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Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


Vegan Chicken That Tastes Identical To The Real Thing? NotCo Artificial Intelligence Has Cracked It

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Chile's NotCo claims that Giuseppe, its patented AI technology platform, has perfectly mimicked the taste of real chicken in a vegan format. The pea protein-based development will be used to create a range of products. Initial chicken launches will take place in Chile, Argentina, and Brazil. Timely expansion into the U.S. and Canada is expected to follow. No confirmation of existing restaurant partner uptake has been offered.


NotCo taps AI to develop new plant-based alternatives - Verdict

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Chilean food-tech start-up NotCo uses artificial intelligence (AI) to identify the optimum combinations of plant proteins when creating vegan alternatives to animal-based food products. The company, set up in 2015, has attracted investment from Amazon founder Jeff Bezos and Future Positive, a US investment fund founded by Biz Stone, the co-founder of Twitter. NotCo's machine learning algorithm compares the molecular structure of dairy or meat products to plant sources, searching for proteins with similar molecular components. NotCo has a database containing over 400,000 different plants, including macronutrient breakdown and chemical composition. These factors are used to predict novel food combinations with the target flavour, texture, and functionality.


GPT-3 Creative Fiction

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What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.


Wine quality rapid detection using a compact electronic nose system: application focused on spoilage thresholds by acetic acid

arXiv.org Machine Learning

It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2) sensors and trained with an approach that uses deep Multilayer Perceptron (MLP) neural network, can perform early detection of wine spoilage thresholds in routine tasks of wine quality control. To obtain rapid and online detection, we propose a method of rising-window focused on raw data processing to find an early portion of the sensor signals with the best recognition performance. Our approach was compared with the conventional approach employed in E-Noses for gas recognition that involves feature extraction and selection techniques for preprocessing data, succeeded by a Support Vector Machine (SVM) classifier. The results evidence that is possible to classify three wine spoilage levels in 2.7 seconds after the gas injection point, implying in a methodology 63 times faster than the results obtained with the conventional approach in our experimental setup.


A new class of foods designed with AI algorithms arrives in Latin America - TheStartupFounder.com

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The first food product invented by an artificial intelligence (AI) system can already be obtained in Argentina. And with this specific meal, AI shows that it can already revolutionize a productive item that, until now, was conservative: the food industry. Dictating an email to the smartphone, driverless cars or combat drones that choose a target and fire without a commander's order are already common. The novelty is that, in laboratories and companies, computational techniques of Machine Learning and Big Data are already used to create new -and recreate old- foods using absolutely novel ingredients. For example, a Chilean startup has just presented in the local market a mayonnaise that has the same taste, smell, color and texture as the traditional one.


iFood Invests in Artificial Intelligence The Rio Times

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RIO DE JANEIRO, BRAZIL – iFood is planning to invest US$20 million in opening an AI learning center to strengthen ties with the tech industry. With an expected staff of 100 people by the end of the year, everything from machine learning, deep learning, behavioral science, and logistics will be covered. All of this is part of iFood's US$500 million funding round that began last year. São Paulo-based iFood is one of Latin America's biggest and most successful startup food delivery company. Seeing how the international food delivery ecosystem is worth around US$94 billion, it's easy to understand why iFood takes digital innovations so seriously.


Artificial Intelligence (Ai) Market In Food And Beverage (F&B) Market Forecast In Top Countries Of APAC, EMEA, Americas Regions 2017-2021 – Herald Telegraph

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Artificial Intelligence (Ai) Market In Food And Beverage (FB) Market report studies the global Artificial Intelligence (Ai) Market In Food And Beverage (FB) Industry market status and forecast categorize the global Artificial Intelligence (Ai) Market In Food And Beverage (FB) Market size (value volume) by key players, type, application, and region. This Artificial Intelligence (Ai) Market In Food And Beverage (FB) Industry industry report focuses on the top players in North America, Europe, Asia-Pacific, South America, and Middle East Africa. Global Artificial Intelligence (Ai) Market In Food And Beverage (FB) Industry industry research report identifies the development opportunities, latest market trends, and competitive scenario of the top industry players. Vital and distinct market information highlighting the growth opportunities, business strategies that will influence the decision-making process of the market players are also discussed in this report. AI technologies have been developed over the years, to assist humans in deliberating, deducing, analyzing, and inventing new technologies that can help end-users.