A new blood test that can detect more than 50 types of cancer has been revealed by researchers in the latest study to offer hope for early detection. The test is based on DNA that is shed by tumours and found circulating in the blood. More specifically, it focuses on chemical changes to this DNA, known as methylation patterns. Researchers say the test can not only tell whether someone has cancer, but can also shed light on the type of cancer they have. Dr Geoffrey Oxnard of Boston's Dana-Farber Cancer Institute, part of Harvard Medical School, said the test was now being explored in clinical trials.
As AI is increasingly incorporated into our workplaces and daily lives, it is poised to fundamentally upend the way we live and work. Concern over this looming shift is widespread. A recent survey of 5,700 Harvard Business School alumni found that 52% of even this elite group believe the typical company will employ fewer workers three years from now. The advent of AI poses new and unique challenges for business leaders. They must continue to deliver financial performance, while simultaneously making significant investments in hiring, workforce training, and new technologies that support productivity and growth.
We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier. We propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay and local adaptation to allow the model to continuously learn from new datasets. We also show that the space complexity of the episodic memory module can be reduced significantly ( 50-90%) by randomly choosing which examples to store in memory with a minimal decrease in performance. We consider an episodic memory component as a crucial building block of general linguistic intelligence and see our model as a first step in that direction.
Artificial intelligence (AI) will change the way we learn and work in the near future. Nearly 400 million workers globally will change their occupations in the next 10 years, and business schools are uniquely situated to respond to the shifts coming to the future of work. However, a recent study, "Implications of Artificial Intelligence on Business Schools and Lifelong Learning," shows that business schools remain cautious in adapting management education to address the changing needs of students, workers and organizations, writes Anne Trumbore in this opinion piece. Trumbore, one of the study's coauthors, is senior director of Wharton Online, a strategic digital learning initiative at the Wharton School of the University of Pennsylvania. In the past few weeks, COVID 19 has moved hundreds of millions of students around the globe from physical to online classes.
People's Google searches, social media posts and even chatbot questions are being used by artificial intelligence to try and predict where the novel coronavirus is going to pop up next. The technology, which has been fine-tuned over the last 15 years, is already feeding information to major health agencies like the World Health Organisation to help them decide where they should focus their efforts. One system, called HealthMap, uses publicly available data from across the internet as well as user-submitted information, according to one of its developers, John Brownstein, a professor at Harvard Medical School. "We work in this hybrid of data mining as well as crowdsourcing," he told the ABC's news podcast The Signal. "What's really phenomenal here is we're seeing incredible international collaboration and a huge amount of data sharing."
IESE Business School has launched a new Artificial Intelligence and the Future of Management Initiative, a multidisciplinary project that will look at how artificial intelligence is impacting management, and prepare executives to put Al to use in their companies in an ethical and socially responsible way. Artificial intelligence, like electricity a century ago, is a general purpose technology that will touch every sphere of economic activity. That places new demands on managers to adapt to the changing competitive landscape, to transform their organizations, and to ensure that employees – and themselves -- have the skills required. IESE's new Artificial Intelligence and the Future of Management Initiative will meet those needs for research and education efforts by: "AI is as much a management challenge as it is a technological challenge," said Dean Franz Heukamp. "With this initiative we want to help current and future managers, as well as policy makers, face the challenges AI presents, enabling them to shape the ways AI is used and ensure that it's a force for good in society."
A classic debate in cognitive science revolves around understanding how children learn complex linguistic rules, such as those governing restrictions on verb alternations, without negative evidence. Traditionally, formal learnability arguments have been used to claim that such learning is impossible without the aid of innate language-specific knowledge. However, recently, researchers have shown that statistical models are capable of learning complex rules from only positive evidence. These two kinds of learnability analyses differ in their assumptions about the role of the distribution from which linguistic input is generated. The former analyses assume that learners seek to identify grammatical sentences in a way that is robust to the distribution from which the sentences are generated, analogous to discriminative approaches in machine learning.
What's important is that you have a faith in people, that they're basically good and smart, and if you give them tools, they'll do wonderful things with them. In the nearly 20 years since I started medical school, I've seen the practice of medicine undergo a wholesale technological transformation. Take medical records as a simple example. I am 100% certain that today's medical students are much slower walkers than me. Because the days of sprinting on rounds to get ahead of the white coat phalanx, pull down a cabinet and open a three-ring binder chart to the next blank page before the intern reaches the door ended a decade ago.
A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of (Weston et al., 2015) and large-scale question answering from (Dodge et al., 2015).
As the deadly 2019-nCov coronavirus spreads, raising fears of a worldwide pandemic, researchers and startups are using artificial intelligence and other technologies to predict where the virus might appear next -- and even potentially sound the alarm before other new, potentially threatening viruses become public health crises. "What we're doing currently with Coronavirus is really trying to get an understanding of what's happening on the ground through as many sources as we can get our hands on," says John Brownstein, chief innovation officer at Boston Children's Hospital and a professor at Harvard Medical School. After SARS killed 774 people around the world in the mid-2000s, his team built a tool called Healthmap, which scrapes information about new outbreaks from online news reports, chatrooms and more. Healthmap then organizes that previously disparate data, generating visualizations that show how and where communicable diseases like the coronavirus are spreading. Healthmap's output supplements more traditional data-gathering techniques used by organizations like the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO).