Organizers of the International Conference on Learning Representations (ICLR) shared details about what will be one of the largest-ever all-digital AI research conferences. The weeklong, online-only affair will feature more than 650 machine learning works. ICLR will include live chat, live Zoom video calls for Q&As and research author meetings, and the ability to upvote questions or vote for speakers using Slido. ICLR was initially scheduled to take place next month in Addis Ababa, Ethiopia, but with a global pandemic underway and shelter in place orders asking one in five people worldwide to stay home, the conference will now take place entirely online. ICLR organizers told VentureBeat they're treating the cancellation as an opportunity to develop a model for remote conferences.

Customers of any type of business expect help instantly and access to their services in a growing number of ways. Banks are turning to chatbots to help deal with massive volumes of customer interactions. Conversational banking frees up agents for more complex issues, while the move to app-based and web banking sees customers more used to dealing with digital interfaces, of which chatbots and AI virtual assistants are just the latest step. Established banks and their challenger rivals are all keen to develop a conversational banking strategy. Those that have been experimenting for some years find themselves with key advantages over banks stepping fresh into the conversational customer service arena.

Artificial intelligence is transforming how we design and build. By 2050, the effects of AI adoption will be widely felt across all aspects of our daily lives. As the world faces a number of urgent and complex challenges, from the climate crisis to housing, AI has the potential to make the difference between a dystopian future and a livable one. By looking ahead, we're taking stock of what's happening, and in turn, imagining how AI can shape our lives for the better. Artificial intelligence is broadly defined as the theory and development of computer systems to perform tasks that normally require human intelligence.

Advancements in the power of machine learning have brought with them major data privacy concerns. This is especially true when it comes to training machine learning models with data obtained from the interaction of users with devices such as smartphones. So the big question is, how do we train and improve these on-device machine learning models without sharing personally-identifiable data? That is the question that we'll seek to answer in this look at a technique known as federated learning. The traditional process for training a machine learning model involves uploading data to a server and using that to train models.

The power grid frequency is the central observable in power system control, as it measures the balance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions and may thus greatly improve power system stability. Here, we develop a weighted-nearest-neighbor (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hour are more precise than averaged daily profiles and could increase the efficiency of frequency control actions. Furthermore, we gain an increased understanding of the specific properties of different synchronous areas by interpreting the optimal prediction parameters (number of nearest neighbors, the prediction horizon, etc.) in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional external perturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequency time series, allowing better understanding of the underlying dynamics.

A primary goal of computer science is to understand which problems can be solved by efficient algorithms. Given the formidable difficulty of proving unconditional computational hardness, stateof-the-art results typically rely on unproven conjectures. While many such results rely only upon the widely-believed conjecture P NP, other results have only been proven under stronger assumptions such as the unique games conjecture [Kho02, Kho05], the exponential time hypothesis [IP01], the learning with errors assumption [Reg09], or the planted clique hypothesis [Jer92, BR13]. It has also been fruitful to conjecture that a specific algorithm (or limited class of algorithms) is optimal for a suitable class of problems. This viewpoint has been particularly prominent in the study of average-case noisy statistical inference problems, where it appears that optimal performance over a large class of problems can be achieved by methods such as the sum-of-squares hierarchy (see [RSS18]), statistical query algorithms [Kea93, BFJ 94], the approximate message passing framework [DMM09, LKZ15], and low-degree polynomials [HS17, HKP 17, Hop18]. It is helpful to have such a conjectured-optimal meta-algorithm because this often admits a systematic analysis of hardness.

This paper proposes a generic approach for crop anomaly detection at the parcel-level based on unsupervised point anomaly detection techniques. The input data is derived from synthetic aperture radar (SAR) and optical images acquired using Sentinel-1 and Sentinel-2 satellites. The proposed strategy consists of four sequential steps: acquisition and preprocessing of optical and SAR images, extraction of optical and SAR indicators, computation of zonal statistics at the parcel-level and point anomaly detection. This paper analyzes different factors that can affect the results of anomaly detection such as the considered features and the anomaly detection algorithm used. The proposed procedure is validated on two crop types in Beauce (France), namely, rapeseed and wheat crops. Two different parcel delineation databases are considered to validate the robustness of the strategy to changes in parcel boundaries.

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this work, we propose a spatially and temporally aware tensor-based neural network for human pose recognition using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental validation of the proposed model indicates that it can achieve state-of-the-art performance. Although in this study, we consider the problem of human pose recognition, our methodology is general enough to be applied to any pattern recognition problem spatiotemporal data from sensor networks.

Countries in the Gulf Cooperation Council are stepping up their use of artificial intelligence tools to halt the spread of the coronavirus pandemic. Governments throughout the GCC -- a group of countries in the Middle East that includes Bahrain, Saudi Arabia, Qatar, Oman, Kuwait and the United Arab Emirates -- have enacted some of world's strictest measures, including suspending passenger flights and imposing curfews on citizens to put brakes on the number of new cases of Covid-19 that currently total over 2 million (2,064,115) globally, according to Johns Hopkins University data. But countries aren't restricting their efforts to simply imploring their residents to stay locked in and shutting down all but the most essential of businesses. They are increasingly deploying sophisticated technology to ensure that movement is limited and social distancing is in place through the use of speed cameras, drones and robots. By applying location-based contact tracing, governments can monitor those who have tested positive for coronavirus, and try to limit their exposure to the population.

Proximie is being deployed across a number of NHS sites, to support the national efforts to fight COVID-19. Proximie uses a combination of machine learning, artificial intelligence and augmented reality, aimed to empower surgeons and clinicians, to virtually and practically interact with each other from anywhere. The platform, which was founded by Dr. Nadine Hachach-Haram FRCS (Plast), BEM, consultant plastic surgeon and head of clinical innovation at Guy's and St. Thomas' NHS Foundation Trust, is being used across a host of NHS sites, as the country battles the pandemic. From enabling self-isolating clinicians to remotely support colleagues on the front line, to virtually connecting MDTs for hand trauma and cancer management, so that every clinician can connect and collaborate off site during COVID-19, the platform is being applied in a number of different ways to support and amplify frontline clinicians. Using augmented reality, healthcare practitioners can remotely interact in a procedure or assessment from start to finish, and mentor a local clinician through a live operation, in a visually and intuitive way.