We revise the definition of clickbait, which lacks current consensus, and argue that the creation of a curiosity gap is the key concept that distinguishes clickbait from other related phenomena such as sensationalism and headlines that do not deliver what they promise or diverge from the article. Therefore, we propose a new definition: clickbait is a technique for generating headlines and teasers that deliberately omit part of the information with the goal of raising the readers' curiosity, capturing their attention and enticing them to click. We introduce a new approach to clickbait detection datasets creation, by refining the concept limits and annotations criteria, minimizing the subjectivity in the decision as much as possible. Following it, we created and release TA1C (for Te Ahorré Un Click, Spanish for Saved You A Click), the first open source dataset for clickbait detection in Spanish. It consists of 3,500 tweets coming from 18 well known media sources, manually annotated and reaching a 0.825 Fleiss' κ inter annotator agreement. We implement strong baselines that achieve 0.84 in F1-score.

Illinois Attorney General Kwame Raoul spoke to CNN's Jim Acosta on Monday about daytime tv talk show host Dr. Phil joining an ICE deportation operation. Talk show host Dr. Phil called out multiple media outlets for expressing outrage over his ride-along with Immigration and Customs Enforcement (ICE) as it apprehended illegal immigrants. Phil McGraw, known as "Dr. Phil," joined border czar Tom Homan and a team of agents as they took various illegal immigrants into custody in Chicago. As part of his show on Merit TV, Dr. Phil filmed a variety of arrests and even interviewed a convicted sex offender and internet predator from Thailand who was being taken into custody.

Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to the general public, and methods to detect distortions from the original work automatically, are crucial. Prior work focused on individual aspects of distortions or worked with unpaired data. In this work, we make three foundational contributions towards addressing this problem: (1) annotating 1,600 instances of scientific findings from academic papers paired with corresponding findings as reported in news articles and tweets wrt. four characteristics: causality, certainty, generality and sensationalism; (2) establishing baselines for automatically detecting these characteristics; and (3) analyzing the prevalence of changes in these characteristics in both human-annotated and large-scale unlabeled data. Our results show that scientific findings frequently undergo subtle distortions when reported. Tweets distort findings more often than science news reports. Detecting fine-grained distortions automatically poses a challenging task. In our experiments, fine-tuned task-specific models consistently outperform few-shot LLM prompting.

Despite protests from San Francisco city leaders, the California Public Utilities Commission last month approved permits for Cruise and another self-driving car company, Waymo, to offer 24/7 paid ride-hailing service in San Francisco. City officials immediately filed a motion against the decision, asking regulators to reconsider the expansion, arguing the city "will suffer serious harm" if the companies are allowed unfettered access to the city's public roads.

It's no longer a secret that artificial intelligence (AI) is here to stay. What once was a puzzling and rather niche area of computer science, has suddenly started to take over our lives with its many applications. As a result, due to this mysterious and unknown characteristic of AI and its more prominent child, machine learning, news sites, and the press, in general, have taken a liking on overstating the reality behind the successes or advances in the field. This phenomenon often leads to articles of an unsavory nature that seems to sensationalize and even fearmonger what's genuinely going on. In this essay, I want to shed some light on this issue.

It's no longer a secret that artificial intelligence (AI) is here to stay. What once was a puzzling and rather niche area of computer science, has suddenly started to take over our lives with its many applications. As a result, due to this mysterious and unknown characteristic of AI and its more prominent child, machine learning, news sites, and the press, in general, have taken a liking on overstating the reality behind the successes or advances in the field. This phenomenon often leads to articles of an unsavory nature that seems to sensationalize and even fearmonger what's genuinely going on. In this essay, I want to shed some light on this issue.

It's no longer a secret that artificial intelligence (AI) is here to stay. What once was a puzzling and rather niche area of computer science, has suddenly started to take over our lives with its many applications. As a result, due to this mysterious and unknown characteristic of AI and its more prominent child, machine learning, news sites, and the press, in general, has taken a liking on overstating the reality behind the successes or advances in the field. This phenomenon often leads to articles of an unsavory nature that seems to sensationalize and even fearmonger what's genuinely going on. In this essay, I want to shed some light on this issue.

Recently, I wrote an article about how deep learning might be hitting its limitations and posed the possibility of another AI winter. I closed that article with a question about whether AI's limitations are defined just as much by philosophy as it is science. This article is a continuation of that topic. The reason I wrote this article is to spur a discussion on why despite so many AI winters and failures, people are still sinking costs to pursue artificial general intelligence. I am presenting a very high-level, non-technical argument that maybe belief systems are driving people's adamacy on what is possible and not just scientific research.

Concerns about how A.I. will affect our lives are necessary, but so is properly talking about them. We're on the cusp of a major change in the way our gadgets do things. Artificial intelligence and machine learning are no longer something you would see in a science fiction novel, and smart machines are being deployed to do even the most mundane tasks, as well as more high-profile things that catch our attention. While I think we're still at least a few years away from the point where we all have our own robotic butlers and flying cars, the possibilities are no longer in doubt. Nobody wants computers that are evil and nobody is building them.

When we imagine a future where humans and robots coexist, it doesn't take long for us to arrive at a conclusion where the human race tragically ends. A robot uprising usually occurs, followed by the inevitable enslavement of all humankind. But when it comes to the future and what will actually unfold, Palmer Luckey, founder of Oculus VR (which Facebook now owns) and inventor of the Oculus Rift virtual reality headset, isn't sweating it. "The reason I'm not creeped out is pretty simple," said Luckey, who sat down with Apple co-founder Steve Wozniak and Re/code journalist Kara Swisher on Saturday at the Silicon Valley Comic Con in San Jose, California. "A lot of people look to science-fiction for representations of technology. It can also be flawed."