You're lost in limbo with these arguments. It's just learning from data and generating from what's learned. It's backed by self-consistent engineering principles.
MACHINE LEARNING
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Building ML Systems: Key Components and Solutions
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Building machine learning systems is hard. Some of the components you need: 1. Data sources
2. Data pipelines
3. Feature stores
4. Model training
5. Model evaluation
6. Model deployment
7. Model monitoring
8. Predictions API At @abacusai we help you with this! -
Three Data Science Career Lessons I Wish I’d Known
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3 Things I Wish I Knew When I Started Data Science: Looking back and realizing how I was wrong about the data science career. https://
kdnuggets.com/2023/01/3-thin
gs-wish-knew-started-data-science.html?utm_source=dlvr.it&utm_medium=twitter&utm_campaign=3-things-i-wish-i-knew-when-i-started-data-science
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LLMs Are English-to-English Machine Translation Systems
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Lost on most people: every time you used http://
translate.google.com for past 20 years, an LLM was used. The main difference now: we’re translating English prompts to chat-inspired predictions also in English. LLMs are basically English-to-English machine translation -
Transfer Learning in NLP Workshop Research Presentation
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Work by Rafal Kocielnik, @SaraKangaslahti @shrimai_ @rmichaelalvarez @caltech @nvidia Appeared at #Neurips workshop on transfer learning in NLP
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Active Learning and Transfer Learning in Few-Shot LLM Settings
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How can we mix active and transfer learning in few-shot learning setting with pre-trained LLMs? We show that you need to label only few samples in-domain and leverage on transfer learning from out-domain along with ingrained knowledge in pretrained models
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Comprehensive Machine Learning Roadmap: From Basics to Advanced Techniques
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Take your #MachineLearning skills to the next level with this comprehensive roadmap From basics to advanced techniques, stay on track and achieve your goals By @ingliguori #DataScience #AI #TechLearning #edutech #innovation #TECH4ALL #DataScience #DataScientists #cyber
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Neuro-Symbolic Reasoning Advances Tutorial at AAAI 2023
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2023 AAAI Tutorial: Advances in Neuro Symbolic Reasoning, Tuesday Feb 7 by @PauloShakASU https://
labs.engineering.asu.edu/labv2/2023-aaa
i-tutorial-advances-in-neuro-symbolic-reasoning/
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REPLUG: Retrieval-Augmented Black-Box Language Models
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REPLUG: Retrieval-Augmented Black-Box Language Models Shi et al.: https://
arxiv.org/abs/2301.12652 #Artificialintelligence #DeepLearning #MachineLearning -
Historical AI Agreements and Paradigm Shift Debates
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Amazing how many people believe this, even when I have written extensively (even this week!) about agreements going back several years. Eg see here 2017 slide https://
garymarcus.substack.com/p/some-things-
garymarcus-might-say
… and see long essay on 2022 https://
garymarcus.substack.com/p/does-ai-real
ly-need-a-paradigm-shift
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