Tuesday November 19, 2019
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Researchers Develop New Programming System for Artificial Intelligence Applications

"Gen" models are black boxes called generative functions (GF), that provide an interface (GFI), exposing capabilities required by inference, researchers said

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In this method, instructions are given to the companies staff members to perform transactions such as money transfers, as well as malicious activity on the company's network. Pixabay

Researchers have developed a novel system named “Gen” that can be used for Artificial Intelligence applications such as computer vision, robotics, and statistics without having to deal with equations or manually writing high-performance codes.

“Gen” includes a number of novel language constructs such as a generative function interface to encapsulate probabilistic models, combinators to create new generative functions from existing ones and an inference library providing high-level inference algorithms.

In the study published in the journal PLDI 2019, researchers from the Massachusetts Institute of Technology demonstrated the probabilistic programming system that aims to be both expressive at the modelling level and efficient at the algorithmic level.

AI
Researchers have developed a novel system named “Gen” that can be used for Artificial Intelligence applications. Pixabay

“Gen” has already showed better performance than existing probabilistic programming systems for a number of different problems such as tracking objects in space, estimating 3D body pose from a depth image, and inferring the structure of a time series, researchers said.

ALSO READ: Researchers Develop an Algorithm to Predict Storms, Cyclones

Based on Julia – a language specialised in numerical analysis and which aims to allow users to express models and create inference algorithms using high-level programming constructs, “Gen” models can be expressed in a number of different ways, each striking a different flexibility/efficiency trade-off. “Gen” provides a built-in modelling language that extends Julia’s syntax for function definition.

“Gen” models are black boxes called generative functions (GF), that provide an interface (GFI), exposing capabilities required by inference, researchers said. The generative function approach is key to making “Gen” suitable for application to a wide range of problems and enables it to use models created in TensorFlow as algorithms written in a programming language or as a result of simulations. (IANS)

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This AI Tool Can Predict Mortality Of Heart Failure Patients

Researchers develop a tool that can predict mortality of heart failure patients

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This artificial intelligence (AI) tool can predict life expectancy in heart failure patients. Pixabay

Researchers have developed an artificial intelligence (AI) tool to predict life expectancy in heart failure patients.

The machine learning algorithm based on de-identified electronic health, records data of 5,822 hospitalised or ambulatory patients with heart failure at UC San Diego Health in the US.

“We wanted to develop a tool that predicted life expectancy in heart failure patients, there are apps where algorithms are finding out all kinds of things, like products you want to purchase,” said Avi Yagil, Professor at University of California.

“We needed a similar tool to make medical decisions. Predicting mortality is important in patients with heart failure. Current strategies for predicting risk, however, are only modestly successful and can be subjective,” Yagil added.

From this model, a risk score was derived that determined low and high risk of death by identifying eight readily available variables collected for the majority of patients with heart failure:Diastolic blood pressure, Creatinine, Blood urea nitrogen, White blood cell count, Platelets, Albumin and Red blood cell distribution.

Yagil said the newly developed model was able to accurately predict life expectancy 88 per cent of the time and performed substantially better than other popular published models.

“This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year,” said researcher Eric Adler.

Heart failure patients
The mortality of a heart failure patient can be predicted. Pixabay

“This is incredibly valuable. It allows us to make informed decisions based on a proven methodology and not have to look into a crystal ball,” he added.

The tool was additionally tested using de-identified patient data from the University of California San Francisco and a data base derived from 11 European medical centers.

“It was successful in those cohorts as well,” said Yagil.

“Being able to repurpose our findings in independent populations is of utmost importance, thus validating our methodology and its results,” Yagil added.

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Researchers said the partnership between physicists and cardiologists was critical to developing a reliable tool and extensive knowledge and experiences from both sides proved synergetic.

The study was published in the European Journal of Heart Failure. (IANS)