Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on the performance of neural models of code. The notion of variable roles (as introduced in the works of Sajaniemi et al. [Refs. 1,2]) has been found to help students' abilities in programming. In this paper, we investigate if this notion would improve the performance of neural models of code. To the best of our knowledge, this is the first work to investigate how Sajaniemi et al.'s concept of variable roles can affect neural models of code. In particular, we enrich a source code dataset by adding the role of individual variables in the dataset programs, and thereby conduct a study on the impact of variable role enrichment in training the Code2Seq model. In addition, we shed light on some challenges and opportunities in feature enrichment for neural code intelligence models.

The companies racing to deploy autonomous cars on the world's roads took a reality check in the 2010s, but multimillion-dollar development efforts remain ongoing across the automotive and tech industries. German supplier Bosch is notably moving full speed ahead with its quest to make driverless cars a reality. Kay Stepper, Bosch's senior vice president of automated driving, sat down with Digital Trends to talk about the state of autonomous driving in 2020, and what's next for the artificial intelligence technology that powers the prototypes it's testing. Bosch has never made a car, so it brings its innovations to the market through partnerships with automakers. It chose Mercedes-Benz parent company Daimler to test autonomous technology in real-world conditions via a ridesharing pilot program in San Jose, California, close to one of the company's research centers.

We introduce a dynamic artificial neural network-based (ANN) adaptive inference process, which learns temporal predictive models of dynamical systems. We term the process REPRISE, a REtrospective and PRospective Inference SchEme. REPRISE infers the unobservable contextual state that best explains its recently encountered sensorimotor experiences as well as accompanying, context-dependent temporal predictive models retrospectively. Meanwhile, it executes prospective inference, optimizing upcoming motor activities in a goal-directed manner. In a first implementation, a recurrent neural network (RNN) is trained to learn a temporal forward model, which predicts the sensorimotor contingencies of different simulated dynamic vehicles. The RNN is augmented with contextual neurons, which enable the compact encoding of distinct, but related sensorimotor dynamics. We show that REPRISE is able to concurrently learn to separate and approximate the encountered sensorimotor dynamics. Moreover, we show that REPRISE can exploit the learned model to induce goal-directed, model-predictive control, that is, approximate active inference: Given a goal state, the system imagines a motor command sequence optimizing it with the prospective objective to minimize the distance to a given goal. Meanwhile, the system evaluates the encountered sensorimotor contingencies retrospectively, adapting its neural hidden states for maintaining model coherence. The RNN activities thus continuously imagine the upcoming future and reflect on the recent past, optimizing both, hidden state and motor activities. In conclusion, the combination of temporal predictive structures with modulatory, generative encodings offers a way to develop compact event codes, which selectively activate particular types of sensorimotor event-specific dynamics.

Brian Lesko and Dan Sherman hate the idea of driverless cars, but for very different reasons. Lesko, 46, a business-development executive in Atlanta, doesn't trust a robot to keep him out of harm's way. "It scares the bejeebers out of me," he says. Sherman, 21, a mechanical-engineering student at the University of Minnesota, Twin Cities, trusts the technology and sees these vehicles eventually taking over the road. But he dreads the change because his passion is working on cars to make them faster.