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Cannes winner Naomi Kawase named producer and senior advisor for Osaka Expo 2025

The Japan Times

Osaka – Film director Naomi Kawase, winner of several Cannes awards, and roboticist Hiroshi Ishiguro were among 10 producers named Monday for the World Exposition set to be held in the city of Osaka in 2025, as the nation began preparing for the event. Kawase will also double as a senior adviser to the event. The expo, to be held for the second time in the city after one in 1970, will have no general producer in charge overall but instead will have 15 senior advisers. The 10 producers, selected by the Japan Association for the 2025 World Exposition, are tasked with designing venues and planning pavilion exhibitions among other sites for the event, which is to be held on Yumeshima, a manmade island in Osaka Bay. Ishiguro, a professor at Osaka University whose creations include his "robot twin," said at a news conference, "The expo 50 years ago had a great impact that can be felt even now. We would like to make the (next) expo one whose legacy will continue for another 50 years."


#TechFightsCOVID: 10 AI use cases to help retail enterprises amid COVID-19 - NASSCOM Community

#artificialintelligence

COVID-19 has had an unparalleled impact on the economy with a slowdown expected in most sectors including retail. In the short to mid-term, COVID-19 and subsequent nation-wide lockdown has further worsened the challenges faced by Indian retailers. With broken supply chains, it has led to a disconnected demand and supply making it difficult for retailers to cater to customer needs. It has also forced customers to rethink their purchase requirements and has led to a shift to contactless mode of deliveries, which is bound to become the new normal going forward. Establishing the right balance between demand and supply becomes key for retailers The Holy Grail for retailers is not only to identify the target customers and their real-time needs but also to proactively procure the right products to cater to the identified demand. This is even more critical amidst the COVID-19 pandemic, when due to broken supply chains there has been a massive demand supply mismatch. Digital enterprises that are utilising the data generated across the retail value chain and customer touchpoints to deploy AI-powered solutions have a significant edge over others. Here are my top 10 picks for AI use cases that can be a good starting point for retail enterprises (specifically amid the pandemic) in their journey towards becoming an intelligent enterprise. These use cases will definitely help retail enterprises survive the crisis and thrive in the long term. Customer Segmentation – Use of AI for creation of customer segments and personas based on real time transaction, demographic and behavioural data, enabling retailers with dynamic pricing for its products, predicting customer behaviour to target and personalise communication, and create cross-sell models. Demand forecasting – Using machine learning and leveraging contextual data to build models enabling retailers to optimise product availability, and gaining a better understanding of sales patterns and anomalies. Store Assortment Optimisation – Customers are restricting their store time with the fear of COVID-19 and that makes getting the right product assortment critical. AI helps store-level customisation of assortments based on store data (returns, purchases, and receipts data). This can also be done for online stores to help increase customer retention. Hyper Targeted Campaigns – It is critical for retailers to identify the right time to push a particular product to ensure maximum sales. AI-powered systems are helpful in suggesting the product and time slot in which it needs marketing. Personalised Marketing – For successful hyper-targeted campaigns it is also important for retailers to ensure the right marketing channel and the right message. Based on a customer’s past behaviour, AI-powered system picks the right way (channel, messaging, and discounts) of communication and sends personalised messages. Fraud Detection – The risk of potential frauds also increases amid these trying times, with a huge volume of online orders. AI-based system can predict potential frauds based on customer profiles and past purchase/returns data. On Time Delivery – With majority of customers opting for home delivery of products, it becomes critical for retailers to ensure on-time delivery. Predictive analytics and AI algorithms can help determine the most cost-effective and energy-efficient route to the destinations. Omni-Channel Customer Service – With restricted access to physical stores, consumers are opting for Omni-channel services. By connecting experiences across channels, building customer knowledge through data and creating discussions within user communities, AI platforms help brands acquire, retain and grow relationships with their customers. Customer Service Chat bot – The need for contactless deliveries has forced many consumers to opt for online purchases. The high volumes also result in larger volumes of queries and concerns. AI-powered chat bot can understand customer’s queries and respond. It can understand a customer’s emotion and can prioritise and alert human customer service agents to intervene. Visual Workforce Monitoring – AI system to detect safety compliance of the workers. This is specifically important in the current COVID-19 times when hygiene factors are critical. If the system detects any violation of safety norms, it can alert and share images for review. NASSCOM Research, NASSCOM CoE – DS&AI along with EY released a report titled “Indian Retail: AI Imperative to Data-Led Growth” focusing on AI opportunities in India’s retail sector. The report provides a unique periodic table of 100+ AI use cases across the retail value chain. The use cases identified in this article are also a part of the report. The report also highlights best practices across retail enterprises that have implemented these use cases. Download the report now: https://tinyurl.com/y9johts2


New AI Enables Rapid Detection of Harmful Bacteria

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Testing for pathogens is a critical component of maintaining public health and safety. Having a method to rapidly and reliably test for harmful germs is essential for diagnosing diseases, maintaining clean drinking water, regulating food safety, conducting scientific research, and other important functions of modern society. In recent research, scientists from University of California, Los Angeles (UCLA), have demonstrated that artificial intelligence (AI) can detect harmful bacteria from a water sample up to 12 hours faster than the current gold-standard Environmental Protection Agency (EPA) methods. In a new study published yesterday in Light: Science and Applications, the researchers created a time-lapse imaging platform that uses two separate deep neural networks (DNNs) for the detection and classification of bacteria. The team tested the high-throughput bacterial colony growth detection and classification system using water suspensions with added coliform bacteria of E. coli (including chlorine-stressed E. coli), K. pneumoniae and K. aerogenes, grown on chromogenic agar as the culture medium.



Coronavirus: Compliance with social distancing during early stages linked to working memory, study finds

FOX News

Dr. Tom Inglesby, director of the Center for Health Security at Johns Hopkins University, joins Chris Wallace on'Fox News Sunday.' A new study published in the Proceedings of the National Academy of Sciences claims compliance in America with social distancing during the early stages of the coronavirus pandemic is linked to working memory. The study, "Working memory capacity predicts individual differences in social-distancing compliance during the COVID-19 pandemic in the United States," assessed the working memory, personality, mood and fluid intelligence of test subjects; the researchers surveyed 850 U.S. residents between March 13 and March 25. The study found a link between working memory and social distancing, and subjects -- noting more benefits than costs -- with higher levels of fluid intelligence, fairness and agreeableness followed the new rules of social distancing compliance, the study found. "The decision of whether or not to follow social distancing guidelines is a difficult one, especially when there is a conflict between the societal benefits (e.g., prevent straining public health resources) and personal costs (e.g., loss in social connection and financial challenges). This decision critically relies on our mental capacity in retaining multiple pieces of potentially conflicting information in our head, which is referred to as working memory capacity," study author Weizhen Xie (Zane) told PsyPost.


Graph Theory & Machine Learning in Neuroscience

#artificialintelligence

Graph theory is the study of graphs, mathematical structures that model the relationships between objects. In this example, we see a social network. A line represents a friendship between the people that it connects. In more technical terms, every person would be called a "node" or "vertex," while every line that connects would be called a "link" or "edge." So, this graph has 5 vertices and 7 edges.



MIT-Designed Robot Could Be Used To Disinfect Schools, Grocery Stores With UV-C Light

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MIT has designed a robot that is capable of disinfecting the floor of a 4,000-square foot warehouse in only half an hour, and it could one day be used to clean your local grocery store or school. The university's Computer Science and Artificial Intelligence Laboratory (CSAIL) worked with Ava Robotics -- a company that focuses on creating telepresence robots -- and the Greater Boston Food Bank (GBFB) to develop a robot that uses a custom UV-C light to disinfect surfaces and neutralize aerosolized forms of the coronavirus. Development on this project began in early April, and one of the researchers said that it came in direct response to the pandemic. The results have been encouraging enough that the researchers say that autonomous UV disinfection could be done in other environments such as supermarkets, factories and restaurants. Covid-19 mainly spreads via airborne transmission, and it is capable of remaining on surfaces for several days.


Using Mathematical Modeling to Simulate an Epidemic

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In this article, we'll explore and visualize a classic mathematical model used for modeling the spread of infectious disease: the SIR model. Our goal is to model how these compartments fluctuate over time, and so we'll consider them to be functions with respect to time: The SIRD model considers another compartment (D) for deceased individuals. In the SIR model, the transition between compartments takes the following path: susceptible infectious recovered. Each transition happens at a different rate. The rate at which susceptible individuals come into contact with infectious individuals, thus contracting the disease, is called the infectious rate (β).


Dermatology researchers: AI tools soon to be 'tightly integrated into daily clinical practice'

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Here they comment that machine learning is "a natural fit for translation into dermatology because dermatology is a specialty that is heavily reliant on …