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Sr. Software Engineer

#artificialintelligence

We're on a mission to provide security teams with the intelligence they need to confront and stop advanced threats like supply chain attacks, zero day exploits, and ransomware attacks. Cyber attackers still have the advantage. Are you ready to help us reclaim the upper hand? Every day, banks, hospitals, government agencies, and entertainment companies rely on Extrahop's Reveal(x) a cloud-based machine learning cyber security platform to understand which users, devices and network activities they can trust. With this knowledge companies prevent fraud, data breaches, and can focus on building better user experiences, instead of worrying about security.


Using machine-learning to distinguish antibody targets

AIHub

The virus's spike proteins (purple) are a key antibody target, with some antibodies attaching to the top (darker purple) and others to the stem (paler zone). A new study shows that it is possible to use the genetic sequences of a person's antibodies to predict what pathogens those antibodies will target. "Our research is in a very early stage, but this proof-of-concept study shows that we can use machine learning to connect the sequence of an antibody to its function," said Nicholas Wu, a professor of biochemistry at the University of Illinois Urbana-Champaign who led the research with biochemistry PhD student Yiquan Wang; and Meng Yuan, a staff scientist at Scripps Research in La Jolla, California. With enough data, scientists should be able to predict not only the virus an antibody will attack, but which features on the pathogen the antibody binds to, Wu said. For example, an antibody may attach to different parts of the spike protein on the SARS-CoV-2 virus.


How the Pandemic Made Algorithms Go Haywire

Slate

Algorithms have always had some trouble getting things right--hence the fact that ads often follow you around the internet for something you've already purchased. But since COVID upended our lives, more of these algorithms have misfired, harming millions of Americans and widening existing financial and health disparities facing marginalized groups. At times, this was because we humans weren't using the algorithms correctly. More often it was because COVID changed life in a way that made the algorithms malfunction. Take, for instance, an algorithm used by dozens of hospitals in the U.S. to identify patients with sepsis--a life-threatening consequence of infection.


Artificial Intelligence In Healthcare Market Report, 2022-2030

#artificialintelligence

The global artificial intelligence in healthcare market size was valued at USD 10.4 billion in 2021 is expected to expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. The growing datasets of patient health-related digital information, increasing demand for personalized medicine, and the rising demand for reducing care expenses are some of the major driving forces of the market growth. The growing global geriatric population, changing lifestyles, rising prevalence of chronic diseases has contributed to the surge in demand for diagnosing and improved understanding of diseases in their initial stages. Artificial Intelligence (AI) and machine learning (ML) algorithms are being widely adopted and integrated into healthcare systems to accurately predict diseases in their early stage based on historical health datasets. Furthermore, deep learning technologies, predictive analytics, content analytics, and Natural Language Processing (NLP) tools are enabling care professionals to diagnose patients' underlying health conditions at an earlier stage. The Covid-19 pandemic positively influenced the demand for AI technologies and unearthed the potential held by these advanced technologies.


Machine Learning Artificial intelligence Market Size 2022-2028: Market Share, World Business Trends, Statistics, Definition, Prime Companies Report Covers, With Impact Of Covid-19 On Domestic and Global Market - Digital Journal

#artificialintelligence

Buy this report @ (Price 4350 USD for a single-user license) https://proficientmarketinsights.com/purchase/20662887 Proficient Market Insights is a credible source for gaining the market reports that will provide you with the lead your business needs. Our aim is to provide the best solution that matches the exact customer requirements. This drives us to provide you with custom or syndicated research reports.


Software Engineer - Machine Learning

#artificialintelligence

Two Six Technologies is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment at Two Six Technologies without regard to race, color, religion, national origin, sex, age, physical and mental disability, sexual orientation, gender identity or expression, genetic information, veteran, marital, pregnancy or citizenship status; or any other status prohibited by applicable national, federal, state or local law. Two Six Technologies Covid-19 Vaccination Policy requires employees to be fully vaccinated. Exceptions to this policy are only granted to those with a company-approved medical or religious accommodation. Prospective or new employees will be required to adhere to this policy and submit proof of vaccination or have an approved exemption prior to the start of their employment.


Senior Research Scientist - On-Device Machine Learning

#artificialintelligence

For U.S. Candidates Only: SRA has adopted a COVID-19 vaccination policy to safeguard the health and well-being of our employees and visitors. As a condition of employment, all employees based in the U.S. are required to be fully vaccinated for COVID-19, unless a reasonable accommodation is approved or as otherwise required by law. Incumbent must make themselves available during core business hours. This position requires the incumbent to travel for work 10% of the time.


A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods

#artificialintelligence

A new approach (SMOTE-RF-SVM) is proposed to identify SARS-CoV-2 epitopes that can be used in vaccine design. Epitope candidates that can be used in vaccine design were determined using machine learning-based in silico and bioinformatics tools. In the unbalanced dataset, generating artificial data with the SMOTE technique increased the model performance. Nonallergic, high antigen (antigen score ≥1.0) and nontoxic 11 possible epitopes candidates were proposed. The search space for vaccine studies was narrowed by SMOTE-RF-SVM.


When governments turn to AI: Algorithms, trade-offs, and trust

#artificialintelligence

As artificial intelligence (AI) and machine learning gain momentum, an increasing number of government agencies are considering or starting to use them to improve decision making. Additionally, COVID-19 has suddenly put an emphasis on speed. In these uncharted waters, where the tides continue to shift, it's not surprising that analytics, widely recognized for its problem-solving and predictive prowess, has become an essential navigational tool. Some examples of compelling applications include those that identify tax-evasion patterns, sort through infrastructure data to target bridge inspections, or sift through health and social-service data to prioritize cases for child welfare and support, or predicting the spread of infectious diseases. They enable governments to perform more efficiently, both improving outcomes and keeping costs down.


Scientists Develop a Machine Learning Model to Predict the Evolution of an Epidemic Accurately - CBIRT

#artificialintelligence

According to a new KAUST study, machine learning approaches can achieve an assumption-free analysis of epidemic case data with amazingly good prediction accuracy and the flexibility to incorporate new data dynamically. Yasminah Alali, an intern in KAUST's 2021 Saudi Summer Internship (SSI) program, developed a proof of concept that reveals a possible alternative to traditional parameter-driven mechanistic models by removing human bias and assumptions from analysis, revealing the underlying story of the data. Using publicly released COVID-19 incidence and recovery data from India and Brazil, Alali leveraged her experience working with artificial intelligence models to design a framework to fit the characteristics and time-evolving nature of epidemic data in collaboration with KAUST's Ying Sun and Fouzi Harrou. To create an effective Gaussian process regression (GPR) based model for forecasting recovered and confirmed COVID-19 cases in two significantly impacted countries, India and Brazil, the researchers first used Bayesian optimization to modify the Gaussian process regression (GPR) hyperparameters. However, the time dependency in the COVID-19 data series is ignored by machine learning models.