Learn how nuclear licensing is being transformed with AI technology to enhance safety and efficiency in power plants.
Category: robotics/AI – Page 99
Evaluating AI and machine learning models in cheminformatics: benchmarking techniques and case studies
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AI-powered microscope predicts and tracks protein aggregation linked to brain diseases
The accumulation of misfolded proteins in the brain is central to the progression of neurodegenerative diseases like Huntington’s, Alzheimer’s and Parkinson’s. But to the human eye, proteins that are destined to form harmful aggregates don’t look any different than normal proteins.
The formation of such aggregates also tends to happen randomly and relatively rapidly—on the scale of minutes. The ability to identify and characterize protein aggregates is essential for understanding and fighting neurodegenerative diseases.
Now, using deep learning, EPFL researchers have developed a ‘self-driving’ imaging system that leverages multiple microscopy methods to track and analyze protein aggregation in real time—and even anticipate it before it begins. In addition to maximizing imaging efficiency, the approach minimizes the use of fluorescent labels, which can alter the biophysical properties of cell samples and impede accurate analysis.
Technology Landscape Review of In-Sensor Photonic Intelligence: From Optical Sensors to Smart Devices
Optical sensors have undergone significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. This review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications.
Will implantable brain-computer interfaces soon benefit people with motor impairments?
A review published in Advanced Science highlights the evolution of research related to implantable brain-computer interfaces (iBCIs), which decode brain signals that are then translated into commands for external devices to potentially benefit individuals with impairments such as loss of limb function or speech.
A comprehensive systematic review identified 112 studies, nearly half of which have been published since 2020. Eighty iBCI participants were identified, mostly participating in studies concentrated in the United States, but with growing numbers of studies from Europe, China, and Australia.
The analysis revealed that iBCI technologies are being used to control devices such as robotic prosthetic limbs and consumer digital technologies.
Future of energy
Imagine if every pattern shaped by nature – like a protein’s fold or cosmic phenomena – is inherently learnable by AI.
ChatGPT now handles 1.7 million requests every minute
OpenAI recently told Axios that their AI tool ChatGPT handles over 2.5 billion user instructions every single day. That’s the equivalent of about 1.7 million instructions per minute or 29,000 per second.
This is a stark increase from December 2024, when ChatGPT was handling about 1 billion messages per day. Having launched in November 2022, it’s become one of the fastest growing consumer apps of all time.