Imagine a data scientist tasked with improving customer segmentation for a marketing campaign. Typically, they might ask an AI, "What is the best clustering algorithm to use for customer segmentation based on purchase history?" This question, while precise, limits the scope of the AI's response to just selecting an algorithm.
Instead, the data scientist decides to use a more open-ended approach: "In what innovative ways can we use data science to understand our customers' behavior and improve our marketing strategies?" This broader question doesn't just seek an algorithm; it opens the door to a wider range of data-driven insights and strategies.
The AI's response suggests not only using clustering algorithms like K-means for segmentation but also incorporating sentiment analysis of customer reviews and feedback to add another layer to understanding customer preferences. It also proposes predictive modeling to forecast future purchasing behaviors based on a combination of historical purchase data and external factors like market trends and seasonal impacts.
Very recently we have also opensourced BUDA, top-down software stack for running ML models on Tenstorrent Hardware: https://github.com/tenstorrent/tt-buda
Metalium being the bottom-up software stack giving open access to Tenstorrent Hardware.
The version in the notebook is just for simple text-based PDFs. I wrote some posts on our company blog[1] about the sheer agonies of dealing with PDF as a data format, so wanted to stick with as simple as possible for now.
That said, I'm planning future notebooks where you can perform text-to-image or image-to-image search, integrate OCR, scale it up, serve it, deploy it, etc.
Don't. PDF is a terrible format for storing machine readable data. You lose a ton of Information while you create the PDF which you then painstakingly have to get back later (if that's even possible)
I may have misworded it (if I wrote those words - PDF rots the brain and my memory likewise).
Agreed on the rest. PDFs don't store machine-readable data. Often just pixelated scanned hot garbage dumpster fire text.
I hate PDFs but have to work with the satanforesaken things. Hence the notebook. It's my little way of trying to give my little PDF-bespoked-hellscape a tiny little glow-up.
I probably didn’t read your comment closely enough. When I hear about PDF parsing or PDF as data I immediately get flashbacks from a project years ago where I had to parse PDF files. I think I am still traumatized by this experience so whenever I hear somebody wants to do this I just want to scream “Nooo. Don’t do this”
Incidentally Jina Hub [0] has a few OCR Executors [1][2] you could integrate into my notebook (though you'd have to do some rewiring to take images into account since it's a text-based notebook)
So much time is taken up on student questions when they could just RTFM. Having an AI take care of that menial bullshit doesn't hurt anyone. There are higher value tasks a teacher can perform then schooling someone who didn't read the curriculum (which this chatbot is based off, after all)
I'm so glad you think an AI is better equipped to answer student questions than an actual human being! I'm sure your years of experience and expertise in the field of education have really helped you develop this groundbreaking opinion.
The book is a pragmatic take on OpenAI's GPT-3 illustrating the capability of this extraordinary model in tackling a wide array of tasks, like having a human-like conversation, text completion, text summarization, and even coding with stunningly good performance.
Instead, the data scientist decides to use a more open-ended approach: "In what innovative ways can we use data science to understand our customers' behavior and improve our marketing strategies?" This broader question doesn't just seek an algorithm; it opens the door to a wider range of data-driven insights and strategies.
The AI's response suggests not only using clustering algorithms like K-means for segmentation but also incorporating sentiment analysis of customer reviews and feedback to add another layer to understanding customer preferences. It also proposes predictive modeling to forecast future purchasing behaviors based on a combination of historical purchase data and external factors like market trends and seasonal impacts.