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Wearable activity trackers combined with AI may aid in early identification of COVID-19

#artificialintelligence

Wearable activity trackers that monitor changes in skin temperature and heart and breathing rates, combined with artificial intelligence (AI), might be used to pick up COVID-19 infection days before symptoms start, suggests preliminary research published in the open access journal BMJ Open. The researchers base their findings on wearers of the AVA bracelet, a regulated and commercially available fertility tracker that monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow, as well as sleep quantity and quality. Typical COVID-19 symptoms may take several days after infection before they appear during which time an infected person can unwittingly spread the virus. Attention has started to focus on the potential of activity trackers and smartwatches to detect all stages of COVID-19 infection in the body from incubation to recovery, with the aim of facilitating early isolation and testing of those with the infection. The researchers therefore wanted to see if physiological changes, monitored by an activity tracker, could be used to develop a machine learning algorithm to detect COVID-19 infection before the start of symptoms. Participants (1163 all under the age of 51) were drawn from the GAPP study between March 2020 and April 2021.


Closing the Gender Data Gap in AI

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The first computer algorithm is said to have been written in the early 1840s, for the prototype of the Analytical Engine. Ada Lovelace, a mathematician dubbed a "female genius" in her posthumous biography. As the field of computing developed over the next century following Lovelace's death, the typing work involved in creating computer programs was seen as "women's work," a role viewed as akin to switchboard operator or secretary. Women wrote the software, while men made the hardware -- the latter seen, at the time, as the more prestigious of the two tasks. And, during the Space Race of the 1950s and '60s, three Black women, known as "human computers," broke gender and racial barriers to help NASA send the first men into orbit.


AI and the ghost in the machine: Losing human jobs is the least of our worries

#artificialintelligence

Artificial intelligence and machine learning are becoming a bigger part of our world, which has raised ethical questions and words of caution. Hollywood has foreshadowed the lethal downside of AI many times over but two iconic films illustrate problems we might soon face. In "2001: A Space Odyssey," the ship is controlled by the HAL 9000 computer. It reads the lips of the astronauts as they share their misgivings about the system and their intention to disconnect it. In the most famous scene, Keir Dullea's Dave Bowman is trapped in an airlock. He says, "Open the pod bay doors, HAL."


A Straightforward HPV16 Lineage Classification Based on Machine Learning

#artificialintelligence

Human Papillomavirus (HPV) is the causal agent of 5% of cancers worldwide and the main cause of cervical cancer and it is also associated with a significant percentage of oropharyngeal and anogenital cancers. More than 60% of cervical cancers are caused by HPV16 genotype, which has been classified into lineages (A, B, C, and D). Lineages are related to the progression of cervical cancer and the current method to assess lineages is by building a Maximum Likelihood Tree (MLT); which is slow, it cannot assess poor sequenced samples, and annotation is done manually. In this study, we have developed a new model to assess HPV16 lineage using machine learning tools. A total of 645 HPV16 genomes were analyzed using Genome-Wide Association Study (GWAS), which identified 56 lineage-specific Single Nucleotide Polymorphisms (SNPs). From the SNPs found, training-test models were constructed using different algorithms such as Random Forest (RF), Support Vector Machine (SVM), and K-nearest neighbor (KNN). A distinct set of HPV16 sequences (n = 1,028), whose lineage was previously determined by MLT, was used for validation. The RF-based model allowed a precise assignment of HPV16 lineage, showing an accuracy of 99.5% in the known lineage samples. Moreover, the RF model could assess lineage to 273 samples that MLT could not determine. In terms of computer consuming time, the RF-based model was almost 40 times faster than MLT. Having a fast and efficient method for assigning HPV16 lineages,...


Could your breath enable your phone to identify you?

FOX News

'The Five' discusses Apple's new software update allowing users the opportunity to edit and unsend unwanted messages. Facial recognition and fingerprint verification are becoming common security features on our phones and now your breath may be a potential option for biometric security, according to a report published in Chemical Communications. Researchers from Kyushu University's Institute for Materials Chemistry and Engineering worked with the University of Tokyo and have developed an olfactory (smell) sensor that can identify a person by analyzing their breath, the report said. "Recently, human scent has been emerging as a new class of biometric authentication, essentially using your unique chemical composition to confirm who you are," first author of the study, Chaiyanut Jirayupat, said in a release. Bangkok, Thailand - December 12, 2015: Apple iPhone5s held in one hand showing its screen for entering the passcode. Researchers from Kyushu University's Institute for Materials Chemistry and Engineering who worked with the University of Tokyo developed an olfactory (smell) sensor that can identify a person by analyzing their breath, the report said.


How AI Can Make Strategy More Human

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The power of AI is now within reach of all companies, opening up a new world of strategy innovation and enabling companies to leave the constraints of legacy architecture behind forever. Three new related high-potential strategies include: Forever Beta, Minimum Viable Idea (MVI), and Co-lab. This article explains each in detail, with examples of companies that are currently using them. Though their specific strategies are distinct, the companies share three important characteristics. First, their technology, business strategy, and execution are so closely intertwined as to be nearly indistinguishable. Second, humans — not machines — are in the driver’s seat. Third, these companies understand that all companies, no matter their industry, are now technology companies. But technology-driven business strategies require farseeing leaders. Those who are able to see opportunities at the new radically human nexus of people and technology will pre-empt disruption and seize the future.


Artificial intelligence may be used to identify benign thyroid nodules

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ATLANTA -- An ultrasound-based artificial intelligence classifier of thyroid nodules identified benign nodules with sensitivity similar to fine-needle aspiration, according to data presented at ENDO 2022. "Artificial analysis of thyroid ultrasound images can identify nodules that are very unlikely to be malignant," Nikita Pozdeyev, MD, PhD, assistant professor at University of Colorado Anschutz Medical Campus, told Healio. "These are mostly spongiform nodules that have a less than 3% probability of malignancy." Pozdeyev and colleagues trained a supervised deep learning classifier of thyroid nodules on 32,545 images of 621 thyroid nodules acquired from University of Washington. The classifier was then tested on an independent set of 145 nodules collected from the University of Colorado.


Artificial intelligence can understand complicated meanings of words

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Researchers have found that artificial intelligence (AI) can understand the complex meanings of words the way humans do. The researchers discovered a simple underlying trick through which machines accomplish the feat, and represent the meanings of words in a manner that correlates with human judgement. The AI system investigated by the researchers has been used to study word meanings over the past decade, by'reading' vast amounts of content on the internet, covering billions of words. The system learns that words that appear frequently together, such as'table' and'chair' have similar meanings, while words that appear rarely together, such as'planet' and'chair' have very different meanings. The AI however, appeared to have one major limitation.


Giuliano Liguori on LinkedIn: #BigData #Analytics #DataScience

#artificialintelligence

The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable. K-NN is a non-parametric algorithm, which means it does not make any assumption on underlying data. It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset. The Naive Bayes classification algorithm is a probabilistic classifier.


Wearable activity trackers + AI might be used to pick up presymptomatic

#artificialintelligence

Wearable activity trackers that monitor changes in skin temperature and heart and breathing rates, combined with artificial intelligence (AI), might be used to pick up COVID-19 infection days before symptoms start, suggests preliminary research published in the open access journal BMJ Open. The researchers base their findings on wearers of the AVA bracelet, a regulated and commercially available fertility tracker that monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow, as well as sleep quantity and quality. Typical COVID-19 symptoms may take several days after infection before they appear during which time an infected person can unwittingly spread the virus. Attention has started to focus on the potential of activity trackers and smartwatches to detect all stages of COVID-19 infection in the body from incubation to recovery, with the aim of facilitating early isolation and testing of those with the infection. The researchers therefore wanted to see if physiological changes, monitored by an activity tracker, could be used to develop a machine learning algorithm to detect COVID-19 infection before the start of symptoms.