Multivariate Adaptive Regression Splines, or MARS, is an algorithm for complex non-linear regression problems. The algorithm involves finding a set of simple linear functions that in aggregate result in the best predictive performance. In this way, MARS is a type of ensemble of simple linear functions and can achieve good performance on challenging regression problems with many input variables and complex non-linear relationships. In this tutorial, you will discover how to develop Multivariate Adaptive Regression Spline models in Python. Multivariate Adaptive Regression Splines (MARS) in Python Photo by Sei F, some rights reserved.
Researchers from Boston University and University of Virginia published a new paper in the Journal of Marketing that examines how consumers respond to AI recommenders when focused on the functional and practical aspects of a product (its utilitarian value) versus the experiential and sensory aspects of a product (its hedonic value). The study, forthcoming in the the Journal of Marketing, is titled "Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The'Word-of-Machine' Effect" and is authored by Chiara Longoni and Luca Cian. More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide recommendations to consumers. As these companies evaluate AI-based assistance, one critical question must be asked: When do consumers trust the "word of machine," and when do they resist it? A new Journal of Marketing study explores reasons behind the preference of recommendation source (AI vs. human).
Only 4 percent of all cancer therapeutic drugs under development earn final approval by the U.S. Food and Drug Administration (FDA). "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, Ph.D., professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." In a paper published October 20, 2020 in Cancer Cell, Ideker and Brent Kuenzi, Ph.D., and Jisoo Park, Ph.D., postdoctoral researchers in his lab, describe DrugCell, a new artificial intelligence (AI) system they created that not only matches tumors to the best drug combinations, but does so in a way that makes sense to humans. "Most AI systems are'black boxes'--they can be very predictive, but we don't actually know all that much about how they work," said Ideker, who is also co-director of the Cancer Cell Map Initiative and the National Resource for Network Biology.
This course has two main sections: one focused on effective business coaching, and the second focused on developing a successful business case. Two separate courses that already have more than 2000 students registered together. The section on The Key Stages of Coaching will involve learners in the process of discovery, goal setting, action planning, and follow-up that distinguishes coaching from other development methods. After completing the second section, you will be able to build an effective business case. You will understand what makes a business case, how to prepare one and how to design business cases to persuade decision makers.
An example of a fundus eye images taken from the UK Biobank. A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson's disease, according to research being presented at the annual meeting of the Radiological Society of North America (RSNA). Parkinson's disease is a progressive disorder of the central nervous system that affects millions of people worldwide. Diagnosis is typically based on symptoms like tremors, muscle stiffness and impaired balance -- an approach that has significant limitations. "The issue with that method is that patients usually develop symptoms only after prolonged progression with significant injury to dopamine brain neurons," said study lead author Maximillian Diaz, a biomedical engineering Ph.D. student at the University of Florida in Gainesville, Florida. "This means that we are diagnosing patients late in the disease process."
Artificial intelligence (AI), the use of human-like intelligence through software and mechanisms, enables the disruption of the most diverse segments. After all, this is an industry that has grown an average of 20% per year for the past 5 years, according to a survey by BBC Research. Many organizations have already joined the "future" and gained space by efficiently applying AI in everyday activities. For example, some banks started to perform financial services without the help of a human; farms use drones capable of identifying points in a crop that need more irrigation and automatically trigger sprinklers. AI is not set to replace the recruiter's work, the importance of the interview, the empathy, and the sparkle in the eye that we sometimes feel when interviewing a candidate.
Growth is fundamental to our personal and professional lives. It challenges us to become better people and make the most of every day. The idea of developing new soft and hard skills, although overwhelming at first, is much more manageable and achievable with the right structure and guidance. Those looking to embrace growth as we close out 2020, and head into a new calendar year, will want to check out this roundup of eLearning bundles on sale for Black Friday. Everything in this roundup is an additional 70% off for a limited time, which means now is a great time to pick up more skills and hit the ground running in 2021.
The receptive field (RF) of a neuron is the term applied to the space in which the presence of a stimulus alters the response of the same neuron. The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input (in mathematics, nonlinear systems represent phenomena whose behavior cannot be expressed as the sum of the behaviors of its descriptors). Conversely, vision models used in science are based on the notion of linear receptive field; in artificial intelligence and machine learning, as artificial neural networks are based on classical models of vision, also use linear receptive fields. "Modeling vision based on a linear receptive field poses several inherent problems: it changes with each input, it presupposes a set of basis functions for the visual system, and it conflicts with recent studies on dendritic computations," asserts Marcelo Bertalmío, first author of a study recently published in the journal Scientific Reports. The paper proposes modeling the receptive field in a nonlinear manner, introducing the intrinsically nonlinear receptive field or INRF.
Nanoparticles could make a reliable blood test for Alzheimer's disease a reality; image credit: National Cancer Institute, Daniel Sone Using nanoparticles with different surface properties, researchers are able to detect subtle changes in the composition of proteins in the plasma years before the presentation of clinical symptoms of Alzheimer's disease, which include memory loss, confusion, and cognitive difficulties. Owing to the unique properties of nanoparticles, different proteins in biological fluids selectively stick onto their surface forming a protein corona, which was found to change during disease. Researchers from the United States and Italy identify these subtle changes in plasma protein patterns to distinguish plasma samples from healthy individuals and those diagnosed with Alzheimer's disease. "Protein corona composition is both influenced by specific health conditions as well as the chemical and physical properties of the nanoparticles themselves," says Dr. Claudia Corbo of the University of Milano-Bicocca and lead author of the study published in Advanced Healthcare Materials. "Binding of proteins to the surface of particles is very precise and dependent on the chemistry and shape of the particles and the chemistry and structure of the proteins," says senior author Professor Omid Farokhzad of Brigham and Women's Hospital and Harvard Medical School.
IPI, the digital contact centre specialist, announced the launch of IPI Cloud AI, a SaaS-based portfolio of IPI's own self-service applications teamed with AI capability from the world's leading vendors. The applications are seamlessly integrated to customers' existing contact centre infrastructures – whether they are on premise or cloud based – providing next-generation AI capability, enabling customers to harness the power of cloud-based self-service functionality simply and cost-effectively, within their existing contact centre system. The initial solutions available include IPI's premier self-service apps: Send Me, which directs customers away from the contact centre to an alternative digital channel; Q4 Me, IPI's own patented end-to-end call-back application; Tell Me, IPI's speech interface for relaying information back to the customers; and ID Me, IPI's ID&V with voice biometrics solution. Alongside this are native integrations to Google Dialogflow CX, Amazon Lex and Microsoft Cognitive Services to support full NLP and intent capture – regardless of channel. "Our research and development team has spent a long time developing a best of breed solution that takes our robust self-service applications and fuses them with next generation AI capabilities from the world's leading vendors," said Steve Murray, Solutions Director at IPI. "We feel that IPI Cloud AI strikes the right balance between cutting edge capabilities and ease of adoption all within an unmatched commercial model. It really is a gamechanger for the market."