They all had some effect, surely. Could I have done it without them? Hang on, what *is* the it that I wouldn't have done? Real life usually lacks counterfactuals. I sense this topic could add some spice to the discussions of those who have been asking about the role of psychoactive substances in art since time immemorial, though the AI component adds nothing fundamentally new.
Food processing and handling is the most important business among the numerous manufacturing businesses in the world that provide the most employment opportunities. The human workforce is critical to the successful production and packaging of food products. The food industry is failing to sustain the demand-supply cycle and is also deficient in food safety as a result of human engagement. Industrial automation is the best approach for overcoming these challenges in the food industry. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques are used to automate everything.
Machine Learning is playing an important role in hospitality management with major focus on food and accommodation. It is because these two sectors are rapidly changing with time, challenging the industry to be proactive and meet the demand of users with minimal efforts. With the applications of ML the hotel owners are now able to deliver superior services. The implementation of ML in food industry and accommodation businesses is moving the industry to a new level, enabling lower costs for storage and transportation and more importantly producing less waste. The costs for storage and transportation is nowadays reduced to a significant level followed by happy customers, quick service, voice searching, and more personalized orders.
A US-based well-funded pizza delivery startup service wanted to predict the ingredients that make up the most popular orders at any given location and predict the denser location. Experts from Frost Digital Ventures built and deployed a system that predicted demand at the ingredient level relative to each market location. This helped the pizza delivery team determine the most effective place for their orders and the most preferred meal by the customers. With the system, the number of accepted orders increased. There was a reduction in food waste because of proper inventory management.
Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week in AI, scientists conducted a fascinating experiment to predict how "market-driven" platforms like food delivery and ride-hailing businesses affect the overall economy when they're optimized for different objectives, like maximizing revenue. Elsewhere, demonstrating the versatility of AI, a team hailing from ETH Zurich developed a system that can read tree heights from satellite images, while a separate group of researchers tested a system to predict a startup's success from public web data. The market-driven platform work builds on Salesforce's AI Economist, an open source research environment for understanding how AI could improve economic policy.
Food manufacturing giant Kraft Heinz has announced it is looking to improve supply chain visibility and day-to-day operations through the adoption of cloud, AI, and digital twins under a new digital transformation partnership it has signed with Microsoft. Under a multi-year deal, Kraft Heinz will migrate the majority of its global datacentre assets to Azure and its enterprise resource planning (ERP) software to SAP on Azure, and create a so-called supply chain control tower that will serve as an "air traffic control" across Kraft Heinz's supply chain of its 85 product categories. Kraft Heinz also plans to develop digital twins of its 34 manufacturing facilities in North America so it can test solutions and processes before applying them on the plant floor, such as looking at methods to reduce mechanical interruptions and address issues before they occur. "As part of our Agile@Scale transformation, we are building a leading tech ecosystem to benefit the entire value chain," Kraft Heinz North America president and executive vice president Carlos Abrams-Rivera said. "Our collaboration with Microsoft is a critical piece of our transformation strategy, providing us with the machine learning and advanced analytics to drive innovation and efficiencies across the supply chain so we can get products into the market faster, better serve our customers and, ultimately, deliver on the sustained and growing consumer demand our iconic brands continue to experience."
Scikit-learn is one of the most popular Python libraries for Machine Learning. It provides models, datasets, and other useful functions. In this article, I will describe the most popular techniques provided by scikit-learn for Model Evaluation. Model Evaluation permits us to evaluate the performance of a model, and compare different models, to choose the best one to send into production. There are different techniques for Model Evaluation, which depend on the specific task we want to solve.
As a former professor in artificial intelligence, one of my favorite – and surely one of the oldest – technological myths is found in the masterpiece, the Iliad. In Homer's poem narrating the Trojan War, the God of metalworking, Hephaestus, engineers one of the first robots known to history, a handmaiden designed to assist him in his forge. Not happy with limiting himself to manufacturing, Hephaestus steps it up by designing Talos, an automated bronze giant whose purpose was to protect ancient Crete from pirates and invaders. While thousands of years have passed since Hephaestus' mythical robots came to life, today's intelligent machines – strong with skillful AI – are making headway in our own workplaces. Take the factories and warehouses adversely affected by the pandemic as an example. With fewer and fewer workers willing and able to assist our manufacturers and fulfilment centers, many are embracing AI and machine learning to automate tasks such as quality control which are traditionally reliant on scores of human workers.
Data science is a field that spans many disciplines. It is not merely in control of the digital world. It is used for everything from internet searches to social media feeds to political campaigns, grocery store inventory, airline routes, and medical appointments. A Data Scientist should acquire a complete set of abilities that covers each building block of the discipline in order to have a successful career. Statistics is one of the building blocks.
This piece explores myths about Artificial Intelligence, such as "I need to go to a university and hire an AI PhD" and "I need to collect millions of images to even know if using AI is possible." As a former professor in artificial intelligence, one of my favorite–and surely one of the oldest–technological myths is found in the masterpiece, the Iliad. In Homer's poem narrating the Trojan War, the God of metalworking, Hephaestus, engineers one of the first robots known to history, a handmaiden designed to assist him in his forge. Not happy with limiting himself to manufacturing, Hephaestus steps it up by designing Talos, an automated bronze giant whose purpose was to protect ancient Crete from pirates and invaders. While thousands of years have passed since Hephaestus' mythical robots came to life, today's intelligent machines–strong of skillful AI–are making headway in our own workplaces.