No matter which news portal you look at: ChatGPT and the artificial intelligence behind it are THE topic of the hour right now. Other AI tools, such as DALL-E, Whisper or Midjourney, are also attracting a lot of attention. So it’s high time to think about the extent to which this technology has what it takes to also support online print shops in their daily business.
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In the third and (for the time being) last part of our chatbot series, we demonstrate how the bots can be used within a company. In the first two parts of our series on the chatbot trend, we looked at the AI app, Replika, and specific use cases in customer service. As service employees or emotional support, chatbots can be in use around the clock – as our first two articles on the subject have already shown.
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A lack of trust in the methods of artificial intelligence has long been considered an obstacle in the relationship between humans and machines. However, research shows that the problem is rather that we tend to exploit AI. Let’s start with the good news: Basically, the relationship between humans and machines is trusting.
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A chat with an artificial intelligence – a gimmick or accessible machine learning? Our author wants to know whether the app “Replika” really is a virtual friend as advertised or just a chatbot. In a self-experiment, he puts the AI app to the test. Influenced by my research into AI methods, I came across a report about the app, Replika. Replika is an instant messaging app that lets you chat 24/7 supposedly with an AI.
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The current episode of the jabberfish podcast is dedicated to looking into the crystal ball: Where is the future of artificial intelligence (AI) heading? At the suggestion of Ingo Eichel (Adobe), Bernd Zipper and Haeme Ulrich discuss the developments at the intersection of real AI and machine learning.
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The new text generator GPT-3 (Generative Pre-trained Transformer) can write texts in the style of Shakespeare, create program codes, and translate foreign languages and legal paragraphs. Is this method the answer to all questions or does it end up as a tragedy because it does not find a suitable method among the 175 billion machine learning parameters?
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