Cars have not been good for the environment, to put it lightly. Someday, self-driving cars will appear widely in the US. Wouldn't it be nice if they also helped reduce greenhouse gas emissions? Trouble is, making an electric car self-driving requires tradeoffs. Electric vehicles have limited range, and the first self-driving cars are expected to be deployed as roving bands of robotaxis, traveling hundreds of miles each day.
Artificial Intelligence is the hottest topic in technology and commerce today, and the field of data science is fundamental to how it works. Courses in data science all now contain a strong AI presence, and a few institutions are already offering specialized undergraduate degrees in AI. The increasing number of colleges and universities offering courses in these subjects indicates industry-wide expectations that there will be a world of rewarding opportunities for those with formal training and accreditation. Well, according to Glassdoor.com the average salary last year for a data scientist stood at $107,000. So, it's certainly a career worth considering if earning a good starting wage is on your list of priorities!
IBM Research, with the help of the University of Texas Austin and the University of Maryland, has created a technology, called BlockDrop, that promises to speed convolutional neural network operations without any loss of fidelity. This could further excel the use of neural nets, particularly in places with limited computing capability. Increase in accuracy level have been accompanied by increasingly complex and deep network architectures. This presents a problem for domains where fast inference is essential, particularly in delay-sensitive and realtime scenarios such as autonomous driving, robotic navigation, or user-interactive applications on mobile devices. Further research results show regularization techniques for fully connected layers, is less effective for convolutional layers, as activation units in these layers are spatially correlated and information can still flow through convolutional networks despite dropout.
Tesla Inc.'s Elon Musk said the carmaker is on the verge of developing technology to render its vehicles fully capable of driving themselves, repeating a claim he's made for years but been unable to achieve. The chief executive officer has long offered exuberant takes on the capabilities of Tesla cars, even going so far as to start charging customers thousands of dollars for a "Full Self Driving" feature in 2016. Years later, Tesla still requires users of its Autopilot system to be fully attentive and ready to take over the task of driving at any time. Tesla's mixed messages have drawn controversy and regulatory scrutiny. In 2018, the company blamed a driver who died after crashing a Model X while using Autopilot for not paying attention to the road.
Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.
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."
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
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.
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.