Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 4

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 .

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.

Researchers discover how the human brain organizes its visual memories through precise neural timing

Researchers at the University of Southern California have made a significant breakthrough in understanding how the human brain forms, stores and recalls visual memories. A new study, published in Advanced Science, harnesses human patient brain recordings and a powerful machine learning model to shed new light on the brain’s internal code that sorts memories of objects into categories—think of it as the brain’s filing cabinet of imagery.

The results demonstrated that the research team could essentially read subjects’ minds, by pinpointing the category of visual image being recalled, purely from the precise timing of the subject’s .

The work solves a fundamental neuroscience debate and offers exciting potential for future brain-computer interfaces, including memory prostheses to restore lost memory in patients with neurological disorders like dementia.

Tesla Autonomy Is AI’s Crowning Jewel; Diner Goes World Wide; Japan Trade Deal Announced

Questions to inspire discussion.

⚡ Q: What advantages does XAI’s proprietary cluster offer? A: XAI’s proprietary clusters, designed specifically for training, are uncatchable by competitors as they can’t be bought with money, creating an unbreachable moat in AI development.

Tesla’s Autonomy and Robotaxis.

🚗 Q: When is Tesla expected to launch unsupervised FSD? A: Tesla is expected to launch unsupervised FSD in the third quarter after polishing and testing, with version 14 potentially being unsupervised even if not allowed for public use.

🤖 Q: What is the significance of Tesla’s upcoming robotaxi launch? A: Tesla’s robotaxi launch is anticipated to be a historic moment, demonstrating that the complexity of autonomous driving technology has been overcome, allowing for leverage and scaling.

💰 Q: How might Tesla monetize its Autonomy feature? A: Tesla may charge monthly fees of $50-$100 for unsupervised use, including insurance, on top of personal insurance costs.

Continue reading “Tesla Autonomy Is AI’s Crowning Jewel; Diner Goes World Wide; Japan Trade Deal Announced” | >

I Tried the World’s First Tesla Diner (11 Hour Wait)

Questions to inspire discussion.

🍳 Q: What can diners expect in terms of food quality? A: The diner emphasizes local sourcing, natural ingredients, and fresh in-house preparation, with a menu designed by Eric Greensman, a professional chef.

Unique Offerings.

🤖 Q: What unique attractions does the Tesla diner offer? A: The diner showcases a fully functional Optimus robot on display and offers Tesla merchandise for purchase.

🍗 Q: Are there any special menu items or services? A: The diner features a self-service club with fried chicken and waffles, a souvenir cup for purchase, and a Tesla burger on the menu.

Practical Amenities.

Continue reading “I Tried the World’s First Tesla Diner (11 Hour Wait)” | >

Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence

Researchers used artificial intelligence to mine global venom proteomes and discovered novel peptides with antimicrobial activity. Several candidates showed efficacy against drug-resistant bacteria in laboratory and animal tests.

Computational clock identifies compounds that may rejuvenate aging brain cells

What if there was a way to make aging brain cells younger again? An international research team from Spain and Luxembourg recently set out to address this question. After developing an aging clock capable of assessing the biological age of the brain, they used it to identify possible brain-rejuvenating interventions. The computational tool they created, recently presented in the journal Advanced Science, constitutes a valuable resource to find compounds with therapeutic potential for neurodegenerative diseases.

As the world population is aging rapidly, with over two billion people projected to be above the age of 60 by 2050, age-related brain disorders are on the rise. Living longer but in is not only a daunting prospect, it also places a substantial burden on health care systems worldwide. The idea of being able to counteract the functional decline of our brain through rejuvenating interventions therefore sounds promising.

The question is, how can we identify compounds that have the potential to efficiently rejuvenate brain cells and to protect the from neurodegeneration? Prof. Antonio Del Sol and his teams of computational biologists, based both at CIC bioGUNE, member of BRTA, and the Luxembourg Centre for Systems Biomedicine (LCSB) from the University of Luxembourg, used their machine learning expertise to tackle the challenge.

Tailored deep brain stimulation improves walking in Parkinson’s disease

For patients with Parkinson’s disease, changes in their ability to walk can be dramatic. “Parkinson’s gait,” as it is often called, can include changes in step length and asymmetry between legs. This gait dysfunction reduces a person’s mobility, increases fall risk, and significantly impacts a patient’s quality of life.

While (DBS) is highly effective for lessening symptoms of tremors, rigidity, and bradykinesia (the slowing of movement), its impact on gait has been more variable and less predictable among patients with advanced gait-related problems. Significant challenges in enhancing DBS outcomes for advanced gait disorders have included the lack of a standardized gait metric for clinicians to use during programming, as well as understanding the impact of different stimulation factors on gait.

In a recent study, researchers at UCSF developed a systematic way to quantify key aspects of gait relevant to Parkinson’s and used machine learning to identify the best DBS settings for each individual. These personalized settings led to meaningful improvements in walking, such as faster, more stable steps, without worsening other symptoms.