Elon Musk sued OpenAI and its Chief Executive Officer Sam Altman, alleging they violated the artificial intelligence startup's founding mission by putting profit ahead of benefiting humanity. The 52-year-old billionaire, who was a co-founder of OpenAI but no longer has a stake, said in a lawsuit filed late Thursday in San Francisco that the company's close relationship with Microsoft Corp. has undermined its original mission of creating open-source technology that wouldn't be subject to corporate priorities. Musk, who is also CEO of Tesla Inc., has been among the most outspoken about the dangers of AI and artificial general intelligence, or AGI. The release of OpenAI's ChatGPT more than a year ago popularized advances in AI technology and raised concerns about the risks surrounding the race to develop AGI, where computers are as smart as an average human. "To this day, OpenAI Inc.'s website continues to profess that its charter is to ensure that AGI'benefits all of humanity,'" the lawsuit said.

Doha, Qatar – At one of the world's largest technology conferences, whether it was on the main stage, its side panels, or at the dozens of glitzy, towering company booths, there was one term on everybody's lips: artificial intelligence (AI). At Web Summit – held for the first time in the Middle East in Doha – and which wrapped up on Thursday, entrepreneurs, investors and business leaders from around the world were all talking about AI's capabilities. Yet alongside that excitement, there are also growing concerns among experts that these technologies could exacerbate inequities dividing the world. Technologies, including AI, run the risk of amplifying biases that already exist, according to Ayo Tometi, co-creator of the US-based antiracist movement Black Lives Matter. "We're seeing quite literally, that prejudice is being programmed into the technologies that are being deployed in our communities. And these biases must be addressed," Tometi said at the summit.

The Jake Gyllenhaal-starring "Road House" remake is facing two major hurdles ahead of its release. Jake Gyllenhaal stars in the remake of "Road House," which is facing both a legal battle and boycott from its director. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Patrick Swayze starred in the original, released in 1989. It was successful upon its original release and gained cult status over the years thanks to cable television. According to the filing, Amazon "repeatedly set and emphasized November 10, 2023 as their self-imposed deadline to complete the 2024 Remake -- the very day before Hill's Termination was to take effect on November 11, 2023. "Hill is further informed and believes and based thereon alleges that Defendants went so far as to take extreme measures to try to meet this November 10, 2023 deadline, at considerable additional cost, including by resorting to the use of AI (Artificial Intelligence) during the 2023 strike of the Screen Actor's Guild ("SAG") to replicate the voices of the 2024 Remake's actors for purposes of ADR (Automatic Dialogue Replacement), all in knowing violation of the collective bargaining agreements of both SAG and the Director's Guild of America (DGA) to which Defendants were signatories.

Hundreds of millions of people now interact with language models, with uses ranging from serving as a writing aid to informing hiring decisions. Yet these language models are known to perpetuate systematic racial prejudices, making their judgments biased in problematic ways about groups like African Americans. While prior research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice: we extend research showing that Americans hold raciolinguistic stereotypes about speakers of African American English and find that language models have the same prejudice, exhibiting covert stereotypes that are more negative than any human stereotypes about African Americans ever experimentally recorded, although closest to the ones from before the civil rights movement. By contrast, the language models' overt stereotypes about African Americans are much more positive. We demonstrate that dialect prejudice has the potential for harmful consequences by asking language models to make hypothetical decisions about people, based only on how they speak. Language models are more likely to suggest that speakers of African American English be assigned less prestigious jobs, be convicted of crimes, and be sentenced to death. Finally, we show that existing methods for alleviating racial bias in language models such as human feedback training do not mitigate the dialect prejudice, but can exacerbate the discrepancy between covert and overt stereotypes, by teaching language models to superficially conceal the racism that they maintain on a deeper level. Our findings have far-reaching implications for the fair and safe employment of language technology.

As LLMs become ubiquitous and integrated into numerous digital applications, it's an increasingly pressing research problem to understand the internal mechanisms that underlie their behaviour - this is the problem of mechanistic interpretability. A fundamental subproblem is to causally attribute particular behaviours to individual parts of the transformer forward pass, corresponding to specific components (such as attention heads, neurons, layer contributions, or residual streams), often at specific positions in the input token sequence. This is important because in numerous case studies of complex behaviours, they are found to be driven by sparse subgraphs within the model (Meng et al., 2023; Olsson et al., 2022; Wang et al., 2022). A classic form of causal attribution uses zero-ablation, or knock-out, where a component is deleted and we see if this negatively affects a model's output - a negative effect implies the component was causally important. More recent work has generalised this to replacing a component's activations with samples from some baseline distribution (with zero-ablation being a special case where activations are resampled to be zero). We focus on the popular and widely used method of Activation Patching (also known as causal mediation analysis) (Chan et al., 2022; Geiger et al., 2022; Meng et al., 2023) where the baseline distribution is a component's activations on some corrupted input, such as an alternate string with a different answer (Pearl, 2001; Robins and Greenland, 1992). Given a causal attribution method, it is common to sweep across all model components, directly evaluating the effect of intervening on each of them via resampling (Meng et al., 2023). However, when working with SoTA models it can be expensive to attribute behaviour especially to small components (e.g.

This paper conducts a user study to assess whether three machine learning (ML) interpretability layouts can influence participants' views when evaluating sentences containing hate speech, focusing on the "Misogyny" and "Racism" classes. Given the existence of divergent conclusions in the literature, we provide empirical evidence on using ML interpretability in online communities through statistical and qualitative analyses of questionnaire responses. The Generalized Additive Model estimates participants' ratings, incorporating within-subject and between-subject designs. While our statistical analysis indicates that none of the interpretability layouts significantly influences participants' views, our qualitative analysis demonstrates the advantages of ML interpretability: 1) triggering participants to provide corrective feedback in case of discrepancies between their views and the model, and 2) providing insights to evaluate a model's behavior beyond traditional performance metrics.

Information extraction (IE) aims to extract complex structured information from the text. Numerous datasets have been constructed for various IE tasks, leading to time-consuming and labor-intensive data annotations. Nevertheless, most prevailing methods focus on training task-specific models, while the common knowledge among different IE tasks is not explicitly modeled. Moreover, the same phrase may have inconsistent labels in different tasks, which poses a big challenge for knowledge transfer using a unified model. In this study, we propose a regularization-based transfer learning method for IE (TIE) via an instructed graph decoder. Specifically, we first construct an instruction pool for datasets from all well-known IE tasks, and then present an instructed graph decoder, which decodes various complex structures into a graph uniformly based on corresponding instructions. In this way, the common knowledge shared with existing datasets can be learned and transferred to a new dataset with new labels. Furthermore, to alleviate the label inconsistency problem among various IE tasks, we introduce a task-specific regularization strategy, which does not update the gradients of two tasks with'opposite direction'. We conduct extensive experiments on 12 datasets spanning four IE tasks, and the results demonstrate the great advantages of our proposed method.

Last year Stack Overflow became one of the first websites to announce it would charge AI giants for access to content used to train chatbots. Now the popular Q&A service for coders has signed up its first customer--Google--in what CEO Prashanth Chandrasekar says is the start of a "meaningful" new stream of revenue. The deal is significant, because it remains unclear how broadly Google and other AI developers will pay for content needed for AI projects. Millions of books and websites have fueled the development of AI systems, but most publishers have not been compensated, and some are suing over what they allege is misuse. Many publishers, including Stack Overflow, appear threatened by ChatGPT and other generative AI products, which can answer queries that would have previously sent coders their way.

It took Ralph Ellison seven years to write Invisible Man. It took J. D. Salinger about 10 to write The Catcher in the Rye. J. K. Rowling spent at least five years on the first Harry Potter book. Writing with the hope of publishing is always a leap of faith. Will you finish the project?

For the past two years, millions of people searching for child abuse videos on Pornhub's UK website have been interrupted. Each of the 4.4 million times someone has typed in words or phrases linked to abuse, a warning message has blocked the page, saying that kind of content is illegal. And in half the cases, a chatbot has also pointed people to where they can seek help. The warning message and chatbot were deployed by Pornhub as part of a trial program, conducted with two UK-based child protection organizations, to find out whether people could be nudged away from looking for illegal material with small interventions. A new report analyzing the test, shared exclusively with WIRED, says the pop-ups led to a decrease in the number of searches for child sexual abuse material (CSAM) and saw scores of people seek support for their behavior.