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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.


Explainable AI for B5G/6G: Technical Aspects, Use Cases, and Research Challenges

arXiv.org Artificial Intelligence

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG), promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.


Eliminating AI Bias

#artificialintelligence

The primary purpose of Artificial Intelligence (AI) is to reduce manual labour by using a machine's ability to scan large amounts of data to detect underlying patterns and anomalies in order to save time and raise efficiency. However, AI algorithms are not immune to bias. As AI algorithms can have long-term impacts on an organisation's reputation and severe consequences for the public, it is important to ensure that they are not biased towards a particular subgroup within a population. In layman's terms, algorithmic bias within AI algorithms occurs when the outcome is a lack of fairness or a favouritism towards one group due to a specific categorical distinction, where the categories are ethnicity, age, gender, qualifications, disabilities, and geographic location. If this in-depth educational content is useful for you, subscribe to our AI research mailing list to be alerted when we release new material. AI Bias takes place when assumptions are made incorrectly about the dataset or the model output during the machine learning process, which subsequently leads to unfair results. Bias can occur during the design of the project or in the data collection process that produces output that unfairly represents the population. For example, a survey posted on Facebook asking about people's perceptions of the COVID-19 lockdown in Victoria finds that 90% of Victorians are afraid of travelling interstate and overseas due to the pandemic. This statement is flawed because it is based upon individuals that access social media (i.e., Facebook) only, could include users that are not located in Victoria, and may overrepresent a particular age group (i.e. To effectively identify AI Bias, we need to look for presence of bias across the AI Lifecycle shown in Figure 1.


On the Opportunities and Risks of Foundation Models

arXiv.org Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.