Your mission We are searching for great machine learning engineers to join the team responsible for: · Extending Criteo's large scale distributed machine learning library (e.g., implementing new distributed and scalable machine learning algorithms, improving their performance) · Building and improving prediction models for ad targeting; proving the business value of the changes and deploying them to production · Gathering and analyzing data, performing statistical modeling You'll have the opportunity to work on highly challenging problems with both engineering and scientific aspects; for example: · Click prediction:ÂHow do you accurately predict in less than a millisecond if the user will click on an ad? Thankfully, you have billions of datapoints to help you. · Offline testing:ÂYou can always compute the classification error on a model predicting the click probability. But will it really correlate with the online performance of this model? · Explore / exploit:ÂIt's easy, UCB and Thomson sampling have low regret. But what happens when new products come and go and when each ad displayed changes the reward of each arm? But what do you do when all data are not equal and when you must distribute the learning overÂthousandsÂof nodes? To qualify for this mission, you need: · MS degree in Computer Science or related quantitative field with 3 years of relevant experience or Ph.D degree in Computer Science or related quantitative field · Good understanding of the mathematical foundations behind machine learning algorithms · Great coding skills.

By building on a recently introduced genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel methodology to compute construction univariate and bivariate construction safety risk at a situational level. Our fully data-driven approach provides construction practitioners and academicians with an easy and automated way of extracting valuable empirical insights from databases of unstructured textual injury reports. By applying our methodology on an attribute and outcome dataset directly obtained from 814 injury reports, we show that the frequency-magnitude distribution of construction safety risk is very similar to that of natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we introduce univariate and bivariate nonparametric stochastic safety risk generators, based on Kernel Density Estimators and Copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithfully to the original data, allowing safetyrelated decision-making under uncertainty to be grounded on extensive empirical evidence. Just like the accurate modeling and simulation of natural phenomena such as wind or streamflow is indispensable to successful structure dimensioning or water reservoir management, we posit that improving construction safety calls for the accurate modeling, simulation, and assessment of safety risk. The underlying assumption is that like natural phenomena, construction safety may benefit from being studied in an empirical and quantitative way rather than qualitatively which is the current industry standard. Finally, a side but interesting finding is that attributes related to high energy levels and to human error emerge as strong risk shapers on the dataset we used to illustrate our methodology.

Such problems appear in a variety of research fields and include image processing [12, 40], data analytics [13, 12], quantum computing [1, 19, 25], systems [30], and sensor localization [23] problems. There are numerous approaches that solve (1), both in its original non-convex form or through its convex relaxation; see [27, 16] and references therein. However, satisfying the rank constraint (or any nuclear norm constraints in the convex relaxation) per iteration requires SVD computations, which could be prohibitive in practice for large-scale settings. To overcome this obstacle, recent approaches reside on non-convex parametrization of the variable space and encode the low-rankness directly into the objective [22, 2, 39, 44, 14, 4, 43, 38, 45, 24, 31, 42, 32, 33].

With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical samples, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, the present paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent categorical (bilinear) models, namely, Probit, Tobit, and Logit. The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios. Leveraging the low intrinsic dimensionality of the sought models, a rank regularized maximum-likelihood estimator is devised, which is then solved recursively via alternating majorization-minimization to sketch high-dimensional categorical data `on the fly.' The resultant procedure alternates between sketching the new incomplete datum and refining the latent subspace, leading to lightweight first-order algorithms with highly parallelizable tasks per iteration. As an extra degree of freedom, the quantization thresholds are also learned jointly along with the subspace to enhance the predictive power of the sought models. Performance of the subspace iterates is analyzed for both infinite and finite data streams, where for the former asymptotic convergence to the stationary point set of the batch estimator is established, while for the latter sublinear regret bounds are derived for the empirical cost. Simulated tests with both synthetic and real-world datasets corroborate the merits of the novel schemes for real-time movie recommendation and chess-game classification.

One of the most important takeways from Davos that quickly became widely spread news, was that the world was about to enter the fourth industrial revolution, resulting from a convergence of a number of big technology changes (autonomous vehicles, sensors, biotechnology, 3D printing, robotics, artificial intelligence). One of the most important technological disruption taking us fast to that extraordinary moment is Deep learning. Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Making an analogy with the way the brain works, deep-learning software tries to imitate what happens in our brains, more exactly in the layers of neurons in the neocortex, where thinking takes place. Ultimately deep learning software aims to recognize patterns in digital representations of sounds, images, and other data.

A team from the Houston Methodist Research Institute says they have developed artificial intelligence software capable of analyzing mammograms for breast cancer with 99 percent accuracy. The technique involves scanning patient charts and cross-checking them with results from mammogram X-rays and clinical reports. "The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining," according to the study published in Cancer. But their algorithm allows for a more comprehensive and accurate analysis that helps avoid false positives -- a very common incident. "We figured out you can mine a clinical report for additional information," said lead researcher Stephen Wong. "Most of the clinical reports are not in a structured format, they are in free form text. So if we can run an AI program to extract the medical information and build a risk assessment model we can score the information and reduce unnecessary biopsies."

It was December 2012, and Doug Burger was standing in front of Steve Ballmer, trying to predict the future. Ballmer, the big, bald, boisterous CEO of Microsoft, sat in the lecture room on the ground floor of Building 99, home base for the company's blue-sky R&D lab just outside Seattle. The tables curved around the outside of the room in a U-shape, and Ballmer was surrounded by his top lieutenants, his laptop open. Burger, a computer chip researcher who had joined the company four years earlier, was pitching a new idea to the execs. He called it Project Catapult. The tech world, Burger explained, was moving into a new orbit.

BERLIN stands for Behavioural Event Reconstruction Linguistic Interface for Narratives. I introduced BERLIN a few blogs ago - in my "final blog." Theoretically after one's final blog, no further blogs are forthcoming. However, I am now posting bonus blogs reflecting aspects of the same closing subject. Today, I will be elaborating on BERLIN's syntax and how its searches are facilitated. As a general rule, the objective of BERLIN is to convert human-friendly narrative into computer-friendly code. It isn't unusual for computer code to be expressed in a human-like language for the purpose of executing a program. A computer program has rigid parameters. BERLIN on the other hand is designed to support "expression." Rather than the code adapting to the needs of a computer program, it is adapted to the requirements of expression. BERLIN remains shaped by a type of program of sorts.

Trucking paid for Scott Spindola to take a road trip down the coast of Spain, climb halfway up Machu Picchu, and sample a Costa Rican beach for two weeks. The 44-year-old from Covina now makes up to 70,000 per year, with overtime, hauling goods from the port of Long Beach. He has full medical coverage and plans to drive until he retires. But in a decade, his big rig may not have any need for him. Carmaking giants and ride-sharing upstarts racing to put autonomous vehicles on the road are dead set on replacing drivers, and that includes truckers.

Transport ministers from the Group of Seven advanced economies agreed Sunday to strengthen cooperation in the railway and airline sectors as they wrapped up their three-day meeting in the resort town of Karuizawa, Nagano Prefecture. Prior to the conclusion of the gathering, ministers from Britain, Canada, France, Germany, Italy, Japan and the United States plus the European Union adopted a declaration Saturday pledging to reinforce international cooperation in creating safety regulations to promote self-driving cars. The conference was the last of the ministerial meetings related to May's G-7 leaders' Ise-Shima summit in Mie Prefecture. "We will cooperate with each other and exercise leadership to support the early commercialization of automated and connected vehicle technologies," the declaration adopted at the Saturday meeting said. "We obtained a common understanding to make efforts in the same direction to create regulation frameworks that (will) tend to vary depending on region," transport minister Keiichi Ishii told a news conference after the meeting.