Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype", and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.

In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahead suggestions. In this work, we present SessionPath, a novel neural network model that improves facet suggestions on two counts: first, the model is able to leverage session embeddings to provide scalable personalization; second, SessionPath predicts facets by explicitly producing a probability distribution at each node in the taxonomy path. We benchmark SessionPath on two partnering shops against count-based and neural models, and show how business requirements and model behavior can be combined in a principled way.

Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy concerns. Given the typical heterogeneous data distributions in such situations, the popular FL algorithm \emph{Federated Averaging} (FedAvg) suffers from weight divergence and thus cannot achieve a competitive performance for the global model (denoted as the \emph{initial performance} in FL) compared to centralized methods. In this paper, we propose the local continual training strategy to address this problem. Importance weights are evaluated on a small proxy dataset on the central server and then used to constrain the local training. With this additional term, we alleviate the weight divergence and continually integrate the knowledge on different local clients into the global model, which ensures a better generalization ability. Experiments on various FL settings demonstrate that our method significantly improves the initial performance of federated models with few extra communication costs.

Learning a proper representation is usually the first step in every machine learning and data analytic tasks. Some recent representation learning methods have been developed in the context of deep learning [1], which are highly parameterized and require a huge amount of labeled data for training. On the other hand, there are methods that learn a proper representation in an unsupervised way and usually do not require learning free parameters. A category of unsupervised representations and distance measures, called link-based distance [2, 3], take into account all the paths between the objects represented in a graph. These distance measures are often obtained by inverting the Laplacian of the base distance matrix in the context of Markov diffusion kernel [2].

Machine learning models have spread to almost every area of life. They are successfully applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a~complex model that fits the training data and results in high accuracy on the test set. The problem arises when models fail confronted with real-world data. This paper describes methodology and tools for model-agnostic audit. Introduced techniques facilitate assessing and comparing the goodness of fit and performance of models. In~addition, they may be used for the analysis of the similarity of residuals and for identification of~outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Presented methods were implemented in the auditor package for R. Due to flexible and~consistent grammar, it is simple to validate models of any classes.

A chatbot that converses like a human should be goal-oriented (i.e., be purposeful in conversation), which is beyond language generation. However, existing dialogue systems often heavily rely on cumbersome hand-crafted rules or costly labelled datasets to reach the goals. In this paper, we propose Goal-oriented Chatbots (GoChat), a framework for end-to-end training chatbots to maximize the longterm return from offline multi-turn dialogue datasets. Our framework utilizes hierarchical reinforcement learning (HRL), where the high-level policy guides the conversation towards the final goal by determining some sub-goals, and the low-level policy fulfills the sub-goals by generating the corresponding utterance for response. In our experiments on a real-world dialogue dataset for anti-fraud in financial, our approach outperforms previous methods on both the quality of response generation as well as the success rate of accomplishing the goal.

But 2019 was the year the earth burned. In Australia, the world watched in horror as bushfires destroyed 10.3 million hectares, marking the continent's most intense and destructive fire season in over 40 years. Earlier that fall, California saw more than 101,000 hectares destroyed, with damages upward of $80 billion. Alaska saw nearly a million. Record-breaking fires also hit Indonesia, Russia, Lebanon -- but nowhere saw the sheer mass of media coverage as the fires that tore through the Amazon nearly all last summer. By year's end, thousands of global media outlets had reported that Brazil's largest rainforest played host to more than 80,000 individual forest fires in 2019, resulting in an estimated 906,000 square hectares of environmental destruction. At the time, Brazil's National Institute for Space Research reported it was the fastest rate of burning since record keeping began in 2013. But amid the charred ruins of one of the largest oxygen-producing environments on the planet, a secret lies buried beneath the soil.

The U.S. Air Force is finally pushing into the world of robot combat drones, vowing to fly the first of its "Skyborg" drones by 2023. The service envisions Skyborg as a merging of artificial intelligence with jet-powered drones. The result will be drones capable of flying alongside fighter jets, carrying out dangerous missions. Skyborg drones will be much cheaper than piloted aircraft, allowing the Air Force to grow its fleet at a lower cost. The Air Force, according to Defense News, will award a total of $400 million to one or more companies to develop different types of Skyborg drones.

Councils in the UK expect to save over £195million (€221 million) in 2020 by introducing artificial intelligence technology techniques, according to a national survey of local authorities. Financial savings, faster resolution of enquiries, freeing up staff to focus on citizen engagement and more accurate processing are the four key reasons behind the trend, revealed in a survey of unitary, borough, county and district councils carried out by local government AI and chatbot specialists Agile Datum . Councils each expect to save an average of £300,000 (€340926) in the next 12 months through greater use of artificial intelligence and another £180,000 ( €204556), on average, through the deployment of self-learning chatbots. One in six councils are anticipating savings between £750,000 (€85231 million) and £1m (1.1 million) just around the introduction of artificial intelligence technology. In all, it amounts to savings of £195m (€221 million) across unitary, borough, district and county councils in the UK.

LOS ANGELES, CA - FEBRUARY 06: Fei-Fei Li speaks onstage during The 2018 MAKERS Conference at ... [ ] NeueHouse Hollywood on February 6, 2018 in Los Angeles, California. After the COVID-19 pandemic is over and the economy reopens, many students will resume work on their careers. But for many young people, their priorities are going to shift. After seeing the pain and suffering caused by a single invisible enemy, some will naturally prioritize biomedical research over other easier and more lucrative trades, like law and finance. And some will choose to pursue possibly the most impactful area, which lies on the borderline of computer science and biomedicine - Artificial Intelligence (AI) for drug discovery.