PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
The creation of the Global Partnership on Artificial Intelligence (GPAI) reflects the growing interest of states in AI technologies. The initiative, which brings together 14 countries and the European Union, will help participants establish practical cooperation and formulate common approaches to the development and implementation of AI. At the same time, it is a symptom of the growing technological rivalry in the world, primarily between the United States and China. Russia's ability to interact with the GPAI may be limited for political reasons, but, from a practical point of view, cooperation would help the country implement its national AI strategy. The Global Partnership on Artificial Intelligence (GPAI) was officially launched on June 15, 2020, at the initiative of the G7 countries alongside Australia, India, Mexico, New Zealand, South Korea, Singapore, Slovenia and the European Union. According to the Joint Statement from the Founding Members, the GPAI is an "international and multistakeholder initiative to guide the responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growth."
AI can save time and money in the search for treatments for emerging diseases, including COVID-19. Artificial intelligence (AI) has been a powerful tool in the search for COVID-19 treatments. In January, BenevolentAI identified a drug for rheumatoid arthritis as a potential therapy for the novel coronavirus. It's now being tested in large-scale trials around the world. AI models and algorithms can save time and money in the search for potential drug leads for emerging diseases.
The COVID-19 pandemic has had a profound impact across industries and healthcare in particular--every aspect of it is undergoing change--from diagnosis to treatment and through the entire continuum of care. This has also created an urgency in the healthcare industry, to look for innovative solutions and a boost to the faster, efficient application of technologies like Artificial Intelligence (AI) and Deep Learning. Pathology is one area which stands to greatly benefit from these applications.
Depending on your opinion, Artificial Intelligence is either a threat or the next big thing. Even though its deep learning capabilities are being applied to help solve large problems, like the treatment and prevention of human and genetic disorders, or small problems, like what movie to stream tonight, AI in many of its forms (such as machine learning, deep learning and cognitive computing) is still in its infancy in terms of being adopted to generate software code. AI is evolving from the stuff of science fiction, research, and limited industry implementations, to adoption across a multitude of fields, including retail, banking, telecoms, insurance, healthcare, and government. However, for the one field ripe for AI adoption – the software industry – progress is curiously slow. Consider this: why isn't an industry, which is built on esoteric symbols, machine syntax, and repetitive loops and functions, all-in on automating code?
Elon Musk plans to link human brains to computers using tiny implants, but a new report warns the implants could leave us vulnerable to hackers. Speaking with Zdnet, Experts said cybercriminals can access these brain-computer interfaces (BCIs) to erase your skills and read thoughts or memories – a breach worse than any other system. To make the technology secure, systems need to'ensure that no unauthorized person can modify their functionality.' This could mean using similar security protocols found in smartphones such as encryption to antivirus software. Musk has been working on his startup Neuralink since 2016, which he says will one-day human brains to computers in order to avoid our species from being outpaced by artificial intelligence.
Currently, Artificial Intelligence (AI) is progressing at a great pace and deep learning is one of the main reasons for this, so all the people need to get a basic understanding of it. Deep Learning is a subset of Machine Learning, which in turn is a subset of Artificial Intelligence. Deep Learning uses a class of algorithms called artificial neural networks which are inspired by the way the biological neural network functions inside the brain. The advancement in the field of deep learning is due to the tremendous increase in computational power and the presence of a huge amount of data. Deep learning is very much efficient in problem-solving as compared to other traditional machine learning algorithms.
The U.S. Department of Education's Institute of Education Sciences has awarded the National Center for Research on Education Access and Choice (REACH) at Tulane University a $100,000 contract to collect data from approximately 150,000 school websites across the country to see how the nation's education system is responding to the coronavirus pandemic. The project, which will track traditional public schools, charter schools and private schools, aims to quickly answer questions that are critical for understanding how students are learning when school buildings are closed. Key questions include: how many schools are providing any kind of instructional support; which are delivering online instruction; what resources are they offering to students and how do students stay in contact with teachers? "This data will also help answer important questions about equity in the school system, showing how responses differ according to characteristics like spending levels, student demographics, internet access, and if there are differences based on whether it is a private, charter or traditional public school," said REACH National Director Douglas N. Harris, Schlieder Foundation Chair in Public Education and chair of economics at Tulane University School of Liberal Arts. REACH will work in cooperation with Nicholas Mattei, assistant professor of computer science at Tulane University School of Science and Engineering, to create a computer program that will collect data from every school and district website in the country.
The drug, baricitinib, is currently marketed by Eli Lilly to treat rheumatoid arthritis. Now, thanks to AI, it is being tested against COVID-19 in a major randomised-controlled trial in collaboration with the U.S. National Institute for Allergies and Infectious Diseases (NIAID) in combination with remdesivir, an antiviral drug from Gilead Sciences that recently won emergency-use approval for COVID-19. Eli Lilly has now commenced its own independent trial of baricitinib as a therapy for COVID-19 in South America, Europe and Asia.
This more than doubles the startup's total raised, and a spokesperson says it will be used to accelerate Sight's operations globally -- with a focus on the U.S. -- as Sight advances R&D for the detection of conditions like sepsis and cancer, as well as factors affecting COVID-19. Blood tests are generally unpleasant -- not to mention costly. On average, getting blood work done at a lab costs uninsured patients between $100 and $1,500. In the developing world, where the requisite equipment isn't always readily available, ancillary costs threaten to drive the price substantially higher. That's why Yossi Pollak, previously at Intel subsidiary Mobileye, and Daniel Levner, a former scientist at Harvard's Wyss Institute for Biologically Inspired Engineering, founded Sight Diagnostics in 2011.