This article aims to reveal the current role of generative artificial intelligence in visual communication design through current examples. The study used a qualitative research method and examined printed and electronic resources. In line with current developments in artificial intelligence, some changes have occurred in visual communication design, as in many design fields. Visual communication design is a wide field of study in the analytical problem-solving process, communication, and aesthetic values. In this context, it is closely related to technology, like many other fields. The rapid acceleration of artificial intelligence has reached the power to affect the sub-fields of visual communication design directly. From illustration to packaging, poster, and logo design, productive artificial intelligence produces work in every field. The role of artificial intelligence in visual communication design also affects designers, and the designer's role is also changing and transforming. Weak artificial intelligence-supported programs require human skills (creativity, intelligence, talent, etc.) and a certain amount of learning processes. In line with today's artificial intelligence developments, only visual or audiovisual elements can be created without the need to learn any program or pay a fee. In addition, 3D models and moving images, which require a long working process, have begun to be created with artificial intelligence. All these developments have changed the role of designers in this field and have brought with them some concerns. Will artificial intelligence take our jobs? Concerns such as speed, low cost, unlimited work, content creation, and visual communication design are also included. Artificial intelligence, which has such abilities, has some features that make it different from humans. However, creativity and creating creative content are still controversial issues. The designer, who produces passive programs on computers, is now faced with an artificial intelligence that produces products close to human limits. This directly affects basic design skills, especially technical processes, and supports revealing the creative and analytical problem-solving side of people. Computational Creativity is a subfield of AI research that largely overlaps with other fields/creative industries. Based on computer science covers fields associated with creativity, such as poetry, storytelling, musical composition and performance, video games, cinema, photography, architecture, industrial and graphic design, and even culinary arts. Another definition of the field is the science of computational systems that undertake specific responsibilities and exhibit behavior that impartial observers would consider creative. Computational creativity studies help us understand human creativity and explore the scheme by which software acts as a creative collaborator rather than a mere tool. Historically, both society and the field of computer science have exhibited skeptical approaches to the creative potential of software.
The main criticism of computational creativity is that the techniques simulate human thought, especially creative thought. The expression of assuming specific responsibilities in computational creativity emphasizes the difference between weak and strong artificial intelligence. Creative responsibilities attributed to a computational system; Develop and use aesthetic criteria to evaluate the works produced; It is like inventing new processes to produce new materials. Deep learning models can be used to evaluate the effectiveness of visual communication. For example, it can suggest what colors, layouts, or symbols to use in user interface design. These points illustrate the potential applications and impacts of deep learning in visual design. The role of deep learning techniques in visual design has great potential to optimize design processes and offer new creative possibilities. Generative artificial intelligence, a part of deep learning, refers to artificial intelligence systems with human-like creativity and productivity abilities. These systems can generally create human-like works in art, music, writing, and design. Productive artificial intelligence systems can produce new content by learning from large data sets and using this learning, such as composing music, painting, writing poetry, and creating stories. Many of the works done by artificial intelligence are derived from other works. Today, in text-based data visualization applications, it is possible to produce artificial intelligence-like works by typing the name of the designer or project you want in the prompt field. While this situation creates risks in creativity and originality, it also causes ethical problems. Generative artificial intelligence, a part of machine learning, has found a place in a wide range of fields of study today. One of these is visual communication design, which is examined within the scope of the article. Some results were obtained within the scope of the application project carried out within the scope of the research.
First of all, generative artificial intelligence helps the designer in technical processes. Since it is fast, it can inspire finding ideas by offering many alternative studies in seconds. However, especially in the label design application, the work needed to comply with the brief given. In some studies, there were distortions in typography; in others, visualizations far from creative ideas were made. Productive artificial intelligence applications did not meet the work brief given within the scope of the study. Since the creative design process involves an idea and analytical problem solving, the works offered by the applications are far from creative ideas. The designer's role is still essential in visual communication design products. As a result of the article, it has been concluded that generative artificial intelligence makes rapid progress in visual communication design technical processes and paves the way for risks.
Yapay zekâ alanındaki güncel gelişmeler doğrultusunda birçok tasarım alanında olduğu gibi görsel iletişim tasarımında da bazı değişimler meydana gelmiştir. Görsel iletişim tasarımı analitik problem çözüm süreci, iletişim ve estetik değerler çerçevesinde geniş çalışma alanına sahiptir. Bu bağlamda birçok alan gibi teknolojiyle yakın ilişki içerisinde olduğu açıktır. Yapay zekânın hızla ivme kazanması özellikle görsel iletişim tasarımının alt alanlarını doğrudan etkileme gücüne ulaşmıştır. İllüstrasyondan, ambalaj tasarımına, afiş tasarımında, logo tasarımına kadar üretken yapay zekâ her alanda çalışmalar ortaya koymaktadır. Yapay zekânın görsel iletişim tasarımındaki rolü tasarımcıları da etkilemekte tasarımcının rolü de değişip dönüşmektedir. Zayıf yapay zekâ destekli programlar insan becerilerine (yaratıcılık, zekâ, yetenek vb.) ve belirli oranda öğrenme süreçlerine ihtiyaç duymaktadır. Günümüzdeki yapay zekâ gelişmeleri doğrultusunda herhangi bir program öğrenme yetisine ya da ücret ödemeye ihtiyaç duymadan sadece görsel ya da görsel-işitsel ögeler oluşturulabilmektedir. Bunun yanı sıra uzun çalışma süreci gerektiren 3 boyutlu modeller ve hareketli görüntüler yapay zekâ ile oluşturulmaya başlanmıştır. Tüm bu gelişmeler tasarımcının bu alandaki rolünü değiştirmekte ve birtakım endişeleri de beraberinde getirmektedir. Yapay zekâ işimizi elimizden alır mı? gibi endişelere görsel iletişim tasarımı da dahildir. Hız, düşük maliyet, sınırsız çalışma ve içerik oluşturma vb. gibi yetilere sahip olan yapay zekâ insanlardan ayrılmasını sağlayan bazı özelliklere sahiptir. Fakat yaratıcılık, yaratıcı içerik oluşturma halen tartışılan bir konudur. Bilgisayarlardaki pasif programlarla üretim gerçekleştiren tasarımcı artık insan sınırlarına yakın üretimler gerçekleştiren yapay bir zekâyla karşı karşıyadır. Bu durum temel tasarım becerilerini özellikle teknik süreçleri doğrudan etkileyerek insanın yaratıcı ve analitik problem çözen yanını ortaya çıkarmayı destekler niteliktedir.
Bu makale üretken yapay zekânın görsel iletişim tasarımındaki rolünü güncel örnekler üzerinden ortaya koymayı amaçlamaktadır. Çalışmada nitel araştırma yöntemi kullanılmış basılı ve elektronik kaynaklar incelenmiştir. Makalenin sonucunda üretken yapay zekânın görsel iletişim tasarımında teknik süreçlerde hızla ilerleme kaydetmesinin yanı sıra risklere de zemin hazırladığına ulaşılmıştır.
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