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Peeking under the hood with ChatGPT plugins

The way these plugins are designed is as open as I can possibly imagine, which means we can peek under the hood at how they are made. That is, if you know where to look. It also offers us a glimpse into potential business ramifications, which we’ll take a look at at the end of this post.
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Announcing Our Investment In Plus One Robotics

We are thrilled to announce that we are leading an investment in Plus One Robotics as part of the Company’s $50M Series C financing. At its core, the company specializes in streamlining the movement of mixed parcels, bags, and products for logistics providers in the likes of DHL, FedEx, and MSC Industrial, many of which Plus One counts amongst its growing rolodex of customers.
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Spot AI: Building the System of Engagement for Enterprise Video Intelligence

Enter Spot AI which is making computer vision practical for underserved users in everyday businesses and industries that power the real economy. Imagine a car wash trying to understand the flow of vehicles through their facility over the course of a day. Or a warehouse operator who wants to know the moment a truck has arrived at the loading dock to begin processing materials. Or even an industrial plant or construction site where a foreman or plant leader wants to monitor safety operations more comprehensively. 
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Introducing: the Scale Generative AI Index

We are closely following the momentum of these overlapping but distinct trends. And to track our own work and share some of the knowledge we’ve accumulated, we’re introducing the Scale Generative AI Index, a list of nearly 200 companies in the space and details about what they’re building. We’ll keep adding to this market map as our research progresses.
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Observations from the Field: The Future of Generative AI

We believe that in this moment of generative AI hype, nothing is more valuable than hearing directly from entrepreneurs and product leaders building in the Generative AI space. That’s why last month we hosted a panel with a group of entrepreneurs and AI practitioners to discuss the key challenges entrepreneurs are facing when it comes to building category-defining generative AI companies.
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Foundation Models Are The New Public Cloud

Over the last few years, building an AI startup used to require “do-it-yourself AI,” which consisted of gathering training data, labeling it, architecting complex data transformations, tuning hyperparameters, and selecting the right model. It was a herculean task, similar in complexity to the workload of the Salesforce engineer above.  But in the last year or two, foundation models have emerged as a time-saving shortcut that enable entrepreneurs to do more faster. These foundation models aren’t specific to particular AI use cases, but are largely general and have something to offer almost anyone. Entrepreneurs can now decouple parts of the training data and model (which comes pre-packaged in a foundation model) from the application layer, which we at Scale call a cognitive application.  
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Natural Language Generation

Writing this content is a desk worker’s low-skill labor, demanding little skill but lots of time. (The quality of GMail autocomplete, introduced in 2018, illustrates just how repetitive business writing is.) It’s this attractive target that makes Natural Language Generation (“NLG”) products so exciting, because this new technology has finally grokked the patterns interwoven in our prose. NLG products are newly feasible, enabled by linguistic “transformer” models like GPT-3 from OpenAI and Jurassic-1 from AI21 Labs.
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