The use of artificial intelligence (AI) can provide an automated and accurate tool to measure a common marker of heart disease in patients undergoing lung cancer screening, according to a study presented today at the annual meeting of the Radiological Society of North America (RSNA). "The new cholesterol guidelines encourage using the calcium score to help physicians and patients decide whether to take a statin," said study co-senior author Michael T. Lu, M.D., M.P.H., director of AI in the Cardiovascular Imaging Research Center (CIRC) at Massachusetts General Hospital (MGH) in Boston in a press release about the findings. "For select patients at intermediate risk of heart disease, if the calcium score is 0, statin can be deferred. If the calcium score is high, then those patients should be on a statin." In this study, researchers trained a deep-learning system on cardiac CTs and chest CTs in which the coronary artery calcium had been measured manually.

In this post we will see how we can classify handwritten digits using convolutional neural network implemented in Keras. Now we can plot some predictions, to see how our model works. If we want to document our model, it can be great to see visually how it looks, and for that the following code can be used. After that, we will have the image with the required specification of our model. Feature maps, or activation maps, are the output activations for a given filter and the definition is the same regardless of what layer you are on.

Every method of computing invites its own security challenges and machine learning (ML) is no exception. Fortunately, the vulnerabilities in this segment of artificial intelligence (AI) are fairly predictable. Unfortunately, however, they are not very easy to spot. When we consider the vast amount of data involved, the fine granularity of that data, and the fact that machine learning both learns and improves as it goes along, therein lies the challenges. Machine learning processes data from patterns that are imperceptible to humans, which is both an asset and a vulnerability.

The transport literature is dense regarding short-term traffic predictions, up to the scale of 1 hour, yet less dense for long-term traffic predictions. The transport literature is also sparse when it comes to city-scale traffic predictions, mainly because of low data availability. The main question we try to answer in this work is to which extent the approaches used for short-term prediction at a link level can be scaled up for long-term prediction at a city scale. We investigate a city-scale traffic dataset with 14 weeks of speed observations collected every 15 minutes over 1098 segments in the hypercenter of Los Angeles, California. We look at a variety of machine learning and deep learning predictors for link-based predictions, and investigate ways to make such predictors scale up for larger areas, with brute force, clustering, and model design approaches.

To delineate image data curation needs and describe a locally designed graphical user interface (GUI) to aid radiologists in image annotation for artificial intelligence (AI) applications in medical imaging. GUI components support image analysis toolboxes, picture archiving and communication system integration, third-party applications, processing of scripting languages, and integration of deep learning libraries. For clinical AI applications, GUI components included two-dimensional segmentation and classification; three-dimensional segmentation and quantification; and three-dimensional segmentation, quantification, and classification. To assess radiologist engagement and performance efficiency associated with GUI-related capabilities, image annotation rate (studies per day) and speed (minutes per case) were evaluated in two clinical scenarios of varying complexity: hip fracture detection and coronary atherosclerotic plaque demarcation and stenosis grading. For hip fracture, 1050 radiographs were annotated over 7 days (150 studies per day; median speed: 10 seconds per study [interquartile range, 3–21 seconds per study]).

Two years ago, Toxic Comment Classification Challenge was published on Kaggle. Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. In this post, we develop a tool that is able to recognize toxicity in comments.

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Amazon Web Services likes to spice things up at its annual AWS re:Invent conference with unexpected hardware announcements, with past surprises such as a semi-truck for transporting data, a machine-learning camera, and a miniature self-driving race car. Now you can add a machine-learning-enabled musical keyboard to the list. Kicking off the event overnight in Las Vegas, the company introduced AWS DeepComposer, a 32-key, two-octave keyboard that the company holds up as an early example of "generative" artificial intelligence. "Generative AI is one of the biggest recent advancements in artificial intelligence technology because of its ability to create something new," Amazon explains on the AWS DeepComposer site. "It opens the door to an entire world of possibilities for human and computer creativity, with practical applications emerging across industries, from turning sketches into images for accelerated product development, to improving computer-aided design of complex objects."

Traditional rule-based AI systems included explainability in AI as part of models, since humans would typically handcraft the inputs to output. But deep learning techniques using semi-autonomous neural-network models can't provide a model's results map to an intended goal. Researchers are working to build learning algorithms that generate explainable AI systems from data. Currently, however, most of the dominant learning algorithms do not yield interpretable AI systems, said Ankur Taly, head of data science at Fiddler Labs, an explainable AI tools provider. "This results in black box ML techniques, which may generate accurate AI systems, but it's harder to trust them since we don't know how these systems' outputs are generated," he said.

DeepMind, an offshoot of Google's parent company, debuted a computer program in January capable of beating professional players at one of the world's toughest video games. StarCraft is a military science fiction franchise set in a universe rife with conflict, where armies of opponents face off to become the most powerful. And DeepMind's program, called AlphaStar, reached StarCraft II's highest rank -- Grandmaster. It can defeat 99.8 percent of human players, according to a study published in the journal Nature in October. StarCraft is one of the most popular, difficult electronic sports in the world.