In a world where seeing is increasingly no longer believing, experts are warning that society must take a multi-pronged approach to combat the potential harms of computer-generated media. As Bill Whitaker reports this week on 60 Minutes, artificial intelligence can manipulate faces and voices to make it look like someone said something they never said. The result is videos of things that never happened, called "deepfakes." Often, they look so real, people watching can't tell. Even Justin Bieber has been tricked by a series of deepfake videos on the social media video platform TikTok that appeared to be of Tom Cruise.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! The collective internet is grappling with misinformation, toxicity, and censorship. In countries all over the world, this is exacerbated by social media networks being restricted or even controlled by the government. Not only does this damage the foundations of free speech and collaboration that the Internet was built on, but it also estranges entire demographics from being able to participate in global dialogue and understanding.

We present an enrichment of the Hateval corpus of hate speech tweets (Basile et. al 2019) aimed to facilitate automated counter-narrative generation. Comparably to previous work (Chung et. al. 2019), manually written counter-narratives are associated to tweets. However, this information alone seems insufficient to obtain satisfactory language models for counter-narrative generation. That is why we have also annotated tweets with argumentative information based on Wagemanns (2016), that we believe can help in building convincing and effective counter-narratives for hate speech against particular groups. We discuss adequacies and difficulties of this annotation process and present several baselines for automatic detection of the annotated elements. Preliminary results show that automatic annotators perform close to human annotators to detect some aspects of argumentation, while others only reach low or moderate level of inter-annotator agreement.

As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e., differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders are affected by clinical and resource considerations, testing may not be uniform in patient populations, giving rise to disparate censorship. Disparate censorship in patients of equivalent risk leads to undertesting in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.

Nir Kaldero is chief AI officer at NEORIS and Adjunct Executive for AI at CEMEX. The world is in constant high-speed transition. We have entered a period in which the battles of good and evil, light and dark, democracy and authoritarianism are at high stake and being defined. Although it might sound like a John le Carré novel, this is the world we are living in. Technology and artificial intelligence (AI), as well as the ideology that will dominate these fields, are also being shaped in real time, even if many of us are unaware of the battles being fought behind our computer screens and closed doors.

A MACHINE with human-level intelligence could be built in the next 30 years and could represent a threat to life on Earth, some experts believe. AI researchers and technology executives like Elon Musk are openly concerned about human extinction caused by machines. The Law of Accelerating Returns is a concept popularized by futurist Ray Kurzweil that states the rate of technological improvement is on a very steep curve. As technology gets more advanced, society and industry are better equipped to improve technology faster and more drastically. "With more powerful computers and related technology, we have the tools and the knowledge to design yet more powerful computers, and to do so more quickly," Kurzweil wrote in his famous 2001 essay.

Business at the Comanche County Courthouse returned to normal on Friday, after it was evacuated Thursday afternoon. The Comanche County Courthouse will be opening at its regular time Friday following an evacuation order Thursday.

A journalist who covers wildfires responds to Premee Mohamed's "All That Burns Unseen." In "All That Burns Unseen," set in a dystopian but not-too-distant future, we finally get the drone sidekick we didn't know we needed. Premee Mohamed's heroine, Vaughn Collins, is a government worker gone rogue as a wildfire burns. Along the way, she rescues a dazed, glitchy fire extinguisher drone. When a funnel of flames heads for Vaughn's truck, threatening everything, her new friend dives into the blaze and sprays.

In 1917, Marcel Duchamp submitted a sculpture to the Society of Independent Artists under a false name. Fountain was a urinal, bought from a toilet supplier, with the signature R. Mutt on its side in black paint. Duchamp wanted to see if the society would abide by its promise to accept submissions without censorship or favor. But Duchamp was also looking to broaden the notion of what art is, saying a ready-made object in the right context would qualify. Then, as before, the debate raged about if something mechanically produced – a urinal, or a soup can (albeit hand-painted by Warhol) – counted as art, and what that meant. Now, the debate has been turned upon its head, as machines can mass-produce unique pieces of art on their own.

This article was published in The Stream (July 6, 2022) under the title "The Church of Artificial Intelligence of the Future" and is republished with permission. There is a church that worships artificial intelligence (AI). Zealots believe that an extraordinary AI future is inevitable. The technology is not here yet, but we are assured that it's coming. We will have the ability to be uploaded onto a computer and thereby achieve immortality.