File Name: artificial intelligence in computer science teaching and research .zip
- The roles of models in Artificial Intelligence and Education research: a prospective view
- Bachelor in Computer Science and Artificial Intelligence
- Exploring the impact of artificial intelligence on teaching and learning in higher education
Artificial intelligence AI is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life.
Artificial intelligence AI is intelligence demonstrated by machines , unlike the natural intelligence displayed by humans and animals , which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. Leading AI textbooks define the field as the study of " intelligent agents ": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
The roles of models in Artificial Intelligence and Education research: a prospective view
Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development.
Failure to do so could result in gaps in transparency, safety, and ethical standards. The emergence of artificial intelligence AI is shaping an increasing range of sectors.
For instance, AI is expected to affect global productivity 1 , equality and inclusion 2 , environmental outcomes 3 , and several other areas, both in the short and long term 4. Reported potential impacts of AI indicate both positive 5 and negative 6 impacts on sustainable development. However, to date, there is no published study systematically assessing the extent to which AI might impact all aspects of sustainable development—defined in this study as the 17 Sustainable Development Goals SDGs and targets internationally agreed in the Agenda for Sustainable Development 7.
Here we present and discuss implications of how AI can either enable or inhibit the delivery of all 17 goals and targets recognized in the Agenda for Sustainable Development. Relationships were characterized by the methods reported at the end of this study, which can be summarized as a consensus-based expert elicitation process, informed by previous studies aimed at mapping SDGs interlinkages 8 , 9 , A summary of the results is given in Fig.
Although there is no internationally agreed definition of AI, for this study we considered as AI any software technology with at least one of the following capabilities: perception—including audio, visual, textual, and tactile e.
This view encompasses a large variety of subfields, including machine learning. Documented evidence of the potential of AI acting as a an enabler or b an inhibitor on each of the SDGs.
The percentages on the top indicate the proportion of all targets potentially affected by AI and the ones in the inner circle of the figure correspond to proportions within each SDG.
The results corresponding to the three main groups, namely Society, Economy, and Environment, are also shown in the outer circle of the figure. The results obtained when the type of evidence is taken into account are shown by the inner shaded area and the values in brackets. For the purpose of this study, we divide the SDGs into three categories, according to the three pillars of sustainable development, namely Society, Economy, and Environment 11 , 12 see the Methods section.
This classification allows us to provide an overview of the general areas of influence of AI. In Fig. A detailed assessment of the Society, Economy, and Environment groups, together with illustrative examples, are discussed next. For instance, in SDG 1 on no poverty, SDG 4 on quality education, SDG 6 on clean water and sanitation, SDG 7 on affordable and clean energy, and SDG 11 on sustainable cities, AI may act as an enabler for all the targets by supporting the provision of food, health, water, and energy services to the population.
It can also underpin low-carbon systems, for instance, by supporting the creation of circular economies and smart cities that efficiently use their resources 13 , AI can also help to integrate variable renewables by enabling smart grids that partially match electrical demand to times when the sun is shining and the wind is blowing However, their consideration is crucial.
Many of these relate to how the technological improvements enabled by AI may be implemented in countries with different cultural values and wealth. Advanced AI technology, research, and product design may require massive computational resources only available through large computing centers.
These facilities have a very high energy requirement and carbon footprint Green growth of ICT technology is therefore essential More efficient cooling systems for data centers, broader energy efficiency, and renewable-energy usage in ICTs will all play a role in containing the electricity demand growth In addition to more efficient and renewable-energy-based data centers, it is essential to embed human knowledge in the development of AI models. Besides the fact that the human brain consumes much less energy than what is used to train AI models, the available knowledge introduced in the model see, for instance, physics-informed deep learning 18 does not need to be learnt through data-intensive training, a fact that may significantly reduce the associated energy consumption.
Although AI-enabled technology can act as a catalyst to achieve the Agenda, it may also trigger inequalities that may act as inhibitors on SDGs 1, 4, and 5. This duality is reflected in target 1. On the other hand, it may also lead to additional qualification requirements for any job, consequently increasing the inherent inequalities 19 and acting as an inhibitor towards the achievement of this target. For targets highlighted in green or orange, we found published evidence that AI could potentially enable or inhibit such target, respectively.
The absence of highlighting indicates the absence of identified evidence. It is noteworthy that this does not necessarily imply the absence of a relationship.
Another important drawback of AI-based developments is that they are traditionally based on the needs and values of nations in which AI is being developed. If AI technology and big data are used in regions where ethical scrutiny, transparency, and democratic control are lacking, AI might enable nationalism, hate towards minorities, and bias election outcomes AI has been recently utilized to develop citizen scores, which are used to control social behavior This type of score is a clear example of threat to human rights due to AI misuse and one of its biggest problems is the lack of information received by the citizens on the type of analyzed data and the consequences this may have on their lives.
It is also important to note that AI technology is unevenly distributed: for instance, complex AI-enhanced agricultural equipment may not be accessible to small farmers and thus produce an increased gap with respect to larger producers in more developed economies 23 , consequently inhibiting the achievement of some targets of SDG 2 on zero hunger.
There is another important shortcoming of AI in the context of SDG 5 on gender equality: there is insufficient research assessing the potential impact of technologies such as smart algorithms, image recognition, or reinforced learning on discrimination against women and minorities. For instance, machine-learning algorithms uncritically trained on regular news articles will inadvertently learn and reproduce the societal biases against women and girls, which are embedded in current languages.
Word embeddings, a popular technique in natural language processing, have been found to exacerbate existing gender stereotypes 2.
In addition to the lack of diversity in datasets, another main issue is the lack of gender, racial, and ethnic diversity in the AI workforce Diversity is one of the main principles supporting innovation and societal resilience, which will become essential in a society exposed to changes associated to AI development Societal resilience is also promoted by decentralization, i. The technological advantages provided by AI may also have a positive impact on the achievement of a number of SDGs within the Economy group.
Although Acemoglu and Restrepo 1 report a net positive impact of AI-enabled technologies associated to increased productivity, the literature also reflects potential negative impacts mainly related to increased inequalities 26 , 27 , 28 , In the context of the Economy group of SDGs, if future markets rely heavily on data analysis and these resources are not equally available in low- and middle- income countries, the economical gap may be significantly increased due to the newly introduced inequalities 30 , 31 significantly impacting SDGs 8 decent work and economic growth , 9 industry, innovation and infrastructure , and 10 reduced inequalities.
Moreover, automation shifts corporate income to those who own companies from those who work there. The interpretation of the blocks and colors is as in Fig. The content of of this figure has not been reviewed by the United Nations and does not reflect its views. Although the identified linkages in the Economy group are mainly positive, trade-offs cannot be neglected.
For instance, AI can have a negative effect on social media usage, by showing users content specifically suited to their preconceived ideas.
This may lead to political polarization 33 and affect social cohesion 21 with consequences in the context of SDG 10 on reduced inequalities. However, there is an underlying risk when using AI to evaluate and predict human behavior, which is the inherent bias in the data.
It has been reported that a number of discriminatory challenges are faced in the automated targeting of online job advertising using AI 35 , essentially related to the previous biases in selection processes conducted by human recruiters. The work by Dalenberg 35 highlights the need of modifying the data preparation process and explicitly adapting the AI-based algorithms used for selection processes to avoid such biases.
The last group of SDGs, i. Benefits from AI could be derived by the possibility of analyzing large-scale interconnected databases to develop joint actions aimed at preserving the environment. Looking at SDG 13 on climate action, there is evidence that AI advances will support the understanding of climate change and the modeling of its possible impacts.
Furthermore, AI will support low-carbon energy systems with high integration of renewable energy and energy efficiency, which are all needed to address climate change 13 , 36 , AI can also be used to help improve the health of ecosystems. The achievement of target Another example is target According to Mohamadi et al. These AI techniques can help to identify desertification trends over large areas, information that is relevant for environmental planning, decision-making, and management to avoid further desertification, or help reverse trends by identifying the major drivers.
However, as pointed out above, efforts to achieve SDG 13 on climate action could be undermined by the high-energy needs for AI applications, especially if non carbon-neutral energy sources are used. Furthermore, despite the many examples of how AI is increasingly applied to improve biodiversity monitoring and conservation 40 , it can be conjectured that an increased access to AI-related information of ecosystems may drive over-exploitation of resources, although such misuse has so far not been sufficiently documented.
A deeper analysis of the gathered evidence was undertaken as shown in Fig. In practice, each interlinkage was weighted based on the applicability and appropriateness of each of the references to assess a specific interlinkage—and possibly identify research gaps. This can be partly due the fact that AI research typically involves quantitative methods that would bias the results towards the positive effects.
However, there are some differences across the Society, Economy and Environment spheres. In the Society sphere, when weighting the appropriateness of evidence, positively affected targets diminish by 5 percentage points p. In the Economy group instead, positive impacts are reduced more 15 p. This can be related to the extensive study in literature assessing the displacement of jobs due to AI because of clear policy and societal concerns , but overall the longer-term benefits of AI on the economy are perhaps not so extensively characterized by currently available methods.
Finally, although the weighting of evidence decreases the positive impacts of AI on the Environment group only by 8 p. This is explained by the fact that, although there are some indications of the potential negative impact of AI on this SDG, there is no strong evidence in any of the targets supporting this claim, and therefore this is a relevant area for future research.
In general, the fact that the evidence on interlinkages between AI and the large majority of targets is not based on tailored analyses and tools to refer to that particular issue provides a strong rationale to address a number of research gaps, which are identified and listed in the section below.
A crucial research venue for a safe integration of AI is understanding catastrophes, which can be enabled by a systemic fault in AI technology. It is therefore very important to raise awareness on the risks associated to possible failures of AI systems in a society progressively more dependent on this technology. Furthermore, although we were able to find numerous studies suggesting that AI can potentially serve as an enabler for many SDG targets and indicators, a significant fraction of these studies have been conducted in controlled laboratory environments, based on limited datasets or using prototypes 45 , 46 , Hence, extrapolating this information to evaluate the real-world effects often remains a challenge.
This is particularly true when measuring the impact of AI across broader scales, both temporally and spatially. We acknowledge that conducting controlled experimental trials for evaluating real-world impacts of AI can result in depicting a snapshot situation, where AI tools are tailored towards that specific environment. However, as society is constantly changing also due to factors including non-AI-based technological advances , the requirements set for AI are changing as well, resulting in a feedback loop with interactions between society and AI.
Another underemphasized aspect in existing literature is the resilience of the society towards AI-enabled changes. Therefore, novel methodologies are required to ensure that the impact of new technologies are assessed from the points of view of efficiency, ethics, and sustainability, prior to launching large-scale AI deployments.
In this sense, research aimed at obtaining insight on the reasons for failure of AI systems, introducing combined human—machine analysis tools 48 , are an essential step towards accountable AI technology, given the large risk associated to such a failure.
Although we found more published evidence of AI serving as an enabler than as an inhibitor on the SDGs, there are at least two important aspects that should be considered. First, self-interest can be expected to bias the AI research community and industry towards publishing positive results.
Bachelor in Computer Science and Artificial Intelligence
I work on algorithmic statistics and machine learning. Many but not all of my papers are available on arXiv. Cross-lingual text classification with minimal resources by transferring a sparse teacher Giannis Karamanolakis, Daniel Hsu, Luis Gravano. Preprint, D, , Dec Wing, Daniel Hsu. Classification vs regression in overparameterized regimes: Does the loss function matter?
Exploring the impact of artificial intelligence on teaching and learning in higher education
Metrics details. This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve. Recent technological advancements and the increasing speed of adopting new technologies in higher education are explored in order to predict the future nature of higher education in a world where artificial intelligence is part of the fabric of our universities.
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Он попытался что-то сказать, но Сьюзан была полна решимости. Ей хотелось поскорее оказаться в Третьем узле, и она достаточно хорошо изучила своего шефа, чтобы знать: Стратмор никуда не уйдет, пока она не разыщет ключ, спрятанный где-то в компьютере Хейла. Ей почти удалось проскользнуть внутрь, и теперь она изо всех сил пыталась удержать стремившиеся захлопнуться створки, но на мгновение выпустила их из рук. Створки стали стремительно сближаться. Стратмор попытался их удержать, но не сумел. За мгновение до того, как они сомкнулись, Сьюзан, потеряв равновесие, упала на пол за дверью. Коммандер, пытаясь приоткрыть дверь, прижал лицо вплотную к узенькой щелке.
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Спустились сумерки - самое романтическое время суток. Он подумал о Сьюзан.