Businesses have discovered that generative AI models are disruptive in marketing, entertainment, and communication. These models can create poetry and artwork using text and images. They employ detailed machine learning models that predict the next image based on what is learned from earlier images. Here we analyze its pros and cons-
Definition of Generative AI:
It can create texts and images like blog posts, program codes, poems, and artworks. These complex machine learning models predict the next word from those that came before or even the next image depending on an earlier word referring to some other pictures. Initially used for consequent word translations in context, LLMs began at Google Brain in 2017. Since then, leading tech companies like Google (BERT and LaMDA) and Facebook (OPT-175B, BlenderBot) have adopted large language and text-to-image models. OpenAI, a non-profit corporation invested mostly by Microsoft has also come up with GPT-3 for text messages, DALL-E2 for pictures, and Whisper for spoken messages as well. Caritative firms such as Midjourney which assisted in winning the art competition as well as open-source generators like HuggingFace have developed generative structures.
Pros of AI in image creation:
1. Marketing: Marketing is by far their most predominant application. AI in Image-generating tools like DALL-E 2 are being utilized in advertisements. One example is when Heinz used an image of a ketchup bottle branded similarly to hers to assert “This is what AI refers to as ‘Ketchup’”. Obviously, it only indicated that the model was based on quite a huge number of photos of Heinz ketchup bottles. To support one of its yoghurts brand Nestle employed an AI-enhanced replica of a Vermeer painting.
2. Chatbots: LLMs are being used at the core of conversational AI or chatbots. They potentially offer greater levels of understanding of conversation and context awareness than current conversational technologies. Facebook designed BlenderBot for dialogue, enabling it to carry on long conversations with humans while maintaining context. Google uses BERT to understand search queries. Google’s LaMDA, another LLM, was also designed for dialogue and conversations, predicting words used in conversation based on past interactions.
3. Fraud Detection: Artificial Intelligence does not only collaborate with artists in generating novel works and sparking new ideas, but it can also help bust fraud and detect art forgeries that pollute the art world. In 2021, a pioneering AI system created by a Swiss company called Art Recognition made headlines by authenticating a disputed artwork, attributing it to Peter Paul Reubens, and suggesting that the painting Samson and Delilah in London’s National Gallery is not by Rubens.
Cons of Generative AI:
1. Deepfake: Deepfakes, where artificial intelligence (AI) creates images and videos that appear real but are not, have impacted media, politics, and entertainment. Until now generating deep fakes required huge technical capacity. Almost everyone would be able to produce them.
2. Ownership: Ownership along property lines with regard to originality in creative works is questionable. These systems claim their job is maintaining individuality which is true but it can be seen as something from preceding textual information together with other captured images.
Without a doubt, the development of these skills would have dramatic consequences for copyright and intellectual property protection but they will probably also change knowledge and creative work. Hence, we need to be considerate when using it.
