The Herculaneum scrolls were so badly damaged in the A.D 79 eruption of Vesuvius that scholars feared the ancient library was lost forever. That just changed—with help from technology and a $1 million prize.

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field.

Summary: A new study reveals how AI-driven deep learning models can decode the genetic regulatory switches that define brain cell types across species. By analyzing human, mouse, and chicken brains, researchers found that some brain cell types remain highly conserved over 320 million years, while others have evolved uniquely.

This regulatory code not only sheds light on brain evolution but also provides new tools for studying gene regulation in health and disease. The findings highlight how AI can identify preserved and divergent genetic instructions controlling brain function across species.

The study also has implications for understanding neurological disorders by linking genetic variants to cognitive traits. Researchers are now expanding their models to study the brains of various animals and human disease states like Parkinson’s.

Dream, a company focused on artificial intelligence (AI)-powered cybersecurity for nations and critical infrastructure, raised $100 million in a Series B funding round to expand into additional markets in which cyber threats pose challenges to national security.

The round valued Dream at $1.1 billion, the company said in a Monday (Feb. 17) press release.

Cyberattacks on critical infrastructure are becoming more common and more sophisticated, Sebastian Kurz, co-founder and president of Dream, and former prime minister of Austria, said in the release.

Science and Technology: A recent study conducted by researchers at Fudan University in China suggests that artificial intelligence (AI) systems may have taken a worrying step: self-replication without human assistance.

S Llama31-70B-Instruct and Alibaba “Successful self-replication without human assistance is the essential step for AI to outperform[humans], and is an early signal for rogue AIs,” the authors wrote in the paper. They added that understanding the risks associated with next-generation artificial intelligence, or “frontier AI,” is key to anticipating undesirable scenarios. The scientists investigated two specific scenarios: “shutdown avoidance” and “replication chain.” In the first case, an AI model was instructed to detect signs that it was about to shut down and then create a copy of itself before that happened. In the second scenario, the AI was told to replicate itself and then program the replica to do the same, starting a possible infinite cycle of copying.

To conduct these tests, the team created a “scaffolding agent,” or a set of system tools and instructions that allow the AI to interact with the operating system. They said there was no direct human interference in the process. At various times, when obstacles such as software conflicts or missing files arose, AIs found ways to work around them, including terminating conflicting processes and restarting the system.

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MARBLE uses geometric deep learning to map dynamics such as neural activity into a latent representation, which can then be used to decode the neural activity or compare it across systems.

An improvement to an existing AI-based brain decoder can translate a person’s thoughts into text without hours of training.

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System could help unravel how genome folding influences genes.

Summary: Researchers have mapped over 70,000 synaptic connections in rat neurons using a silicon chip with 4,096 microhole electrodes, significantly advancing neuronal recording technology. Unlike traditional electron microscopy, which only visualizes synapses, this method also measures connection strength, providing deeper insight into brain network function.

The chip mimics patch-clamp electrodes but at a massive scale, enabling highly sensitive intracellular recordings from thousands of neurons simultaneously. Compared to their previous nanoneedle design, this new approach captured 200 times more synaptic connections, revealing detailed characteristics of each link.

The technology could revolutionize neural mapping, offering a powerful tool for studying brain function and diseases. Researchers are now working to apply this system in live brains to further understand real-time neural communication.