Hiring developer talent is a business priority, but not all roles are created equal. As startups introduce new ways to apply technologies and large enterprises continue their quest to digitally transform, hiring needs to evolve for all companies looking to hire top tech talent. Data from Hired's marketplace reveals that global demand for blockchain engineers is through the roof, at a 517% increase year over year. For developers interested in blockchain roles, don't let the titles fool you. For engineers with an expertise in blockchain, they typically hold titles such as backend engineer, systems engineer or solutions architect, with blockchain being listed as a desired skill for the role.

We propose a new approach for constructing synthetic pseudo-panel data from cross-sectional data. The pseudo panel and the preferences it intends to describe is constructed at the individual level and is not affected by aggregation bias across cohorts. This is accomplished by creating a high-dimensional probabilistic model representation of the entire data set, which allows sampling from the probabilistic model in such a way that all of the intrinsic correlation properties of the original data are preserved. The key to this is the use of deep learning algorithms based on the Conditional Variational Autoencoder (CVAE) framework. From a modelling perspective, the concept of a model-based resampling creates a number of opportunities in that data can be organized and constructed to serve very specific needs of which the forming of heterogeneous pseudo panels represents one. The advantage, in that respect, is the ability to trade a serious aggregation bias (when aggregating into cohorts) for an unsystematic noise disturbance. Moreover, the approach makes it possible to explore high-dimensional sparse preference distributions and their linkage to individual specific characteristics, which is not possible if applying traditional pseudo-panel methods. We use the presented approach to reveal the dynamics of transport preferences for a fixed pseudo panel of individuals based on a large Danish cross-sectional data set covering the period from 2006 to 2016. The model is also utilized to classify individuals into 'slow' and 'fast' movers with respect to the speed at which their preferences change over time. It is found that the prototypical fast mover is a young woman who lives as a single in a large city whereas the typical slow mover is a middle-aged man with high income from a nuclear family who lives in a detached house outside a city.

The first product of Apple's self-driving electric car project codenamed "Titan", could actually be an electric van instead of a car, the media reported. "According to multiple unnamed sources of German business publication -- Manager Magazin, prototypes of Apple's work have been seen painted in black and silver and the main highlight is that vans are being tested, rather than cars," Apple Insider reported on Thursday. In August 2018, Tesla's former engineering Vice President, Doug Field was appointed by Apple to lead team "Titan". "The'Apple Car' which could be arriving between 2023 and 2025, has undergone development at Apple in a variety of different. Originally working on an entire vehicle, the project changed its focus towards self-driving vehicle systems, though there are some signs it is shifting back towards overall vehicular design," the report said.

In simple terms, that "most important role" is the cycle of observation followed by critical thinking followed by action. It's important to bear in mind that the proper goal of Machine Learning (ML) is not abdication of human responsibility for decision-making. Rather, it's improving our individual and collective ability to make better decisions by leveraging increased speed, accuracy and absence of bias. Our context here is supply chain planning and execution, but there is no reason to limit the scope of Machine Learning. When it comes to designing and creating technology solutions for supply chain analytics and business intelligence, this is not a throw-away idea buried in a long-forgotten PowerPoint presentation.

The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, Data Scientist on Oracle's Information Management Platform Team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that lies with using machine learning in a big data environment.

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-scale neural graph learning framework that allows us to train embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels. Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including image classification and triplet ranking. We provide case studies to demonstrate that, qualitatively, image retrieval based on Graph-RISE effectively captures semantics and, compared to the state-of-the-art, differentiates nuances at levels that are closer to human-perception.

The late Paul Allen's research vessel, the Petrel, has found another historic warship at the bottom of the ocean. In the wake of an initial discovery in late January, the expedition crew has confirmed that it found the USS Hornet, an aircraft carrier that played a pivotal role in WWII through moments like the Doolittle Raid on Japan and the pivotal Battle of Midway. It was considered lost when it sank at the Battle of Santa Cruz in October 1943, but modern technology spotted it nearly 17,500 feet below the surface of the South Pacific Ocean, near the Solomon Islands. The team initially narrowed down its search area by using data from the era, such as action reports and deck logs from other ships involved in the Santa Cruz fight. From there, tech took over.

Meteorological forecasting provides reliable prediction about the future weather within a given interval of time. Meteorological forecasting can be viewed as a form of hybrid diagnostic reasoning and can be mapped onto an integrated conceptual framework. The automation of the forecasting process would be helpful in a number of contexts, in particular: when the amount of data is too wide to be dealt with manually; to support forecasters education; when forecasting about underpopulated geographic areas is not interesting for everyday life (and then is out from human forecasters' tasks) but is central for tourism sponsorship. We present logic MeteoLOG, a framework that models the main steps of the reasoner the forecaster adopts to provide a bulletin. MeteoLOG rests on several traditions, mainly on fuzzy, temporal and probabilistic logics. On this basis, we also introduce the algorithm Tournament, that transforms a set of MeteoLOG rules into a defeasible theory, that can be implemented into an automatic reasoner. We finally propose an example that models a real world forecasting scenario.

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by "irrelevant" aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data. We introduce an extension of the Multinomial Logit (MNL) model, called the context dependent random utility model (CDM), which allows for a particular class of choice set effects. We show that the CDM can be thought of as a second-order approximation to a general choice system, can be inferred optimally using maximum likelihood and, importantly, is easily interpretable. We apply the CDM to both real and simulated choice data to perform principled exploratory analyses for the presence of choice set effects.

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model. Car-following behavior produced by the model were compared with that observed in the empirical NGSIM data, to demonstrate the model's ability to follow a lead vehicle safely, efficiently, and comfortably. Results show that the model demonstrates the capability of safe, efficient, and comfortable velocity control in that it 1) has small percentages (8\%) of dangerous minimum time to collision values (\textless\ 5s) than human drivers in the NGSIM data (35\%); 2) can maintain efficient and safe headways in the range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth acceleration. The results indicate that reinforcement learning methods could contribute to the development of autonomous driving systems.