The xenobots – made from frog cells – are the first multicellular organisms found to reproduce in this way. Xenobots were first created last year, using cells taken from the embryo of the frog species Xenopus laevis. Under the right lab conditions, the cells formed small structures that could self-assemble, move in groups and sense their environment. Now, the researchers behind the work have found that xenobots can also self-replicate. Josh Bongard at the University of Vermont and Michael Levin at Tufts University in Massachusetts and their colleagues began by extracting rapidly dividing stem cells that are destined to become skin cells from frog embryos.
The seafood industry has long been a vital economic force in Massachusetts, generating $14 billion annually in sales and employing more than 127,000. But despite the strength of the industry here and our rich fishing grounds and strong ports, the Bay State still imports far more seafood than it produces. Today the U.S. imports 90% of the seafood we eat, and it's clear that wild capture fisheries alone can't meet our increasing demand for seafood. It's time for the United States take action to diversify our food supply by encouraging development of the nascent aquaculture industry. Aquaculture -- or fish farming -- needs to play a bigger role in producing sustainable protein for our growing population.
Running a business optimally is not only about finding and convincing customers to buy your product. It's also about retaining customers who've already bought your product, so they will remain loyal to your business. Stopping customer "churn" has long been a big business behind the scenes, but historically it's been a slow, tedious process. Boston, Massachusetts-based marketing-tech startup OfferFit, which announced $14 million in series A funding today, has made halting customer churn its life's ambition in part by replacing standard manual A/B testing with an AI implementation that's faster to respond to customers, more accurate, and more efficient to use. A/B testing (also known as split-testing) is the process of comparing two versions of a web page, email, or other marketing asset and measuring the difference in performance.
Training a single AI model can emit as much carbon as five cars in their lifetimes MIT Tech Review Training an AI model has the equivalent carbon footprint as five American cars, including fuel usage, according to researchers at the University of Massachusetts, who performed life cycle assessments for training several large AI models. While that figure relates to a neural net with more than 200 million parameters, the study highlights the unbelievable efficiency of the human brain. The bigger question now is whether we will build machines that rival the brain for efficiency. To be energy-efficient, brains predict their perceptions Quanta Magazine Many neuroscientists view the brain as a "prediction machine" which, through predictive processing, uses knowledge of the world to make inferences or generate hypotheses about the causes of incoming information. Computational neuroscientists are building artificial neural networks that learn to make predictions about incoming information.
Valerie Johnson is an avid traveler who loves the outdoors. Next on her list is a trip to Walden Pond, in Massachusetts. But the 27-year-old Texan won't need a plane ticket; all she'll need is a video game. Johnson was recently diagnosed with idiopathic intracranial hypertension, a neurological disorder that can cause headaches, impaired vision, and joint pain. These symptoms make travel--particularly to the outdoors--daunting.
The Secret Service has designated Nantucket a "No Drone Zone" ahead of President Biden's Thanksgiving visit. "Due to protective operations in Nantucket, MA, the Secret Service, in coordination with the Federal Aviation Administration (FAA), has established a drone flight restriction from November 23, 2021 to November 28, 2021," the agency said in a press release. The restriction applies to all aircraft and pilots, including drone pilots. The Bidens were scheduled to arrive at Nantucket Memorial Airport at 7:10 p.m. Tuesday, the White House Press Office announced this week. The 46th president is a frequent visitor to the Massachusetts coastal island.
Atrial fibrillation--an irregular and often rapid heart rate--is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. As described in a study published in Circulation, a team led by researchers at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard has developed an artificial intelligence–based method for identifying patients who are at risk for developing atrial fibrillation and could therefore benefit from preventative measures. The investigators developed the artificial intelligence–based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH. Next, the scientists applied their method to three large data sets from studies including a total of 83,162 individuals. The AI-based method predicted atrial fibrillation risk on its own and was synergistic when combined with known clinical risk factors for predicting atrial fibrillation. The method was also highly predictive in subsets of individuals such as those with prior heart failure or stroke.
Liu Leqi is a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, Pittsburgh, PA, USA. Her research interests include AI and human-centered problems in machine learning. Dylan Hadfield-Menell is an assistant professor of artificial intelligence and decision-making at the Massachusetts Institute of Technology, Cambridge, MA, USA. His recent work focuses on the risks of (over-) optimizing proxy metrics in AI systems. Zachary C. Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University, Pittsburgh, PA, USA, and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research spans core machine learning methods, applications to clinical medicine and NLP, and the impact of automation on social systems. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab's website (acmilab.org).
Early last year, researchers at the Massachusetts Institute of Technology (mit) used a machine-learning algorithm to look for new antibiotics. After training the system on molecules with antimicrobial properties, they let it loose on huge databases of compounds and found one that worked. Because it operated in a different way, even bacteria that had developed a resistance to traditional antibiotics could not evade the new drug.
EARLY LAST year, researchers at the Massachusetts Institute of Technology (MIT) used a machine-learning algorithm to look for new antibiotics. After training the system on molecules with antimicrobial properties, they let it loose on huge databases of compounds and found one that worked. Because it operated in a different way, even bacteria that had developed a resistance to traditional antibiotics could not evade the new drug. Your browser does not support the audio element. Behind the success was a deeper truth: the algorithm was able to spot aspects of reality that humans had not contemplated, might not be able to detect and may never comprehend.