Partly’s Post

When we want to make sure something is backwards compatible, we need it to interface with something old even though it's ("paradigmatically") new. The less specific and niche this is, i.e. broader the required (backwards) compatibility, the closer you get to a threshold where after needing more and more universality, the optimal solution is intelligence. This is because the increasing heterogeneity you need to interface with eventually makes a stack of custom solutions computationally too extensive, unfeasible, thus entirely unpractical and unrealistic. Intelligence subsumes all those, for it includes building countless custom solutions if necessary. So general intelligence is the ultimate (backwards) compatibility. Intelligence is ultimately an interfacing problem, and generality in intelligence is what allows universal approximation of all solutions. Which is why intelligence is ultimately an interfacing problem. Which is why it's the ultimate translation layer. Which is why many custom layers we in this engineering paradigm built into our systems will disappear, to make place for intelligence. To make place for self-generating translation layers, acting like interfacing glue. #generalintelligence #selfgeneratingsystems #universalinterfacing

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please check my article if want to learn about request param annotation

Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings https://lnkd.in/dBD2FGRk A paper from DeepMind on the evaluation of text-to-image models, but there are several important contributions that go well beyond T2I evaluation and are useful for many other types of LLM evaluation. I'll share a detailed review later Data and code are not released it but will be released soon

We all know AI is advancing at lightning speed. But Open Interpreter caught my eye because of the endless possibilities it opens up especially for automating robust systems. #automation Any computer scientist will tell you there’s a big difference between a program and its execution. it’s like the difference between a recipe and the actual cake. Open Interpreter gives the recipe; and it delivers the cake on top. Here is a 5 minute demonstration of this: https://lnkd.in/gUE-a6bm And their official website has great documentation to help you get started: https://lnkd.in/g2NZarJQ Let me know how you would put this into use! #ArtificialIntelligence #Automation #OpenInterpreter #TechInnovation #FutureOfWork

Easy to understand the Transformer Transformers work by processing huge volumes of data, and encoding language tokens (representing individual words or phrases) as vector-based embeddings (arrays of numeric values). You can think of an embedding as representing a set of dimensions that each represent some semantic attribute of the token. The embeddings are created such that tokens that are commonly used in the same context are closer together dimensionally than unrelated words. As a simple example, the following diagram shows some words encoded as three-dimensional vectors, and plotted in a 3D space:

Day 16 of #100daysofdataAnalysis I should have posted this yesterday, but there was no power—so technically, yesterday was Day 16! Today, I continued working on my dataset and kept improving my dashboard. It’s funny—I’ve redone this project around five times, but I get better each time. I think I’ll finally finish it today! I also regarding the community challenge learned about transformers, a model architecture introduced in the paper Attention is All You Need. Transformers were originally designed for language translation. The main parts include: 1. Input Text: The raw text. 2. Preprocessing: Breaking down the text into tokens (tokenization). 3. Encoder: Converts tokens into vector embeddings. 4. Embedding: Generates vector representations. 5. Decoder: Processes embeddings to create an output. 6. Output Layer: Produces the final result. Transformers include both an encoder and a decoder, and there are different types of transformers. I also learned about the differences between transformers and large language models (LLMs).

Claude 3 function calling for Intelligent Document Processing 👉 More info on using multi-modality combined with function calling https://buff.ly/3KH701G #AWS #AI #GenAI

Claude 3 function calling for Intelligent Document Processing 👉 More info on using multi-modality combined with function calling https://buff.ly/3KH701G #AWS #AI #GenAI

Bounding Box vs. Semantic Segmentation: Choosing the Right Annotation Method https://lnkd.in/g4xc4ra9 Read more about Infosearch's Bounding box annotation - https://lnkd.in/gbGyZQf Semantic Segmentation - https://lnkd.in/gipcAeMm Contact us to outsource your annotation services to Infosearch. enquiries(@)infosearchbpo(.)com #boundingBox #boundingBoxAnnotation #boundingBoxAnnotationOutsourcing #SemanticSegmentation #SemanticSegmentationAnnotation #SemanticSegmentationOutsourcing