Tech giants dominate research but the line between real breakthrough and product showcase can be fuzzy. Some scientists have had enough.
If AI is really going to make a difference to patients we need to know how it works when real humans get their hands on it, in real situations.
Mark Rober’s Tesla crash story and video on self-driving cars face significant scrutiny for authenticity, bias, and misleading claims, raising doubts about his testing methods and the reliability of the technology he promotes.
Questions to inspire discussion.
Tesla Autopilot and Testing 🚗 Q: What was the main criticism of Mark Rober’s Tesla crash video? A: The video was criticized for failing to use full self-driving mode despite it being shown in the thumbnail and capable of being activated the same way as autopilot. 🔍 Q: How did Mark Rober respond to the criticism about not using full self-driving mode? A: Mark claimed it was a distinction without a difference and was confident the results would be the same if he reran the experiment in full self-driving mode. 🛑 Q: What might have caused the autopilot to disengage during the test?
A new study finds that staying up late, known as having an “evening chronotype,” is associated with a higher risk of depression.
La Niña conditions are waning, and a transition to ENSO-neutral is favored in the next month.
Researchers turn to the vascular system of plants to solve a major bioengineering problem blocking the regeneration of human tissues and organs.
Long non-coding RNAs (long ncRNAs,) are a type of RNA, generally defined as transcripts more than 200 nucleotides that are not translated into protein.
Long non-coding transcripts are found in many species.
LncRNAs are extensively reported to be involved in transcriptional regulation, and epigenetic regulation.
Long non coding RNA has been proven to be associated with multiple diseases, such as cardiovascular diseases, rheumatic diseases, cancer etc.
More detailed information ons are provided in the link below.
Unveiled at CES 2025, Roborock’s innovative robot vacuum with an arm, Saros Z70, is now available as a pre-order bundle in the US store. According to the company, consumers can get the Saros Z70 for $1,899 with another Robocok product. This device’s availability is expected in early May.
Roborock previewed the Saros Z70 to BGR a little before its official announcement at CES, and the company’s view for the future of the robot vacuum segment future is impressive. Roborock says the Saros Z70 features a foldable robotic arm with five axes that can deploy itself to clean previously obstructed areas and put away small items such as socks, small towels, tissue papers, and sandals under 300g.
While I can understand the appeal of the robot vacuum going a step further–I think the ability to climb different areas is more interesting with the latest Roborock Qrevo Curv and Saros 10R –it feels a bit too much not removing your dirty socks from the floor; you know?
The healthcare industry faces a significant shift towards digital health technology, with a growing demand for real-time and continuous health monitoring and disease diagnostics [1, 2, 3]. The rising prevalence of chronic diseases, such as diabetes, heart disease, and cancer, coupled with an aging population, has increased the need for remote and continuous health monitoring [4, 5, 6, 7]. This has led to the emergence of artificial intelligence (AI)-based wearable sensors that can collect, analyze, and transmit real-time health data to healthcare providers so that they can make efficient decisions based on patient data. Therefore, wearable sensors have become increasingly popular due to their ability to provide a non-invasive and convenient means of monitoring patient health. These wearable sensors can track various health parameters, such as heart rate, blood pressure, oxygen saturation, skin temperature, physical activity levels, sleep patterns, and biochemical markers, such as glucose, cortisol, lactates, electrolytes, and pH and environmental parameters [1, 8, 9, 10]. Wearable health technology includes first-generation wearable technologies, such as fitness trackers, smartwatches, and current wearable sensors, and is a powerful tool in addressing healthcare challenges [2].
The data collected by wearable sensors can be analyzed using machine learning (ML) and AI algorithms to provide insights into an individual’s health status, enabling early detection of health issues and the provision of personalized healthcare [6,11]. One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare. This enables individuals and healthcare providers to proactively address symptomatic conditions before they become more severe [12,13,14,15]. Wearable devices can also encourage healthy behavior by providing incentives, reminders, and feedback to individuals, such as staying active, hydrating, eating healthily, and maintaining a healthy lifestyle by measuring hydration biomarkers and nutrients.