Description Job Description: The Leidos Innovations Center (LInC) seeks a Machine Learning Research Engineer primarily focused on cognitive signal processing, to work in our Arlington, VA office. The candidate will research & develop new, state-of-the-art machine learning algorithms and implement them across the RF domain (e.g., communications, radar, electronic warfare, spectrum sensing, and signals intelligence [SIGINT]), in both modelling and simulation environments and real time software embed systems. The candidate will also contribute to technology developments in signal processing, optimization, detection & estimation, deep learning, and adaptive decision and control. Requires basic knowledge of and ability to apply machine learning and radar/signal processing principles, theories, and concepts in support of direct programs, IR&D, and marketing efforts. Primary Responsibilities Designs and develops methods, algorithms, and systems that apply machine learning technologies to support advanced signal processing concepts.
In the past decade, advances in genetic disease and precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment . The enormous divergence of signaling and transcriptional networks mediating the cross talk between healthy, diseased, stromal, and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. Unexpectedly, the conclusion of the human genome did not translate into a burst of new drugs. The pharmaceutical industry rather announced a declining output in terms of the number of new drugs approved despite increasing commercial efforts of drug research and development [2, 3]. In contrast, machine learning (ML) as well as network and systems biology are innovating with impactful discoveries and are now starting to be seamlessly integrated into the biomedical discovery pipeline .
Overstating the importance of Artificial Intelligence is difficult. When implemented efficiently, AI holds the capacity to boost your billing business tenfold. In many cases, AI is the thing that is scaling the business rather than the physical workforce. The question on many business minds is how does AI change the way business is done? To help answer this question, we analyzed many billing and coding companies.
Uses AI to: Find combinations of genomic, phenotypic, and clinical features that define disease risk, prognosis, and therapy response in a complex disease population. Allows researchers to: Find novel drug targets in existing datasets, identify drug repurposing opportunities, and improve biomarker-driven patient stratification strategies.
Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM).
Imagine having to choose from over 14,000 different treatment scenarios to decide which drugs might be best for a child or a loved one affected by epilepsy. This is what faces many families according to the experts at Stanford and doc.ai who have announced a new type of clinical trial using artificial intelligence (AI). The project's goal is to help make the process more scientific using population data and less prone to lengthy individual trial-and-error. Researchers are analyzing medications, side effects, genomic information, environmental exposures, activity and even physical traits. This type of work produces vast amounts of information and requires so much processing power that it can only be performed by the latest AI systems.
Suicide is the second leading cause of death in people aged 15 to 29. According to the World Health Organisation, about 800,000 individuals commit suicide every year, with many more people attempting suicide. In China, an AI bot is looking to reduce that figure. Founded in 2018, the Tree Hole bot finds potential suicidal intentions posted on social media network Weibo and connects the posters to volunteer psychologists, consultants and psychological scholars. In China, a "tree hole"--inspired by an Irish story about a man who told his secrets to a tree--is where people post secrets online for others to read.
On 2 December 2019, Amazon expanded its automatic transcription service for AWS to include support for medical speeches. Transcriptions can be of many types -- for movies and entertainment content, transcribing audio for the hearing impaired, audio for voice-over, etc -- but one of the most essential application is in the field of medical practices. If you have a doctor in the family, you would've seen them spend a significant amount of time talking into a recorder about the medical conditions to document them later. And by later, we mean that there is a medical transcriber who the next day or so and'transcribes' the recording into a format that can be documented and archived. Although you can say that the transcriber removes the unnecessary'eh' and'uhs' from the recording, a transcriber does more than that.
No one knows who gave Rahul Roy tuberculosis. Roy's charmed life as a successful trader involved traveling in his Mercedes C class between his apartment on the plush Nepean Sea Road in South Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai's weather. He seldom rolled down his car windows – his ambient atmosphere, optimized for his comfort, rarely changed. Historically TB, or "consumption" as it was known, was a Bohemian malady; the chronic suffering produced a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in part, because consumption, like aristocracy, was thought to be hereditary. Even after Robert Koch discovered that the cause of TB was a rod-shaped bacterium – Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral peer, Syphilis, and unaesthetic cousin, leprosy. TB became egalitarian in the early twentieth century but retained an aristocratic noblesse oblige. George Orwell may have contracted TB when he voluntarily lived with miners in crowded squalor to understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with poor people. For Roy, there was nothing heroic about getting TB. He was embarrassed not because of TB's infectivity; TB sanitariums are a thing of the past. TB signaled social class decline. He believed rickshawallahs, not traders, got TB.
Artificial Intelligence (AI) is acquiring increasing importance in many applications that support decision-making in various areas, including healthcare, consumption, and risk classification of individuals. The growing impact of AI on people's lives naturally raises the question about its ethical and moral components. Are AI decisions ethically acceptable? How can we ensure that AI remains ethical over time? Should we dominate AI and impose specific behavioural rules, possibly limiting its enormous potential, or should we allow AI to develop its own ethics, possibly ultimately subjugating us to intellectual slavery?