The Colombian Nobel laureate Gabriel García Márquez, who spent some of his happiest years chronicling life in Caracas, once declared journalism "the best job in the world". Not so if you are reporting on today's Venezuela, where journalists are feeling the heat as the South American country lurches towards full-blown dictatorship under President Nicolás Maduro. In the four weeks since Venezuela's disputed election, local journalists have come up with a distinctly 21st-century tactic to avoid being arrested for reporting on 21st-century socialism: using artificial intelligence avatars to report all the news Maduro's regime deems unfit to print. In daily broadcasts, the AI-created newsreaders have been telling the world about the president's post-election crackdown on opponents, activists and the media, without putting the reporters behind the stories at risk. Carlos Eduardo Huertas, the director of Connectas, the Colombia-based journalism platform coordinating the initiative, said far from being a gimmick, the use of AI was a response to "the persecution and the growing repression that our colleagues are suffering in Venezuela, where the uncertainty over the safety of doing their job … grows by the minute".

This study explores the application of evolutionary generative algorithms in music production to preserve and enhance human creativity. By integrating human feedback into Differential Evolution algorithms, we produced six songs that were submitted to international record labels, all of which received contract offers. In addition to testing the commercial viability of these methods, this paper examines the long-term implications of content generation using traditional machine learning methods compared with evolutionary algorithms. Specifically, as current generative techniques continue to scale, the potential for computer-generated content to outpace human creation becomes likely. This trend poses a risk of exhausting the pool of human-created training data, potentially forcing generative machine learning models to increasingly depend on their random input functions for generating novel content. In contrast to a future of content generation guided by aimless random functions, our approach allows for individualized creative exploration, ensuring that computer-assisted content generation methods are human-centric and culturally relevant through time.

Rehabilitation tasks demand robust and accurate trajectory-tracking performance, mainly achieved with parallel robots. In this field, limiting the value of the force exerted on the patient is crucial, especially when an injured limb is involved. In human-robot interaction studies, the admittance controller modifies the location of the robot according to the user efforts driving the end-effector to an arbitrary location within the workspace. However, a parallel robot has singularities within the workspace, making implementing a conventional admittance controller unsafe. Thus, this study proposes an admittance controller that overcomes the limitations of singular configurations by using a real-time singularity avoidance algorithm. The singularity avoidance algorithm modifies the original trajectory based on the actual location of the parallel robot. The complemented admittance controller is applied to a 4 degrees of freedom parallel robot for knee rehabilitation. In this case, the actual location is measured by a 3D tracking system because the location calculated by the forward kinematics is inaccurate in the vicinity of a singularity. The experimental results verify the effectiveness of the proposed admittance controller for safe knee rehabilitation exercises

This paper aims to develop an approach for the reconfiguration of a parallel kinematic manipulator (PKM) with four degrees of freedom (DoF) designed to tackle tasks of diagnosis and rehabilitation in an injured knee. The original layout of the 4-DoF manipulator presents Type-II singular configurations within its workspace. Thus, we proposed to reconfigure the manipulator to avoid such singularities (owing to the Forward Jacobian of the PKM) during typical rehabilitation trajectories. We achieve the reconfiguration of the PKM through a minimization problem where the design variables correspond to the anchoring points of the robot limbs on fixed and mobile platforms. The objective function relies on the minimization of the forces exerted by the actuators for a specific trajectory. The minimization problem considers constraint equations to avoid Type-II singularities, which guarantee the feasibility of the active generalized coordinates for a particular path. To evaluate the proposed conceptual strategy, we build a prototype where reconfiguration occurs by moving the position of the anchoring points to holes bored in the fixed and mobile platforms. Simulations and experiments of several study cases enable testing the strategy performance. The results show that the reconfiguration strategy allows obtaining trajectories having minimum actuation forces without Type-II singularities.

Although parallel manipulators (PMs) started with the introduction of architectures with 6 Degrees of Freedom (DoF), a vast number of applications require less than 6 DoF. Consequently, scholars have proposed architectures with 3 DoF and 4 DoF, but relatively few 4 DoF PMs have become prototypes, especially of the two rotation (2R) and two translation (2T) motion types. In this paper, we explain the mechatronics design, prototype and control architecture design of a 4 DoF PM with 2R2T motions. We chose to design a 4 DoF manipulator based on the motion needed to complete the tasks of lower limb rehabilitation. To the author's best knowledge, PMs between 3 and 6 DoF for rehabilitation of lower limbs have not been proposed to date. The developed architecture enhances the three minimum DoF required by adding a 4 DoF which allows combinations of normal or tangential efforts in the joints, or torque acting on the knee. We put forward the inverse and forward displacement equations, describe the prototype, perform the experimental setup, and develop the hardware and control architecture. The tracking accuracy experiments from the proposed controller show that the manipulator can accomplish the required application.

It's also important for the speaker to regularly converse with native speakers to understand cultural jargon, slang, and colloquial expressions. But it's not always easy to visit the country whose language you want to learn, and consuming a foreign language's media can be difficult when you're still a novice speaker. To help with this issue, Google announced a new interactive feature within Google Search to help people learn English. The feature is currently available to Android users in Argentina, Colombia, India, Indonesia, Mexico, and Venezuela. Users can translate to or from English in Google Search, and Search will provide them with prompts, short practice sessions, and personalized feedback.

Urban environments are intricate systems where the breakdown of critical infrastructure can impact both the economic and social well-being of communities. Electricity systems hold particular significance, as they are essential for other infrastructure, and disruptions can trigger widespread consequences. Typically, assessing electricity availability requires ground-level data, a challenge in conflict zones and regions with limited access. This study shows how satellite imagery, social media, and information extraction can monitor blackouts and their perceived causes. Night-time light data (in March 2019 for Caracas, Venezuela) is used to indicate blackout regions. Twitter data is used to determine sentiment and topic trends, while statistical analysis and topic modeling delved into public perceptions regarding blackout causes. The findings show an inverse relationship between nighttime light intensity. Tweets mentioning the Venezuelan President displayed heightened negativity and a greater prevalence of blame-related terms, suggesting a perception of government accountability for the outages.

In the fall of 2020, gig workers in Venezuela posted a series of images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles--including a particularly revealing shot of a young woman in a lavender T-shirt sitting on the toilet, her shorts pulled down to mid-thigh. The images were not taken by a person, but by development versions of iRobot's Roomba J7 series robot vacuum, the company which Amazon recently acquired for $1.7 billion in a pending deal. They were then sent to Scale AI, a startup that contracts workers around the world to label data used to train artificial intelligence. Earlier this year, MIT Technology Review obtained 15 screenshots of these private photos, which had been posted to closed social media groups.

Spanish is one of the most spoken languages in the globe, but not necessarily Spanish is written and spoken in the same way in different countries. Understanding local language variations can help to improve model performances on regional tasks, both understanding local structures and also improving the message's content. For instance, think about a machine learning engineer who automatizes some language classification task on a particular region or a social scientist trying to understand a regional event with echoes on social media; both can take advantage of dialect-based language models to understand what is happening with more contextual information hence more precision. This manuscript presents and describes a set of regionalized resources for the Spanish language built on four-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities; as well as examples of using regional resources on message classification tasks.

We are in the middle of a business model revolution. And we are active participants in that revolution. We have been transitioning from a society where possession and application of physical commodities defined wealth and power, to a society where possession and application of knowledge define wealth and power. Throughout the 20th century, oil had been the most valuable commodity in defining wealth and power. Possession and application of oil defined the fortunes of individuals (John D. Rockefeller, H.L. Hunt, Paul Getty), companies (Standard Oil, ExxonMobil, Shell, BP), and countries (United States, Russia, Saudi Arab, Iraq, Canada, Venezuela).