Biotech firms are turning to artificial intelligence to boost productivity and address talent shortages. Insilico Medicine is building a multi‑task AI platform that can generate disease hypotheses, design candidate molecules and even repurpose existing drugs, aiming to speed drug discovery and cut costs. GenEditBio is using AI to design engineered protein delivery vehicles that target specific tissues for in‑vivo CRISPR therapy, recently receiving FDA clearance for a corneal‑dystrophy trial. Both companies stress the need for richer, more diverse data to improve model accuracy and envision future tools such as digital twins for virtual clinical testing. Leia mais →

Synthetic data promises speed and scalability for AI development, especially when real data is scarce. However, industry experts warn that reliance on artificially generated datasets can create blind spots, particularly in complex, high‑pressure environments where unpredictable human behavior and subtle variations matter. Real‑world data, captured from sensors, field operations, and digital twins, offers a more accurate foundation, improving model reliability, regulatory compliance, and trust. The shift toward reality‑first training is seen as essential for AI systems that must adapt continuously to the nuances of actual operating conditions. Leia mais →

Synthetic data promises speed and scalability for AI development, especially when real data is scarce. However, industry experts warn that reliance on artificially generated datasets can create blind spots, particularly in complex, high‑pressure environments where unpredictable human behavior and subtle variations matter. Real‑world data, captured from sensors, field operations, and digital twins, offers a more accurate foundation, improving model reliability, regulatory compliance, and trust. The shift toward reality‑first training is seen as essential for AI systems that must adapt continuously to the nuances of actual operating conditions. Leia mais →

Synthetic data promises speed and scalability for AI development, especially when real data is scarce. However, industry experts warn that reliance on artificially generated datasets can create blind spots, particularly in complex, high‑pressure environments where unpredictable human behavior and subtle variations matter. Real‑world data, captured from sensors, field operations, and digital twins, offers a more accurate foundation, improving model reliability, regulatory compliance, and trust. The shift toward reality‑first training is seen as essential for AI systems that must adapt continuously to the nuances of actual operating conditions. Leia mais →

Synthetic data promises speed and scalability for AI development, especially when real data is scarce. However, industry experts warn that reliance on artificially generated datasets can create blind spots, particularly in complex, high‑pressure environments where unpredictable human behavior and subtle variations matter. Real‑world data, captured from sensors, field operations, and digital twins, offers a more accurate foundation, improving model reliability, regulatory compliance, and trust. The shift toward reality‑first training is seen as essential for AI systems that must adapt continuously to the nuances of actual operating conditions. Leia mais →

Synthetic data promises speed and scalability for AI development, especially when real data is scarce. However, industry experts warn that reliance on artificially generated datasets can create blind spots, particularly in complex, high‑pressure environments where unpredictable human behavior and subtle variations matter. Real‑world data, captured from sensors, field operations, and digital twins, offers a more accurate foundation, improving model reliability, regulatory compliance, and trust. The shift toward reality‑first training is seen as essential for AI systems that must adapt continuously to the nuances of actual operating conditions. Leia mais →