Therapeutic Area

Alphabet AI is helping release sterile mosquitoes in Singapore


In many parts of the world, mosquitoes are more than just a campsite nuisance -- they carry that cause an estimated 725,000 deaths per year. On Singapore, the effect isn't so terrible -- some mosquitoes carry dengue fever, but it affects less than a dozen people per year. But because it's a city and an island, Singapore is the perfect testing ground to see how easy it might be to get rid of the disease-carrying bugs, all sans gene editing. That's what Alphabet-owned healthcare company Verily hopes to do. The company, along with Singapore's environmental agency, plans to release male mosquitoes that carry Wolbachia, a naturally-occurring bacteria that reduces the bugs' ability to transmit disease and prevents their eggs from hatching.

Huami's 'first AI-powered wearable chipset' takes aim at Apple Watch


It's been the month of wearable silicon – and off the back of the news of the Amazfit Verge, Huami announced that its developed the "world's first AI-powered wearable chipset" – the Huangshan No. 1. For the uninitiated, Huami is a Chinese hardware company, pretty much backed by Xiaomi. It's been turning out some pretty impressive devices, including the Amazfit Bip and Amazfit Stratos, both of which have mustered decent Wareable reviews. Well, Gizmochina says that the Huangshan No.1 features four core intelligence engines: namely, a cardiac biometric engine, ECG, ECG Pro and Heart Rhythm Abnormality engine. That's clearly a pretty big claim to Apple, aping (in true Huami style) pretty much all the lead features of the Apple Watch Series 4 unveiled last week.

Love, Death, and Other Forgotten Traditions - Issue 64: The Unseen


The science-fiction writer Robert Heinlein once wrote, "Each generation thinks it invented sex." He was presumably referring to the pride each generation takes in defining its own sexual practices and ethics. But his comment hit the mark in another sense: Every generation has to reinvent sex because the previous generation did a lousy job of teaching it. In the United States, the conversations we have with our children about sex are often awkward, limited, and brimming with euphemism. At school, if kids are lucky enough to live in a state that allows it, they'll get something like 10 total hours of sex education.1

Brain scientists dive into deep neural networks


At the Conference on Cognitive Computational Neuroscience this month, researchers presented new tools for comparing data collected from living brains with readouts from computational models known as deep neural networks. Such comparisons might offer up new hypotheses about how humans interpret sights and sounds, understand language, or navigate the world. Until recently, artificial intelligence couldn't come anywhere close to human performance on tasks like recognizing sounds or classifying images. But deep neural networks, loosely inspired by the brain, have logged increasingly impressive performances, especially on visual tasks. That has led some neuroscientists to wonder whether these models could yield insight into how our own brains process information.

Fighting the Opioid Crisis Through Artificial Intelligence and Machine Learning


In the United States, an average of 135 people die per day as a result of opioid-induced overdose, according to the National Institute on Drug Abuse. A November 2017 report issued by the Council of Economic Advisers estimates that the total economic cost of the opioid crisis was $504 billion in 2015. This likely underestimates the total cost, given the difficulty of quantifying social impact on people suffering from opioid use disorder, as well as the financial hardships of family members and communities affected by addiction and fatal overdose. The opioid epidemic is a national health crisis that requires intervention by state, local, and national policymakers. To reduce the prevalence of opioid-induced mortality, stakeholders need access to more opportune data that will drive evidence-based policy recommendations and patient-centric treatment pathways.

Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018


Edge services and edge computing have been in talks since at least the 90s. When Edge computing is extended to the cloud it can be managed and consumed as if it were local infrastructure. It's the same as how humans find it hard to interact with infrastructure that is too far away. Edge Analytics is the exciting area of data analytics that is gaining a lot of attention these days. While traditional analytics, answer questions like what happened, why it happened, what is likely to happen and options on what you should do about it Edge analytics is data analytics in real time.

Machine Learning Confronts the Elephant in the Room Quanta Magazine


In a new study, computer scientists found that artificial intelligence systems fail a vision test a child could accomplish with ease. "It's a clever and important study that reminds us that'deep learning' isn't really that deep," said Gary Marcus, a neuroscientist at New York University who was not affiliated with the work. The result takes place in the field of computer vision, where artificial intelligence systems attempt to detect and categorize objects. They might try to find all the pedestrians in a street scene, or just distinguish a bird from a bicycle (which is a notoriously difficult task). The stakes are high: As computers take over critical tasks like automated surveillance and autonomous driving, we'll want their visual processing to be at least as good as the human eyes they're replacing.

Artificial Intelligence Continues to Change Health Care


One key way is with diagnostic and imaging tools, like MRIs and CT and PET scans. Algorithms can be trained, for instance, to accurately measure all of the lymph nodes from a cancer patient's CT scan to see if they're changing size. It's a huge job that algorithms can do much more quickly than humans. The clinician can then take the results and decide whether a therapeutic regime is working or needs to be adjusted or changed. We now also use machine-learning tools in stroke detection and classification.

Machine Learning vs Machine Reasoning: Know the Difference


Since ancient times, humans have been interested in finding systematic approaches to reasoning and logical thinking. Now, we want to make machines "think" like us and endow them with the reasoning ability that, unfortunately, we don't quite understand ourselves. But, why do we need machines that can deconstruct truths and validate reasons like we do? One of our most recent AI-related posts discusses the story of an AI system that can detect skin cancer more accurately than dermatologists. No doubt, this is a big deal in that an early diagnosis is one of the most effective methods for providing successful cancer treatments.

New AI Strategy Mimics How Brains Learn to Smell Quanta Magazine


Today's artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there's a car in an image, at differentiating between depictions of cats and dogs. "But they are rather pathetic at composing music or writing short stories," said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. "They have great trouble reasoning meaningfully in the world." To overcome those limitations, some research groups are turning back to the brain for fresh ideas.