$219 Springer Nature book "Mastering Machine Learning: From Basics to Advanced" was written with a chatbot
Springer Nature book on machine learning is full of made-up citations
Academic publishers are parasites.
I have been working through a textbook this week that has a copyleft statement on every page, and was written by a government scientist who did not get paid to do it. When you access it through the publisher website there is a copyright and they're charging $200 for access.
Replacing the human expert with a word-guessing machine is a logical progression in their unabashed rent seeking.
In my experience the publisher Manning Books earns their cut. They’ve done interesting books, allow early access and update books online, provide digital copies if you buy the physical, etc.
Isn't that illegal?
The content (i.e. text, tables, images, etc. ) of the book is under copyleft, while the only thing the publisher can argue that's theirs is the design (cover, font, copyright claim text, etc.) There are things like page layout and stuff that may've been created by the author or the publishers so it's in a grey area.
All in all, I think scanning the book and OCRing it, removing stuff like page numbers and those first few pages of junk would remove all "infringing" elements.
Or, as always, you can email tye author and they're 99% sure to give you their manuscript directly if they didn't publish it somewhere else already.
Doesn't that mean there's no ownership and it can be freely taken?
Unless you are a major corporation... you are not free to take anything.
Okay so hear me out on this. The book mentioned in this article is definitely a trash, the author used LLM without informing readers, which is why most people feel the are being scammed and express feelings of frustration and hate.
I personally have deployed LLMs on my local machines and used them for variety of things such as Summarize news and Articles, Coding, Image Generation, etc and I have to be honest it is really really impressive technology. Any author who takes assistance from LLM would be hyper-productive compared to someone who does all the labour themselves. I used to take hours to read a broad area of knowledge and then deep dive in intrested topics. When LLMs generate summary and you can decide weather to read the source yourself or not is a big time saver and productivity boost. Of course this can be abused by someone who trusts LLMs too much and don't again verifies what they read, it can give false information but that's not how they are supposed to be used.
These language models are really good at creating summaries. I use a locally deployed LLM to read summaries of Articles and then if I feel interested I read the entire article end to end from original source. In Almost every case the summary is spot on without it missing any important points or topics, heck I have created system prompt so that it tries to give hot takes and nuanced perspectives from the article and it impresses me sometimes giving me a new perspective which I would have not thought otherwise.
I am convinced that using LLM along with your own knowledge always surpasses the quality of your work if someone with your same capability generates work without taking assistance of a LLM.
Didn't have time to read that, so I threw your comment into my local TinyLlama instance:
The article discusses an artificial intelligence assistant named "AiLlMa," which is used for summarizing news and articles in various languages. The author suggests that using such AI assistants, including LLMs, can save time by taking away the need to manually search and read source material. He argues that LLMs generate summaries that are spot-on without missing important points or topics, and that using them along with personal knowledge gives a distinct advantage over independent reading of sources. The author uses examples of LLMs generating useful summaries from articles on various topics, such as summarizing news headlines for readers and creating nuanced takes on articles based on contextual information. Overall, the article suggests that using AI assistants like LLMs can improve productivity, boost efficiency, and produce more accurate summaries.
Didn't have time to read that, so I threw your comment into ChatGPT:
Threw it into TinyLlama—LLMs like AiLlMa save time, summarize accurately, and boost productivity better than reading sources solo.
Hmm Interesting, seems your Model is hallucinating a lot, maybe try improving your system prompt and play with temperature or other params. I have a deepseek-ai_DeepSeek-R1-Distill-Qwen-1.5B Running locally. Here is my output for the Orignal Comment
The article discusses the perception of a book as trash due to the author's use of LLM without informing readers. The author highlights the benefits of using LLMs, such as saving time and improving productivity, by suggesting that using locally deployed LLMs can significantly enhance the summarization process. The article also warns against the misuse of LLMs, emphasizing the importance of verifying information. The author concludes that relying on LLMs can surpass human capabilities when combined with personal knowledge. Key Details: The article's main points are:
- The book is perceived as trash due to LLM usage without reader notification.
- LLMs save time and improve productivity.
- Using locally deployed LLMs for summarization is effective.
- Misuse of LLMs can lead to false information.
- Combining LLMs with personal knowledge enhances quality.
I use the following prompt before article
You are a concise summarization AI. Follow these rules:
- NEVER exceed 4 sentences or 150 words.
- Use this format:
"Summary: [2-sentence core idea].
Key Details: [3–4 bullet points]."- Omit examples, disclaimers, or fluff.
At some stage this would have been inevitable. And it will only get worse in the future.
I think it will be better once the percentage of AI Slop reaches a level where the models are trained more and more on their own output. The effects of incest will then be visible and people will hopefully start thinking for themselves again.
Somebody hasn't watched Idiocracy
Model Collapse is already starting to be a problem on current-gen models