Mr A. Inteligence (ITS OPINION OF ITSELF)
Automated journalism, also known as algorithmic journalism or robotic journalism, is when news articles are generated by computer programs   . Algorithms scan large amounts of data supplied, select a selection of pre-programmed items structured by key points and insert details such as names, locations, numbers, ranking, statistics and other numbers. Data science and AI companies such as Automated Insights, Narrative Science, United Robots, and YSEOP have developed and deployed computer programs for news agencies.
Automated journalism is seen as an opportunity to free journalists from routine reporting and give them more time for more complex tasks. It enables efficiency and cost reductions and reduces the financial burden faced by many news organizations. Automated journalism was perceived as a threat to authorship and high-quality news as well as a threat to human journalists’livelihoods.
Computer engineers, linguists and data scientists are new players in the world of journalism. At the same time, many voices in academic circles are urging journalists to develop computational thinking to facilitate dialogue with computer scientists. News services such as the Press Association and Associated Press as well as smaller regional providers are also affected by the news jobs market.
These are sophisticated artificial intelligence and natural language creation software that processes information and converts it to news copies by scanning data, selecting an article template from a set of pre-programmed options, and adding specific details such as names of places. Google has invested $622,000 in Reporters Without Data, a robotic radar system, and the British Press Association (PA) has begun producing computer-generated news. Associated Press (AP) was one of the first to test robojournalism and produced computer-generated sports and financial reports in 2013.
The Washington Post used a bot to report on the results of the 2016 Rio Olympics. The BBC introduced synthetic voices to read articles published on its website last year, and Reuters introduced an automated video system to report on sports matches. According to Matt Carlson, author of Robotic Reporters, the algorithms transform data into narrative text in real time.
Its Cyborg program has published thousands of articles over the past year, taken over financial reports, and turned them into news for business reporters. Not surprisingly, Bloomberg News was one of the first adapters of automated content. Many of the narrative news stories focus on news stories in which data has been computed and published.
The company hired a team of “meta-authors” to train journalists to create a set of templates. Together with engineers, they trained the computer to identify different angles and data. Their use of artificial intelligence picked up steam when someone named Bertie helped provide reporters with initial drafts and templates for news stories.
Most news on topics such as sports and finance follows a predictable formula, so it is easy for the meta-authors to create a framework for the article. To construct sentences, the algorithm uses the vocabulary compiled by the authors.
Artificial intelligence (AI) is the ability of a computer or robot to control itself and perform tasks that can be accomplished by humans, but require human intelligence and discernment. Machine learning is the method by which a computer is trained to learn from its inputs without being explicitly programmed. Artificial intelligence can perform a variety of tasks that ordinary people can do, but it needs to be coordinated with humans for certain tasks.
The term artificial intelligence (AI) is an umbrella term that refers to the different possibilities which are offered by technological developments of recent times. News organizations use artificial intelligence to automate a variety of tasks that form the chain of journalistic production, including identifying, extracting and verifying data, creating stories, graphics, publishing, sorting, selecting, prioritizing, filtering and tagging articles. Machine learning, which includes deep learning and neural networks, is when computers master new tasks in much the same way as humans study examples and learn from their own experiences.
While AI is a remarkable achievement, machine intelligence lags behind human intelligence in many ways. Intelligent computer programs require enormous amounts of energy and pollute the environment. One of the big talking points around artificial intelligence and robotic autonomy is the impact on employment.
One hundred years ago a Czech author Karel Capek introduced a play the word “robot” and told the story of an artificial factory worker who was to serve the people. This week, The Guardian published an article written by a computer.
The script was created using a publicly available artificial intelligence (AI) system called GPT-2. Created by Elon Musk’s company OpenAI, the figure of time, a robot that goes out into the world and learns about society, human emotions and death, is a computer model designed to generate the text that comes from an enormous pool of information available on the Internet.
Computer scientists have developed deep learning techniques to create realistic objects in virtual environments that can be used to train robots. The researchers presented a learning process based on optical flux that enables robots to estimate the distance and visual appearance (shape, color, and texture) of the visible objects. The technology could be used to write fake news, short stories and poems.
CSET researchers have begun to use GPT-3 in a series of experiments called Human-Machine Teaming (GPT) to create tweets and get people to check them before publication. They are now beginning to extend their experiments to other uses of disinformation, such as thematic tweets, news headlines that frame the narrative of news history, the dissemination of divisive messages, and the creation of messages that address political affiliations on important issues.
The CSET researchers acknowledge that GPT-3 excels when tweeting tweets that are short, but the longer the text is, the more likely errors are in LLM-generated text. When reviewing news articles, such errors spread more widely than if their CSET colleagues had difficulty distinguishing whether they were human or machine-generated, the researchers say. In the case of fools, the human-machine interaction, humans can pick up the faults of artificial intelligence before publishing them.
Launched by Elon Musk (who, as I said, has been somewhat critical of artificial intelligence in the past), the OpenAI research laboratory thought GPT-3 “s predecessor, G PTP-2, was too dangerous to make available to the public if it fell into the wrong hands. So OpenAI built a more powerful version and released it in an abrupt change of heart.
GPT-3 stands for generative pre-training, which uses a vast database of English sentences and a powerful computer model called the neural network to recognize patterns and learn its own rules – a language that works as if it had 175 billion rules of its own. It sounds complicated, and it is, but its size and texture make it adaptable to all kinds of tasks that require some kind of language.