AI tools increasingly shape how we discover, make and experience music. While these tools can have the potential to empower creativity, they may fundamentally redefine relationships between stakeholders, to the benefit of some and the detriment of others. In this position paper, we argue that these tools will fundamentally reshape our music culture, with profound effects (for better and for worse) on creators, consumers and the commercial enterprises that often connect them. By paying careful attention to emerging Music AI technologies and developments in other creative domains and understanding the implications, people working in this space could decrease the possible negative impacts on the practice, consumption and meaning of music. Given that many of these technologies are already available, there is some urgency in conducting analyses of these technologies now. It is important that people developing and working with these tools address these issues now to help guide their evolution to be equitable and empower creativity. We identify some potential risks and opportunities associated with existing and forthcoming AI tools for music, though more work is needed to identify concrete actions which leverage the opportunities while mitigating risks.
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AI biedt enorme kansen voor webwinkels, maar welke tools zijn écht waardevol? Het initiatief RankMyAI onthult hoe je door de hype navigeert met objectieve data over 25.000+ AI-oplossingen die je e-commerce direct efficiënter, slimmer en winstgevender kunnen maken.
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De Nederlandse AI-sector ontwikkelt zich in razendsnel tempo, met elke maand weer nieuwe tools en bedrijven. Maar waar vinden deze AI-innovaties plaats? Welke sectoren lopen voorop? En welke bedrijven winnen het meeste terrein?
MULTIFILE
We present an evaluation of tools for assessing the impact of AI in the Dutch media sector. Our evaluation of the ECP AIIA tool shows the need for clear guidelines in the adoption of various AI applications within Dutch media organisations. We conclude that the adoption of impact assessment tools, such as the ECP AIIA, is not held back by common media practice, but rather by commercial considerations.
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In deze keynote verken ik de balans tussen menselijke expertise en AI: leidt AI tot cognitieve luiheid? En wat zegt de wetenschap hierover? Hoe zet je AI in zonder je vakmanschap te verliezen? Wat gebeurt er als je te veel uit handen geeft aan tools? En wanneer gebruik je technologie juist om beter te worden in je werk?
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In this paper, we report on the initial results of an explorative study that aims to investigate the occurrence of cognitive biases when designers use generative AI in the ideation phase of a creative design process. When observing current AI models utilised as creative design tools, potential negative impacts on creativity can be identified, namely deepening already existing cognitive biases but also introducing new ones that might not have been present before. Within our study, we analysed the emergence of several cognitive biases and the possible appearance of a negative synergy when designers use generative AI tools in a creative ideation process. Additionally, we identified a new potential bias that emerges from interacting with AI tools, namely prompt bias.
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In my previous post on AI engineering I defined the concepts involved in this new discipline and explained that with the current state of the practice, AI engineers could also be named machine learning (ML) engineers. In this post I would like to 1) define our view on the profession of applied AI engineer and 2) present the toolbox of an AI engineer with tools, methods and techniques to defy the challenges AI engineers typically face. I end this post with a short overview of related work and future directions. Attached to it is an extensive list of references and additional reading material.
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From the article: The ethics guidelines put forward by the AI High Level Expert Group (AI-HLEG) present a list of seven key requirements that Human-centered, trustworthy AI systems should meet. These guidelines are useful for the evaluation of AI systems, but can be complemented by applied methods and tools for the development of trustworthy AI systems in practice. In this position paper we propose a framework for translating the AI-HLEG ethics guidelines into the specific context within which an AI system operates. This approach aligns well with a set of Agile principles commonly employed in software engineering. http://ceur-ws.org/Vol-2659/
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Het doen van onderzoek is een tijdrovend en inspannend proces. Dat is enerzijds heel goed, als we die tijd besteden aan het diep verwerken en nadenken over de onderzoeksinhoud. Maar bij andere aspecten van onderzoek zoals het eindeloos verdwalen in de literatuur of het verwerken van data kunnen we wel wat hulp van AI gebruiken. Gelukkig zijn er allerlei tools die ons hierbij kunnen ondersteunen in de verschillende fases van het onderzoeksproces.
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Recently, the job market for Artificial Intelligence (AI) engineers has exploded. Since the role of AI engineer is relatively new, limited research has been done on the requirements as set by the industry. Moreover, the definition of an AI engineer is less established than for a data scientist or a software engineer. In this study we explore, based on job ads, the requirements from the job market for the position of AI engineer in The Netherlands. We retrieved job ad data between April 2018 and April 2021 from a large job ad database, Jobfeed from TextKernel. The job ads were selected with a process similar to the selection of primary studies in a literature review. We characterize the 367 resulting job ads based on meta-data such as publication date, industry/sector, educational background and job titles. To answer our research questions we have further coded 125 job ads manually. The job tasks of AI engineers are concentrated in five categories: business understanding, data engineering, modeling, software development and operations engineering. Companies ask for AI engineers with different profiles: 1) data science engineer with focus on modeling, 2) AI software engineer with focus on software development , 3) generalist AI engineer with focus on both models and software. Furthermore, we present the tools and technologies mentioned in the selected job ads, and the soft skills. Our research helps to understand the expectations companies have for professionals building AI-enabled systems. Understanding these expectations is crucial both for prospective AI engineers and educational institutions in charge of training those prospective engineers. Our research also helps to better define the profession of AI engineering. We do this by proposing an extended AI engineering life-cycle that includes a business understanding phase.
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