Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if the other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising solution: privacy is considered sufficient if presence of individual patients cannot be distinguished. However, differentially private learning with current methods does not improve predictions with feasible data sizes and dimensionalities. Here we show that useful predictors can be learned under powerful differential privacy guarantees, and even from moderately-sized data sets, by demonstrating significant improvements with a new robust private regression method in the accuracy of private drug sensitivity prediction. The method combines two key properties not present even in recent proposals, which can be generalised to other predictors: we prove it is asymptotically consistently and efficiently private, and demonstrate that it performs well on finite data. Good finite data performance is achieved by limiting the sharing of private information by decreasing the dimensionality and by projecting outliers to fit tighter bounds, therefore needing to add less noise for equal privacy. As already the simple-to-implement method shows promise on the challenging genomic data, we anticipate rapid progress towards practical applications in many fields, such as mobile sensing and social media, in addition to the badly needed precision medicine solutions.

Dash presents its new chart library for the web. We like how the article goes in depth with how the dash library designed. They describe how the handled error cases and multiple integrations in a smart way. We found their comparison to Excel and the R ecosystem on point. One implementation challenge is that you need to maintain a flask application and have a ReactJS friendly front-end.

When it comes to effectiveness of machine learning, more data almost always yields better results--and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments.

Scientists are using artificial intelligence (AI) to help predict which embryos will result in IVF success. In a new study, AI was found to be more accurate than embryologists at pinpointing which embryos had the potential to result in the birth of a healthy baby. Experts from Sao Paulo State University in Brazil have teamed up with Boston Place Clinic in London to develop the technology in collaboration with Dr Cristina Hickman, scientific adviser to the British Fertility Society. They believe the inexpensive technique has the potential to transform care for patients and help women achieve pregnancy sooner. During the process, AI was "trained" in what a good embryo looks like from a series of images.

Women have long claimed their monthly period makes them more irritable or stressed. Now, however, scientists have said the idea that a woman's menstrual cycle affects her thinking is nothing more than a myth. According to some previous studies, women are more impulsive and moody before their period, and more'rational' afterwards. Scientists have said the idea that a woman's menstrual cycle affects her thinking is nothing more than a myth But the latest research says that while women may feel their thinking became altered, this was not the case when studied scientifically. It seems that despite hormone levels fluctuating enormously in a woman's body, they have no effect on her ability to remember or make decisions.

Artificial intelligence is getting pretty good at besting humans in things like chess and Go and dominating at trivia. Now, AI is moving into the arts, aping van Gogh's style and creating a truly trippy art form called Inceptionism. A new AI project is continuing to push the envelope with an algorithm that only produces original styles of art, and Chris Baraniuk at New Scientist reports that the product gets equal or higher ratings than human-generated artwork. Researchers from Rutgers University, the College of Charleston and Facebook's AI Lab collaborated on the system, which is a type of generative adversarial network or GAN, which uses two independent neural networks to critique each other. In this case, one of the systems is a generator network, which creates pieces of art.

Snapchat's controversial new feature allows other people to see exactly where you are in real time, and parents are being advised to take steps to protect their children's privacy. Snap Map uses data such as your location, speed of travel and phone usage to work out where you are and what you're doing, and shares this information with your friends on an interactive map. The map, which you can launch by pinching the Snapchat camera home screen, is precise enough to show not only what street you're on, but also whereabouts on that street you are. Parents are being urged to read up on it, and to ensure their children are aware of the risks that can come with sharing too much information through social media. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

In the first and second parts of this series, I introduced the Outbrain Click Prediction machine learning competition and my initial tasks to tackle the challenge. I presented the main techniques used for exploratory data analysis, feature engineering, cross-validation strategy and modeling of baseline predictors using basic statistics and machine learning. In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%). One of the popular approaches for CTR Prediction is Logistic Regression with a Follow-the-Regularized-Leader (FTRL) optimizer, which have been used in production by Google to predict billions of events per day, using a correspondingly large feature space. It is a linear model with a lazy representation of the coefficients (weights) and, in conjunction with L1 regularization, it leads to very sparse coefficient vectors. This sparsity property shrinks memory usage, making it scalable for feature vectors with billions of dimensions, because each instance will typically have only a few hundreds of nonzero values.

During a book tour stop in Portland, Oregon earlier this year, author Jeff VanderMeer (Borne, the Southern Reach Trilogy) met up with two speculative-fiction contemporaries: Omar El Akkad and Lidia Yuknavitch. Like VanderMeer, both had recently published dystopian-ish novels set against a backdrop of climate change. El Akkad's American War chronicles a fossil-fuel civil war in the U.S.; in Yuknavitch's The Book of Joan, a new Joan of Arc for a global-warming era battles fascist forces. Given the obvious real-world resonances of the three books (the admittedly more fantastical Borne tackles out-of-control capitalism via a futuristic desert city terrorized by a giant flying psychotic bear), VanderMeer organized a three-way conversation to examine what he calls their "parallel evolution"--as well as dicuss how to take on a troubling present reality in an meaningful and productive way. They feel like boundaries that mean less and less, attempts at containment or to say "this couldn't possibly happen to anyone reading this now."

Two-Class Boosted Decision Tree module creates a machine learning model that is based on the boosted decision trees algorithm. A boosted decision tree is an ensemble learning method in which the second tree corrects for the errors of the first tree, the third tree corrects for the errors of the first and second trees, and so forth. Predictions are based on the entire ensemble of trees together that makes the prediction. Step 1 Add the Boosted Decision Tree module to the experiment. Step 2 Specify how you want the model to be trained, by setting the Create trainer mode option.