Sparrow is Amazon’s new intelligent robotic system that streamlines the fulfillment process by moving individual products before they get packaged—a major technological advancement to support our employees.
SAN FRANCISCO — Two weeks after closing a $44 billion deal to buy Twitter, Elon Musk painted a bleak financial picture for the social media company and outlined a series of changes for employees in his first companywide emails to staff.
In two emails sent to workers late on Wednesday, Mr. Musk said the economy was challenging. He added that he planned to end Twitter’s remote work policy and wanted employees to renew their focus on generating revenue and fighting spam.
“Sorry that this is my first email to the company, but there is no way to sugarcoat the message,” Mr. Musk, 51, wrote in one email. “The economic picture ahead is dire.” Twitter was too heavily dependent on advertising and vulnerable to pullbacks in brand spending, he added, and would need to bolster the revenue it gets from subscriptions.
This past Monday (Nov. 7th) I was given a behind the scenes look at Dr. Alfonso Fasano’s neuromodulation clinic, one of the busiest of its kind in the world.
On his schedule for that day were about 25 patients, which is fairly typical for him. But these were not exactly 25 patients that a typical doctor might see in a given day. They were patients whom the Canadian medical system had funneled to him, individuals who not even most movement disorder specialists, let alone neurologists or general practitioners, could properly treat. For him and his team though, it was just another Monday.
In the effort to efficiently convert heat into electricity, easily accessible materials from harmless raw materials open up new perspectives in the development of safe and inexpensive so-called thermoelectric materials. A synthetic copper mineral acquires a complex structure and microstructure through simple changes in its composition, thereby laying the foundation for the desired properties, according to a study published recently in the journal Angewandte Chemie.
The novel synthetic material is composed of copper, manganese, germanium, and sulfur, and it is produced in a rather simple process, explains materials scientist Emmanuel Guilmeau, CNRS researcher at CRISMAT laboratory, Caen, France, who is the corresponding author of the study. The powders are simply mechanically alloyed by ball-milling to form a precrystallized phase, which is then densified by 600 degrees Celsius.
The Celsius scale, also known as the centigrade scale, is a temperature scale named after the Swedish astronomer Anders Celsius. In the Celsius scale, 0 °C is the freezing point of water and 100 °C is the boiling point of water at 1 atm pressure.
We are still struggling to account for consciousness. A new hypothesis by psychologist Nicholas Humphrey challenges the basis of the discussion and argues sentience isn’t what we think.
The SisAl Pilot project produces solar-grade silicon from Spanish quartz without using coal and with zero CO2 emissions. The company behind the project claims that the process is cheaper and more sustainable.
China gave up on their SLS clone, deciding that building a giant rocket that blows up with every mission is a bad idea. Maybe Congress, who is forcing NASA to build the SLS, could learn from this.
When China started to get serious about sending its astronauts to the Moon in the middle of the last decade, the country’s senior rocket scientists began to plan a large booster to do the job.
In 2016 the country’s state-owned rocket developer, the China Academy of Launch Vehicle Technology, began designing the “Long March 9” rocket. It looked more or less like the large heavy lifter NASA was designing at the time, the Space Launch System. Like NASA’s large rocket, the Long March 9 had a core stage and boosters and was intended to be fully expendable.
Continue reading “China is scrapping plans for an SLS-like rocket in favor of reusable booster” »
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Wearable technology is capable of tracking various measures of human health and is getting better all the time. New research shows how this could come to mean real-time feedback on posture and body mechanics. A research team at Cornell University has demonstrated this functionality in a novel camera system for the wrist, which it hopes to work into smartwatches of the future.
The system is dubbed BodyTrak and comes from the same lab behind a face-tracking wearable we looked at earlier in the year that is able to recreate facial expressions on a digital avatar through sonar. This time around, the group made use of a tiny dime-sized RGB camera and a customized AI to construct models of the entire body.
The camera is worn on the wrist and relays basic images of body parts in motion to a deep neural network, which had been trained to turn these snippets into virtual recreations of the body. This works in real time and fills in the blanks left by the camera’s images to construct 3D models of the body in 14 different poses.
The number of AI and, in particular, machine learning (ML) publications related to medical imaging has increased dramatically in recent years. A current PubMed search using the Mesh keywords “artificial intelligence” and “radiology” yielded 5,369 papers in 2021, more than five times the results found in 2011. ML models are constantly being developed to improve healthcare efficiency and outcomes, from classification to semantic segmentation, object detection, and image generation. Numerous published reports in diagnostic radiology, for example, indicate that ML models have the capability to perform as good as or even better than medical experts in specific tasks, such as anomaly detection and pathology screening.
It is thus undeniable that, when used correctly, AI can assist radiologists and drastically reduce their labor. Despite the growing interest in developing ML models for medical imaging, significant challenges can limit such models’ practical applications or even predispose them to substantial bias. Data scarcity and data imbalance are two of these challenges. On the one hand, medical imaging datasets are frequently much more minor than natural photograph datasets such as ImageNet, and pooling institutional datasets or making them public may be impossible due to patient privacy concerns. On the other hand, even the medical imaging datasets that data scientists have access to could be more balanced.
In other words, the volume of medical imaging data for patients with specific pathologies is significantly lower than for patients with common pathologies or healthy people. Using insufficiently large or imbalanced datasets to train or evaluate a machine learning model may result in systemic biases in model performance. Synthetic image generation is one of the primary strategies to combat data scarcity and data imbalance, in addition to the public release of deidentified medical imaging datasets and the endorsement of strategies such as federated learning, enabling machine learning (ML) model development on multi-institutional datasets without data sharing.
