Things to Do in L.A. Tap to enable a layout that focuses on the article. Chance of more showers in L.A., with a new storm set to hit Thursday A driver navigates a flooded street during a storm Monday in Santa Barbara. This is read by an automated voice. Please report any issues or inconsistencies here . Showers could linger in Los Angeles on Tuesday following four straight days of rain -- and even more rain is likely on Thursday and Friday.

Claire chatted to Matt Beane from the University of California, Santa Barbara about how humans can learn to work with intelligent machines. Matt Beane conducts field research on robots and AI in the workplace, focusing on positive exceptions applicable to the broader world of work. He has published his award-winning research in top management journals and presented on the TED stage. He's been recognized as a Human-Robot Interaction Pioneer and named to the Thinkers50 Radar list. Matt is an assistant professor in the Technology Management department at the University of California, Santa Barbara, and a Digital Fellow with Stanford's Digital Economy Lab and MIT's Initiative on the Digital Economy.

We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios in day-ahead grid planning. Our primary motivation is screening of probabilistic scenarios for realized load and renewable generation, in order to identify scenarios most relevant for operational risk mitigation. To handle the high-dimensionality of the scenarios across asset classes and intra-day periods, we employ functional measures of depth to sub-select outlying scenarios that are most likely to be the riskiest for the grid operation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserves shortfall and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics.

The ocean can be a dangerous place, even for a shark. Despite sitting at the top of the food chain, these predators are now reeling from destructive human activities like overfishing, pollution and climate change. Researchers at UC Santa Barbara focused on a particularly troublesome issue for sharks: tangles with the longline tuna fishery. Using data from regional fisheries management organizations and machine learning algorithms, the scientists were able to map out hotspots where shark species face the greatest threat from longline fishing. The findings, published in Frontiers in Marine Science, highlight key regions where sharks can be protected with minimal impact on tuna fisheries.

We study a scenario where an aircraft has multiple heterogeneous sensors collecting measurements to track a target vehicle of unknown location. The measurements are sampled along the flight path and our goals to optimize sensor placement to minimize estimation error. We select as a metric the Fisher Information Matrix (FIM), as "minimizing" the inverse of the FIM is required to achieve small estimation error. We propose to generate the optimal path from the Hamilton-Jacobi (HJ) partial differential equation (PDE) as it is the necessary and sufficient condition for optimality. A traditional method of lines (MOL) approach, based on a spatial grid, lends itself well to the highly non-linear and non-convex structure of the problem induced by the FIM matrix. However, the sensor placement problem results in a state space dimension that renders a naive MOL approach intractable. We present a new hybrid approach, whereby we decompose the state space into two parts: a smaller subspace that still uses a grid and takes advantage of the robustness to non-linearities and non-convexities, and the remaining state space that can by found efficiently from a system of ODEs, avoiding formation of a spatial grid.

Fran was a celebrity whale – the most photographed humpback in the San Francisco Bay, with 277 recorded sightings since 2005. Last month, she was hit by a ship and killed. Her death marked a grim milestone: Fran was the fifth whale to be killed by a ship strike in the area this year, according to the Marine Mammal Center. Collisions with ships are one of the leading causes of death for endangered whales, who breed, eat and travel in deep channels in the same busy waters that cargo ships frequent. Whales that spend their lives near the surface – such as humpbacks and right whales – are especially at risk. One 2019 study likened their plight to those of land animals forced to criss-cross the highways that cut through their habitats.

Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus on the decision-making development for the intervention of COVID-19, aiming to provide mathematical models and efficient machine learning methods, and justifications for related policies that have been implemented in the past and explain how the authorities' decisions affect their neighboring regions from a game theory viewpoint.

Did you miss a session at the Data Summit? MixMode, which today announced a $45 million series B funding round, has a massive opportunity ahead to deploy its self-learning, "third-wave" AI system to proactively secure customers against previously unknown cyberattacks, CEO John Keister told VentureBeat. A significant portion of the hundreds of billions of dollars spent each year on cybersecurity is focused on signature-based solutions, which only protect against the 20% of successful attacks that had previously been seen, Keister said. But the other 80% of cyberattacks (according to figures from the Ponemon Institute) are novel attacks -- and identification of those requires advanced AI capabilities, he said. "The existing systems simply don't address that 80%," Keister said.

When we hear about AI in the contact center, it's usually about chatbots and augmented agent. But in this conversation, we hear at how contact center AI can help with sales and marketing. Invoca is doing some fascinating stuff with its conversational intelligence engine. The company's technology has analyzed over 1.5B conversational minutes. Its customers analyze their call center interactions to optimize marketing, improve digital conversion rates, automate contact center QA, and enable agent coaching. Invoca just announced it saw 70% revenue growth during the past 12 months. Invoca's customer base now includes over 2,300 of the leading B2C brands across a number of industries. The common theme, according to Gregg, is they tend to have complex interactions. The small company seems to be doing a few things right. It was named a Leader in The Forrester Wave: Conversation Intelligence: Sales And Marketing, Q4 2021 report. Just this week it was selected for the Innovation Showcase at Enterprise Connect. Invoca was also recognized in the Inc. Best Workplaces of 2019 list and achieved the difficult Great Place to Work certification. Dave Michels 0:12 Welcome to talking here today, Evan and I will be talking with Brent Johnson of invoca. But before that Evon must be the pandemic is over, because it's time for Enterprise Connect. I know I'm gonna be there. Evan Kirstel 0:24 You know, after a two year hiatus, I will be there in person at Enterprise Connect in Orlando, and at the Innovation Showcase which you are spearheading I really actually looking forward to it to seeing, well, you not so much, but a lot of other people that I haven't seen in person for a while. You mentioned the Innovation Showcase, because that is without doubt the most valuable session.