Last week I had an amazing opportunity to present about machine learning and innovation to 110 people leaders at my company. It was a mixed audience of technical and non technical people. My message is that machine learning needs to be understood by everyone in the business, not just the data scientists. Domain knowledge with machine learning will really enable successful data projects. A few months ago I followed a tutorial on http://course.fast.ai

New research from Alphabet's life science company, Verily, and Google shows that one day there might be a far easier test to determine risk of heart disease. Researchers were able to find new indicators of heart disease risk present in pictures of retinas by analyzing them with artificial intelligence, according to a paper published Thursday by researchers at Google and Verily, which has not been peer reviewed. Doctors today rely heavily on blood tests to determine risk of heart disease; a potential test based on retinal images would be less invasive, easier to obtain, and faster to analyze with AI. While mainstream medicine has found a number of signals that indicate heart disease on retinal images, the Google and Verily algorithm was able to find indicators of age, gender, whether the patient smoked, blood pressure, and information about the level of sugar in a person's blood on its own. Then after analyzing how those traits influenced future cardiovascular attacks, Google claims the system nearly matched the accuracy of a standard test for estimating the risk of heart failure, the European SCORE system. That system uses data from a blood test.

In lab tests involving simulated tabletop earthquakes, researchers at the Los Alamos National Laboratory in New Mexico demonstrated that machine-learning technology can play a role in predicting major tremors by analyzing acoustic signals to find failing fault lines. For the experiment, earthquakes were modeled by the researchers using two large blocks of steel, which were put under stress. This resulted in them rubbing against one another like tectonic plates on the Earth's surface. The movement released energy in the form of seismic waves -- which was then analyzed by the team's artificial intelligence. "We discovered that an artificial intelligence can learn to discern a very specific pattern in the sound emitted by the fault before it ruptures," Bertrand Rouet-LeDuc, one of the researchers on the project, told Digital Trends.

The HireVue app uses a mixture of video interviewing and artificial intelligence to make recruiting more efficient and match the right candidates with the right jobs. Job recruitment has always been a hassle. Sometimes recruiters are swarmed with 300 applicants for a single position,. Once they've narrowed down the options, the 50 prime candidates still need to be screened and interviewed. It would be more efficient to hire the ideal candidate as soon as possible, rather than dragging them through the job search process longer than necessary.

Engineers and product managers at Apple's headquarters at Cupertino these days are obsessed with the idea of embedding artificial intelligence (AI) into every aspect of consumer's digital touch points. That's because Apple chief Tim Cook wants AI to be like air, all pervasive yet invisible. "The beauty of AI is that the user does not have to think about launching an application or think like'okay, I am going to do some AI now'. This is embedded in things which you don't even get to know. If you talk to our various teams -- the software team, Apple TV team, the mail team, the home pod team -- all of them have AI projects going on. AI is sought of like air. It's invisible yet all permeating," Cook told BusinessLine in a recent interview.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints. Often, identifying weak (but scientifically interesting) associations between a set of predictors and a response necessitates pooling datasets from multiple diverse labs or groups. While there is a rich literature in statistical machine learning to address distributional shifts and inference in multi-site datasets, it is less clear ${\it when}$ such pooling is guaranteed to help (and when it does not) -- independent of the inference algorithms we use. In this paper, we present a hypothesis test to answer this question, both for classical and high dimensional linear regression. We precisely identify regimes where pooling datasets across multiple sites is sensible, and how such policy decisions can be made via simple checks executable on each site before any data transfer ever happens. With a focus on Alzheimer's disease studies, we present empirical results showing that in regimes suggested by our analysis, pooling a local dataset with data from an international study improves power.

Conventional modeling approaches have found limitations in matching the increasingly detailed neural network structures and dynamics recorded in experiments to the diverse brain functionalities. On another approach, studies have demonstrated to train spiking neural networks for simple functions using supervised learning. Here, we introduce a modified SpikeProp learning algorithm, which achieved better learning stability in different activity states. In addition, we show biological realistic features such as lateral connections and sparse activities can be included in the network. We demonstrate the versatility of this framework by implementing three well-known temporal codes for different types of cognitive tasks, which are MNIST digits recognition, spatial coordinate transformation, and motor sequence generation. Moreover, we find several characteristic features have evolved alongside the task training, such as selective activity, excitatory-inhibitory balance, and weak pair-wise correlation. The coincidence between the self-evolved and experimentally observed features indicates their importance on the brain functionality. Our results suggest a unified setting in which diverse cognitive computations and mechanisms can be studied.

Preprocessing data is an important step before any data analysis. In this paper, we focus on one particular aspect, namely scaling or normalization. We analyze various scaling methods in common use and study their effects on different statistical learning models. We will propose a new two-stage scaling method. First, we use some training data to fit linear regression model and then scale the whole data based on the coefficients of regression. Simulations are conducted to illustrate the advantages of our new scaling method. Some real data analysis will also be given.