An anonymous reader writes: Google Calendar is now 10 years old. What better way to celebrate than by adding a new goals feature to the service? The new feature lets you set a personal goal in Google Calendar, which will then find time in your schedule so you can achieve your goal. The feature is available for mobile-only users in all countries and languages where Google Calendar works. The goal is dependent on two main questions: "how often?" and "best time?" [Once you answer those questions], it will then find the best time slot in your schedule to pencil in your new goal.

Of course you can use R to analyse the GA Demo data. It s real data from the Google Merchandise Store so you might be interested in applying machine learning algorithms, or create beautiful visualizations and dashboards. In more than one occasion in this blog I shared examples of GA dashboards made with R and Shiny. Some readers asked me for the original dataset in order to reproduce the code, cause they did not have access to any GA account. With the demo account available, now it s easy to export the data and import it into R, let say in a .csv

Drone safety, from privacy issues to crashing over unsuspecting pedestrians, has been a concern since, well, the dawn of the drone. But one startup is working to use artificial intelligence to help drone pilots pick the safest route. Flock, an artificial intelligence company formed out of Imperial College London, Oxford University and Cambridge University, is currently developing a risk analysis program for commercial drones, from aerial photographers to drone use on a larger scale, such as delivering Amazon packages. The program uses real-time weather information and the location of buildings. But what's perhaps even more impressive is that the system can also predict what areas will be full of people so it can choose a route around congested areas or a time when those areas will be less crowded.

Since Facebook announced a bot developer framework and distribution platform in April, the media has been hyperventilating over its impact. I know we're a big part of this, and I don't apologize. Bots, as a new (or revisited) paradigm for human-computer interaction, are here, and we're observing hundreds of companies, billions in funding, and thousands of bots flying in your browsers and messaging apps. You can download the full landscape here, and more rich data is coming soon. This article is part of the Bots Landscape.

SAS is the recognized incumbent in the analytics, statistics and data science tool space. As the software celebrates its 50th birthday this year, it has evolved into a broad suite of tools and approaches that tries to do everything. From basic inference to the most complex clinical trials, SAS is trying to provide a framework for everyone. Even with 50 years of code (or perhaps because of 50 years of code), there are some areas where SAS may be falling behind. People interested in data science have been watching open source statistical environments develop as alternate solutions for full cycle data science programs. Over the last 5 years, two contenders, R and Python, have proven themselves to be capable and worthwhile investments professionally and organizationally.

Least squares regression can cause impossible estimates such as probabilities that are less than zero and greater than 1.So, when the predicted value is measured as a probability, use Logistic Regression We use the log of the odds rather than the odds directly because an odds ratio cannot be a negative number--but its log can be negative. Notice that we have randomly initialized our coefficients for income and other predictors. These will be adjusted by Solver based on a likelihood function.We will cover them later Column H tells us the predicted probability of the borrower's actual behavior, whether that behavior is repayment or default--not simply, as in Column G, the predicted probability of defaulting on the loan. One property of logarithms is that their sum equals the logarithm of the product of the numbers on which they're based The logarithms of probabilities are always negative numbers, but the closer a probability is to 1.0, the closer its logarithm is to 0.0. I haven't covered cross-validation, which is commonly used to validate a logistic regression equation.If you don't always have a large number of cases to work with, a different approach is to use statistical inference.

Learn how to design a spam classifier using machine learning APIs with this webinar. Syed is an IT Engineering Manager with 14 years of experience in software development, technology management, IT strategy, technology consulting, and product development.

In the world of machine learning, deep learning neural networks (DLNN) is the fastest growing field. World Scientific's latest book "Deep Learning Neural Networks: Design and Case Studies" shows how DLNN can be a powerful computational tool for solving prediction, diagnosis, detection and decision problems based on a well-defined computational architecture. The applications in this field serve as a major decision tool in Big Data applications. DLNN successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance. Their range of applications covers almost any problem whose input data, performance evaluation and target decision can be numerically expressed.

Brain–computer interfaces (BCIs) have traditionally been used to enable communication and control for paralyzed patients.1 However, it is also thought that BCIs hold promise for fulfilling the longstanding goal of creating artificial systems (i.e., which can perform with the adaptability, robustness, and general intelligence of humans). To augment the sensing and processing capabilities of such artificial systems, BCI systems can thus be used on healthy individuals. In this way, the biological machinery that enables human cognition can be leveraged. Image triage--a visual target search over a set of images--is a prime application for this new class of BCI.

When Bloomberg first built the terminal system, back in the early 1980s, most of its customers -- mainly finance professionals -- didn't have computers on their desks. The internet was not yet a commonly-accepted technical protocol for networking and hardware of the terminal's kind hadn't been seen before. So Bloomberg's engineers had to go about inventing the tech themselves -- from the set of instructions to carry data across a network, to custom-built hardware so traders could use a keyboard, and monitors you could stack. It created a great culture of invention at Bloomberg, which has more software engineers than journalists. But cultivating that culture to create new products within came at a small cost.