Health & Medicine


Progress in AI seems like it's accelerating, but here's why it could be plateauing

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"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.


AI could tell you when you're about to get sick

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The devices will help gather, analyze, and display a crush of health data he wants to collect about himself--and, he hopes, from millions of others. It starts with your DNA sequence and includes data from Fitbit-style wearables that measure your steps, heart rate, and sleep patterns. Now Imagu's engineers are working with counterparts at ICX to create what they call a "virtual health brain" that will interpret the thousands of data points ICX wants to collect on each customer. During his stint as BGI's CEO, Wang helped build the company into one of the largest sequencing operations in the world.


Progress in AI seems like it's accelerating, but here's why it could be plateauing

#artificialintelligence

"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.


Artificial Intelligence is our future. But will it save or destroy humanity?

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If tech experts are to be believed, artificial intelligence (AI) has the potential to transform the world. Artificial intelligence is software built to learn or problem solve -- processes typically performed in the human brain. Neither Musk nor Hawking believe that developers should avoid the development of AI, but they agree that government regulation should ensure the tech does not go rogue. However, Shostak doesn't believe sophisticated AI will end up enslaving the human race -- instead, he predicts, humans will simply become immaterial to these hyper-intelligent machines.


Progress in AI seems like it's accelerating, but here's why it could be plateauing

@machinelearnbot

"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.


Forget Police Sketches: Researchers Perfectly Reconstruct Faces by Reading Brainwaves

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Using brain scans and direct neuron recording from macaque monkeys, the team found specialized "face patches" that respond to specific combinations of facial features. In the early 2000s, while recording from epilepsy patients with electrodes implanted into their brains, Quian Quiroga and colleagues found that face cells are particularly picky. In a stroke of luck, Tsao and team blew open the "black box" of facial recognition while working on a different problem: how to describe a face mathematically, with a matrix of numbers. In macaque monkeys with electrodes implanted into their brains, the team recorded from three "face patches"--brain areas that respond especially to faces--while showing the monkeys the computer-generated faces.


Uncle Sam Wants Your Deep Neural Networks

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Earlier this year, Kaggle ran a $1 million contest to build algorithms capable of identifying signs of lung cancer in CT scans, helping to fuel a larger effort to apply neural networks to health care.


Artificial Intelligence ushers in the era of superhuman doctors

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"How long would you say that's been going on?" In primary care, one in 20 patients will get a wrong diagnosis. These are worrying figures, driven by the complex nature of diagnosis, which can encompass incomplete information from patients, missed hand-offs between care providers, biases that cloud doctors' judgement, overworked staff, overbooked systems, and more. This is why many want to use the constant and unflappable power of artificial intelligence to achieve more accurate diagnosis, prompt care and greater efficiency.


7 Startups Giving Artificial Intelligence (AI) Emotions - Nanalyze

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Founded in 2012, Israeli startup Beyond Verbal has taken in $10.1 million in 4 rounds of funding to develop a technology that "analyzes emotions from vocal intonations". Like CrowdEmotion, nViso's technology tracks the movement of 43 facial muscles using a simple webcam and then uses AI to interpret your emotions. The Company uses a branch of artificial intelligence called Natural Language Processing (NLP) techniques to capture people's emotions, social concerns, thinking styles, psychology, and even their use of parts of speech. The startup developed a technique to "read" human emotional state called Transdermal Optical Imaging (TOI) using a conventional video camera to extract information from the blood flow underneath the human face.


Top 10 Data Mining Algorithms, Explained

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A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. Orange, an open-source data visualization and analysis tool for data mining, implements C4.5 in their decision tree classifier. Support vector machine (SVM) learns a hyperplane to classify data into 2 classes. The balls represent data points, and the red and blue color represent 2 classes.