Cloud and edge computing are coming together as never before, leading to huge opportunities for developers and organizations around the world. Digital twins, mixed reality, and autonomous systems are at the core of a massive wave of innovation from which our customers already benefit. From the outside, it's not always apparent how this technology converges or the benefits that can be harnessed by bringing these capabilities together. This is why at Microsoft Build we talk about the possibilities this convergence creates, how customers are already benefitting, and our journey to making this technology easier to use and within reach of every developer and organization. Imagine taking any complex environment and applying the power of technology to create awe-inspiring experiences and reach new business heights that were previously unimaginable.
FREMONT, CA: The COVID-19 epidemic ushered in a time of tremendous acceleration in the industrial sector's digital transformation endeavors. What ordinarily takes years to develop and implement took only a few months. This transition has been aided by technologies that were already available but whose adoption has been accelerated. As a result, flexible manufacturing augmented reality, predictive maintenance, edge computing, and digital thread are more widely adopted in 2021. The rate at which manufacturers respond to market changes is known as flexible manufacturing.
Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. And also, not all users receive the same offer, and that is the challenge to solve with this data set. The data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app.
The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime, people must repurpose the materials and processes available to make shelter, distribute food, etc. Repurposing is equally important in professional life (e.g. clinicians often repurpose medicines off-license) and in addressing societal challenges (e.g. finding new roles for waste products,). Despite the importance of repurposing, the topic has received little academic attention. By considering examples from a variety of domains such as every-day activities, drug repurposing and natural disasters, we identify some principle characteristics of the process and describe some technical challenges that would be involved in modelling and simulating it. We consider cases of both substitution, i.e. finding an alternative for a missing resource, and exploitation, i.e. identifying a new role for an existing resource. We argue that these ideas could be developed into general formal theory of repurposing, and that this could then lead to the development of AI methods based on commonsense reasoning, argumentation, ontological reasoning, and various machine learning methods, to develop tools to support repurposing in practice.
Today, I will talk about discovering interesting patterns in data, what is called pattern mining, and in particular about how the concept of taxonomy can be useful to find interesting patterns. There has been a lot of research on pattern mining over the years to find various types of interesting patterns in data, and numerous algorithms have been designed for that. To explain the interest for taxonomies in finding patterns, I will talk about a classical problem in pattern mining called high utility itemset mining. High utility Itemset mining aims at searching in data to find itemsets (sets of values) that have a high importance as measured by a utility function. There are many applications of this problem, but let me illustrate it with shopping data as it is a popular example.
Amazon on Wednesday said it's bringing its "Just Walk Out" shopping technology to two Whole Foods stores, giving it an opportunity to test the cashierless payment system in a larger retail space. Next year, with the system in place at stores in Washington, DC, and Sherman Oaks, Calif., shoppers will have the option to skip the checkout line. Amazon acquired Whole Foods for $13.7 billion in 2017. Meanwhile, the tech giant first introduced the "Just Walk Out" system at its first Amazon Go store in 2016. The system uses computer vision, sensor fusion and deep learning to eliminate checkout lines.
Have you watched the "Silicon Valley" comedy series of HBO? If so, I bet you remember the Not Hotdog app that Jian Yang developed. Here is a clip to refresh your memory. So basically this app identifies whether something is Hot dog or not. Well, we can train with other types of objects to identify them as well.
New York, NY, Aug. 19, 2021 (GLOBE NEWSWIRE) -- Facts and Factors have published a new research report titled "Artificial Intelligence (AI) in Food and Beverages Market By Organization Size (Small, Medium & Large Enterprises), By Application (Food Sorting, Quality Control, and Safety Compliance, Consumer Engagement, Production and Packaging, Maintenance, and Other Applications), By End-Use Industry (Food Processing, Grocery, Hotels & Beverages Industry, and Others Region: Global & Regional Industry Perspective, Comprehensive Analysis, and Forecasts, 2021 – 2026". "According to the recent research report, the demand of global Artificial Intelligence (AI) in Food and Beverages Market size & share expected to reach to USD 29.45 Billion by 2026 from USD 3.07 Billion in 2020, at a compound annual growth rate (CAGR) of 45.70% during the forecast period 2021 to 2026" Artificial Intelligence (AI) is the process of developing intelligent robots that act and react similarly to humans. The objective is to teach machines how to think intelligently in the same way humans do. Until recently, the machines did exactly what they were programmed to do. AI, on the other hand, will allow machines to think and behave in the same way that people do. In the food processing industry, artificial intelligence is being utilized to improve a variety of products, streamline operations, and improve the customer experience.
Artificial intelligence (AI) and machine-learning (ML) have quickly grown beyond a few major tech companies and hardcore academic researchers. Every marketing organization can tap into the power of AI to streamline operations and grow the business. The new book The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing provides a growth framework for business and marketing leaders to implement AI using a five-stage model called the "AI Marketing Canvas." On this episode of Marketing Smarts, I speak with co-author Rajkumar Venkatesan about how he and his co-writer developed those stages by studying leading global brands. We cover examples of brands―including Google, Lyft and Coca-Cola―that have successfully woven AI into their marketing strategies. This is not a conversation about coding for AI models. Raj and I talk about how marketing leaders can go from "zero to hero" with AI in marketing, and what that means for your team and your company culture. Listen to the entire show now from the link above, or download the mp3 and listen at your convenience.