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Artificial Intelligence in Sound Design: A Revolution or a Threat?

In this article, Roman Ponomarenko, sound designer and composer with 20+ years of professional experience, explores the transformative potential of AI in sound design and what this means for the future of music.

Artificial intelligence (AI) is already making significant progress in music and sound design. However, will the sophisticated AI of the future eventually replace human professionals in these fields? Navigating such a complex issue proves to be quite challenging, as AI brings forth a mix of exciting opportunities and daunting challenges.

New AI Robot with Human Brain Shocks the World! (They’ve Crossed the Line)

Recent advancements in AI and robotics have led to significant breakthroughs, including a robot with a brain-on-a-chip in China and Skild AI’s development of a universal robot brain for complex tasks. Meanwhile, Zen Technologies in India has introduced Prahasta, a four-legged robot equipped with advanced LIDAR and AI for defense purposes, capable of navigating difficult terrains and carrying heavy loads. These innovations highlight the rapid evolution of robotics technology, blending artificial intelligence with physical capabilities in unprecedented ways, poised to transform industrial, defense, and healthcare sectors.

#ai #robot

Unlock Gene Networks Using Limited Data with AI Model Geneformer

Geneformer is a recently introduced and powerful AI model that learns gene network dynamics and interactions using transfer learning from vast single-cell transcriptome data. This tool enables researchers to make accurate predictions about gene behavior and disease mechanisms even with limited data, accelerating drug target discovery and advancing understanding of complex genetic networks in various biological contexts.

Developed by researchers at the Broad Institute of MIT and Harvard and their collaborators, the AI model Geneformer uses the highest-expressed genes in sc-RNA expression data to generate a dense representation of each cell, which can be used as features for various downstream predictive tasks. What makes Geneformer unique, however, are the capabilities its architecture enables, even when trained on very little data.

Geneformer has a BERT-like transformer architecture and was pre-trained on data from about 30M single-cell transcriptomes across various human tissues. Its attention mechanism enables it to focus on the most relevant parts of the input data. With this context-aware approach, the model can make predictions by considering ‌relationships and dependencies between genes.

The Emergence Of Organoid Intelligence: Reshaping AI With Miniature Brains

Replicating these processes in AI systems is a significant challenge. One of the most exciting applications is in this field. Leveraging OI can help in training AI models more effectively. The dynamic neural networks in organoids can serve as a blueprint for creating more human-like AI systems.

The development of AI-enabled organoids is a promising field that combines AI with organoids to create more precise models of human organ functionality and diseases. This convergence could revolutionize drug discovery, disease diagnosis and the development of advanced treatments. AI helps organoids by guiding them through three crucial dimensions:

1. Hybrid Intelligence: A potential future scenario involves merging OI with traditional AI systems. This fusion could result in a new era of “hybrid intelligence” that combines the analytical power of AI with the nuanced understanding of human-like cognition.

Artificial Intelligence is Learning to ‘Think’ More Like Humans, New Research Suggests

Artificial intelligence (AI) isn’t just performing with high accuracy; for the first time, new research suggests that it is “thinking” very much like humans.

Work on AI models has long focused on the scale of tasks or accuracy, but a group of researchers is looking more closely at how AI makes decisions. By developing a process more similar to the human mind, troubling tendencies for AI “hallucinations” may be mitigated.

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