Goto

Collaborating Authors

Results


U.S. government agencies to use AI to cull and cut outdated regulations

#artificialintelligence

WASHINGTON (Reuters) - The White House Office of Management and Budget (OMB) said Friday that federal agencies will use artificial intelligence to eliminate outdated, obsolete, and inconsistent requirements across tens of thousands of pages of government regulations. A 2019 pilot project used machine learning algorithms and natural language processing at the Department of Health and Human Services. The test run found hundreds of technical errors and outdated requirements in agency rulebooks, including requests to submit materials by fax. OMB said all federal agencies are being encouraged to update regulations using AI and several agencies have already agreed to do so. Over the last four years, the number of pages in the Code of Federal Regulations has remained at about 185,000.


FDA Proposes New Regulatory Framework for Artificial Intelligence/Machine Learning Algorithm

#artificialintelligence

For quite a while, artificial intelligence and machine learning models are leveraged in the healthcare industry to improve patient outcomes. They have been utilized in various scans, for diagnosing various diseases, for the drug manufacturing and planning the treatment for various diseases. The involvement of these AI/ML models is observed in the surgical process as well. With the amount of data being generated nowadays, the traditional AI/M- based software models are often scrutinized under the lens of performance and accuracy. As new advances are shaping the future of healthcare, the modification of the existing software models has been recognized by healthcare professionals.


FDA Proposes New Regulatory Framework for Artificial Intelligence/Machine Learning Algorithm

#artificialintelligence

For quite a while, artificial intelligence and machine learning models are leveraged in the healthcare industry to improve patient outcomes.


U.S. government agencies to use AI to cull and cut outdated regulations

#artificialintelligence

WASHINGTON (Reuters) - The White House Office of Management and Budget (OMB) said Friday that federal agencies will use artificial intelligence to eliminate outdated, obsolete, and inconsistent requirements across tens of thousands of pages of government regulations. A 2019 pilot project used machine learning algorithms and natural language processing at the Department of Health and Human Services. The test run found hundreds of technical errors and outdated requirements in agency rulebooks, including requests to submit materials by fax. OMB said all federal agencies are being encouraged to update regulations using AI and several agencies have already agreed to do so. Over the last four years, the number of pages in the Code of Federal Regulations has remained at about 185,000.


A New Model of the Brain's Real-Life Neural Networks - Neuroscience News

#artificialintelligence

Summary: A new computational model predicts how information deep inside the brain could flow from one network to another, and how neural network clusters can self optimize over time. Researchers at the Cyber-Physical Systems Group at the USC Viterbi School of Engineering, in conjunction with the University of Illinois at Urbana-Champaign, have developed a new model of how information deep in the brain could flow from one network to another and how these neuronal network clusters self-optimize over time. Their work, chronicled in the paper "Network Science Characteristics of Brain-Derived Neuronal Cultures Deciphered From Quantitative Phase Imaging Data," is believed to be the first study to observe this self-optimization phenomenon in in vitro neuronal networks, and counters existing models. Their findings can open new research directions for biologically inspired artificial intelligence, detection of brain cancer and diagnosis and may contribute to or inspire new Parkinson's treatment strategies. The team examined the structure and evolution of neuronal networks in the brains of mice and rats in order to identify the connectivity patterns.


Mathematics: The Tao of Data Science · Harvard Data Science Review

#artificialintelligence

Confucius once said, "Fish forget they live in water; people forget they live in the Tao" (Lin, 2007). Analogously, it may be easy for data scientists to forget they live in a world defined and permeated by mathematics. The two pieces, "Ten Research Challenge Areas in Data Science" by Jeannette M. Wing and "Challenges and Opportunities in Statistics and Data Science: Ten Research Areas" by Xuming He and Xihong Lin, provide an impressively complete list of data science challenges from luminaries in the field of data science. They have done an extraordinary job, so this response offers a complementary viewpoint from a mathematical perspective and evangelizes advanced mathematics as a key tool for meeting the challenges they have laid out. Notably, we pick up the themes of scientific understanding of machine learning and deep learning, computational considerations such as cloud computing and scalability, balancing computational and statistical considerations, and inference with limited data.


Trust Algorithms? The Army Doesn't Even Trust Its Own AI Developers - War on the Rocks

#artificialintelligence

Last month, an artificial intelligence agent defeated human F-16 pilots in a Defense Advanced Research Projects Agency challenge, reigniting discussions about lethal AI and whether it can be trusted. Allies, non-government organizations, and even the U.S. Defense Department have weighed in on whether AI systems can be trusted. But why is the U.S. military worried about trusting algorithms when it does not even trust its AI developers? Any organization's adoption of AI and machine learning requires three technical tools: usable digital data that machine learning algorithms learn from, computational capabilities to power the learning process, and the development environment that engineers use to code. However, the military's precious few uniformed data scientists, machine learning engineers, and data engineers who create AI-enabled applications are currently hamstrung by a lack of access to these tools.


FDA proposes new regulatory framework on artificial intelligence, machine learning technologies

#artificialintelligence

The findings come from a cross-sectional study, published in BMJ Open, of the comments submitted to the US Food and Drug Administration (FDA) 'Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)--Discussion Paper and Request for Feedback'. Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform health care, continually incorporating insights from the vast amount of data generated every day during the delivery of health care. Many such devices must have regulatory approval or clearance before being available for clinical practice, and in the US that regulation falls to the FDA. The suitability of traditional medical device regulatory pathways for AI/ML have been called into question because the nature of the technology means it is continually evolving and adapting to improve performance. Under the current framework it would mean that as devices evolved they would require further review and approval, which could be time consuming and may affect patient safety and interests.


You can now hum into Google Search and it will find the song

ZDNet

Google on Thursday announced a handful of updates to its Search function, touting that it has implemented artificial intelligence (AI) and machine learning to improve the user experience. Users can now hum, whistle, or sing a melody to Google via the mobile app by tapping the mic icon and saying, "What's this song?" or by clicking the "Search a song" button. Humming for 10-15 seconds will give Google's machine learning algorithm the chance to match the song. The feature is currently available in English on iOS, and in around 20 languages on Android, with more languages coming to both platforms in the future, Google said. The search giant's AI updates also span spelling and general search queries. This includes a new spelling algorithm that uses a deep neural net, which Google claims has significantly improved its ability to decipher misspellings.


Machine learning uncovers potential new TB drugs

#artificialintelligence

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability. Using this new approach, which allows computer models to account for uncertainty in the data they're analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that cause tuberculosis. This method, which has previously been used by computer scientists but has not taken off in biology, could also prove useful in protein design and many other fields of biology, says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "This technique is part of a known subfield of machine learning, but people have not brought it to biology," Berger says.