'Explainable AI' can efficiently detect AR/VR cybersickness

Explainable AI for AR/VR Cybersickness Detection

Augmented Reality (AR) and Virtual Reality (VR) technologies have been gaining popularity in recent years. These technologies have the potential to revolutionize various industries, including gaming, education, healthcare, and more. However, one of the major challenges associated with AR/VR is cybersickness.

Cybersickness is a type of motion sickness that occurs when a person experiences a disconnect between what they see and what they feel. This can lead to symptoms such as nausea, dizziness, and headaches. Cybersickness can be a significant barrier to the adoption of AR/VR technologies, as it can limit the amount of time people can spend using these technologies.

Fortunately, Explainable AI can help efficiently detect AR/VR cybersickness. Explainable AI is a type of AI that provides explanations for its decisions. This is particularly important in the context of cybersickness detection, as it allows users to understand why they are experiencing symptoms.

Explainable AI works by analyzing various data points, such as head movements, eye movements, and heart rate. By analyzing this data, the AI can detect patterns that are indicative of cybersickness. Once cybersickness is detected, the AI can provide an explanation for why the user is experiencing symptoms.

Explainable AI has several advantages over traditional methods of cybersickness detection. Firstly, it is more accurate than self-reporting, as users may not always be aware of their symptoms. Secondly, it is less invasive than physiological monitoring, as it does not require the use of sensors or other devices. Finally, it provides users with an explanation for their symptoms, which can help them better understand how to prevent cybersickness in the future.

In conclusion, Explainable AI is a powerful tool for detecting AR/VR cybersickness. By providing explanations for its decisions, it can help users better understand why they are experiencing symptoms. This can lead to more effective prevention strategies and a better overall AR/VR experience.

https://www.lifetechnology.com/blogs/life-technology-technology-news/explainable-ai-can-efficiently-detect-ar-vr-cybersickness

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Uber Shares Jump as it Reports Higher Sales

Uber, the ride-hailing giant, reported higher sales in its latest earnings report, causing its shares to jump in the stock market.

The company reported a revenue of $3.17 billion in the second quarter of 2021, which is a 105% increase from the same period last year. This is also higher than the expected revenue of $3.1 billion.

Uber's net loss for the quarter was $509 million, which is an improvement from the $1.78 billion loss in the same period last year.

The company's CEO, Dara Khosrowshahi, attributed the strong performance to the recovery of the ride-hailing business as more people get vaccinated and travel restrictions are lifted.

Uber's food delivery business, Uber Eats, also saw a significant increase in revenue, with a 85% year-over-year growth. The company's delivery business has been a key driver of growth during the pandemic as more people order food online.

The positive earnings report caused Uber's shares to jump by more than 10% in after-hours trading.

Overall, the strong performance of Uber in the second quarter of 2021 is a promising sign for the company's future growth and profitability.

https://www.lifetechnology.com/blogs/life-technology-technology-news/uber-shares-jump-as-it-reports-higher-sales

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Recycling Valuable Metals from Spent Lithium Ion Batteries using Spinning Reactors

As the world becomes more dependent on technology, the demand for lithium ion batteries has increased significantly. However, the disposal of spent batteries has become a major environmental concern. These batteries contain valuable metals such as cobalt, nickel, and lithium, which can be recycled and reused in new batteries.

Spinning reactors have been developed as an efficient and cost-effective method for recycling these valuable metals from spent lithium ion batteries. The process involves shredding the batteries into small pieces and then placing them in a spinning reactor. The reactor uses centrifugal force to separate the metals from the other materials in the battery.

The spinning reactor method has several advantages over traditional recycling methods. Firstly, it is a much faster process, taking only a few minutes to separate the metals. Secondly, it is a more environmentally friendly method as it does not require the use of harmful chemicals. Finally, it is a more cost-effective method as it requires less energy and fewer resources.

The recycled metals can then be used in the production of new batteries, reducing the need for new mining and extraction of these valuable metals. This not only reduces the environmental impact of mining but also helps to conserve natural resources.

In conclusion, the use of spinning reactors for recycling valuable metals from spent lithium ion batteries is a promising solution to the growing problem of battery waste. It is an efficient, environmentally friendly, and cost-effective method that can help to conserve natural resources and reduce the environmental impact of mining.

https://www.lifetechnology.com/blogs/life-technology-technology-news/recycling-valuable-metals-from-spent-lithium-ion-batteries-using-spinning-reactors

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Neural Networks on Photonic Chips: Harnessing Light for Ultra-Fast and Low-Power Artificial Intelligence

Artificial intelligence (AI) has become an integral part of our lives, from voice assistants to self-driving cars. However, the current AI systems are limited by their high power consumption and slow processing speeds. To overcome these limitations, researchers are exploring the use of photonic chips to build neural networks.

What are Photonic Chips?

Photonic chips are electronic circuits that use light instead of electricity to transmit and process information. They are made of silicon and other materials that can manipulate light waves. Photonic chips have several advantages over traditional electronic chips, including:

• Higher processing speeds
• Lower power consumption
• Less heat generation
• Greater bandwidth

Neural Networks on Photonic Chips

Neural networks are a type of AI system that mimics the structure and function of the human brain. They consist of interconnected nodes that process and transmit information. Neural networks are used in a variety of applications, including image recognition, speech recognition, and natural language processing.

By building neural networks on photonic chips, researchers can take advantage of the high processing speeds and low power consumption of photonic circuits. This can lead to AI systems that are faster, more efficient, and more accurate than current systems.

Applications of Photonic Neural Networks

The use of photonic neural networks has several potential applications, including:

• Self-driving cars: Photonic neural networks can process visual information from cameras and lidar sensors in real-time, allowing for faster and more accurate decision-making.
• Medical imaging: Photonic neural networks can analyze medical images, such as X-rays and MRIs, to detect abnormalities and assist in diagnosis.
• Speech recognition: Photonic neural networks can process speech signals with greater accuracy and speed than current systems, improving the performance of voice assistants and other speech-based applications.

Conclusion

The use of photonic chips to build neural networks has the potential to revolutionize the field of artificial intelligence. By harnessing the power of light, researchers can create AI systems that are faster, more efficient, and more accurate than current systems. The applications of photonic neural networks are vast, and we can expect to see them in a variety of industries in the near future.

https://www.lifetechnology.com/blogs/life-technology-technology-news/neural-networks-on-photonic-chips-harnessing-light-for-ultra-fast-and-low-power-artificial-intelligence

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Mapping the Inequalities of Low-Carbon Electricity

Low-carbon electricity is an important aspect of reducing greenhouse gas emissions and combating climate change. However, the distribution of low-carbon electricity is not equal across the globe. Some countries have access to more low-carbon electricity than others, and within countries, there are often disparities in access based on income and location.

Mapping these inequalities can help identify areas that need more investment in low-carbon electricity infrastructure and policies to ensure that everyone has access to clean energy.

Global Inequalities

According to the International Energy Agency, in 2019, only 27% of global electricity generation came from low-carbon sources such as renewable energy and nuclear power. However, the distribution of low-carbon electricity is not equal across the globe. Europe and North America have the highest share of low-carbon electricity, while Africa and Asia have the lowest.

Within countries, there are often disparities in access to low-carbon electricity based on income and location. In the United States, for example, low-income households and communities of color are more likely to live near polluting power plants and have less access to renewable energy sources.

Mapping Inequalities

Mapping the inequalities of low-carbon electricity can help identify areas that need more investment in clean energy infrastructure and policies. For example, mapping the distribution of low-carbon electricity in the United States can help identify areas where low-income households and communities of color have less access to renewable energy sources.

Mapping can also help identify areas where renewable energy sources are underutilized. For example, in some parts of Africa, there is great potential for solar energy, but the infrastructure and policies needed to support it are lacking.

Conclusion

Mapping the inequalities of low-carbon electricity is an important step in ensuring that everyone has access to clean energy. By identifying areas that need more investment in clean energy infrastructure and policies, we can work towards a more equitable distribution of low-carbon electricity and a more sustainable future.

https://www.lifetechnology.com/blogs/life-technology-technology-news/mapping-the-inequalities-of-low-carbon-electricity

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New Inorganic Wide-Bandgap Perovskite Subcells that are both Efficient and Stable

Perovskite solar cells have been gaining popularity in recent years due to their high efficiency and low cost. However, the stability of these cells has been a major concern, as they tend to degrade quickly in the presence of moisture and heat. To address this issue, researchers have been exploring new materials that can improve the stability of perovskite solar cells.

One promising material is inorganic wide-bandgap perovskite, which has been shown to be both efficient and stable. In a recent study, researchers developed a new type of perovskite solar cell that uses inorganic wide-bandgap perovskite subcells.

The new subcells have a bandgap of 1.9 eV, which is wider than the bandgap of traditional perovskite subcells. This wider bandgap allows the cells to absorb more sunlight and convert it into electricity more efficiently. In addition, the inorganic nature of the material makes it more stable than traditional perovskite materials.

The researchers tested the new subcells and found that they had an efficiency of 21.7%, which is comparable to the efficiency of traditional perovskite solar cells. However, the new subcells showed much better stability, with no degradation observed after 1,000 hours of exposure to light and heat.

These results are promising for the future of perovskite solar cells. By using inorganic wide-bandgap perovskite subcells, researchers can improve the efficiency and stability of these cells, making them a more viable option for renewable energy production.

https://www.lifetechnology.com/blogs/life-technology-technology-news/new-inorganic-wide-bandgap-perovskite-subcells-that-are-both-efficient-and-stable

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