What can you actually make with Machine Learning? -Image classification
-Prediction models
-Speech recognition
-Natural language processing (GPT-3)
-Recommendation systems The possibilities are endless. (Personally, I think Natural Language Processing is the most )
AI
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Common Applications of Machine Learning Explained
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Explaining the Difference Between AI and ML
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First off, what's the difference between AI and ML? "AI" is a broad field that encompasses the development of intelligent machines and software, while Machine Learning is a specific approach to achieving artificial intelligence using algorithms & models that can learn from data.
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Code Capture as Foundation for RLHF Self-Bootstrapping
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“capture code” could be a very cool basis for RLHF/retraining. this thing is bootstrapping itself!
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Telemetry Insights and Alternative Metrics for AI Suggestion Success
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enjoyed the telemetry bit, shows how much they care about reporting success of this thing perhaps time based measurement might be buggy if eg i walk away from my desk after seeing a horrible suggestion perhaps could measure based on #’s of edits after a suggestion was accepted
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Debug Mode for Copilot: Expose Scores, Latencies, and Prompts
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idea for extension: offer a “debug mode” modified version of copilot with all these scores and latencies and prompts exposed, so that people can gain an intuition of whats coming in and out as they type
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Weekend Meme Sunday AI Computer Vision Humor Post
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The weekend is not the same without "Meme-orable" Sunday!
#memes #aimemes #AI #funny #computervision -
Automated Bin Picking: New Dimensions for Success
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Automated Bin Picking: New Dimensions for Success
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Online Data Training GPT-4: Privacy and Security Implications
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PSA: Everything you are writing online anywhere on any subject is being used as training data for #GPT4. So are the books you write and youtube videos and podcasts you record. We are all training #GPT now. #ChatGPT #GPT3 #openai #artificialinteligence #infosec #ooda
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Model Prediction Challenges and Product Differentiation in AI
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this is definitely a tough issue…given how unpredictable research it is, it's hard for us to predict exactly when a model will be ready or what it will be capable of. but generally speaking its safe to assume models will get much better. product differentiation is good!
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MLflow 2.0 Released with Enhanced MLOps Features
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Availability of #MLflow 2.0 is here Incorporating extensive user feedback, MLflow 2.0 introduces new features & improvements to simplify data science workflows & deliver innovative, first-class tools for MLOps. Learn more https://
dbricks.co/3tAv3qy