Europe is leading one of the most important space travel efforts of this century.
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How Europe Will Save Space Travel
Posted in futurism
The new service lets consumers contribute to medical research, but still poses privacy concerns.
- By Karen Weintraub on November 17, 2018
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NASA engineers are using some of the most powerful supercomputers in the world to design rotary wing vehicles that combine both piloted and autonomous operations. Urban Air Mobility is a safe and efficient system that can transport a small number of passengers and cargo, without the need for long runways. Check it out: https://go.nasa.gov/2FvGPfH
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Amidst the ongoing threat of the California wildfires, a Tesla Model 3 owner has posted a brief demonstration of the electric sedan’s capability to maintain the air quality inside its cabin, despite the vehicle not being equipped with the Model S and X’s hospital-grade HEPA filter or a dedicated “Bioweapon Defense Mode.”
Elon Musk took to Twitter last week to offer the Model S and Model X as vehicles that can be used to transport people away from the ongoing CA wildfires. The Model S and X are capable of scrubbing the air inside the car, thanks to their large HEPA filters that are fitted with separate acid and alkaline gas neutralization layers. Later social media updates and anecdotes from Model S and X owners driving through the CA area indicate that Bioweapon Defense Mode helped maintain the air quality inside their vehicles.
In a follow-up tweet, Elon Musk noted that the Model 3’s air filtration system is not on the same “hospital-grade” level as that of the Model S and X, since the smaller vehicle does not have enough space to accommodate the HEPA filtration system in Tesla’s two flagship vehicles. This could be seen in the parts catalog for the vehicles, where the Model X HEPA filter was listed as “FILTER, HEPA, MDL X,” while the Model 3’s system was simply listed as “HVAC, CABIN FILTER, M3.”
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The Cancer Genome Atlas (TCGA) provides a high-quality resource of molecular data on a large variety of human cancers. Corces et al. used a recently modified assay to profile chromatin accessibility to determine the accessible chromatin landscape in 410 TCGA samples from 23 cancer types (see the Perspective by Taipale). When the data were integrated with other omics data available for the same tumor samples, inherited risk loci for cancer predisposition were revealed, transcription factors and enhancers driving molecular subtypes of cancer with patient survival differences were identified, and noncoding mutations associated with clinical prognosis were discovered.
Science, this issue p. eaav1898; see also p. 401.
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The brain has always been considered the main inspiration for the field of artificial intelligence(AI). For many AI researchers, the ultimate goal of AI is to emulate the capabilities of the brain. That seems like a nice statement but its an incredibly daunting task considering that neuroscientist are still struggling trying to understand the cognitive mechanism that power the magic of our brains. Despite the challenges, more regularly we are seeing AI research and implementation algorithms that are inspired by specific cognition mechanisms in the human brain and that have been producing incredibly promising results. Recently, the DeepMind team published a paper about neuroscience-inspired AI that summarizes the circle of influence between AI and neuroscience research.
You might be wondering what’s so new about this topic? Everyone knows that most foundational concepts in AI such as neural networks have been inspired by the architecture of the human brain. However, beyond that high level statement, the relationship between the popular AI/deep learning models we used everyday and neuroscience research is not so obvious. Let’s quickly review some of the brain processes that have a footprint in the newest generation of deep learning methods.
Attention is one of those magical capabilities of the human brain that we don’t understand very well. What brain mechanisms allow us to focus on a specific task and ignore the rest of the environment? Attentional mechanisms have become a recent source of inspiration in deep learning models such as convolutional neural networks(CNNs) or deep generative models. For instance, modern CNN models have been able to get a schematic representation of the input and ignore irrelevant information improving their ability of classifying objects in a picture.
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