Applying Artificial Intelligence to Promote Sustainability
Abstract
:1. Introduction
2. Materials and Methods
2.1. Type of Study
- First coined by Kevin Ashton in 1999, IoT is a technology paradigm contemplated as a network of digitally connected devices and machines. Here, the digital connection of devices, systems, and humans occurs over the Internet. The term refers to the network of physical objects—“things”—that can be embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the Internet. IoT has grown rapidly in recent years due to the decline in the cost of connected sensors and the increase in Internet accessibility. It will continue to affect various sectors, such as home automation, healthcare, agriculture, and industrial automation.
- Big data typically involves collecting large and complex data sets and originates from different sources [24]. Next to the characteristics of high volume and variety in data format, it is also known for its velocity. That is, it is close to real-time collection speed [22]. The amount of data requires significant storage and sophisticated technology to process and gain insights. Two of the main concerns that go hand-in-hand with big data revolve around data security and privacy. Much of the growth in big data is driven by increased data capture across various devices and platforms, including social media interactions, e-commerce transactions, sensors embedded in devices (part of the Internet of Things), and mobile devices. The concept of big data has been evolving with the advancement of technology. As storage capacities have grown and computational power has increased, they can handle vast amounts of data quickly and relatively inexpensively.
- Regarding data storage, cloud computing and its related extensions offer an alternative to local data storage. It has also become particularly relevant with the rise of big data that require management [22]. Users can store and access data remotely through a network of remote servers, typically offered as a service by different providers. Cloud computing has five technical characteristics: large-scale computing resources, high scalability and elastic shared resource pool, dynamic resource scheduling, and general purpose [25]. Its benefits include increased reliability, security, flexibility, and accessibility.
- The field of AI is understood to be mimicking human intelligence [26]. Therefore, the other concepts below, like ML and deep learning, are also considered to be included when talking about AI. A subsection of AI is rule-based AI, which, as the name indicates, runs based on pre-defined rules, usually in the form of if–then statements. It is a comparatively simpler form of AI than the ones below and can only handle limited complexity.
- ML is a subfield of AI based on the idea that computer systems are taught to recognize patterns and make predictions and decisions through various methods and algorithms using data to fit models [3]. Traditionally, these systems were programmed to conduct a specific task; however, with ML, the approach is to train them more broadly, which offers benefits including increased automation, accuracy, and adaptability. ML focuses on building systems that learn from and make data-based decisions. Unlike traditional programming, where humans explicitly code all the rules, machine learning algorithms use statistical techniques to learn patterns in data and make predictions or decisions without being explicitly programmed to perform the task. ML has evolved from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. It powers recommendation systems on platforms like Netflix and Amazon, tailoring content to individual user preferences. It is used extensively in services like Google Photos and voice-activated assistants like Siri and Alexa. ML can also be used to predict diseases from various medical data and personalize treatment. In finance, e.g., it is used for credit scoring, algorithmic trading, and risk management.
- DL (deep learning), in return, is a subset of ML and consequently a narrower approach. The main differences between ML and DL lie in the data type and size they can process and the complexity of the various models. ML typically has a simpler, one-layer structure. At the same time, DL can process multiple layers simultaneously [22] and is thus able to solve more complex problems and be used in image and speech recognition. DL has revolutionized many fields of machine learning through its ability to process large volumes of data and automatically discover the representations needed for detection or classification. DL has become a foundational technology for many modern AI applications, offering substantial improvements over previous techniques in terms of performance and flexibility. Deep learning excels in image recognition, object detection, and video analysis. This capability is used in applications from automated surveillance systems to medical diagnostic tools. DL applied techniques like recurrent neural networks (RNNs) and transformers have improved the ability of machines to understand and generate human language, powering systems like chatbots and translation services. It has powered the language understanding and speech recognition capabilities of virtual assistants like Siri, Alexa, and Google Assistant. DL’s development is closely tied to broader AI and machine learning trends, shaping the future of how intelligent systems are designed and implemented.
- Blockchain is a database or ledger that records and stores data blocks and operates decentrally. When adding a new block containing data to the chain, it is equipped with a timestamp and a hash, i.e., a cryptographic “fingerprint” [8], which is linked to the hash of the previous block and is almost instantaneously shared with the entire network. The blocks cannot be modified unless consent is given by the network, thus rendering the chain tamper-proof. The technology has gained much attention, particularly for its immutable and more transparent, secure, and permanent abilities compared to previously existing methods. Based on what it is combined with, it can be helpful in a wide range of applications and industries, the most prominent ones probably being cryptocurrencies and supply chain management. However, there are concerns around BC, particularly scalability and high energy consumption due to the required computing power. Lastly, its decentralized nature limits oversight and law enforcement, which governments are attempting to address through different approaches.
2.2. Search of Literature
2.3. Analysis
3. Results
3.1. Clarifying the Scope of AI
3.2. The Role of Artificial Intelligence across the Food Value Chain in the Industry Context
3.3. AI Applications throughout the FVC
3.4. Challenges and Drawbacks
3.5. Gaps and Outlook
4. Discussion
Author Contributions
Funding
Informed Consent Statement
Conflicts of Interest
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Ta, M.D.-P.; Wendt, S.; Sigurjonsson, T.O. Applying Artificial Intelligence to Promote Sustainability. Sustainability 2024, 16, 4879. https://doi.org/10.3390/su16124879
Ta MD-P, Wendt S, Sigurjonsson TO. Applying Artificial Intelligence to Promote Sustainability. Sustainability. 2024; 16(12):4879. https://doi.org/10.3390/su16124879
Chicago/Turabian StyleTa, Miriam Du-Phuong, Stefan Wendt, and Throstur Olaf Sigurjonsson. 2024. "Applying Artificial Intelligence to Promote Sustainability" Sustainability 16, no. 12: 4879. https://doi.org/10.3390/su16124879