Lifeboat Foundation

WIRED asked experts from all corners of society and academia to answer questions about the future of technology, artificial intelligence, and humanity itself.

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Continue reading “Experts Predict the Future of Technology, AI & Humanity” »

The use of artificial intelligence has played an important role in science teaching and learning. The purpose of this study was to fill a gap in the current review of research on AI in science education (AISE) in the early stage of education by systematically reviewing existing research in this area. This systematic review examined the trends and research foci of AI in the science of early stages of education. This review study employed a bibliometric analysis and content analysis to examine the characteristics of 76 studies on Artificial Intelligence in Science Education (AISE) indexed in Web of Science and Scopus from 2013 to 2023. The analytical tool CiteSpace was utilized for the analysis.

Like Tom Hanks, victims of AI-generated trickery will need help getting out the message that they are not the ones who generated the content that’s being ascribed to them. The value of human expertise and decision-making in crisis management will become even more evident.

The AI revolution will have permanent repercussions beyond anything we can perhaps imagine today. Soon, organizations exploring and implementing the use of AI will invite unprecedented levels of risk.

Deepfake AI is now being used to create voice clones, convincing images and video hoaxes, all of which can be used to destroy an individual’s reputation or livelihood.

Artificial intelligence (AI) and emerging technologies have ushered in a new era, bringing unprecedented opportunities and challenges. In today’s rapidly evolving digital landscape, addressing these multifaceted challenges necessitates a collaborative effort spanning various sectors and calls for policy reforms while emphasizing global cooperation.

The rapid advancement of technologies, particularly artificial intelligence, has introduced transformative possibilities alongside a range of concerns. While AI holds the potential to revolutionize industries and enhance our daily lives, it also raises pressing issues related to data privacy, misinformation, and cybersecurity.

Experts have proposed adopting the “information environment” framework to address these multifaceted challenges. This framework comprises three essential components:

Google DeepMind and academic partners have unveiled an AI that trains robots for generalized tasks using the “Open X-Embodiment” dataset. ConceptGraphs, on the other hand, offers a new 3D scene representation, improving robot perception and planning by combining vision and language.

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Continue reading “Google’s RT-2-X Generalist AI Robots: 500 Skills, 150,000 Tasks, 1,000,000+ Workflows” »

To build the supercomputer that powers OpenAI’s projects, Microsoft says it linked together thousands of Nvidia graphics processing units (GPUs) on its Azure cloud computing platform. In turn, this allowed OpenAI to train increasingly powerful models and “unlocked the AI capabilities” of tools like ChatGPT and Bing.

Scott Guthrie, Microsoft’s vice president of AI and cloud, said the company spent several hundreds of millions of dollars on the project, according to a statement given to Bloomberg. And while that may seem like a drop in the bucket for Microsoft, which recently extended its multiyear, multibillion-dollar investment in OpenAI, it certainly demonstrates that it’s willing to throw even more money at the AI space.

The dean of Stanford University’s medical school thinks artificial intelligence will transform the medicines you take, the care you get, and the training of doctors.

A team of New York University computer scientists has created a neural network that can explain how it reaches its predictions. The work reveals what accounts for the functionality of neural networks—the engines that drive artificial intelligence and machine learning—thereby illuminating a process that has largely been concealed from users.

The breakthrough centers on a specific usage of neural networks that has become popular in recent years—tackling challenging biological questions. Among these are examinations of the intricacies of RNA splicing—the focal point of the study—which plays a role in transferring information from DNA to functional RNA and protein products.

“Many neural networks are black boxes —these algorithms cannot explain how they work, raising concerns about their trustworthiness and stifling progress into understanding the underlying biological processes of genome encoding,” says Oded Regev, a computer science professor at NYU’s Courant Institute of Mathematical Sciences and the senior author of the paper, which was published in the Proceedings of the National Academy of Sciences.

MindsDB connects AI models to datasources. Contribute to/mindsdb development by creating an account on GitHub.