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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."


64% of people want more regulation to make AI safer

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People want increased regulation and more accountability in the field of artificial intelligence (AI), new research by Fountech.ai The AI firm commissioned an independent survey among 2,000 UK adults to uncover their attitudes towards the current state of AI development. It found that the majority (64%) want to see more regulation introduced so that the technology is safer to use and does not pose threats to society. Those aged over 55 appear more sceptical of AI, with almost three quarters (73%) keen to see additional guidelines introduced to improve safety standards. This is in comparison to just over half (53%) of those aged between 18 and 34 who held this view.


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

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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


Artificial Intelligence Landscape -- 100 great articles and research papers

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Back in 2015 I had written an article on 100 Big Data papers to help demystify landscape. On the same lines I thought it would be good to do one for AI. The initial part is about the basics and provides some great links to strengthen your foundation. The latter part has links to some great research papers and is for advanced practitioners who want to understand the theory and details. AI is a revolution that is transforming how humans live and work.


Face Detection in Flutter Using Firebase's ML Kit

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In the last piece in this series on developing with Flutter, we looked at how we can implement [image labeling using ML Kit, which belongs to the Firebase family. In this 7th installment of the series, we'll keep working with ML Kit, this time focusing on implementing face detection. Using face detection in Firebase's ML Kit enables you to detect faces in an image, without providing additional data. The face detection algorithm returns rectangular bounding boxes that you can then plot on the detected faces. It's also able to detect key facial landmarks such as eyes, mouth, nose, etc.


Banking on AI: The time is ripe for Indian banks to embrace artificial intelligence – IAM Network

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Therefore, banks must leverage AI to balance the need for privacy and security with personalisation and engagement. Creative implementation of AI by start-ups and fintechs has helped further this trend. From personalisation to customer service, fraud detection and prevention to compliance, and risk monitoring to intelligent contract documents, AI has helped banks gain better control and predictability.Related NewsToday, customers expect faster, personal, and meaningful services and interactions with their banks and little tolerance for generic unsolicited messages. Therefore, banks must leverage AI to balance the need for privacy and security with personalisation and engagement. That said, the Indian banking sector has some amount of catching up to do.While Indian banks have explored the use of AI, it has primarily been used to improve customer experience by adding chatbots as an additional interface for customers like SIA by State Bank of India, Eva by HDFC and iPal by ICICI.


Explore the Gendering of AI Voice Assistants

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THINK PIECE 2 of I'd blush if I could, is the first in-depth UN examination of the gendering of AI technology. Using the example of digital voice assistants such as Amazon's Alexa and Apple's Siri technology, it explains how gender imbalances in the digital sector can be'hard-coded' into technology products.


gradio-app/gradio

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At Gradio, we often try to understand what inputs a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced gradio, a python library that allows you to quickly create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. To get a sense of gradio, take a look at a few of these examples, and find more on our website: www.gradio.app. Gradio is very easy to use with your existing code. Let's start with a basic function (no machine learning yet!) that greets an input name.


NASA to Use Machine Learning to Enhance Search for Alien Life on Mars

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Researchers at NASA have been hard at work on a pilot AI system intended to help future exploration missions find evidence of life on other planets in our solar system. Machine learning algorithms will help exploration devices analyze soil samples on Mars and return the most relevant data to NASA. The pilot program is currently slated for a test run during the ExoMars mission that will see its launch in mid-2022. As IEEE Spectrum reports, the decision to use machine learning and artificial intelligence to aid the search for life on other planets was driven largely by Erice Lyness, the head of the Goddard Planetary Environments Lab at NASA. Lyness needed to come up with ways of automating aspects of geochemical analyses of samples taken in other parts of our solar system.


How to Choose a Machine Learning Technique

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Why are there so many machine learning techniques? The thing is that different algorithms solve various problems. The results that you get directly depend on the model you choose. That is why it is so important to know how to match a machine learning algorithm to a particular problem. In this post, we are going to talk about just that. First of all, to choose an algorithm for your project, you need to know about what kinds of them exist.