We can't stop progress or robots, but we can adopt wise policies that maximize both supply and demand for human capital, encouraging employers to pay the best price for high-quality labor. Right now, we're trying to force them to pay more than the labor is really worth because our government has not been able to establish a better set of labor, immigration and education policies. BurgerFlipBot is a symbol of that government failure -- and a stark warning of what lies at the end of that road.

Many data and analytics leaders face a chicken and egg situation. Without experienced data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, getting serious business commitment to fund data science projects and hire data scientists is equally challenging. Most organisations are still in the early phases of their data science journey and struggle to understand what machine learning and data science can do for them. They don't exactly know which skills are needed and hiring data scientists seems really difficult.

Powell High School teacher Jimmy Waters stands in front of the San Jose Semaphore in California. Waters cracked a code broadcast by the building that had been unsolved for more than four years. Jimmy Waters, 31, teaches advanced algebra, geometry and trigonometry at Powell High School. Last summer, Waters devoted a month of his vacation time to solving the puzzle presented by the San Jose Semaphore, a project created by New York-based artist Ben Rubin in 2006. Adobe planned on rewarding Waters with a one-year subscription to its Creative Cloud software, but at his request, the company is donating 40 one-year subscriptions to Powell High School's computer lab, on top of a 3-D printer "to help the students push the boundaries of creativity even further," according to a company news release.

We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets. We formulate the learning approach using a covariance-fitting methodology, and show that the resulting predictor has desirable computational and distribution-free properties: It is implemented online with a runtime that scales linearly in the number of samples; has a constant memory requirement; avoids local minima problems; and prunes away redundant feature dimensions without relying on restrictive assumptions on the data distribution. In conjunction with the split conformal approach, it also produces distribution-free prediction confidence intervals in a computationally efficient manner. The method is demonstrated on both real and synthetic datasets.

Learning parameters of latent graphical models (GM) is inherently much harder than that of no-latent ones since the latent variables make the corresponding log-likelihood non-concave. Nevertheless, expectation-maximization schemes are popularly used in practice, but they are typically stuck in local optima. In the recent years, the method of moments have provided a refreshing angle for resolving the non-convex issue, but it is applicable to a quite limited class of latent GMs. In this paper, we aim for enhancing its power via enlarging such a class of latent GMs. To this end, we introduce two novel concepts, coined marginalization and conditioning, which can reduce the problem of learning a larger GM to that of a smaller one. More importantly, they lead to a sequential learning framework that repeatedly increases the learning portion of given latent GM, and thus covers a significantly broader and more complicated class of loopy latent GMs which include convolutional and random regular models.

Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario suffers from a tunnel vision effect: without recourse to independent sampling, the urge to query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of AS methods and standard classifiers. We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried next. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. From these observations we propose D3TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, matching or exceeding the performance of competing methods on seven real network datasets in our evaluation.

In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.

Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain–computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)--two of them in permanent CLIS and two entering the CLIS without reliable means of communication--learned to answer personal questions with known answers and open questions all requiring a "yes" or "no" thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%.

Machines, you may have heard, are coming for all the jobs. Artificial intelligence handles insurance claims and basic bookkeeping, manages investment portfolios, does legal research, and performs basic HR tasks. Human labor doesn't stand a chance against them--after the "automation apocalypse," only those with spectacular abilities and the owners of the robots will thrive. But before you start campaigning for a universal basic income and set up a bunker, you might want to also familiarize yourself with the competing theory: In the long run, we're going to be just fine. Our modern fear that robots will steal all the jobs fits a classic script. Nearly 500 years ago, Queen Elizabeth I cited the same fear when she denied an English inventor named William Lee a patent for an automated knitting contraption.

It's 3/14, also known as Pi Day – a mathematics holiday to celebrate the irrational, transcendental number we learned in school, for the most part, to calculate the circumference or area of circles. While there are a number of fulfilling Pi(e) related activities you can indulge in, from feasting on scrumptious pies to chasing down the value of Pi (good luck!), it is also an apt moment to turn attention to where children across the world stand in mathematics achievement and other learning outcomes. While countries have made impressive gains in access to education, a recurring theme is that not nearly enough learning is happening. The 2018 World Development Report (WDR) takes on the learning crisis and its possible underlying factors. The report also takes stock of a growing evidence base to identify key principles and effective interventions to improve learning, challenges in taking successful interventions to scale, and strategies to overcome those challenges.