Collaborating Authors

Machine Learning Engineer - The Machine Learning Conference


Overjet is an early-stage VC-backed startup building the future of data-driven dentistry. We are using AI to transform the $130B dental care market and improve patient outcomes. We are seeking an entrepreneurially-minded a highly skilled developer who is comfortable with backend software development including deploying machine learning models, loves challenges and is passionate about impacting lives. Please email your resume to Develop machine learning pipelines Deploy machine learning models for inference Implement and maintain metrics for tracking ML models performance Design and develop microservices and APIs related to data ingestion, machine learning and product quality Ensuring responsiveness of applications.

Human sound systems are shaped by post-Neolithic changes in bite configuration


Biomechanical models of the speech apparatus show that labiodentals incur about 30% less muscular effort in the overbite and overjet configuration than in the edge-to-edge bite configuration. This difference is not present in similar articulations that place the upper lip, instead of the teeth, against the lower lip (as in bilabial "m," "w," or "p"). Our models also show that the overbite and overjet configuration reduces the incidental tooth/lip distance in bilabial articulations to 24 to 70% of their original values, inviting accidental production of labiodentals. The joint effect of a decrease in muscular effort and an increase in accidental production predicts a higher probability of labiodentals in the language of populations where overbite and overjet persist into adulthood. When the persistence of overbite and overjet in a population is approximated by the prevalence of agriculturally produced food, we find that societies described as hunter-gatherers indeed have, on average, only about one-fourth the number of labiodentals exhibited by food-producing societies, after controlling for spatial and phylogenetic correlation.

A survey of statistical learning techniques as applied to inexpensive pediatric Obstructive Sleep Apnea data Machine Learning

Obstructive sleep apnea (OSA), a form of sleep-disordered breathing characterized by recurrent episodes of partial or complete airway obstruction during sleep, is a serious health problem, affecting an estimated 1-5% of elementary school-aged children [9, 2]. Even mild forms of untreated pediatric OSA may cause high blood pressure, behavioral challenges, or impeded growth. Compared to adults, the symptoms of childhood-onset OSA are more varied and change continuously with development, making diagnosis a difficult challenge. The complexity of the data from surveys, biomedical measurements, 3D facial photos, and time-series data calls for state of the art techniques from mathematics and data science. Clinical data, including that considered in confirming or ruling out a diagnosis of pediatric OSA, consist of high-dimensional multi-mode data with mixtures of variables of disparate types (e.g., nominal and categorical data of different scales, interval data, time-to-event and longitudinal outcomes) also called mixed or noncommensurate data.

Artificial Intelligence And Other Tech Innovations Are Transforming Dentistry 7wData


From analyzing X-rays to documenting the results of your visit, Artificial Intelligence will be relied upon to make your dental appointment more efficient and to enhance your care. Dentem created a platform that integrates machine learning APIs, including the ability to auto-populate tooth charting. It offers dental practices software services that synchronize appointments across all platforms and maintains all patients' records electronically. They currently offer Dx Vision that uses machine learning to assess dental images for areas of concern and soon will offer D Assistant, a virtual assistant that will respond to a dentist's voice commands. As with other healthcare applications, Artificial Intelligence will be able to support dentists as a virtual second opinion when they determine a care plan.

These are the 64 startups unveiled at Y Combinator W18 Demo Day 2


Microbiome therapeutics, Photoshop for augmented reality, and cancer treatments were some of the ideas presented at Day 2 of startup accelerator Y Combinator's Winter 2018 Demo Day. YC is increasingly using its massive class size (141 startups this time around) to fund especially risky frontier technology and biotech moonshots, while tempering the portfolio with more predictable enterprise companies. Investors say that valuations for post-Demo Day raises have risen steeply recently. Some speculate that people who made a fortune on cryptocurrency are trying to invest their returns elsewhere, driving up demand for YC startups. The accelerator still admits many international copycats of U.S. successes, and YC is also repeating itself a bit. The Podcast App pitched the exact same product and strategy as Breaker, which debuted at YC exactly a year ago. But there were plenty of ambitious and unique businesses unveiled today on the Mountain View Computer History Museum stage, and the room was -- as always -- packed with a who's who of tech investors. Check out our coverage of all 64 startups that launched on the record yesterday, plus our picks for the top 7 companies from yesterday. Here are the 60 startups that launched at YC's Winter 2018 Demo Day 2: Callisto is a sexual misconduct reporting software built for victims. The company's product works by asking people who are looking to report a perpetrator to give certain unique identifiers, like a LinkedIn profile or phone number. If two victims name the same perpetrator, they are put in touch with each other and then with with an "options counselor," a lawyer who can give them options on how to proceed in handling the situation. The company says that victims that visit Callisto's website are 5x more likely to take action.