Well File:


Artificial Intelligence and Food Safety: Hype vs. Reality


To understand the promise and peril of artificial intelligence for food safety, consider the story of Larry Brilliant. Brilliant is a self-described "spiritual seeker," "social change addict," and "rock doc." During his medical internship in 1969, he responded to a San Francisco Chronicle columnist's call for medical help to Native Americans then occupying Alcatraz. Then came Warner Bros.' call to have him join the cast of Medicine Ball Caravan, a sort-of sequel to Woodstock Nation. That caravan ultimately led to a detour to India, where Brilliant spent 2 years studying at the foot of the Himalayas in a monastery under guru Neem Karoli Baba. Toward the end of the stay, Karoli Baba informed Brilliant of his calling: join the World Health Organization (WHO) and eradicate smallpox. He joined the WHO as a medical health officer, as a part of a team making over 1 billion house calls collectively. In 1977, he observed the last human with smallpox, leading WHO to declare the disease eradicated. After a decade battling smallpox, Brilliant went on to establish and lead foundations and start-up companies, and serve as a professor of international health at the University of Michigan. As one corporate brand manager wrote, "There are stories that are so incredible that not even the creative minds that fuel Hollywood could write them with a straight face."[1]

Building the engine that drives digital transformation

MIT Technology Review

This is the consensus view of an MIT Technology Review Insights survey of 210 members of technology executives, conducted in March 2021. These respondents report that they need--and still often lack-- the ability to develop new digital channels and services quickly, and to optimize them in real time. Underpinning these waves of digital transformation are two fundamental drivers: the ability to serve and understand customers better, and the need to increase employees' ability to work more effectively toward those goals. Two-thirds of respondents indicated that more efficient customer experience delivery was the most critical objective. This was followed closely by the use of analytics and insight to improve products and services (60%).

MIT Engineers Create a Programmable Digital Fiber – With Memory, Sensors, and AI


MIT researchers have created the first fabric-fiber to have digital capabilities, ready to collect, store and analyze data using a neural network. In a first, the digital fiber contains memory, temperature sensors, and a trained neural network program for inferring physical activity. MIT researchers have created the first fiber with digital capabilities, able to sense, store, analyze, and infer activity after being sewn into a shirt. Yoel Fink, who is a professor of material sciences and electrical engineering, a Research Laboratory of Electronics principal investigator, and the senior author on the study, says digital fibers expand the possibilities for fabrics to uncover the context of hidden patterns in the human body that could be used for physical performance monitoring, medical inference, and early disease detection. Or, you might someday store your wedding music in the gown you wore on the big day -- more on that later.

GBT Is Researching To Develop An AI Empowered, Wireless Patient Health Monitoring System


SAN DIEGO, June 03, 2021 (GLOBE NEWSWIRE) -- GBT Technologies Inc. ( OTC PINK: GTCH) ("GBT" or the "Company") has commenced research with the goal of developing an AI empowered, wireless patient monitoring system. The project's internal code name is "Apollo". The technology will be based on radio waves and empowered by machine learning. Currently health related monitoring devices are typically wearable or invasive types. These devices are self-reporting systems and typically monitoring patient's vitals, keeping logs, track sleeping habits and similar.

Intelicare awarded $100K grant from NSSN to improve machine learning for assisted living – Software


InteliCare has been awarded a $100,000 grant from the New South Wales Smart Sensing Network ("NSSN") to develop its machine learning (ML) capability in conjunction with the University of Sydney (USyd) and Macquarie University (MU). The company is negotiating an agreement with USyd, MU and the NSSN to use these funds to help fund a one-year joint project delivered by the universities' Computer Science Departments. The goal is to build ML algorithms that can predict and prevent chronic disease and mental health deterioration that can lead to a loss of independence and an increased risk of injury. In addition to the NSSN funds, InteliCare will provide a co-contribution of $152,898 in cash and the universities will provide $161,021 of in-kind support. Ongoing development beyond the initial project will require the company to budget from working capital.

Machine Learning Data Scientist


Oura is an award-winning and fast-growing startup that helps people track all stages of sleep and activity using the Oura Ring and connected app. By providing daily feedback and practical steps to inspire healthy lifestyles, we've helped hundreds of thousands of people improve their sleep, understand their bodies, and transform their health. We're on a mission to empower every person to own their inner potential, and we're seeking candidates who want to make an impact on our journey. We are looking for a Machine Learning Data Scientist to work in our Research Algorithm Platform squad in North America. Working closely with health and physiology scientists, you will develop machine learned models to capture indicators of health and wellness.

What is AI Pose Estimation Technology and How You Can Use It?


If you list the biggest and fastest-growing technologies over the past decade, artificial intelligence (AI) will inarguably top the charts. The global AI market was valued at 39.9 billion in 2019 and is projected to grow at a CAGR of 42.2% during 2020-2027, according to Grand View Research. AI has found applications in every industry, and the fitness sector is no different. Smart fitness wearables, AI-powered fitness apps, and AI and Machine Learning (ML) for gym management are common use cases of AI in fitness. But not many people thought that there would come a time when AI will be on the verge of replacing personal trainers and fitness coaches.

A Nutrition Label for AI


It can be difficult to understand exactly what's going on inside of a deep learning model, which is a real problem for companies concerned about bias, ethics, and explainability. Now IBM is developing something called AI FactSheets, which it describes as a nutrition label for deep learning that explains how models work and that can also detect bias. AI FactSheets is a new addition to Watson Open Scale that will provide a plain-language description of what's going on inside deep learning models. The software, which is expected to be generally available soon, can work with AI models developed by Watson Studio, or any other AI model accessible from a REST API. After being exposed to the model, AI FactSheets generates a PDF with information about bias, trust, and transparency aspects of a given deep learning model.

Edge Intelligence for Empowering IoT-based Healthcare Systems

arXiv.org Artificial Intelligence

The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.

Adaptive Degradation Process with Deep Learning-Driven Trajectory

arXiv.org Machine Learning

Remaining useful life (RUL) estimation is a crucial component in the implementation of intelligent predictive maintenance and health management. Deep neural network (DNN) approaches have been proven effective in RUL estimation due to their capacity in handling high-dimensional non-linear degradation features. However, the applications of DNN in practice face two challenges: (a) online update of lifetime information is often unavailable, and (b) uncertainties in predicted values may not be analytically quantified. This paper addresses these issues by developing a hybrid DNN-based prognostic approach, where a Wiener-based-degradation model is enhanced with adaptive drift to characterize the system degradation. An LSTM-CNN encoder-decoder is developed to predict future degradation trajectories by jointly learning noise coefficients as well as drift coefficients, and adaptive drift is updated via Bayesian inference. A computationally efficient algorithm is proposed for the calculation of RUL distributions. Numerical experiments are presented using turbofan engines degradation data to demonstrate the superior accuracy of RUL prediction of our proposed approach.