Shanghai/Beijing – Huawei Technologies founder Ren Zhengfei's global ambitions are marked in bricks and mortar at a new company campus in southern China, where the buildings are replicas from European cities. Zhang Yiming, founder of ByteDance, the operator of short video app TikTok, has plastered his Beijing headquarters with posters including a cover of former Google CEO Eric Schmidt's book "How Google Works," and has long said he will build a global firm that can compete with U.S. tech giants. But the two companies that best exemplify China's ambitions to challenge U.S. tech dominance are now stymied by strains in relations between China and countries including the United States, India, Australia and Britain. Chinese companies with world-beating technology -- including drone-maker DJI, artificial intelligence firms Megvii, SenseTime and iFlytek, surveillance camera vendor Hikvision and e-commerce conglomerate Alibaba Group -- are also among those losing access to markets. Smaller companies are being forced to rethink too. "What we are experiencing now is unprecedented," said a Chinese startup founder who has operations in the United States and India but asked not to be identified as he is now considering walking away.

Shudu Gram is a striking South African model. She's what fashion likes to call "one to watch," with a Balmain campaign in 2018, a feature in Vogue Australia on changing the face of fashion, and a red carpet appearance at the 2019 BAFTAs in a custom Swarovski gown. I'm from Canada, although I live in New York City now. Unlike Shudu, who's considered a "new face," I've been in the business for almost five years. I am also a futurist; I spend a lot of time researching emerging technologies and educating young people about the future of work through my startup WAYE.

Washington – The United States and close ally Australia held high-level talks on China on Tuesday and agreed on the need to uphold a rules-based global order, but the Australian foreign minister stressed that Canberra's relationship with China was important and it had no intention of injuring it. U.S. Secretary of State Mike Pompeo and Defense Secretary Mark Esper held two days of talks in Washington with their Australian counterparts, Foreign Minister Marise Payne and Defense Minister Linda Reynolds, who had flown around the world for the meetings despite the COVID-19 pandemic and face two weeks of quarantine on their return. At a joint news conference, Pompeo praised Australia for standing up to pressure from China and said Washington and Canberra would continue to work together to reassert the rule of law in the South China Sea, where China has been pressing its claims. Payne said the United States and Australia shared a commitment to the rule of law and had reiterated their commitment to hold countries to account for breaches, such as China's erosion of freedoms in Hong Kong. She said the two sides had also agreed to form a working group to monitor and respond to harmful disinformation and would look at ways to expand cooperation on infectious diseases, including access to vaccines.

A new study has found that the masks which protect people from spreading the coronavirus also have a second use, breaking facial recognition algorithms. Researchers from the National Institute of Standards and Technology have found that the best facial recognition algorithms had significantly higher error rates when trying to identify someone wearing a cloth covering. The researchers tested one-to-one matching algorithms, where a photo is compared to a different photo of the same person. This verification method is commonly used to unlock smartphones, or check passports. It drew digital masks onto the faces in a trove of border crossing photographs, and then compared those photos against another database of unmasked people seeking visas and other immigration benefits.

UneeQ Creator with Google Dialogflow unlocks the power of conversational AI to improve customer experience and influence consumer behavior with customizable digital employees. UneeQ, a United States and New Zealand-based digital human company, announces the launch of first-ever conversational AI digital human creator platform, UneeQ Creator. Equipped with native integrations into Google, Microsoft, IBM, Amazon and many others, UneeQ Creator offers ground-breaking machine learning functionality and the ability to easily create and deploy unique digital human employees or brand ambassadors capable of recreating natural human interaction across any industry. "The use case potential for digital employees is immeasurable," said Danny Tomsett, founder and CEO, UneeQ. "For example, imagine if the next time you went to order fast food, instead of using a touch screen, your order was taken by the friendly face of a digital human who could provide recommendations. All of these scenarios are rapidly becoming reality and are in development as we speak, with growing possibilities across many new emerging needs in a post-pandemic world every day."

Robotic systems are more present in our society everyday. In human-robot environments, it is crucial that end-users may correctly understand their robotic team-partners, in order to collaboratively complete a task. To increase action understanding, users demand more explainability about the decisions by the robot in particular situations. Recently, explainable robotic systems have emerged as an alternative focused not only on completing a task satisfactorily, but also in justifying, in a human-like manner, the reasons that lead to making a decision. In reinforcement learning scenarios, a great effort has been focused on providing explanations using data-driven approaches, particularly from the visual input modality in deep learning-based systems. In this work, we focus on the decision-making process of a reinforcement learning agent performing a simple navigation task in a robotic scenario. As a way to explain the goal-driven robot's actions, we use the probability of success computed by three different proposed approaches: memory-based, learning-based, and introspection-based. The difference between these approaches is the amount of memory required to compute or estimate the probability of success as well as the kind of reinforcement learning representation where they could be used. In this regard, we use the memory-based approach as a baseline since it is obtained directly from the agent's observations. When comparing the learning-based and the introspection-based approaches to this baseline, both are found to be suitable alternatives to compute the probability of success, obtaining high levels of similarity when compared using both the Pearson's correlation and the mean squared error.

As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.

This paper introduces an enhanced meta-heuristic (ML-ACO) that combines machine learning (ML) and ant colony optimization (ACO) to solve combinatorial optimization problems. To illustrate the underlying mechanism of our enhanced algorithm, we start by describing a test problem -- the orienteering problem -- used to demonstrate the efficacy of ML-ACO. In this problem, the objective is to find a route that visits a subset of vertices in a graph within a time budget to maximize the collected score. In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known. Specifically, classification models are used to classify an edge as being part of the optimal route, or not, using problem-specific features and statistical measures. We have tested several classification models including graph neural networks, logistic regression and support vector machines. The trained model is then used to predict the probability that an edge in the graph of a test problem instance belongs to the corresponding optimal route. In the second phase, we incorporate the predicted probabilities into the ACO component of our algorithm. Here, the probability values bias sampling towards favoring those predicted high-quality edges when constructing feasible routes. We empirically show that ML-ACO generates results that are significantly better than the standard ACO algorithm, especially when the computational budget is limited. Furthermore, we show our algorithm is robust in the sense that (a) its overall performance is not sensitive to any particular classification model, and (b) it generalizes well to large and real-world problem instances. Our approach integrating ML with a meta-heuristic is generic and can be applied to a wide range of combinatorial optimization problems.

Recent developments in machine learning techniques have allowed automatic generation of video game levels that are stylistically similar to human-designed examples. While the output of machine learning models such as generative adversarial networks (GANs) is notoriously hard to control, the recently proposed latent variable evolution (LVE) technique searches the space of GAN parameters to generate outputs that optimize some objective performance metric, such as level playability. However, the question remains on how to automatically generate a diverse range of high-quality solutions based on a prespecified set of desired characteristics. We introduce a new method called latent space illumination (LSI), which uses state-of-the-art quality diversity algorithms designed to optimize in continuous spaces, i.e., MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to effectively search the parameter space of theGAN along a set of multiple level mechanics. We show the performance of LSI algorithms in three experiments in SuperMario Bros., a benchmark domain for procedural content generation. Results suggest that LSI generates sets of Mario levels that are reliably mechanically diverse as well as playable.

The goal of this work is to build a classifier that can identify text complexity within the context of teaching reading to English as a Second Language (ESL) learners. To present language learners with texts that are suitable to their level of English, a set of features that can describe the phonological, morphological, lexical, syntactic, discursive, and psychological complexity of a given text were identified. Using a corpus of 6171 texts, which had already been classified into three different levels of difficulty by ESL experts, different experiments were conducted with five machine learning algorithms. The results showed that the adopted linguistic features provide a good overall classification performance (F-Score = 0.97). A scalability evaluation was conducted to test if such a classifier could be used within real applications, where it can be, for example, plugged into a search engine or a web-scraping module. In this evaluation, the texts in the test set are not only different from those from the training set but also of different types (ESL texts vs. children reading texts). Although the overall performance of the classifier decreased significantly (F-Score = 0.65), the confusion matrix shows that most of the classification errors are between the classes two and three (the middle-level classes) and that the system has a robust performance in categorizing texts of class one and four. This behavior can be explained by the difference in classification criteria between the two corpora. Hence, the observed results confirm the usability of such a classifier within a real-world application.