This book is a beautiful introduction to simulations of natural systems. We get concise prose, clear illustrations, and carefully chosen systems with a guided tour through the math. All done in the Processing language, which is fun and easy to pick up. The reason I like this book so much is probably due to timing. In 2013, I was on the verge of abandoning software development. I was fed up. (That's a story for another time.) Instead, I planned to go into cognitive psychology full-time as a researcher. This book brought back the joy in programming for me. Sometimes, a good book is the book that motivated you.
They just published a secondary analysis of the active trial that showed that um those who did processing speed training with uh booster sessions at one and three years had a significantly decreased risk of dementia 20 years later.
Our market reach is far greater than any any TPU can any ASA can possibly have. And so if you look at our position, uh we're the only company that that accelerates applications of all kinds.
Over thirteen lessons, you’ll learn about the past, present, and future of meat, from the ways animals and humans are treated in meat processing plants to emerging meat alternatives.
Booth's formally trained (and her grandparents are watercolor artists!) but her use of color is just so free and unexpected, it makes you want to experiment yourself and join along in the fun.
AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm (Software 2.0) because both are fundamentally about the automation of digital information processing.
It's the same reason why I recommend learning how to type and learning your editor so well you don't even have to think about the action because the people that have to... Even if you just look down, that's still mental processing power you have to spend looking at a keyboard in which you already know where the key is.
And the history of NLP and language processing instruction, tuning and tasks per language model used to be like one language model did one task, and then in the instruction tuning literature, there's this point where you start adding more and more tasks together where it just starts to generalize to every task. And we don't know where on this curve we are.
And we'll get into the details of the models and again and again as we try to get deeper into how the models were trained, we will say things like the data processing, data filtering data quality is the number one determinant of the model quality.
I also read a ton of technical books last year, but the most impactful one for me was Neural Network Methods for Natural Language processing by Yoav Goldberg
I liked Mem’s editing experience within notes because it resembled word processing, without extra steps getting in the way. But I found the app otherwise surprisingly lacking in basic functionality, despite its polished appearance.
But in terms of what we value the most as humans, which is to say our feelings, our emotions, our sense of what the world is in a very personal way that I think means as much or more to people than their information processing. And that’s where I don’t think that AI necessarily will become conscious because I think it’s the property of life.
When I'm writing articles etc that need to be shared with an editor, I typically write in Microsoft Word. I hate all word processing software, and anyone who insists that Google Docs is so much better than Word is a liar.
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