Tag: ai

Why I don’t understand the hype about translation with AI

Artificial Intelligence (AI) in translation is nothing new.

The craft of translation has always been influence by ICT, because it’s all about communication. We were amongst the first who could use fax machines. We were amongst the first who could use pc’s, even if it was only to have an output which looked more neat. And so we were also the first who used pc’s to install databases on them and to be able to use automated dictionaries and spelling checkers.

One of the first benefits I like about translating using a computer, was that we could preserve the layout of the original document. The days of numbering blocks of text to make clear which part of the translation was related to which part of the original text, were gone. The client finally got a document which he could incorporate in his work flow and product chain without much ado.

When computer memories became big and fast enough to store source and target of previous translations, we could start reaping the benefits of our previous work, by automating what we had already been done for decades: found translations of reviewed documents on the translations of the original documents. But this time we didn’t have to plough through huge stacks of material and type it over. The computer did part of the work for us, and we could deliver faster a better result.

That last evolution was the introduction of computer aided translation, or CAT. Once those programs were used, it was simply a matter of time before we could implement logic to compare source material with target material of various kinds, so that the program could support us to amend the target.

Inevitable that lend to an increasing intelligence of the programs, and finally to what we call machine translation, or MT.

That’s why I don’t understand the hype about translating with AI. As a matter of fact MT IS translating with AI. MT systems often don’t use the traditional CAT tools as their fundamental work system, and traditional CAT tools have implemented some MT elements to make it possible to use MT to prepare for the definitive work by the translation to speed up the work and at the same time to avoid mistakes by the automated systems.

MT doesn’t work without MTPE: machine translation post editing.  The translator does the post editing of the texts produced by MT. That is the way translation with AI works. The only difference I see between translation with AI and MTPE is the terminology: it are two different expressions to mention the same thing. Therefore the huge hype about AI in the field of translation is nothing more than a shift in providers: some say they use AI to draw clients, but they’re doing the same thing that the translation business has been doing for several years now. AI in translation is more about marketing and window dressing than about offering a new, more advanced way of working.

But Can AI be Creative

AI can be creative in certain ways, though its approach to creativity is very different from human creativity. When we talk about creativity in AI, it often comes down to how well an AI can generate new ideas, solve problems in novel ways, or produce art, music, or writing that feels fresh or innovative. However, its creative output is typically bound by the data and patterns it has been trained on, as well as the algorithms that guide its process. Here’s a closer look at what AI creativity involves:

1. Generative Creativity

  • Examples: AI-generated art, music, and writing are among the most visible examples of generative creativity. Models like DALL-E, GPT, and others use vast datasets of images, text, and audio to create something new by combining and transforming existing patterns in unique ways.
  • Mechanism: Generative models use sophisticated algorithms to synthesize data, but they’re not truly creating from scratch or from personal inspiration; instead, they combine elements learned from training data. So, while the output may look creative to us, it’s a reconfiguration of prior data rather than an entirely novel concept.

2. Problem-Solving and Innovation

  • Examples: AI algorithms can help discover new drugs, suggest innovative materials, or optimize systems in ways that weren’t obvious to humans. AI excels at exploring many possibilities at once, especially in structured fields like mathematics, physics, and chemistry.
  • Mechanism: Using methods like reinforcement learning or evolutionary algorithms, AI can “explore” solutions by trial and error or adapt previous successful strategies to new challenges. In many cases, the solutions AI finds can surprise or even inspire human researchers, as they may break with traditional thinking or assumptions.

3. Augmenting Human Creativity

  • Examples: AI can serve as a collaborator in creative fields, helping artists, musicians, and writers experiment with new ideas, overcome creative blocks, or find unexpected directions. For instance, musicians might use AI to generate a melody based on a given style or genre.
  • Mechanism: In this role, AI acts more like a tool that expands the human creative process, giving artists and creators a starting point, reference, or unexpected twist. The final creative control and decision-making, however, remain with the human.

4. Limitations in True Creativity

  • Context and Intent: Human creativity is driven by emotions, experiences, and intentions, all of which give depth and meaning to creative work. AI doesn’t experience these things; it operates without consciousness, intuition, or intent.
  • Originality: While AI can generate outputs that look original, it isn’t creating from a true sense of originality; it’s generating based on patterns and probabilities in its training data. Even when it makes surprising connections, they lack the subjective motivation that often drives human creativity.

In Summary

AI can generate creative outputs by recombining existing elements in innovative ways, and it can even inspire or collaborate with humans. However, because it lacks true consciousness, personal experience, and emotional depth, AI’s creativity differs from human creativity in significant ways

Thanks to https://divvyhq.com/content-automation/the-ultimate-ai-test-how-creative-can-ai-get-in-content-creation/

The Creativity of AI

The creativity of AI is both fascinating and transformative, especially as we begin to see machines take on tasks that were traditionally exclusive to human imagination and ingenuity. AI’s approach to creativity is rooted in its ability to process vast datasets, recognize patterns, and generate new combinations of ideas, images, or words. Unlike human creativity, which draws from personal experience, intuition, and often an emotional or cultural connection, AI creativity is based on algorithms, probabilities, and learned data structures. But as it evolves, AI is beginning to exhibit behaviors that are eerily reminiscent of human creativity, inspiring a new dialogue about what it means to “create.”

For example, generative models like GPT-4 and DALL-E (which creates images from text prompts) showcase AI’s ability to produce art, music, literature, and design concepts. These systems don’t “feel” creativity as humans do, but they mimic it through a process called “generative synthesis,” blending learned elements to produce something original within a given framework. This approach has led to surprising, sometimes profound results, such as AI-written novels, original paintings, and new styles of music.

One powerful aspect of AI creativity is its capacity for collaboration. Human artists, writers, and scientists increasingly use AI as a partner, integrating its suggestions or novel outputs into their own work. In these hybrid environments, AI offers humans fresh ideas and perspectives that may not arise from a purely human-centered creative process. This symbiosis—where AI can suggest unanticipated plot twists in a novel or offer design combinations never before considered—hints at a future where creativity is shared between human and machine.

Still, AI’s creativity raises ethical and philosophical questions. Can an algorithmically generated piece be truly considered art? Who owns the creative rights? And what happens when AI begins to mimic human creativity so closely that it’s hard to tell the difference? These questions challenge our traditional notions of authorship, originality, and even the purpose of creativity itself.

As AI continues to develop, its role in creative fields could fundamentally change the landscape of human culture. Whether it remains a tool or grows into something closer to a collaborator, AI’s creativity will no doubt drive innovation and broaden our understanding of what creativity can be.

Artificial Intelligence and translation

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There are two things which shouldn’t be forgotten about AI in general:
First it relies heavily on human input. E.g. the Tesla AI Autopilot is based on a massive amount of pictures on which objects are lined out, so that the software can compare those line-outs with images captured by the LIDAR (which is just some kind of visible light camera system). Without all those people analyzing the photographs, that Autopilot would not function. In other words: without the back-up of human intelligence, that artificial intelligence wouldn’t work.
Secondly: the input is analyzed by statistical means, not be actually intelligent systems in the way we understand ‘intelligence’. That means AI prefers frequent occurences of phenomena, not necessarily correct occurences of phenomena, i.e. not necessarily occurences which are the results of laws of nature.
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