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Machine-learning system flags remedies that might do more harm than good

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Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death. Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient's deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient's risk of death. To help clinicians avoid remedies that may potentially contribute to a patient's death, researchers at MIT and elsewhere have developed a machine-learning model that could be used to identify treatments that pose a higher risk than other options.


Researchers explain why they believe Facebook mishandles political ads

NPR Technology

Facebook has worked for years to revamp its handling of political ads -- but researchers who conducted a comprehensive audit of millions of ads say the social media company's efforts have had uneven results. The problems, they say, include overcounting political ads in the U.S. -- and undercounting them in other countries. And despite Facebook's ban on political ads around the time of last year's U.S. elections, the platform allowed more than 70,000 political ads to run anyway, according to the research team that is based at the NYU Cybersecurity for Democracy and at the Belgian university KU Leuven. Their research study was released early Thursday. They also plan to present their findings at a security conference next August.


DeepMind tests the limits of large AI language systems with 280-billion-parameter model

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Language generation is the hottest thing in AI right now, with a class of systems known as "large language models" (or LLMs) being used for everything from improving Google's search engine to creating text-based fantasy games. But these programs also have serious problems, including regurgitating sexist and racist language and failing tests of logical reasoning. One big question is: can these weaknesses be improved by simply adding more data and computing power, or are we reaching the limits of this technological paradigm? This is one of the topics that Alphabet's AI lab DeepMind is tackling in a trio of research papers published today. The company's conclusion is that scaling up these systems further should deliver plenty of improvements.


Chan Zuckerberg Commits $500 Million to Harvard Neuroscience and AI Institute

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Chan Zuckerberg Initiative co-founders and co-CEOs Mark Zuckerberg '06, L.L.D. '17 and Priscilla Chan '07, announced today a gift to establish the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard. The new institute, which will have dedicated space in the recently completed Science and Engineering Complex in Allston (see "A 500-Year Building"), is named after Karen Kempner Zuckerberg, the mother of the Meta CEO and founder (Facebook, Instagram, and WhatsApp are Meta Platforms Inc.'s best-known apps) and her parents. According to Jeff MacGregor, vice president of science communications for the Chan Zuckerberg Initiative (CZI), the Kempner Institute will receive $500 million in funding during the next 15 years. The gift will support 10 new faculty appointments, new computing infrastructure, and resources for students--from undergraduates to post-doctoral fellows--that will allow them to pursue knowledge in an uninhibited way across labs and disciplines. Zuckerberg and Chan, a pediatrician, will donate an additional $2.9 billion to support biomedical research focused on improving human health, with the aim of ultimately ending all human disease.


The Next Frontiers of AI and Machine Learning in Data - Fintech Schweiz Digital Finance News - FintechNewsCH

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With data becoming a core business asset for financial companies, artificial intelligence and machine learning (AI/ML) continues to be a focal point in maximising the competitive advantage of this'new oil'. This is according to a new study by LSEG Labs, The Defining Moment for Data Scientists, on the applications of AI/ML in financial services. The study found that the adoption of AI/ML within organisations has remained steady since 2018. Over the last three years, between 40-50% of respondents reported deploying AI/ML in multiple areas. The survey is based on responses from 482 data scientists, quants, model governance professionals and C-suite executives, from both sell-side and buy-side financial institutions.


The Next Frontiers of AI and Machine Learning in Data - Fintech Singapore

#artificialintelligence

With data becoming a core business asset for financial companies, artificial intelligence and machine learning (AI/ML) continues to be a focal point in maximising the competitive advantage of this'new oil'. This is according to a new study by LSEG Labs, The Defining Moment for Data Scientists, on the applications of AI/ML in financial services. The study found that the adoption of AI/ML within organisations has remained steady since 2018. Over the last three years, between 40-50% of respondents reported deploying AI/ML in multiple areas. The survey is based on responses from 482 data scientists, quants, model governance professionals and C-suite executives, from both sell-side and buy-side financial institutions.


@Radiology_AI

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Model training strategies are described for use in scenarios in which there are limited datasets available. Models developed with data-driven approaches have great potential to shape the future practice of radiology. A wide variety of strategies are available to enhance the performance of models in data-limited settings, including transfer learning, data augmentation, semisupervised training techniques, efficient annotation strategies, federated learning, few-shot learning, and different neural network architectures. Research in diagnostic radiology decision support has progressed rapidly because of the availability of large datasets, powerful machine learning (ML) techniques, and computers to efficiently run ML techniques (1–3). Advances in medical image analysis research include multiple systems that aim to help radiologists detect disease (4–7), identify disease progression (8), localize abnormalities (9), automate time-consuming tasks, and improve the radiology workflow. The performance of deep learning–based algorithms depends on the availability of large-scale annotated data (3,10,11). A large dataset with diverse, high-quality images curated from multiple institutions and different geographic areas is preferable to ensure the generalizability of a model for clinical use (12). However, curating large datasets is challenging because of their volume, limited radiologist availability, and tedious annotation processes. It is particularly challenging to curate data for rare diseases. Additionally, many complexities are introduced in the data de-identification process to comply with patient privacy rules, institutional review board requirements, and local ethical committee protocols (13). If training data are limited, deep learning–based models may suffer from overfitting, which results in poor generalizability. Several reviews have described deep learning–based frameworks for medical imaging (2,3,14).


Two-stage Deep Neural Network via Ensemble Learning for Melanoma Classification

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Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; and the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the informative features; finally we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC $2017$ challenge...


4 Reasons Your Deal Forecasts Probably Aren't Accurate

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Sales and deal forecasting are vital parts of any business's planning, but it is also hard to argue that there are major issues with how we prepare for the future. Based on a number of sources, the level of inaccuracy with current tools is astounding. A study found that only 28.1% of sales teams were within a 5% deviation of their forecast, and 47% of 90-day predictions were off by a margin of more than half -- and sales reps overestimated by an average $91,000 and underestimated by only $47,000. CSO Insights cites that 60% of forecast deals do not close, and even organizations that formally track and review their processes still lose 40% of predicted closures. A SiriusDecisions' analysis pegged that "79 percent of sales organizations miss their sales forecast by more than 10 percent", and in another analysis, an asset manager says he just cuts 20% off the top of a forecast since he doesn't think they're reliable.


Working from home increases your risk of making mistakes, scientists say

Daily Mail - Science & tech

Working from home increases your risk of making mistakes, a study examining the quality of chess play has found. The standard was significantly worse when players competed online instead of face to face, researchers discovered, suggesting that not being in the office is harmful to productivity. They monitored nearly 215,000 chess moves made by players during in-person and digital tournaments, checking them against what was the best play by using artificial intelligence. Such was the impact on performance when playing remotely, it would have taken Norwegian grandmaster Magnus Carlsen, the world's top-rated player, to the same rating as the current 20th-best player, according to Dainis Zegners from Rotterdam School of Management, one of the study's co-authors. He said the research showed that remote working could hinder people's ability to carry out mentally-intense tasks while alone.