Last month, Elon Musk's Neuralink demonstrated that it is possible to monitor brain activity from our phones. There were speculations around Neuralink of what potential it has for the future generations. Decoding brain signals has great implications in medicine. A disabled person can be assisted, can understand what a speechless person is feeling and more. So, can we know what someone is thinking?
TikTok appears to have avoided a US ban at the last minute... probably. President Trump has agreed to a deal "in concept" (via CNBC) that theoretically allays US security issues while letting it operate in the country. True to earlier discussions, Oracle and Walmart would claim a 20% investment stake in a newly formed TikTok Global company that will run the social video service's business in the US and "most of the users" worldwide. Oracle would become TikTok's "secure cloud provider" and hold on to American data, while Walmart would wield its e-commerce and advertising technology. The deal will also see TikTok Global pay over $5 billion in "new tax dollars" to the US Treasury, and join with Oracle, Walmart and investors like Coatue and Sequoia to launch an AI-powered educational video curriculum. The program would teach kids basics like math, reading and science, as well as more advanced subjects like computer engineering.
President Donald Trump said Saturday he has approved a deal in principle in which Oracle and Walmart will partner with the viral video-sharing app TikTok in the U.S., allowing the popular app to avoid a shutdown. "I have given the deal my blessing -- if they get it done that's great, if they don't that's okay too," Trump told reporters on the White House South Lawn before departing for North Carolina. "I approved the deal in concept." The U.S. Department of Commerce announced it would delay the prohibition of U.S. transactions with TikTok until next Sunday. Shortly after Trump's comments, Oracle announced it was chosen as TikTok's secure cloud provider and will become a minority investor with a 12.5% stake.
Li Xian, who works at a publishing company in Shanghai, says the Chinese mobile-payments app Alipay is indispensable. Over the past week she's used it to order and pay for dinner through a delivery service, buy movie tickets, pay her utility bills, and rent a bike. "It's my lifeline," Li says. "I can't remember the last time I used cash." She is far from an outlier.
Throughout the world, dust storms wreak havoc on many aspects of human life including health, aviation, solar power generation, and agriculture, among others. Given the hazards that this natural phenomena causes, it is imperative that societies are prepared for the onset of these storms to minimize economic loss and save lives. Utilizing the data received from Earth observation satellites, it is possible for atmospheric scientists to detect developing dust storms; however, even for experts, it can be difficult to detect dust storms in satellite images obscured by clouds, smoke, or nighttime conditions. Furthermore, manual detection requires atmospheric scientists to gather together the relevant satellite images, which takes time before a complete analysis can be made. The ability to automatically detect dust is potentially a large boon for the Earth science community.
Elon Musk is hailed as an innovator and disrupter who went from knowing next to nothing about building cars to running the world's most valuable automaker in the space of 16 years. But his record shows he is more of a fast learner who forged alliances with firms that had technology Tesla lacked, hired some of their most talented people, and then powered through the boundaries that limited more risk-averse partners. Now, Musk and his team are preparing to outline new steps in Tesla's drive to become a more self-sufficient company less reliant on suppliers at its "Battery Day" event on Tuesday. Musk has been dropping hints for months that significant advances in technology will be announced as Tesla strives to produce the low-cost, long-lasting batteries that could put its electric cars on a more equal footing with cheaper gasoline vehicles. New battery cell designs, chemistries and manufacturing processes are just some of the developments that would allow Tesla to reduce its reliance on its long-time battery partner, Japan's Panasonic, people familiar with the situation said.
Artificial intelligence can be used to diagnose cancer, predict suicide, and assist in surgery. In all these cases, studies suggest AI outperforms human doctors in set tasks. But when something does go wrong, who is responsible? There's no easy answer, says Patrick Lin, director of Ethics and Emerging Sciences Group at California Polytechnic State University. At any point in the process of implementing AI in healthcare, from design to data and delivery, errors are possible.
By Sumit Pandey Taoyuan City (Taiwan), Sep 20 (UNI) Even as the world awaits a COVID vaccine, Artificial intelligence (AI) can be used for detecting pneumonia caused by the pandemic which has claimed nearly a million lives globally. The dataset commonly used for this work is open source chest X-ray images from Kaggle or other open-source websites. Some of these models have reported an accuracy even greater than 98 percent, experts have said. The experts while calling for integrating the AI systems into the medical practice, said it would build a mutually-beneficial relationship between AI and Medicine. In future AI would offer greater efficiency or cost-effectiveness and Doctors (or Medical Staff) would offer AI the essential medical exposure of complex cases.
A brain mechanism referred to as "replay" inspired researchers at Baylor College of Medicine to develop a new method to protect deep neural networks, found in artificial intelligence (AI), from forgetting what they have previously learned. The study, in the current edition of Nature Communications, has implications for both neuroscience and deep learning. Deep neural networks are the main drivers behind the recent fast progress in AI. These networks are extremely good at learning to solve individual tasks. However, when they are trained on a new task, they typically lose the ability to solve the previously learned task completely.
Machine learning becomes engaging when we face various challenges and thus finding suitable datasets relevant to the use case is essential. Flexibility refers to the number of tasks that it supports. For example, Microsoft's COCO( Common Objects in Context) is used for object classification, detection, and segmentation. Add a bunch of captions for the same, and we can use it as a dataset for an image caption generator as well. Well, when we are just starting, we shall be working with some of the small and standard machine learning datasets like the CIFAR-10, MNIS, Iris, etc.