Plastic pollution is one of those problems everyone can see, yet few know how to tackle it effectively. I grew up walking the beaches around Tramore in County Waterford, Ireland, where plastic debris has always been part of the coastline, including bottles, fragments of fishing gear and food packaging. According to the UN, every year 19-23 million tonnes of plastic lands up in lakes, rivers and seas, and it has a huge impact on ecosystems, creating pollution and damaging animal habitats. Community groups do tremendous work cleaning these beaches, but they're essentially walking blind, guessing where plastic accumulates, missing hot spots, repeating the same stretches while problem areas may go untouched. Years later, working in marine robotics at the University of Limerick, I began developing tools to support marine clean-up and help communities find plastic pollution along our coastline.

The dust coated the Alps and caused'blood rain' in England. In the light of the setting sun, the sky forms a veil of Saharan dust over the Wurmberg in Lower Saxony, Germany. Breakthroughs, discoveries, and DIY tips sent six days a week. The wild winds of winter typically bring snow in the Northern Hemisphere. But sometimes, they carry dust .

It generally takes 18 to 36 months to build a hyperscale AI site - such as, presumably, one of the world's most powerful supercomputers. It generally takes 18 to 36 months to build a hyperscale AI site - such as, presumably, one of the world's most powerful supercomputers. From press release to scrap metal site: the Essex'supercomputer' that's still a scaffolding yard Nscale's AI project still in use as depot ahead of pledged completion date - with planning permission filed after Guardian's inquiries Revealed: UK's multibillion AI drive is built on'phantom investments' T he press releases announcing a gleaming supercomputer on the outskirts of north London depict a glass and concrete building, rising from a tree-lined street. Accompanied by images of glowing blue robot faces, it looks like the centre of a technological revolution. By the end of this year, that artist's impression is supposed to be a reality.

Neural Information Processing Systems http://nips.cc/

We propose a Bayesian decision making framework for control of Markov Decision Processes (MDPs) with unknown dynamics and large, possibly continuous, state, action, and parameter spaces in data-poor environments. Most of the existing adaptive controllers for MDPs with unknown dynamics are based on the reinforcement learning framework and rely on large data sets acquired by sustained direct interaction with the system or via a simulator. This is not feasible in many applications, due to ethical, economic, and physical constraints. The proposed framework addresses the data poverty issue by decomposing the problem into an offline planning stage that does not rely on sustained direct interaction with the system or simulator and an online execution stage. In the offline process, parallel Gaussian process temporal difference (GPTD) learning techniques are employed for near-optimal Bayesian approximation of the expected discounted reward over a sample drawn from the prior distribution of unknown parameters. In the online stage, the action with the maximum expected return with respect to the posterior distribution of the parameters is selected. This is achieved by an approximation of the posterior distribution using a Markov Chain Monte Carlo (MCMC) algorithm, followed by constructing multiple Gaussian processes over the parameter space for efficient prediction of the means of the expected return at the MCMC sample. The effectiveness of the proposed framework is demonstrated using a simple dynamical system model with continuous state and action spaces, as well as a more complex model for a metastatic melanoma gene regulatory network observed through noisy synthetic gene expression data.

The "black-box" nature of representations, combined with the known susceptibility of networks to learn spurious correlations [

However, these models suffer from quadratic computational cost in the input sequence lengthn to compute pairwise attention in each layer.