Using a raised eyebrow or smile, people with speech or physical disabilities can now operate their Android-powered smartphones hands-free, Google said Thursday. Two new tools put machine learning and front-facing cameras on smartphones to work detecting face and eye movements. Users can scan their phone screen and select a task by smiling, raising eyebrows, opening their mouth or looking to the left, right or up. "To make Android more accessible for everyone, we're launching new tools that make it easier to control your phone and communicate using facial gestures," Google said. The Centers for Disease Control and Prevention estimates that 61 million adults in the United States live with disabilities, which has pushed Google and rivals Apple and Microsoft to make products and services more accessible to them.

An informative measurement is the most efficient way to gain information about an unknown state. We give a first-principles derivation of a general-purpose dynamic programming algorithm that returns a sequence of informative measurements by sequentially maximizing the entropy of possible measurement outcomes. This algorithm can be used by an autonomous agent or robot to decide where best to measure next, planning a path corresponding to an optimal sequence of informative measurements. This algorithm is applicable to states and controls that are continuous or discrete, and agent dynamics that is either stochastic or deterministic; including Markov decision processes. Recent results from approximate dynamic programming and reinforcement learning, including on-line approximations such as rollout and Monte Carlo tree search, allow an agent or robot to solve the measurement task in real-time. The resulting near-optimal solutions include non-myopic paths and measurement sequences that can generally outperform, sometimes substantially, commonly-used greedy heuristics such as maximizing the entropy of each measurement outcome. This is demonstrated for a global search problem, where on-line planning with an extended local search is found to reduce the number of measurements in the search by half.

Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequential patterns existing in the counterpart, i.e., the item side. Although a few studies investigate the dynamics involved in the dual sides, the complex user-item interactions are not fully exploited from a global perspective to derive dynamic user and item representations. In this paper, we devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe). To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice and exploits time-sliced graph neural networks to learn user and item representations. Moreover, to enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices based on temporal point process. Comprehensive experiments on three public real-world datasets demonstrate DRL-SRe outperforms the state-of-the-art sequential recommendation models with a large margin.

Attention scorers have achieved success in parsing tasks like semantic and syntactic dependency parsing. However, in tasks modeled into parsing, like structured sentiment analysis, "dependency edges" are very sparse which hinders parser performance. Thus we propose a sparse and fuzzy attention scorer with pooling layers which improves parser performance and sets the new state-of-the-art on structured sentiment analysis. We further explore the parsing modeling on structured sentiment analysis with second-order parsing and introduce a novel sparse second-order edge building procedure that leads to significant improvement in parsing performance.

Add the United Kingdom to the list of countries that says an artificial intelligence can't be legally credited as an inventor. Per the BBC, the UK Court of Appeal recently ruled against Dr. Stephen Thaler in a case involving the country's Intellectual Property Office. In 2018, Thaler filed two patent applications in which he didn't list himself as the creator of the inventions mentioned in the documents. Instead, he put down his AI DABUS and said the patent should go to him "by ownership of the creativity machine." The Intellectual Property Office told Thaler he had to list a real person on the application.

DeepImageJ has been updated to DeepImageJ 2.1. The format of the models in previous versions are not compatible with DeepImageJ 2.1. Please, try to update your models using DeepImageJ Build Bundled Model or do not update DeepImageJ in Fiji using the Update Sites until you can update your models. Contact us if you have any question! DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.

India is an agricultural country. It is clear that today when the country is in the throes of an economic downturn, we have failed to address the economic policies of industrialization and agriculture. There is always a feeling of empathy in all classes about farmers. However, it is also clear that only if agricultural economic policies and the proper use of modern technology change, agricultural income will increase, India will make its mark in the international market and Indian farmers will grow. Many scientists have been able to identify the unique characteristics of plant components to make them resistant to drought and pests through genetic engineering.

After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and accessibility, it also opens up new opportunities in terms of content mining and the next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve and explore information from this 'big data of the past'. Among semantic indexing opportunities, the recognition and classification of named entities are in great demand among humanities scholars. Yet, named entity recognition (NER) systems are heavily challenged with diverse, historical and noisy inputs. In this survey, we present the array of challenges posed by historical documents to NER, inventory existing resources, describe the main approaches deployed so far, and identify key priorities for future developments.

Distributed computing enables large-scale computation tasks to be processed over multiple workers in parallel. However, the randomness of communication and computation delays across workers causes the straggler effect, which may degrade the performance. Coded computation helps to mitigate the straggler effect, but the amount of redundant load and their assignment to the workers should be carefully optimized. In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation. The goal is to minimize the communication plus computation delay of the slowest task. We propose worker assignment, resource allocation and load allocation algorithms under both dedicated and fractional worker assignment policies, where each worker can process the encoded tasks of either a single master or multiple masters, respectively. Then, the non-convex delay minimization problem is solved by employing the Markov's inequality-based approximation, Karush-Kuhn-Tucker conditions, and successive convex approximation methods. Through extensive simulations, we show that the proposed algorithms can reduce the task completion delay compared to the benchmarks, and observe that dedicated and fractional worker assignment policies have different scopes of applications.