Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.

On the last day of February, NYU professor Gary Marcus published an essay entitled "The threat of automated misinformation is only getting worse." He warned about the easiness with which you can create misinformation backed by fake references using Bing "with the right invocations." Shawn Oakley, dubbed by Marcus as a "jailbreaking expert" said that "standard techniques" suffice to make it work, providing evidence that the threat of automatic AI-generated misinformation at scale is increasing. Marcus shared his findings on Twitter and FoundersFund's Mike Solana responded: My interpretation of Solana's sarcastic tweets is that claiming that an AI model is a dangerous tool for misinformation (or, more generally, harm of some kind) isn't a good argument if you've consciously broken its filters--he implies the problem isn't the tool's nature but your misuse, and thus you're to blame and not the company that created the tool. His "analogy" between Bing Chat and a text editor misses the point (i.e., language models can generate human-sounding misinformation at scale--you can't do that with Microsoft Word but can with Microsoft Bing) but, even if Marcus is right, there's some truth in Solana's implied stance.

Notably, the Soldex trading platform will have machine learning algorithms and a team of data sciences and experts.

To help solve this problem, developers are now merging blockchain and Artificial Intelligence (AI) technologies to create an automated and …

The day I'm scheduled to appear on his show, the television physician and Oprah protege has assembled a cast of victims and villains that includes a college communications major who caught chlamydia from a Tinder date; a 24-year-old woman who was stabbed 21 times by the fiance she met online; and Douglas Hines, a cartoonish engineer donning a white lab coat and outsize bowtie, who claims to have created the world's first sex robot. NSFW Warning: This story may contain links to and descriptions or images of explicit sexual acts. I'm here, presumably, to act as a voice of reason in a segment called "Rise of the sex robots: Why experts are issuing warnings!" My co-panelists, a sex therapist and a psychologist, are well-trained in the art (or is it science?) of near-scripted debate. While one is slightly more optimistic, their arguments are essentially two sides of the same coin: Sex robots either can or will pose a threat to human relationships, or worse. As TV doctors do, they've both developed firmly planted opinions on the subject without any first-hand experience. It's not lost on me that I'm the only one in the room who's actually seen a sex robot IRL. After the doctors deliver their warnings, Oz turns to me, a look of concern written in deep lines across his forehead. He wants to know if people can have real, intimate relationships with robots.

Samantha, a sex robot stands, in the home of robotics expert Dr Sergi Santos and his partner of 16 years, Maritsa Kissamitaki. The couple deisgned the artificial intelligence-driven robot that they say is capable of enjoying sex. Want to have sex with no strings attached? How about sex with wires attached (or at least wires involved)? Sex robots (or so-called sexbots) are not just coming, they are already here.

It's been just short of a year since I locked eyes with Harmony, RealDoll's first sex robot, at her home in Southern California. It was an arresting experience that has remained cemented in memory. In that moment, I suddenly understood the uncanny valley, a theory posited by roboticist Masahiro Mori, nearly half a century prior. It attempts to explain the feeling of revulsion and eeriness that human onlookers experience when they encounter an artificial life-form that appears nearly, but not quite human. Today, she's ventured out of the dimly lit R&D room at Abyss Creations to meet me at CES.

It's been just short of a year since I locked eyes with Harmony, RealDoll's first sex robot, at her home in Southern California. It was an arresting experience that has remained cemented in memory. In that moment, I suddenly understood the Uncanny Valley, a theory posited by roboticist Masahiro Mori, nearly half a century prior. It attempts to explain the feeling of revulsion and eeriness that human onlookers experience when they encounter an artificial life form that appears nearly, but not quite human. Today, she's ventured out of the dimly lit R&D room at Abyss Creations to meet me at CES.

We present a tool that, from automatically recognised names, tries to infer inter-person relations in order to present associated people on maps. Based on an in-house Named Entity Recognition tool, applied on clusters of an average of 15,000 news articles per day, in 15 different languages, we build a knowledge base that allows extracting statistical co-occurrences of persons and visualising them on a per-person page or in various graphs.