In the old Camden Town Hall opposite London's St Pancras station, away from the white tie and tails of the pageantry at Windsor Castle, was perhaps the most substantive display of the consequences of Donald Trump's state visit. In front of Prime Minister Sir Keir Starmer, many members of the British and US cabinets and the cream of the European tech industry, a highly-crafted video played, featuring the long history of UK science. It included George Stephenson, Charles Babbage, Ada Lovelace, Alan Turing and Sir Demis Hassabis, with dozens of UK start-up companies from every corner of the country listed. It was a cross between a UK government investment promotion video and the Danny Boyle 2012 Olympic Opening Ceremony, except for one crucial detail - it was voiced by Jensen Huang, the American Nvidia artificial intelligence (AI) and microchip magnate. This week, Trump said the tech tycoon was taking over the world and the boss of the company, which hit a market value of $4tn (£2.9tn) this summer, appears to have gone all-in on the UK in quite an extraordinary way.

Hosted by The Alan Turing Institute, AI UK is a yearly event that brings together representatives from government, academia and industry to showcase data science and AI research and innovation in the UK. This year, the two-day conference featured talks, panel discussions, and hands-on workshops, and participants could attend in-person or remotely. One of the sessions focussed on an on-going case study in a London borough whereby the local council is using data and AI to help inform their decision making, and to improve what they do. Tariq set the scene by describing the borough of Camden, an area that not only houses institutions such as University College London and the Francis Crick Institute, and companies such as Google, but also some of the poorest communities in Europe. The council wants to tackle inequality and sees the use of data as one potential avenue.

Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world. These difficulties have in turn generated research on approximate Bayesian inference methods for ABMs and on constructing differentiable approximations to arbitrary ABMs, but little work has been directed towards designing approximate Bayesian inference techniques for the specific case of differentiable ABMs. In this work, we aim to address this gap and discuss how generalised variational inference procedures may be employed to provide misspecification-robust Bayesian parameter inferences for differentiable ABMs. We demonstrate with experiments on a differentiable ABM of the COVID-19 pandemic that our approach can result in accurate inferences, and discuss avenues for future work.

To leverage this potential, effective forest and consumption, improve urban air quality, reduce urban tree management is essential. This requires detailed wind speeds, and mitigating the urban heat information about tree species, composition, health and geographical island effect. Urban trees also play a key role in location of each tree in order to create a long term climate change mitigation and global warming by sustainable plan for plantation and forestation sites, pruning capturing and storing atmospheric carbon-dioxide schedules and mitigation of potential problems (Baeten & which is the largest contributor to greenhouse Bruelheide, 2018). It also helps to monitor tree species diversity gases. Automated tree detection and species classification and track health and growth rate to creates a more using aerial imagery can be a powerful robust ecosystem with better productivity and greater resilience tool for sustainable forest and urban tree management.

One of my favorite things about deep reinforcement learning is that, unlike supervised learning, it really, really doesn't want to work. Throwing a neural net at a computer vision problem might get you 80% of the way there. Throwing a neural net at an RL problem will probably blow something up in front of your face -- and it will blow up in a different way each time you try. A lot of the biggest challenges in RL revolve around two questions: how we interact with the environment effectively (e.g. In this post, I want to explore a few recent directions in deep RL research that attempt to address these challenges, and do so with particularly elegant parallels to human cognition. This post will begin with a quick review of two canonical deep RL algorithms -- DQN and A3C -- to provide us some intuitions to refer back to, and then jump into a deep dive on a few recent papers and breakthroughs in the categories described above.

One of my favorite things about deep reinforcement learning is that, unlike supervised learning, it really, really doesn't want to work. Throwing a neural net at a computer vision problem might get you 80% of the way there. Throwing a neural net at an RL problem will probably blow something up in front of your face -- and it will blow up in a different way each time you try. A lot of the biggest challenges in RL revolve around two questions: how we interact with the environment effectively (e.g. In this post, I want to explore a few recent directions in deep RL research that attempt to address these challenges, and do so with particularly elegant parallels to human cognition. This post will begin with a quick review of two canonical deep RL algorithms -- DQN and A3C -- to provide us some intuitions to refer back to, and then jump into a deep dive on a few recent papers and breakthroughs in the categories described above.

Over the last years we have witnessed a rapid increase in the availability of GPSreceiving devices, such as smart phones or car navigation systems. The devices generate vast amounts of temporal positioning data that have been proven invaluable in various applications, from traffic management (Kühne et al., 2003) and route planning (Gonzalez et al., 2007; Li et al., 2011; Kowalska et al., 2015) to inferring personal movement signatures (Liao et al., 2006). Critical to the utility of GPS data is their accuracy. The data suffer from measurement errors caused by technical limitations of GPS receivers and sampling errors caused by their receiving rates. When digital maps are available, it is common practice to improve the accuracy of the data by aligning GPS points with the road network. The process is known as map-matching. Most map-matching algorithms align GPS trajectories with the road network by considering positions of each GPS point, either in isolation or in relation to other GPS points in the same trajectory.

The ability to track a moving vehicle is of crucial importance in numerous applications. The task has often been approached by the importance sampling technique of particle filters due to its ability to model non-linear and non-Gaussian dynamics, of which a vehicle travelling on a road network is a good example. Particle filters perform poorly when observations are highly informative. In this paper, we address this problem by proposing particle filters that sample around the most recent observation. The proposal leads to an order of magnitude improvement in accuracy and efficiency over conventional particle filters, especially when observations are infrequent but low-noise.

Stephon Alexander is a theoretical physicist and jazz musician.

Everyone had his or her favorite drink in hand. There were bubbles and deep reds, and the sound of ice clinking in cocktail glasses underlay the hum of contented chatter. Gracing the room were slender women with long hair and men dressed in black suits, with glints of gold necklaces and cuff links. But it was no Gatsby affair. It was the annual Imperial College quantum gravity cocktail hour. The host was dressed down in black from head to toe--black turtleneck, jeans, and trench coat.