The Register: MIT apologizes, permanently pulls offline huge dataset that taught AI systems to use racist, misogynistic slurs

The Register: MIT apologizes, permanently pulls offline huge dataset that taught AI systems to use racist, misogynistic slurs. “The training set, built by the university, has been used to teach machine-learning models to automatically identify and list the people and objects depicted in still images. For example, if you show one of these systems a photo of a park, it might tell you about the children, adults, pets, picnic spreads, grass, and trees present in the snap. Thanks to MIT’s cavalier approach when assembling its training set, though, these systems may also label women as whores or bitches, and Black and Asian people with derogatory language. The database also contained close-up pictures of female genitalia labeled with the C-word.”

BetaNews: How COVID-19 sparked a revolution in healthcare machine learning and AI

BetaNews: How COVID-19 sparked a revolution in healthcare machine learning and AI. “As with nearly every element of the healthcare system, applications of machine learning and artificial intelligence (AI) have also been transformed by the pandemic. Although the power of machine learning and AI was being put to significant use prior to the Coronavirus outbreak, there is now increased pressure to understand the underlying patterns to help us prepare for any epidemic that might hit the world in the future.”

Neowin: Google releases AI Explorables to make machine learning more accessible and participatory

Neowin: Google releases AI Explorables to make machine learning more accessible and participatory . “…much of the existing literature on machine learning, barring a few resources, can come off as abstruse and pedantic. Considering this, Google has taken an initiative called AI Explorables to make the core concepts of machine learning more accessible via a series of interactive explanations.”

SiliconANGLE: OpenAI debuts Jukebox, a machine learning framework that creates its own music

SiliconANGLE: OpenAI debuts Jukebox, a machine learning framework that creates its own music. “Artificial intelligence research outfit OpenAI Inc. has published a new machine learning framework that can generate its own music after being trained on raw audio. The new tool is called Jukebox, and the results are pretty impressive. Although the songs it made don’t quite sound like the real thing, they’re very close approximations to the originals.”

MIT News: Automating the search for entirely new “curiosity” algorithms

MIT News: Automating the search for entirely new “curiosity” algorithms. “Engineers have discovered many ways of encoding curious exploration into machine learning algorithms. A research team at MIT wondered if a computer could do better, based on a long history of enlisting computers in the search for new algorithms.”

EurekAlert: Using machine learning to estimate COVID-19’s seasonal cycle

EurekAlert: Using machine learning to estimate COVID-19’s seasonal cycle. “One of the many unanswered scientific questions about COVID-19 is whether it is seasonal like the flu – waning in warm summer months then resurging in the fall and winter. Now scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) are launching a project to apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to tease out the answer.”

The Next Web: An explanation of machine learning models even you could understand

The Next Web: An explanation of machine learning models even you could understand . “If you are new to data science, this title is not intended to insult you. It is my second post on the theme of a popular interview question that goes something like: ‘explain [insert technical topic] to me as though I were a five-year-old.’ Turns out, hitting the five-year-old comprehension level is pretty tough. So, while this article may not be perfectly clear to a kindergartener, it should be clear to someone with little to no background in data science (and if it isn’t by the end, please let me know in the comments).”

Johns Hopkins University: JHU researchers to use machine learning to predict heart damage in COVID-19 victims

Johns Hopkins University: JHU researchers to use machine learning to predict heart damage in COVID-19 victims. “Johns Hopkins researchers recently received a $195,000 Rapid Response Research grant from the National Science Foundation to, using machine learning, identify which COVID-19 patients are at risk of adverse cardiac events such as heart failure, sustained abnormal heartbeats, heart attacks, cardiogenic shock and death. Increasing evidence of COVID-19’s negative impacts on the cardiovascular system highlights a great need for identifying COVID-19 patients at risk for heart problems, the researchers say. However, no such predictive capabilities currently exist.”

Brandeis NOW: How artificial intelligence is helping scientists find a coronavirus treatment

Brandeis NOW: How artificial intelligence is helping scientists find a coronavirus treatment. “More than 50,000 academic articles have been written about COVID-19 since the virus appeared in November. The volume of new information isn’t necessarily a good thing. Not all of the recent coronavirus literature has been peer reviewed, while the sheer number of articles makes it challenging for accurate and promising research to stand out or be further studied. Computer science and linguistics professor James Pustejovsky is leading a Brandeis team in creating an artificial intelligence platform called Semantic Visualization of Scientific Data — or SemViz — that can sort through the growing mass of published work on coronavirus and help biologists who study the disease gain insights and notice patterns and trends across research that could lead to a treatment or cure.”

Berkeley Lab: Machine Learning Tool Could Provide Unexpected Scientific Insights into COVID-19

Berkeley Lab: Machine Learning Tool Could Provide Unexpected Scientific Insights into COVID-19. “A team of materials scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day. The tool, live at covidscholar.org, uses natural language processing techniques to not only quickly scan and search tens of thousands of research papers, but also help draw insights and connections that may otherwise not be apparent.”

AWS (Amazon): AWS launches machine learning enabled search capabilities for COVID-19 dataset

AWS (Amazon): AWS launches machine learning enabled search capabilities for COVID-19 dataset. “As the world grapples with COVID-19, researchers and scientists are united in an effort to understand the disease and find ways to detect and treat infections as quickly as possible. Today, Amazon Web Services (AWS) launched CORD-19 Search, a new search website powered by machine learning that can help researchers quickly and easily search tens of thousands of research papers and documents using natural language questions.”

Campus Technology: Coursera Machine Learning Tool Matches On-Campus Courses with MOOC Resources

Campus Technology: Coursera Machine Learning Tool Matches On-Campus Courses with MOOC Resources. “CourseMatch can ‘ingest’ on-campus course catalogs in more than 100 languages and map them to the most relevant Coursera courses in any of the languages available on the platform. It then returns up to five ‘matches’ along with a relevance score, with higher scores given for stronger matches.”

Towards Data Science: Shakespeare Meets Google’s Flax

Towards Data Science: Shakespeare Meets Google’s Flax. “Google Researcher introduced Flax, a new rising star in Machine Learning, a few months ago. A lot has happened since then and the pre-release has improved tremendously. My own experiments with CNNs on Flax are bearing fruit and I am still amazed about the flexibility compared to Tensorflow. Today I will show you an application of RNNs in Flax: Character-Level Language Model.”