Our zombie solar cells could power indoor devices without sunlight by Marina Freitag, Newcastle University Internet connected devices need power. That either means connecting them to the grid, which limits what we can use them for, or using batteries. To avoid this, my colleagues and I are helping develop a new type of smart solar cell that can adapt to the amount of available light. Last week, that all died. Sidewalk Labs canceled the Quayside project on May 7. More Unknown Devices on Corporate Networks A report published this week by Sepio Systems suggests the number of devices being attached to corporate networks since the start of the COVID-19 pandemic began has increased sharply.

As tensions and tech rivalry between the U.S. and China intensify, artificial intelligence is taking center stage. During the recent Tortoise Global AI Summit, panelists discussed the increasingly fraught relationship between these global superpowers, whose rivalry had shown signs of bitterness even before President Trump launched a trade war. While this competition extends across a wide range of technologies, the panelists agreed AI has increasingly become a focal point, thanks to the essential role many predict it will play in the coming decades. And not only is the race for AI supremacy pitting China against the U.S., it is forcing every other country to reassess their place in this technological duel. "We're seeing a technology competition in the context of a worsening relationship between the world's two great powers," said John Sawers, former head of the U.K.'s MI6 spy agency.

There's a push for AI/ML to reduce the staggering number of preventable deaths due to surgical error. The first and second leading causes of death in the United States are heart disease and cancer. The third is now coronavirus. Astoundingly, the answer is preventable medical errors. Surgical errors in particular account for 26% of these deaths and cost somewhere north of $36 billion in the U.S. I did a double take when I read those stats while reporting this story.

Customer's expectations are the guard rails that guide how their relationships progress with any business. The pandemic has made the predictable unpredictable, erasing marketing personas of the past and re-writing them in real-time. Old guard rails and expectations are changing fast. Having an accurate outside-in view from the customer's perspective is the value VoC programs deliver, with the best ones providing data to guide strategy. Pure e-commerce orders have grown 110% since January, and e-commerce revenue has increased by 96%.

The system, outlined May 14 in The Lancet: Digital Health, utilizes data from multiple institutions across Europe and was validated using scans from more than 500 patients in India. Compared with manual assessment, the CNN produced similarly accurate measurements, allowing clinicians to quantify lesion burden and progression.

Deep reinforcement learning techniques have shown to be a promising path to solve very complex tasks that once were thought to be out of the realm of machines. However, while humans and animals learn incrementally during their lifetimes and exploit their experience to solve new tasks, standard deep learning methods specialize to solve only one task at a time and whatever information they acquire is hardly reusable in new situations. Given that any artificial agent would need such a generalization ability to deal with the complexities of the world, it is critical to understand what mechanisms give rise to this ability. We argue that one of the mechanisms humans rely on is the use of discrete conceptual representations to encode their sensory inputs. These representations group similar inputs in such a way that combined they provide a level of abstraction that is transverse to a wide variety of tasks, filtering out irrelevant information for their solution. Here, we show that it is possible to learn such concept-like representations by self-supervision, following an information-bottleneck approach, and that these representations accelerate the transference of skills by providing a prior that guides the policy optimization process. Our method is able to learn useful concepts in locomotive tasks that significantly reduce the number of optimization steps required, opening a new path to endow artificial agents with generalization abilities.

Emergency managers receive communication training about the importance of being 'first, right and credible', and taking into account the psychology of their audience and their particular reasoning under stress and risk. But we believe that citizens should be similarly trained about how to deal with risk communication. In particular, such messages necessarily carry a part of uncertainty since most natural risks are difficult to accurately forecast ahead of time. Yet, citizens should keep trusting the emergency communicators even after they made forecasting errors in the past. We have designed a serious game called Vigiflood, based on a real case study of flash floods hitting the South West of France in October 2018. In this game, the user changes perspective by taking the role of an emergency communicator, having to set the level of vigilance to alert the population, based on uncertain clues. Our hypothesis is that this change of perspective can improve the player's awareness and response to future flood vigilance announcements. We evaluated this game through an online survey where people were asked to answer a questionnaire about flood risk awareness and behavioural intentions before and after playing the game, in order to assess its impact.

Spoken dialog systems have seen applications in many domains, including medical for automatic conversational diagnosis. State-of-the-art dialog managers are usually driven by deep reinforcement learning models, such as deep Q networks (DQNs), which learn by interacting with a simulator to explore the entire action space since real conversations are limited. However, the DQN-based automatic diagnosis models do not achieve satisfying performances when adapted to new, unseen diseases with only a few training samples. In this work, we propose the Prototypical Q Networks (ProtoQN) as the dialog manager for the automatic diagnosis systems. The model calculates prototype embeddings with real conversations between doctors and patients, learning from them and simulator-augmented dialogs more efficiently. We create both supervised and few-shot learning tasks with the Muzhi corpus. Experiments showed that the ProtoQN significantly outperformed the baseline DQN model in both supervised and few-shot learning scenarios, and achieves state-of-the-art few-shot learning performances.

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches based on the local label imbalance, namely MLSOL and MLUL. By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

In this paper, the decades-old clustering method k-means is revisited. The original distortion minimization model of k-means is addressed by a pure stochastic minimization procedure. In each step of the iteration, one sample is tentatively reallocated from one cluster to another. It is moved to another cluster as long as the reallocation allows the sample to be closer to the new centroid. This optimization procedure converges faster to a better local minimum over k-means and many of its variants. This fundamental modification over the k-means loop leads to the redefinition of a family of k-means variants. Moreover, a new target function that minimizes the summation of pairwise distances within clusters is presented. We show that it could be solved under the same stochastic optimization procedure. This minimization procedure built upon two minimization models outperforms k-means and its variants considerably with different settings and on different datasets.