Results


Machine Learning Tailors Training to the Student

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"You don't want to present each learner with the same experience, with the same knowledge," explains Luke DeVore, business development director at Design Interactive of Orlando, Fla., a human-centered design firm with a focus on training. Design Interactive is already applying machine learning to training through its ScreenAdapt system, which a Window-based training application for x-ray images. By tracking users' ability to identify areas of concern within an image – and also identify when trainees fail to scan the entire image – the system provides invaluable personalized feedback to both trainees and instructors. "We've spoken with a lot of radiologists and they've said there's a real difference between the way a novice will scan a medical image versus an experienced performer.


4 ways Google Cloud will bring AI, machine learning to the enterprise

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Last November, when Google announced that machine learning research luminary Fei-Fei Li, Ph.D. would join Google's Cloud Group Platform group, a lot was known about her academic work. At this point in time, most enterprises do not have the technical capabilities to build and train custom machine learning models that would utilize the Machine Learning Engine. These companies can apply machine learning with Google's pre-trained models (full list) using APIs to add machine learning capability to their applications, such as understanding natural language, images and natural language. Li drew on her experience building the open-source ImageNet data set of over 15 million labeled images that enabled advances in deep learning research.


After Uber, Snapchat's boom & tech ethics #103

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What if algorithms can spot suicide risks before the people around you can? In a soon to be published paper, a researcher claims to be able to spot suicide risks with 80% accuracy up to two years in advance. Similar peer-reviewed work has applied machine learning to long-term observation of physical symptoms to improve early identification of suicide risk. Even Facebook has developed an algorithm that can spot suicide risk based on your status updates.


Artificial intelligence used to identify skin cancer Stanford News

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Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists. During testing, the researchers used only high-quality, biopsy-confirmed images provided by the University of Edinburgh and the International Skin Imaging Collaboration Project that represented the most common and deadliest skin cancers – malignant carcinomas and malignant melanomas. The algorithm's performance was measured through the creation of a sensitivity-specificity curve, where sensitivity represented its ability to correctly identify malignant lesions and specificity represented its ability to correctly identify benign lesions. "Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," said Susan Swetter, professor of dermatology and director of the Pigmented Lesion and Melanoma Program at the Stanford Cancer Institute, and co-author of the paper.


AI's good at diagnosing skin cancer

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Classifying skin lesions using images is challenging, owing to fine-grained variabilities in their appearance. Convolutional Neural Networks (CNN) offer potential for dealing with fine-grained object categories. Trained CNN used a dataset of 129,450 clinical images and 2,000 skin lesions. Equipped with CNN, mHealth can potentially extend dermatologists' reach beyond their clinics.


Wearable AI system can detect a conversation's tone

MIT News

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute of Medical Engineering and Science (IMES) say that they've gotten closer to a potential solution: an artificially intelligent, wearable system that can predict if a conversation is happy, sad, or neutral based on a person's speech patterns and vitals. As a participant tells a story, the system can analyze audio, text transcriptions, and physiological signals to determine the overall tone of the story with 83 percent accuracy. The system also captured audio data and text transcripts to analyze the speaker's tone, pitch, energy, and vocabulary. "The team's usage of consumer market devices for collecting physiological data and speech data shows how close we are to having such tools in everyday devices," says Björn Schuller, professor and chair of Complex and Intelligent Systems at the University of Passau in Germany, who was not involved in the research.


This AI Can Diagnose a Rare Eye Condition as Well as a Human Doctor

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To this end, a group of Chinese ophthalmologists and computer scientists has demonstrated a machine learning algorithm for identifying congenital cataracts, a rare eye disease that's nonetheless responsible for some 10 percent of all vision loss in children worldwide. The algorithm is based on convolutional neural networks (CNNs), a class of machine learning models that attempts to imitate the neural processing that occurs in the visual cortex of animals. CNNs are widely used for visual recognition tasks but also other domains, like playing Go, natural language processing, and drug discovery. It only started to falter when tasked with making decisions about follow-up care, where the network registered a relatively large number of false positives.


Genetic algorithms for feature selection in Data Analytics

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Here the function to optimize is the generalization performance of the predictive model, represented by the error on a selection data set. The fist step is to create and initialize the individuals in the population As the genetic algorithm is an stochastic optimization method, the genes of the individuals are usually initialized at random. To evaluate the fitness, we need to train the predictive model with the training data, and then evaluate its selection error with the selection data. Once the selection operator has chosen half of the population, the crossover operator recombines the selected individuals to generate a new population.


Artificial intelligence used to identify skin cancer Stanford News

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Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists. During testing, the researchers used only high-quality, biopsy-confirmed images provided by the University of Edinburgh and the International Skin Imaging Collaboration Project that represented the most common and deadliest skin cancers – malignant carcinomas and malignant melanomas. The algorithm's performance was measured through the creation of a sensitivity-specificity curve, where sensitivity represented its ability to correctly identify malignant lesions and specificity represented its ability to correctly identify benign lesions. "Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," said Susan Swetter, professor of dermatology and director of the Pigmented Lesion and Melanoma Program at the Stanford Cancer Institute, and co-author of the paper.


Deep learning algorithm does as well as dermatologists in identifying skin cancer

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In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of dermatologists. During testing, the researchers used only high-quality, biopsy-confirmed images provided by the University of Edinburgh and the International Skin Imaging Collaboration Project that represented the most common and deadliest skin cancers--malignant carcinomas and malignant melanomas. The algorithm's performance was measured through the creation of a sensitivity-specificity curve, where sensitivity represented its ability to correctly identify malignant lesions and specificity represented its ability to correctly identify benign lesions. "Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," said Susan Swetter, professor of dermatology and director of the Pigmented Lesion and Melanoma Program at the Stanford Cancer Institute, and co-author of the paper.