Conversational robots and virtual characters can enhance learning and expand entertainment options for children, a trio of studies by Disney Research shows, though exactly how these autonomous agents interact with children sometimes depends on a child's age. Pre-school children responding to an on-screen character, for instance, may be happiest if the character simply waits for their responses or repeats a question. Older children talking with a robot, on the other hand, appreciate it when the robot references their previous conversations, while younger children are just as happy if the robot treats each conversation as a new encounter. "Teasing out these nuances is necessary if we are to make the interactions between automated characters and children as engaging as possible," said Jill Fain Lehman, senior research scientist. Lehman and other staff members of Disney Research will present findings from the three studies at the Interaction Design and Children Conference in Palo Alto, Calif., June 27-30. "Though parent-child interaction remains the most important factor in child development, the prospect of automated characters that can interact with children offers exciting opportunities for further enhancing learning and play," said Markus Gross, vice president at Disney Research.

For more than 60 years, the Lawrence Livermore National Laboratory (LLNL) has applied science and technology to make the world a safer place. We have multiple openings for Postdoctoral Research Staff Members to engage in the research, design, and deployment of machine learning and statistical methods to solve important data and science problems stemming from the Laboratory's mission spaces. You will work as part of collaborative, multidisciplinary teams to support a variety of application areas (such as material science, high energy physics, predictive medicine, cybersecurity, climate modeling). These positions are in the Center for Applied Scientific Computing (CASC) Division within the Computation Directorate. Essential Duties - Research, design, implement, and apply a variety of advanced data science methods for multiple applications in a collaborative scientific environment.

Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly incorporating all higher level context information. Moreover, it can keep the dimensions and hierarchical structures of the feature maps, which makes it flexible to be integrated with any CNN-based models. Empirical experiments show that our results are monotonically getting better with increasing number of FPMs. Without bells and whistles, PFPN outperforms the state-of-the-art methods significantly on five benchmark datasets under various evaluation metrics.

At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.

Recent methods for training generalized additive models (GAMs) with pairwise interactions achieve state-of-the-art accuracy on a variety of datasets. Adding interactions to GAMs, however, introduces an identifiability problem: effects can be freely moved between main effects and interaction effects without changing the model predictions. In some cases, this can lead to contradictory interpretations of the same underlying function. This is a critical problem because a central motivation of GAMs is model interpretability. In this paper, we use the Functional ANOV A decomposition to uniquely define interaction effects and thus produce identifiable additive models with purified interactions. To compute this decomposition, we present a fast, exact, mass-moving algorithm that transforms any piecewise-constant function (such as a tree-based model) into a purified, canonical representation. We apply this algorithm to several datasets and show large disparity, including contradictions, between the apparent and the purified effects. An important question in data analysis is whether two variables act in concert to affect an outcome. But this unconstrained additive model has fundamental flaws.

Farmland in Fukushima that was rendered unusable after the disastrous 2011 nuclear meltdown is getting a second chance at productivity. A group of Japanese investors have created a new plan to use the abandoned land to build wind and solar power plants, to be used to send electricity to Tokyo. The plan calls for the construction of eleven solar power plants and ten wind power plants, at an estimated cost of $2.75 billion. Fukushima has been aggressively converting land damaged by the 2011 meltdown, such as this golf course (pictured above) into a source of renewable energy. A new $2.75 billion plan will add eleven new solar plants and ten wind power plants to former farmland The project is expected to be completed in March of 2024 and is backed by a group of investors, including Development Bank of Japan and Mizuho Bank.

The growing demand for ML/AI technologies, as well as for ML/AI talent, in the pharmaceutical industry is driving the formation of a new interdisciplinary field: data-driven drug discovery/healthcare. Consequently, there is a growing number of AI driven startups offering technology solutions for drug discovery/development. In drug development, preclinical phase (in vitro and in vivo), also named preclinical studies and nonclinical studies, is a stage of research that begins before clinical trials, and during which important feasibility, iterative testing and drug safety data are collected. According to a detailed mind-map prepared by Pharma Division of Deep Knowledge Analytics (updated Q1 2019): the AI for Drug Discovery, Biomarker Development and Advanced R&D Industry Landscape counts so far 400 investors, 170 companies and 50 corporations. This article focuses only on the AI startups and the AI investors trying to overcome the above 4 challenges during design and execution of the preclinical phase.

Visual 1st, the annual Silicon-Valley imaging conference for industry leaders and upstarts, once again brought together a worldwide audience for a day-and-a-half executive conference. The event, held Oct. 2-3 at the Golden Gate Club in San Francisco, addresses topics as far-reaching as artificial intelligence and as every day as printing. As with most conferences, the real meat of the event is the hallway discussions and informal meetings over a beer or wine at the reception. Below are some photos from the conference, courtesy of sponsor, Sweet Escapes. Each year, a panel of high-powered industry experts presented the four Visual 1st Awards to the most outstanding among 30 products competing in this year's show-and-tell demo sessions.

Alexa, Google Assistant, Siri, and Cortana can answer all sorts of questions that pop into users' heads, and they're improving every day. But what happens when a company like Amazon decides to crowdsource answers to fill gaps in its platform's knowledge? The result can range from amusing and perplexing to concerning. Alexa Answers allows any Amazon customer to submit responses to unanswered questions. When the web service launched in general availability a few weeks ago, Amazon gave assurances that submissions would be policed through a combination of automatic and manual review.

Learning with rejection (LWR) allows development of machine learning systems with the ability to discard low confidence decisions generated by a prediction model. That is, just like human experts, LWR allows machine models to abstain from generating a prediction when reliability of the prediction is expected to be low. Several frameworks for this learning with rejection have been proposed in the literature. However, most of them work for classification problems only and regression with rejection has not been studied in much detail. In this work, we present a neural framework for LWR based on a generalized meta-loss function that involves simultaneous training of two neural network models: a predictor model for generating predictions and a rejecter model for deciding whether the prediction should be accepted or rejected. The proposed framework can be used for classification as well as regression and other related machine learning tasks. We have demonstrated the applicability and effectiveness of the method on synthetically generated data as well as benchmark datasets from UCI machine learning repository for both classification and regression problems. Despite being simpler in implementation, the proposed scheme for learning with rejection has shown to perform at par or better than previously proposed methods. Furthermore, we have applied the method to the problem of hurricane intensity prediction from satellite imagery. Significant improvement in performance as compared to conventional supervised methods shows the effectiveness of the proposed scheme in real-world regression problems.