Trump says US hit'big facility' linked to alleged Venezuelan drug boats Donald Trump has said the US has carried out a strike on a dock area linked to alleged Venezuelan drug boats. The US president said there had been a major explosion where they load the boats up with drugs - but did not give more details. Venezuela's government is yet to respond. The explosion was caused by a drone strike carried out by the CIA, CNN and the New York Times reported, citing sources familiar with the matter. If confirmed, it would be the first known US operation inside Venezuela.

Plus: Cisco discloses a zero-day with no available patch, Venezuela accuses the US of a cyberattack, and more. Federal contracting records reviewed by WIRED this week show that United States Customs and Border Protection is transitioning from testing small drones to using them as standard surveillance tools, a move that will further expand CBP's already extensive dragnet that in some cases extends far beyond US land borders. Meanwhile, US Immigration and Customs Enforcement is planning to incorporate a broad cybersecurity contract that will include expanding employee surveillance and monitoring . The move comes as the US government is escalating leak investigations and condemning internal dissent. The Chinese-language artificial intelligence app Haotian can be used to create "nearly perfect" face swaps during live video chats, and it is a favorite tool of Southeast Asian scammers.

Abuse reporting services collect reports about abuse victims have suffered. Accurate classification of the submitted reports is fundamental to analyzing the prevalence and financial impact of different abuse types (e.g., sextortion, investment, romance). Current classification approaches are problematic because they require the reporter to select the abuse type from a list, assuming the reporter has the necessary experience for the classification, which we show is frequently not the case, or require manual classification by analysts, which does not scale. To address these issues, this paper presents a novel approach to classify cryptocurrency abuse reports automatically. We first build a taxonomy of 19 frequently reported abuse types. Given as input the textual description written by the reporter, our classifier leverages a large language model (LLM) to interpret the text and assign it an abuse type in our taxonomy. We collect 290K cryptocurrency abuse reports from two popular reporting services: BitcoinAbuse and BBB's ScamTracker. We build ground truth datasets for 20K of those reports and use them to evaluate three designs for our LLM-based classifier and four LLMs, as well as a supervised ML classifier used as a baseline. Our LLM-based classifier achieves a precision of 0.92, a recall of 0.87, and an F1 score of 0.89, compared to an F1 score of 0.55 for the baseline. We demonstrate our classifier in two applications: providing financial loss statistics for fine-grained abuse types and generating tagged addresses for cryptocurrency analysis platforms.

In general-sum games, the interaction of self-interested learning agents commonly leads to collectively worst-case outcomes, such as defect-defect in the iterated prisoner's dilemma (IPD). To overcome this, some methods, such as Learning with Opponent-Learning Awareness (LOLA), shape their opponents' learning process. However, these methods are myopic since only a small number of steps can be anticipated, are asymmetric since they treat other agents as naive learners, and require the use of higher-order derivatives, which are calculated through white-box access to an opponent's differentiable learning algorithm. To address these issues, we propose Model-Free Opponent Shaping (M-FOS). M-FOS learns in a meta-game in which each meta-step is an episode of the underlying inner game. The meta-state consists of the inner policies, and the meta-policy produces a new inner policy to be used in the next episode. M-FOS then uses generic model-free optimisation methods to learn meta-policies that accomplish long-horizon opponent shaping. Empirically, M-FOS near-optimally exploits naive learners and other, more sophisticated algorithms from the literature. For example, to the best of our knowledge, it is the first method to learn the well-known Zero-Determinant (ZD) extortion strategy in the IPD. In the same settings, M-FOS leads to socially optimal outcomes under meta-self-play. Finally, we show that M-FOS can be scaled to high-dimensional settings.

Artificial Intelligence is knowledge shown by machines, rather than normal insight shown by creatures including people. AI is a wide term that alludes to the utilisation of specific sorts of investigation to follow through with responsibilities from driving a vehicle to fraud detection. Previously, fraud detection has been done by rules-based calculations which are regularly convoluted and not generally extremely difficult to evade. These strategies risk missing a lot of fraud activities or proceeding to have exorbitant measures of bogus up-sides, where client's cards get declined because of misidentified and dubious ways of behaving. Customary models are likewise entirely unyielding which is an issue in an application where fraudulent are continually tracking down better approaches to sneak by the radar.

One day after work last month, Tom Otieno* went to a shopping centre in Nairobi to pick up groceries before heading home. He got a call from someone he had been chatting to for a week on Grindr, a social networking app for gay, bi, trans and queer people. The man had already tried ringing several times during the day while Otieno was with colleagues and was keen to meet. Otieno, 29, mentioned where he was but said that he did not want to see the man. Then, as he was heading to his car, he got another call.

Stakeholders in the Nigerian maritime industry have identified deeper application of technology as a way to achieving an efficient port system in Nigeria. At a recent one-day Town Hall Meeting on Hitch Free Port Operations in Nigeria organised by JournalNG in Lagos, they urged the federal government to consider applying the Webb Port system being used in neighbouring Benin Republic. While making a presentation at the event, Managing Director of Webb Fontaine Nigeria Limited, Ope Babalola disclosed that his company has assisted Benin Republic in achieving ICT port system that harmonised the country's interests through a single transaction. According to him, the system has helped in saving time, producing more accurate results, protecting government revenue and facilitating trade. Tankian Coulibaly an official from Webb Fontaine in Benin Republic said his company helped in Beninois government to set up a port community integration system called Webb Port.

Deepfakes may have innocent and fun applications -- companies like RefaceAI and Morphin enable users to swap their faces with those of popular celebrities in a GIF or digital content format. But like a double-edged sword, the more realistic the content looks, the greater the potential for deception. Deepfakes have been ranked by experts as one of the most serious artificial intelligence (AI) crime threats based on the wide array of applications it can be used for criminal activities and terrorism. A study by University College London (UCL) identified 20 ways AI can be deployed for the greater evil and these emerging technologies were ranked in order of concern in accordance with the severity of the crime, the profit gained, and the difficulty in combating their threats. When the term was first coined, the idea of deepfakes triggered widespread concern mostly centered around the misuse of the technology in spreading misinformation, especially in politics.

It's painfully common for data to be exposed online. But just because it happens so often that doesn't make it any less dangerous. Especially when that data comes from a slew of dating apps that cater to specific groups and interests. Security researchers Noam Rotem and Ran Locar were scanning the open internet on May 24 when they stumbled upon a collection of publicly accessible Amazon Web Services "buckets." Each contained a trove of data from a different specialized dating app, including 3somes, Cougary, Gay Daddy Bear, Xpal, BBW Dating, Casualx, SugarD, Herpes Dating, and GHunt.

Indeed, even 10 years back, the notice of Artificial Intelligence (AI) would refer to the dread that it would remove human employments and render them expendable. Slice to the present and that dread has now been supplanted with a progressively rational methodology where AI is being viewed as an approach to expand human capacities in an undeniably digital time. New AI frameworks have beyond-human cognitive abilities, which a significant number of us fear could conceivably dehumanize the eventual fate of work. Nonetheless, via automating these skills, AI will drive human experts up the range of abilities stepping stool into exceptionally human abilities, for example, inventiveness, social capabilities, sympathy, and sense-production, which machines can't automate. Subsequently, AI will make the working environment progressively human, not less.