and Harvard University and the Massachusetts Institute of Technology (MIT) in the US.<\/p>\n

The transparency of advanced algorithms is another hot topic. The details of how these tools operate are often kept secret by developers. In some cases, the code is so complex that it is not possible to understand how the algorithm arrives at a decision and what its implications are. Systems such as these, which are opaque to external scrutiny, are known as \u201cblack box algorithms.\u201d The debate has gained momentum after research into an experimental tool, namely, Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), which is used in the US legal system to make sentencing recommendations and even to predict the risk that a defendant will reoffend. The study, which was conducted by the ProPublica organization in 2016, revealed that the COMPAS system is 77% more likely to classify black defendants as possible reoffenders than whites. Northpointe, which is the private company that created the algorithm, declined to share the code. \u201cAlgorithms used by public bodies should not be created or developed without the participation of public managers and administrators, as they are not neutral,\u201d says S\u00e9rgio Amadeu da Silveira, who is a researcher at the Center for Engineering, Modeling, and Applied Social Sciences at the Federal University of ABC (UFABC).<\/p>\n

In 2017, Kate Crawford, who is the head of research at Microsoft Research, and Meredith Whittaker, who is the leader of Google\u2019s Open Research Group, founded the AI Now Institute, which is an organization that is dedicated to understanding the social implications of artificial intelligence. Based at New York University, USA, the institute\u2019s approach involves computer scientists, lawyers, sociologists, and economists. In October, it released a report that offered guidelines on the use of artificial intelligence algorithms. One recommendation was that public agencies such as those that are responsible for criminal justice, healthcare, welfare, and education should not use systems whose algorithms are not well known. According to the document, black-box algorithms should be subject to public auditing and validation tests to implement corrective mechanisms when necessary.<\/p>\n

Another objective of artificial intelligence algorithms is to free human beings from repetitive tasks\u2014and there is frequent debate over the implications of AI software on the labor market. \u201cThe Future of Employment,\u201d which is a report that was published in 2013 by economists Carl Frey and Michael Osborne from the Oxford Martin School, UK, estimated that sophisticated algorithms could soon replace 140 million professional jobs worldwide. The paper specifies examples, such as the increasing automation of decision-making in the financial market and even the impact on the work of software engineers\u2014machine learning and algorithms can improve and accelerate various programming tasks. \u201cProcedural intellectual activities that involve repetitive tasks, such as translating documents, have a great chance of one day being executed by computer algorithms,\u201d says S\u00e9rgio Amadeu, who is from UFABC. It is important that we discuss the side effects of artificial intelligence, according to Marcondes Cesar, who is from USP; however, for now, they are far outweighed by the remarkable contributions that are made by these algorithms to the solution of problems of many types.<\/p>\n

\"\"

Hoobox Robotics<\/span><\/a> An algorithm translates facial expressions into commands for controlling motorized wheelchairsHoobox Robotics<\/span><\/p><\/div>\n

Facial expression<\/strong>
\nHoobox Robotics, which is a company that was founded by researchers from UNICAMP in 2016, has developed a system for motorized wheelchairs that enables quadriplegics to control the chair using only facial expressions. The algorithm that is used by the software, which is called Wheelie, translates up to 11 facial expressions, such as a smile or a raised eyebrow, into commands to move forward, backward, left, and right. The program is being tested by 39 patients in the USA, where the company has a research unit at the Johnson & Johnson laboratory in Houston. The system uses a 3D camera to capture dozens of facial points.<\/p>\n

\u201cThe user can configure a command for each expression. A smile, for example, can move the chair forward, a kiss, back,\u201d explains computer scientist Paulo Gurgel Pinheiro, who is the director of Hoobox. To learn to recognize key expressions, the Wheelie algorithm studied a set of facial data from 103 truck drivers. \u201cWe partnered with a transportation company to install cameras in trucks and record the facial expressions of volunteers over three months,\u201d Gurgel explains. <\/div>\n

Identifying parasites<\/strong>
\nAn IME-USP research project is being conducted in collaboration with UNICAMP\u2019s Laboratory of Image Data Science (LIDS) to improve the diagnosis of parasite infections using computer vision. Marcelo Finger, who is a computer scientist from IME, is testing an algorithm that can identify parasites by analyzing images of stool samples. \u201cWe have been able to identify 15 parasites in humans and some in animals, such as cattle, dogs, and cats,\u201d he says. Diagnoses are currently obtained by examining stool samples under a microscope. \u201cA lab worker can usually analyze about six blades at a time. The aim is to automate this process,\u201d says Finger. It seems simple; however, because algorithms operate by identifying patterns, any background noise creates an obstacle for the researchers. \u201cIt is one thing for the algorithm to be able to identify the parasite in a photo from a book; doing the same with an image in which the parasite is surrounded by dirt is quite another,\u201d says the researcher.<\/div>\n
\"\"

Projeta Sistemas<\/span><\/a> System uses computer vision to estimate the weight of cattleProjeta Sistemas<\/span><\/p><\/div>\n

Cattle weight<\/strong>
\nProjeta Sistemas, which is a startup that is based in Vit\u00f3ria, Esp\u00edrito Santo State, Brazil, has created an algorithm for assisting cattle farmers. The system, which is called Olho do Dono, uses 3D images to estimate the weights of cows. \u201cThe process of weighing cattle is very costly, time-consuming, and involves moving the animals around, which can cause stress and even weight loss,\u201d explains computer scientist Pedro Henrique Coutinho, who is the director of the company. The software was developed based on computer vision techniques and associates the weights of the cattle with images that were captured by cameras. The system relies on a robust database. \u201cWe monitor the weighing of livestock on ranches throughout Brazil. Our algorithm is based on thousands of recorded images,\u201d says Coutinho. Development began in 2015 and the software will go to market in September.<\/div>\n
Lost animals<\/strong>
\nCrowdPet is a smartphone applicationthat was created by SciPet, which is a company that is based at UNICAMP, for helping find lost animals. Thesystem uses an algorithm to compare pictures of lost pets that were provided by their owners with photos of animals on the streets that were taken by volunteers. \u201cThe application can match two images through visual recognition methods and uses geolocation to locate where the photo of the lost animal was taken,\u201d says Fabio Rog\u00e9rio Piva, who is the director of SciPet. The Animal Control Center in the municipality of Vinhedo, S\u00e3o Paulo, began using the application last year to register animals during welfare campaigns. SciPet has developed a prototype that can identify dogs and cats with 99% accuracy.<\/div>\n","protected":false},"excerpt":{"rendered":"Logical computer systems have a growing impact on everyday life","protected":false},"author":421,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[169],"tags":[219,264],"coauthors":[740],"class_list":["post-284678","post","type-post","status-publish","format-standard","hentry","category-technology","tag-computation","tag-information-technology"],"acf":[],"_links":{"self":[{"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/posts\/284678","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/users\/421"}],"replies":[{"embeddable":true,"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/comments?post=284678"}],"version-history":[{"count":3,"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/posts\/284678\/revisions"}],"predecessor-version":[{"id":285400,"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/posts\/284678\/revisions\/285400"}],"wp:attachment":[{"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/media?parent=284678"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/categories?post=284678"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/tags?post=284678"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/revistapesquisa.fapesp.br\/en\/wp-json\/wp\/v2\/coauthors?post=284678"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}