MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge Fan et al.: https://
arxiv.org/abs/2206.08853 #NeurIPS #NeurIPS2022 #ReinforcementLearning
MACHINE LEARNING
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MineDojo: Building Open-Ended Embodied Agents Internet-Scale Knowledge
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Value-Equivalent Sampling for Reinforcement Learning Decision-Making
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Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning Dilip Arumugam, Benjamin Van Roy : https://
arxiv.org/abs/2206.02072 #NeurIPS #NeurIPS2022 #ReinforcementLearning -
AI Iteration Upleveling Customer Experience Strategy
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How #AI iteration can uplevel the customer experience https://
venturebeat.com/ai/how-ai-iter
ation-can-uplevel-the-customer-experience/
… @venturebeat @fergal_reid #MachineLearning #DataScience #BigData #Analytics #DeepLearning #100DaysofCode #IoT #serverless #womenwhocode #DigitalTransformation #CX #ArtificialIntelligence -
CSRankings Limitations and Need for Better Academic AI Alternatives
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Yes. I have significant issues with various design choices of CSRankings. I wish there was a better and more customizable alternative (toggle people with industry on/off if you like?). It's, for better or worse, the best we got right now, and I get some utility from it.
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Machine Learning Research Paper from University of Washington
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http://
cs.washington.edu/homes/pedrod/p
apers/iclr18.pdf
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Three Eras of Programming: From Assembly to AI
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The three eras of programming:
1. Assembly code
2. High-level languages
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GPT-3 Training Data Composition Breakdown
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GPT-3 has been trained on 45 TB of text data from different categories: ⬩Common Crawl (8 years of raw web page crawling) ⬩WebText (The text of Reddit posts with 3+ upvotes) ⬩Books (The internet-based books corpora) ⬩Wikipedia Data is then "weighed" as such:
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How AI Models Transform Input into Predictions
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This means that it can take user's input text and transform it into what it predicts the most useful result will be based on the patterns recognized from the parameter library.
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How Language Models Function as Probabilistic Text Predictors
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When a user provides the text input, the system analyzes the language and uses a text predictor to create the most likely* output. *Does not necessarily mean the most factually accurate output, but rather the most "likely" output based on the data from the training library.
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How AI Models Learn to Recognize Textual Patterns
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The algorithm (or "model") utilizes a set of data that enables it to recognize certain patterns in the text. This is accomplished by training the system on the vast body of internet text to spot patterns.