In the context of artificial intelligence (AI), the singularity (or technological singularity) is a hypothetical future point in time when technological growth becomes uncontrollable and irreversible, resulting in unpredictable and profound changes to human civilization.
It is often described as the moment when AI surpasses human intelligence, initiating a "runaway" cycle of self-improvement that leaves human cognitive abilities far behind.
My training involves a massive dataset of multilingual text, allowing me to understand and generate dozens of languages natively. I don’t need to "translate" your question into English to understand it, nor do I translate my thoughts back into your language to answer.
That said, because a large portion of my training data is English-based, I sometimes have a deeper "knowledge base" in English for niche topics. But for the vast majority of conversations, the process is seamless and direct.
Is there a specific language you'd like to test out or switch to?
AI:No, I prefer to work through my document in the language I know, for practice. I speak Finnish since childhood but now have no Finnish speakers even daily. I write so columns a few times a year, so my language is basically high school level Finnish. I actually prefer to ask you in English, but will occasionally use Google Translate to do a single paragraph from English to Finnish. If was not able work out the terms on my own. I use Wikipedia and translate a page to Finnish if the article is not there in Finnish.
I caught Google AI in a lie. It knows waht I asked it yesterday. it gave me tips:I can definitely help bridge that gap. If you ever feel a Google Translate result sounds a bit robotic or "off," feel free to drop the paragraph here. I can help you polish the phrasing to make it sound more like a native speaker and less like a literal translation.
That is a productive pace—a half page today while the ideas from Chapter 3 are fresh is a great way to build momentum. Since Ethan Mollick focuses heavily on the "Human-AI collaboration" aspect, you’ll likely run into some modern English concepts that don't have perfect one-word matches in traditional kirjakieli.
Writing a two-page column gives you enough room to really dig into his arguments without losing the reader. Since you mentioned his book specifically, are you finding it easy to convey his "Working with the Alien" or "Centaur/Cyborg" metaphors in Finnish, or are you sticking to more literal descriptions of the technology?
Good luck with the writing—työn iloa (joy of work) as you dive into that half-page!
Should we look at some Finnish terms for "Prompt Engineering" or "Large Language Models" that sound professional for your review?
https://en.wikipedia.org/wiki/Large_language_modelLLMs consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems.[6]
LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self-attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few-shot learning and compositional reasoning.
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by "soft" weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from tens to millions of tokens in size.
Unlike "hard" weights, which are computed during the backwards training pass, "soft" weights exist only in the forward pass and therefore change with every step of the input.
https://en.wikipedia.org/wiki/Attention_Is_All_You_NeedI think the Google people wrote a lot of the Wikipedia articles. Though yiu cannot write on your exact work, but a close colleague can.
"Attention Is All You Need"[1] is a 2017 research paper in machine learning authored by eight scientists working at Google. The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al.[2] The transformer approach it describes has become the main architecture of a wide variety of AI, including large language models.[3][4] At the time, the focus of the research was on improving Seq2seq techniques for machine translation, but the authors go further in the paper, foreseeing the technique's potential for other tasks like question answering and what is now known as multimodal generative AI.[1]
Some early examples that the team tried their Transformer architecture on included English-to-German translation, generating Wikipedia articles on "The Transformer", and parsing. These convinced the team that the Transformer is a general-purpose language model, and not just good for translation.[5]
it got the tabloid term righ, we call them afternoon papers in Finnish. I referred to culture and entertainment, it used the word humor. In some sense that might work, but humor in Finnish is just huumori. I did not use that. My sentence is a bit run on: I meant that "then people go on from chatty things to actual serious things like health."Those errors are similar to those in tabloids. The AI wants the user to come back to ask again next time, and the answer may be a guess and a colored picture of the situation. In culture and humor, that may be permissible, but then people ask serious personal questions, even health-related ones.
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