Spyros Makridakis

This paper examines the transformative impact of Artificial General Intelligence (AGI), poised to redefine society and organizations by surpassing narrow AI's capabilities. Drawing on historical technological revolutions, we analyze AGI’s potential to enhance problem-solving, address global challenges like climate change and healthcare disparities, and reshape labor, governance, and human purpose. Through a human-AI collaborative approach, we present four scenarios—ranging from utopian synergy to existential risks—to assess AGI’s societal and economic implications. Key considerations include cognitive automation, ethical governance, and equitable access. We propose actionable recommendations for governments, firms, and educational institutions, emphasizing reskilling, international cooperation, and ethical frameworks to ensure AGI fosters inclusive prosperity while mitigating risks like job displacement and inequality.

ChatGPT, a state-of-the-art large language model (LLM), is revolutionizing the AI field by exhibiting humanlike skills in a range of tasks that include understanding and answering natural language questions, translating languages, writing code, passing professional exams, and even composing poetry, among its other abilities. ChatGPT has gained an immense popularity since its launch, amassing 100 million active monthly users in just two months, thereby establishing itself as the fastest-growing consumer application to date. This paper discusses the reasons for its success as well as the future prospects of similar large language models (LLMs), with an emphasis on their potential impact on forecasting, a specialized and domain-specific field. This is achieved by first comparing the correctness of the answers of the standard ChatGPT and a custom one, trained using published papers from a subfield of forecasting where the answers to the questions asked are known, allowing us to determine their correctness compared to those of the two ChatGPT versions. Then, we also compare the responses of the two versions on how judgmental adjustments to the statistical/ML forecasts should be applied by firms to improve their accuracy. The paper concludes by considering the future of LLMs and their impact on all aspects of our life and work, as well as on the field of forecasting specifically. Finally, the conclusion section is generated by ChatGPT, which was provided with a condensed version of this paper and asked to write a four-paragraph conclusion.

Forecasting competitions are the equivalent of laboratory experimentation widely used in physical and life sciences. They provide useful, objective information to improve the theory and practice of forecasting, advancing the field, expanding its usage, and enhancing its value to decision and policymakers. We describe 10 design attributes to be considered when organizing forecasting competitions, taking into account trade-offs between optimal choices and practical concerns, such as costs, as well as the time and effort required to participate in them. Consequently, we map all major past competitions in respect to their design attributes, identifying similarities and differences between them, as well as design gaps, and making suggestions about the principles to be included in future competitions, putting a particular emphasis on learning as much as possible from their implementation in order to help improve forecasting accuracy and uncertainty. We discuss that the task of forecasting often presents a multitude of challenges that can be difficult to capture in a single forecasting contest. To assess the caliber of a forecaster, we, therefore, propose that organizers of future competitions consider a multicontest approach. We suggest the idea of a forecasting-“athlon” in which different challenges of varying characteristics take place.

The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field in order to identify best practices and highlight their practical implications. However, whether the findings of the M5 competition can be generalized and exploited by retail firms to better support their decisions and operation depends on the extent to which the M5 data is sufficiently similar to unit sales data of retailers that operate in different regions, sell different types of products, and consider different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporaci\'on Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest that there are only small discrepancies between the examined data sets, supporting the representativeness of the M5 data.

Forecasting competitions are the equivalent of laboratory experimentation widely used in physical and life sciences. They provide useful, objective information to improve the theory and practice of forecasting, advancing the field, expanding its usage and enhancing its value to decision and policymakers. We describe ten design attributes to be considered when organizing forecasting competitions, taking into account trade-offs between optimal choices and practical concerns like costs, as well as the time and effort required to participate in them. Consequently, we map all major past competitions in respect to their design attributes, identifying similarities and differences between them, as well as design gaps, and making suggestions about the principles to be included in future competitions, putting a particular emphasis on learning as much as possible from their implementation in order to help improve forecasting accuracy and uncertainty. We discuss that the task of forecasting often presents a multitude of challenges that can be difficult to be captured in a single forecasting contest. To assess the caliber of a forecaster, we, therefore, propose that organizers of future competitions consider a multi-contest approach. We suggest the idea of a forecasting "athlon", where different challenges of varying characteristics take place.

Blockchain is a new technology, often referred to as the Internet of Value. As with all new technologies, there is no consensus on its potential value, with some people claiming that it will bring more disruptive changes than the Internet and others contesting the extent of its importance. Despite predictions that the future is perilous, there is evidence that blockchain is a remarkable, new technology that will change the way transactions are made, based on its ability to guarantee trust among unknown actors, assure the immutability of records, while also making intermediaries obsolete. The importance of blockchain can be confirmed by the interest in digital currencies, the great number of published blockchain papers, as well as MDPI’s journal Future Internet which exclusively publishes blockchain articles, including this special issue covering present and future blockchain challenges. This paper is a survey of the fast growing field of blockchain, discussing its advantages and possible drawbacks and their implications for the future of the Internet and our personal lives and societies in general. The paper consists of the following parts; the first provides a general introduction and discusses the disruptive changes initiated by blockchain, the second discusses the unique value of blockchain and its general characteristics, the third presents an overview of industries with the greatest potential for disruptive changes, the forth describes the four major blockchain applications with the highest prospective advantages, and the fifth part of the paper ends with a discussion on the most notable subset of innovative blockchain applications—Smart Contracts, DAOs (Decentralized Autonomous Organizations) and super safe networks—and their future implications. There is also a concluding section, which summarizes the paper, describes the future of blockchain, and mentions the challenges to be overcome.

Marine transport has grown rapidly as the result of globalization and sustainable world growth rates. Shipping market risks and uncertainty have also grown and need to be mitigated with the development of a more reliable procedure to predict changes in freight rates. In this paper, we propose a new forecasting model and apply it to the Baltic Dry Index (BDI). Such a model compresses, in an optimal way, information from the past in order to predict freight rates. To develop the forecasting model, we deploy a basic set of predictors, add lags of the BDI and introduce additional variables, in applying Bayesian compressed regression (BCR), with two important innovations. First, we include transition functions in the predictive set to capture both smooth and abrupt changes in the time path of BDI; second, we do not estimate the parameters of the transition functions, but rather embed them in the random search procedure inherent in BCR. This allows all coefficients to evolve in a time‐varying manner, while searching for the best predictors within the historical set of data. The new procedures predict the BDI with considerable success.